Annual Report, FY16 2 Department of Computational and Systems Biology Department of COMPUTATIONAL & SYSTEMS BIOLOGY

School of Medicine University of Pittsburgh

Ivet Bahar, Ph.D. Distinguished Professor and John K. Vries Chair

Fiscal Year 2016

Section I : General Description (Activities and Mission) 4

Section II : Research and Other Scholarly Activities 9

Section III : Teaching Activities 64

Section IV : Faculty Data 78

Section V : Three-Year Bibliography 92

Section VI : Financial Plan for Fiscal Year 2016 118

(2001-2016)

Annual Report, FY16 Department of Computational and Systems Biology 3

Section I General Description

Computational and Systems Biology – Who We Are The Department of Computational and Systems Biology (CSB) has continued to be a leader in the field. Our increasing focus on multi-scale interactions in biological systems enabled us to tackle more research at an integrated, Systems level. The post-genomic and big data era has brought a new excitement to science. With this excitement also comes new challenges that require new and innovative approaches with which to tackle them. CSB continues to play a leading role in driving discovery through continued interdisciplinary efforts to answer the research challenges of today and define the new approaches to address the questions of tomorrow. Concomitant, with our research foci, we also make concerted efforts in educating the next generation of scientists, who will continue this work. The unique, hands-on research opportunities that we provide to students at the graduate, undergraduate, and even high school levels provide these students with the necessary interdisciplinary and hands-on training to prepare them for careers in these fields. At the same time, these efforts provide numerous opportunities for our postdoctoral fellows and junior faculty to serve as mentors for our scientists-in-training, especially those at the undergraduate and high school level. These early experiences in mentoring are valuable opportunities that allow these scientists to hone their mentoring skills, while working together with some of the best and brightest in the next generation.

A Brief History Computational biology was established as a research discipline at the University of Pittsburgh with the vision of the Senior Vice Chancellor, Dr. Arthur S. Levine, who recruited Dr. Bahar to create a new Center for Computational Biology and Bioinformatics in March 2001, which soon became a highly visible center. The creation of the Department of Computational Biology within the School of Medicine followed in October 2004. Built on the premise of the founding Center for Computational Biology and Bioinformatics, the department was created with Dr. Ivet Bahar as the Founding Chair, to establish a home uniquely welcoming to highly interdisciplinary faculty, and to firmly establish the University of Pittsburgh in a nationally recognized leadership role in a field of tremendous growth and excitement. The department name was changed to Department of Computational and Systems Biology in 2009, to reflect the expansion in research and educational goals and activities of the department.

In 2016, the contributions of our Faculty were highlighted by various awards and promotions. Within our department: Dr. Panagiotis Benos was promoted to Professor; Jianhua Xing was promoted to Associate Professor with Tenure; S. Chakra Chennubhotla was promoted to Associate Professor with Tenure; Dr. Joseph Ayoob was promoted to Associate Professor; Dr. Bing Liu was promoted to Research Assistant Professor; Dr. Anne-Ruxandra Carvunis was recruited as an Assistant Professor in the Tenure stream and will join the department in October; and Dr. Zuckerman was selected as the 2015-2016 DISTINGUISHED MENTOR in the School of Medicine. Dr. Clark was featured in Pitt Med highlighting his work in gene evolution and the Center for Causal Discovery (PI: Cooper, Bahar and Berg) was the cover story for the Winter 2016 issue. Dr. Jeremy Berg will be reducing his role in the Department as he moves on taking the position of Editor in Chief of Science for a 5-year term. Dr. Berg is the 20th Editor in Chief since the journal’s inception in 1880. We are committed to providing a home that welcomes highly interdisciplinary faculty, and that firmly establishes the University of Pittsburgh in nationally recognized leadership roles in rapidly growing and evolving fields that are driving the next wave of scientific discovery.

CSB Research – Leading the Field with Cutting-edge Science Increasingly complex biological problems and extremely large biological data sets have necessitated new approaches to answer many of today’s current research challenges. As one of the fields in what are now being called the “New Biologies”, Computational and Systems Biology encompasses an interdisciplinary approach to harness the power of computation to tackle the emerging big data challenges and to answer questions in biology not amenable to traditional approaches. Thus, to rise to these challenges and to continue to be a leader in this newly emerging field, CSB has set our research mission to: Advance the scientific understanding of biological systems through computational tools and theoretical approaches based on the fundamental principles of physical sciences.

Annual Report, FY16 4 Department of Computational and Systems Biology

Design new computational/mathematical models and methods for simulating complex biological processes and for extracting useful information from accumulating data.

CSB investigators pursue this mission by investigating leading questions in biology and medicine in four major areas of specialization/growth in the field: 1. Bioinformatics and Computational Genomics and Proteomics 2. Cell and Systems Biology 3. Molecular Structural Biology 4. Pharmacology and Drug Discovery

Bioinformatics and Computational Genomics and Proteomics The genomics and proteomics fields are primarily based on computer science and statistics and aim to digest the daunting quantity of “-omics” data now available by systematic development and application of probability and statistics theories, information technologies and data mining techniques. Within Genomics there are three main categories: functional genomics, structural genomics, and comparative genomics. Functional genomics generally refers to the high throughput determination of gene functions. Structural genomics is the systematic effort to gain a complete structural description of a defined set of molecules, ultimately for an organism’s entire proteome. Comparative genomics uses evolutionary relationships between various organisms to understand the structure and function of the genome. Proteomics aims at quantifying the expression levels of the complete protein complement (the proteome) in a cell at any given time. While proteomics research was initially focused on two-dimensional gel electrophoresis for protein separation and identification, proteomics now refers to any procedure that characterizes the function of large sets of proteins. Proteomics may be considered as a subset of functional genomics.

Cell and Systems Biology Systems biology seeks to integrate different levels of information to understand how biological systems function at multiple scales, with the goal of developing an understandable model of a whole system. This is accomplished by studying the relationships and interactions between various parts of the particular system of interest, which could range over orders of magnitude, from molecular assemblies to subcellular -, multicellular-, and tissue-level systems.

Molecular Structural Biology Studying the architecture, shape, and dynamics of biological macromolecules is paramount to understanding the basic mechanisms that drive the essential processes of all life. Macromolecules such as proteins and nucleic acids carry out most of the functions of a cell, and are able to perform these functions by adopting ensembles of structures under native state conditions. Structural biology is concerned with the driving forces and interactions that determine the three-dimensional shapes and dynamics of biomolecules. Moreover, by applying the fundamental principles of the physical sciences, we are beginning to establish sequence-structure-dynamics-function relationships that enable deeper levels of discoveries, and summon the possibility of de novo structural and functional predictions at the proteome level.

Pharmacology and Drug Discovery Computational and theoretical approaches are revolutionizing Pharmacology and Drug Discovery. Predicting, modeling, and simulating potential therapeutic agents and their interactions with target molecules is a powerful new first step in the drug discovery process. This in silico approach streamlines the often laborious, expensive, and slow process of identifying and testing lead compounds for use as treatments. Combining these advances with high-throughput and high-content cellular- and systems- level pharmacological and poly-pharmacological approaches is profoundly impacting medical science. The UPDDI is committing major efforts in "Quantitative Systems Pharmacology" (QSP) alongside "Chemistry and Molecular Biophysics", while all technical areas of drug discovery and development will continue to be employed and extended in our programs.

While the four areas may appear distinct, there is considerable overlap, and they all share a common goal: quantitative evaluation/prediction of biological data/processes. Traditionally, these areas have been advanced by researchers specialized in one of the specific disciplines. However, it is now widely recognized that they can advantageously complement/guide each other, and their combined use can induce synergistic effects. Thus, multidisciplinary expertise is essential for efficient development and use of computational biology tools.

CSB Educational Endeavors – Training the Next Generation of Scientists As one of the first departments in computational and systems biology in the nation, the faculty and staff of the department strive to establish a successful model for inter-disciplinary research by collaborating

Annual Report, FY16 Department of Computational and Systems Biology 5

with experimentalists and theoreticians alike and training a new generation of researchers capable of meeting the next wave of scientific challenges and thereby propagating scientific advances and breakthroughs. To this end, we have also set forth the educational goals of offering first-class programs at the graduate, undergraduate, and high school levels in computational biology, all of which serve multiple purposes: (i) to introduce computational biology problems and methods to students from chemistry, physics, engineering, mathematics, and computer sciences, (ii) to teach fundamental concepts of the quantitative and physical sciences to biology and biomedical sciences students, (iii) to prepare and encourage students at all levels for a career in computational biology research, and (iv) to reach out to and recruit students from backgrounds that are underrepresented in the sciences. In all cases, the aim is to train students and postdoctoral researchers to identify and tackle complex biological problems that are beyond the reach of traditional approaches.

Joint CMU-Pitt Ph.D. Program in Computational Biology To support this educational mission, the Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) was developed as a collaborative effort between the University of Pittsburgh and Carnegie Mellon University. This program was conceived as a cross-institutional (Pitt and CMU) and cross-campus (Pitt SOM and SAS) Ph.D. program that would exploit the complementing strengths of the different universities or schools. (For additional details about this program please refer to the Teaching Activities section of the department’s Annual Report.)

The CPCB is supported through the Graduate Studies Office of the School of Medicine. In the first year of the program, students’ stipends and tuition expenses are covered by their respective universities. Beginning in their second year, students’ stipend costs are supported by their respective mentors through research dollars and/or other funding sources. Tuition costs for those students enrolled in the University of Pittsburgh are allocated to their respective departments via the university’s annual step down cost model. In its first year, the CPCB enrolled six students (4 at Pitt and 2 at CMU). Including the 12 students who joined the program in Fall 2016, the program has a total of 44 students, of which 20 are domestic. About 39% of our students are female. Through aggressive recruiting efforts and wide dissemination of program information, CPCB expects to steadily increase enrollment each year. In FY17, the following activities are planned: Creation and distribution of a more dynamic program flyer; direct mail approach to attendees of ABRCMS conferences to reach out to students underrepresented in the sciences; increased recruitment at research conferences and workshops; further updates and improvements to program website; social networking. In FY 17, we anticipate that 4-5 students will apply for graduation to complete their thesis requirements.

In November 2005, Carnegie Mellon University, in partnership with the University of Pittsburgh, was selected to receive a $1 million grant from the Howard Hughes Medical Institute (HHMI) to support the development of the CPCB. In FY09, our program was awarded an NIBIB T32 Training Grant: Integrated, Interdisciplinary, Inter-University Ph.D. Program in Computational Biology (PIs: Ivet Bahar and Robert Murphy). This training grant was renewed in August 2014 (PIs: Russell Schwartz, Panayiotis Benos, Ivet Bahar and Robert Murphy) and supports 6 students each year

Other Graduate Programs Besides their role in CPCB, CSB faculty play an active role in mentoring and teaching students enrolled in several other graduate programs at the University of Pittsburgh. Panayiotis Benos is acting as Associate Director of a new interdisciplinary program, Integrated Systems Biology (ISB), led by the Developmental Biology Department. Other programs include the Interdisciplinary Biomedical Graduate Program, Molecular Biophysics + Structural Biology Graduate Program, Biomedical Informatics Training Program, Human Genetics Ph.D. Program, and Physics Ph.D. Program. In FY17 and beyond, CSB faculty will continue to make themselves available as advisors/co-advisors in these programs.

Undergraduate Summer Programs In 2003, The University of Pittsburgh was one of only 11 institutions funded for a summer training initiative in computational biology. This NIH-NSF-funded program, “BBSI @ Pitt: Simulation and Computer Visualization of Biological Systems at Multiple Scales” was a multi-institutional program between the University of Pittsburgh (lead institution), Duquesne University, the Pittsburgh Supercomputing Center, and Carnegie Mellon University. This program was conducted from Summers 2003 to 2009. In order to continue our commitment to providing graduate-level research experiences to undergraduate students, we applied for and received an NSF award for a Research Experiences for Undergraduates (REU) program that we began hosting in summer 2010, maintaining the continuity of our undergraduate mentoring efforts from the BBSI program. For this new REU summer program entitled Training and Experimentation in Computational Biology (TECBio), we have built off of the success of our BBSI program, and have kept many of the components that made the previous program a success. In our first summer we had 150 applicants for our 10-week program, which yielded a group of high quality

Annual Report, FY16 6 Department of Computational and Systems Biology

students for inaugural program. Today our application pool has sky-rocketed to over 300 applicants per year. Our applicants are always a very diverse group who come from many academic and ethnic backgrounds, including many students from groups underrepresented in the sciences and those that are at universities that have limited research opportunities (together these groups compose over 80% of our participants). In 2012, we submitted and obtained a 4-year renewal of our TECBio program. Since the program received one of the top scores in the review process, the Department of Defense is now a co- sponsor of our program. Recently, we just submitted a second renewal for five years. With the TECBio REU program, we look forward to continuing our traditions of undergraduate mentoring and training to inspire the next great generation of scientists.

High School Summer Program The summer of 2011 saw the start of a new initiative in the CSB that reached out to high school students in the Pittsburgh area. In collaboration with the Department of Biomedical Informatics, we started a new component of the University of Pittsburgh Cancer Institute (UPCI) High School Academy, which we called the Computational and Systems Biology and Biomedical Informatics (CoSBBI) Academy. In the first two summers of this initiative, we accepted 13 students in our program and matched them up with a research mentor for an 8-week summer research experience. In the summer of 2013, the CoSBBI program expanded into 2 separate and larger programs, one still housed in Biomedical Informatics and one hosted by CSB in partnership with the UPDDI. This latter new program, which is called the Drug Discovery, Systems and Computational Biology (DiSCoBio) Academy, offers computational and experimental training opportunities to our cohort. The mentored research component was complemented by numerous other enrichment activities, including didactic training in the major disciplines in drug discovery and computational and systems biology, hands-on and demonstration sessions, research and career seminars, tours of labs and research facilities on campus, educational and social field trips, and a number of opportunities for the students to present their work and hone their communication skills. Many of our faculty, postdoctoral fellows, graduate students, and summer undergraduate students were involved in this program through their efforts as research advisors, mentors, lecturers, information session panelists, and/or hosts of students on various tours and other events. Students in the DiSCoBio program were also specifically integrated in the tiered-mentoring framework with the graduate and undergraduate students in the CSB to provide them with important sources of information on how to conduct their research, and what it is like to be an undergraduate/ graduate student and how to prepare oneself to go about the application processes for both.

Research Associates Research associates form an integral part of every research environment, as they are capable of mostly independent research, and are also in a position to assist students during their dissertation work. New faculty recruited to the CSB received three years of support for research associates. In addition, research associates in the CSB are encouraged by their faculty mentors to seek out fellowships, awards, and other sources of funding.

Computational Biology Classroom For effective teaching of computational biology to students and/or other individuals, it is important to have an infrastructure capable of such education. Unlike other disciplines, computational biology is best taught “hands-on,” where the instructor has the option of demonstrating concepts using the appropriate audio-visual technology/equipment, and students can simultaneously participate. The CSB classroom (3073 BST3) has been designed to meet this necessity. In the classroom, there are 22 17" Intel Core i5 Dell laptops at each desk, plus an additional one at the presentation podium, which are all set up for wireless access and configured with specific software to assist and complement the teaching of computational biology courses.

The classroom has served as a major hub for many of our educational programs and other departmental activities. Throughout the academic year, the classroom houses lectures for the courses in our graduate program, a host of special seminars, many lab meetings, and various other events. In the summers, this classroom is utilized by the REU program for courses, seminars, journal clubs, research projects, and other activities. The DiSCoBio program also uses the classroom for their didactic activities and also as a place for the students to work on their research projects. Throughout FY17, CSB will continue to both seek out state-of-the-art equipment and software to optimize this classroom for teaching of computational biology courses and workshops and also provide high-level, cutting-edge research, and didactic training experiences to students at all levels.

Annual Report, FY16 Department of Computational and Systems Biology 7

Annual Report, FY16 8 Department of Computational and Systems Biology

Tenure track faculty p. 10 Section II Non-tenure track faculty p. 28 Research and Other Activities p. 40 Scholarly Activities

Annual Report, FY16 Department of Computational and Systems Biology 9 10 Section II : Research and Other Scholarly Activities

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Department of Computational and Systems Biology Systems and Computational of Department promoters promoters (Corcoran, Pandit, 4: promoters was used to identify network modules that were associated with (IPF). In particular, we found that FY16 Report, Annual evolutionary evolutionary information Bioinformatics (Kadri, based on framework the and hierarchical was proven correctly hidden predicting to the Markov miRNA be very even for model distantly related species. In addition, we efficient used in a support vector machine algorithm RNA to polymerase II identify core these algorithms to analyze high nexus. We are also interested in the role of chromatin in gene expression and the information. SNP integration and expression gene with of data seq ATAC miRNA genes and gene networks. developed a new algorithm identification (HHMMiR) for of microRNA genes without using (Mahony, (Mahony, pairwise and multiple DNA motif alignments, clustering phylogenetic tree constructions of and comparisons DNA and participated in patterns the analysis and (the annotation of the opossum genome, equivalent the first (Mikkelsen, non sequenced of BLAST and 177; Mahony, CLUSTALW for DNA DiSCo identifies motifs.) transcription factor We sub also certain co started started developing multi heterogeneous, machine learning algorithms that DNA motifs. can for the identification of integrate DNA and 15 efficiently analyze i283 Our group is interested in understanding gene regulation, its dynamics, the mechanistic characteristics of the protein models and computational heterogeneous algorithms high to study factors transcription and microRNA (miRNA) genes in the of the context miRNA diseases. terminal genes are gene short (20 processes as well as in many diseases and abnormal phenotypes in humans. More recently, our group transcriptional transcriptional gene regulation; modeling data. clinical and genetic biomedical, of gene networks; statistical analysis of heterogeneous Gene expression regulation is a fundamental process in the life of way, the its cell. It response determines, to in a changing unique environment, communication fate. with Mis other cells, and its developmental diseases. autoimmune to cancer Current Current Research Interests: Associate Professor Associate Affairs of Faculty Chair Vice Panayiotis (Takis) Benos, Ph.D. Benos, (Takis) Panayiotis 14 Section II : Research and Other Scholarly Activities

plcto o tee loihs o imdcl aaes a ucvrd e ad nw disease known and new uncovered has datasets biomedical to algorithms mechanisms in chroniclung diseasesand has identified biomarkers to cancer therapy. these of Application of indicative is which structure, network infer to methods fast develop they and information biomedical represent to models graphical using are they goal Towardsframework.unifyingthat a underdata)clinical and omics as heterogeneous(suchdatasets multi Benos analyze to development method learning Machine with associated is (Coronnello, that Benos, SNP a of discovery (Coronnello,8: pathway the ER the by in demonstrated a perturbation the was certain through on osteoporosis that binding based effect miRNA the is in predicting in have ComiR, ComiR SNPs of algorithm, effectiveness new The algorithms. The prediction between overlap target improves mRNAs. existing that shown given have we of which model, binding expression thermodynamic comprehensive the influence to expected 39: Benos, miRNA Athanassiou, (Huang, information and (static) prior server mRNA and data web expression from and gene networks (mirConnX) algorithm regulatory new predicts a published that laboratory (http://www.benoslab.pitt.edu/mirconnx/) the year, Last diseases. other and IPF in networks regulatory gene of context the in play miRNAs of role the of understanding our enhance Corcoran, (Pandit, feed a

1080) ae, e eeoe CmR e sre t mk or loih body available broadly algorithm our make to server web ComiR developed we Later, e1002830.) W416 ’ - forward - aoaoy eae neetd n eeoig loihs ht a seiial analyze specifically can that algorithms developing in interested became laboratory 2. Ti ya w dvlpd nw loih fr rdcig o a e o mRA ee is genes miRNA of set a how predicting for algorithm new a developed we year This 423.) - op hc i cuil o te pteil o eecya tasto (M) n t IPF to and (EMT) transition mesenchymal to epithelial the for crucial is which loop t al. et Nucl Acids Res Acids Nucl , m ep rt ae Med Care Crit Resp J Am

(2013) 41:

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- between clinical and other variables. other and clinical between 2) Teefnig aeepce to expected are findings These 229). Department of Computational and Systems Biology t al. et

n h ls fw years, few last the In , LS opt Biol Comput PLoS Nucl Acids Res Res Acids Nucl

Annual Report, FY16

(2011) (2012)

Dr.

Section II : Research and Other Scholarly Activities . 15 - Leu - Lys - s numerous Ser ’ - X - X - X -

President President of the American Society for Biochemistry and -

. He has been using free energy perturbation methods to determine if the observed differences in - Associate Senior Vice Chancellor for Science Strategy and and Strategy Science for Chancellor Vice Senior Associate Sciences Health the in Planning Biology Systems and Computational Professor, Jeremy Berg, Ph.D. Berg, Jeremy for Institute Director, and Chair Foundation Pittsburgh Medicine Personalized

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual His major research activities His onhave focused major research onstudies activities the computational complex between the human peroxisomal targeting receptor Pex5p and peptide containing targeting sequences. He and his research group had previously performed extensive experimental studies examining the ability of Pex5p to bind peptides containing a range of variants of the type 1 peroxisome targeting sequence COO computationally. reproduced be can energy free binding of Medicine of the National Academies, and a public service American awards from Society the Biophysical for Society, the Biochemistry and American Molecular Society Biology, for Cell the Biology. American He is Chemical past Molecular Biology and continues to be indeeply involved Society, public and policy discussions initiatives related and the enterprise. research biomedical the and science to computational computational biology as well as research in anesthesiology, wound healing, and critical care related to trauma. and burn Prior to his appointment as NIGMS director, Dr. Berg directed the Institute for Basic Biomedical Sciences at the Johns Hopkins University School of Medicine in Baltimore, MD, where he also served as professor and director of the department of biophysics and awards biophysical and honors are the American chemistry. Chemical Society Award in Pure Chemistry, election Among to the Institute Dr. Berg Institute Institute of Personalized Medicine, Associate Vice Chancellor for Science Strategy Health and Sciences, Planning Professor in of the Computational and Systems Biology, and University of Pittsburgh. Professor Prior to ofcoming to Pittsburgh in Chemistry August 2011, at Dr. Berg served the as the director of the National Institute of General Medical Sciences (NIGMS) where he oversaw at a $2 billion budget the that funded basic research National in the areas of Institutes cell biology, biophysics, of Health (NIH), genetics, developmental biology, pharmacology, physiology, biological chemistry, bioinformatics and Jeremy Jeremy M. Berg is Pittsburgh Foundation Professor and Director of the 16 Section II : Research and Other Scholarly Activities

Associate Professor Carlos Camacho, Ph.D. (Dr. Transcription Sobol); Dr. other collaborations. with collaboration several in (in well as workingwhile Straub), (Dr.Hypertension repairFinn); (Dr. invasion cancer USC); DNA Roninson, targets: in first several obtained have for we area, inhibitors this class In chemistry. MCR using molecules small into points entry these well target process recognition the of onset definedthe at which PPIs, on have protein, acceptor the in area novel compounds.This platform builds theon role anchor side chains,i.e., those that bury large surface million screen and develop to chemistry (MCR) reaction small interactionwide protein of development protein rational on the in to interested technology molecular are weight (ant)agonists.Specifically, access we seek to benefit from the growing structural information who open chemists new and radically (PPIs) a developing are Doemling facilitate the true Dr.collaboration amongstbiologists, say, experts specificon protein with collaboration in PI In 3. understand to theory quantitativemolecular interaction, including signal firsttransduction andpromiscuous interactions.the of development the to led disordered also these have throughefforts Thesetransduction motifs? signal regulate Nature does why and How as: answer such to questionsmolecules disordered with interactions of analysis protein quantitative the novel from predictresulting insights to peptide technology this of using Besides structure affinities. bound the model semi first the developed has PI the studies, crystallographic from Using insights regulation. cell in roles multiple their reveal to critical is specificity post binding their characterizing to protein due mediating rearrangements peptides, disordered bind that domains substrates their with peptidesand proteinsdisordered of interaction the predict and understand to technologiesDeveloping 2. and on RNA factors, Based transcription of families high. important miRNA targets. of quite specificity binding is the delineatecurrently are positives to we devotedtechniques, optimization falsemodern Using chains. RNA of and DNA stranded numbersingle score the DNA, protein of insights and of mechanistic sites binding of nature length short the degenerate to due However, intrinsically DNA. the scan to used are matrices weight or patterns consensus with combination in which sequences, sequencebinding known on from based obtained informationare prediction target for non used methods common of most the Currently, basis interaction. molecular the proteins. of binding DNA understanding of specificity the of analysis the and estimates Protein Understanding 1. the from Specific researchprojects include: interactions, molecular are interests biophysics of molecular recognition to the prediction research and validation of physical interactions. main My nesad h atn yokltn ewr, hc i iprat o svrl udmna biological fundamental several for important Speicfically,alpharemodeling.surface is and motility cell tocontraction muscle fromprocesses, which network, cytoskeleton actin the understand post disordered intrinsically and via signaling cell proteins of context the in networks regulatory of understanding the AssemblyDisassembly4.andmacromolecular of complexes.

- silico high throughput discovery of ant (agonists) of protein of (agonists) ant of discoverythroughput high silico –

sic “ druggable - protein interactions (PPIs), exemplified in the Protein Data Bank (PDB), to develop a PDB a develop to (PDB), Bank Data Protein the in exemplified (PPIs), interactions protein ytm biologysystem

” - rnlto mdfctos I claoain ih r Wls w ae rig to trying are we Wells, Dr. with collaboration In modifications. translation Specialemphasisunderstanding. in is pockets, and the use of chemical mimicry of these functional groups to redesign to groups functional these of mimicry chemical of use the and pockets,

- DNA and RNA interactions, including the development of accurate free energy free accurate of development the including interactions, RNA and DNA –

prah ht obn bohscl nihs ih utpe component multiple with insights biophysical combine that approach - rnltoa mdfctos Aatr ae ihy rmsuu, and promiscuous, highly are Adapters modifications. translational - N ad N eonto, e r dvlpn nw ol o ok and dock to tools new developing are we recognition, RNA and DNA

- dpe cmlxs n etmt ter boue binding absolute their estimate and complexes adapter - pcfc n seii protein specific and specific - size “ Recently, major a interest in the lab has been adapter proteins adapter - virtual virtual rti itrcin, s el structural well as interactions, protein - protein interactions protein - rti itrcin, e r using are we interactions, protein Department of Computational and Systems Biology libraries of chemically accessible chemically of libraries h P i dvlpn a structural developing a is PI The - flexible docking method to method docking flexible ” such as PDZ, SH2/3, WW SH2/3, PDZ, as such - N ad N binding RNA and DNA - . Funded. NIH,bythe

protein interactions Annual Report, FY16

- actinin -

Section II : Research and Other Scholarly Activities . 17 In In the translational translational - play play an important domain domain complexes of

- per se reactivity, reactivity, thereby elucidating the structural - family family of actin crosslinking proteins specific to - reactive reactive antimicrobial or cleaved peptide immune - react with peptides extracted from antigens and/or microbes, microbes, and/or from extracted react antigens with peptides -

binding protein. protein. binding - muscle cells. We are studying how crosslinking is regulated by three phosphorylation states, ions and ions and states, by three isphosphorylation regulated muscle how crosslinking We studying are cells. -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

hypothesis hypothesis that autoimmunity could result from cross responses targeting limited structural motifs. In collaboration with Dr. Ascherman, the PI is developing a more precise mapping of B cell over responses time to help define the of sequence molecular recognition events leading to epitope component of spreading this process. A major is application the study of and the Hsp90 as citrullination a biomarker for antigen cross molecular of processes regarding insight is provide to of study this goal ultimate The arthritis. rheumatoid autoimmunity. to lead ultimately and autoantigens multiple generate that mimicry conventional conventional paradigm of humoral immunity, B cells recognize their cognate antigen target in its native form. However, it resemblance is to native protein can trigger antibodies recognizing higher well order structures found in native known protein. Recently, that we have shown that structural relatively criteria rather than sequence unstable role peptides in both bearing antigen only recognition cross disease with individuals autoimmune and partial affinity structural maturation. Moreover, examples even exist in where the sera absence from of common linear amino acid sequences. These observations have led to the non of crosslinking actin basis molecular to the pertinent insight provide to aim studies our Collectively, lipids. by ACTN4, modifications while can result in robust signals by revealing assembling and disassembling multi a actin the quantitative framework for understanding 5. Develop how novel structural post Specifically in insights the into context of antigenicity, citrullinated Hsp90 and isoforms as the autoantigens development in rhermatoid of arthritis. autoimmune diseases. 4 (ACTN4) is a calcium sensitive member of the spectrin 18 Section II : Research and Other Scholarly Activities

ofrainl nlss n obnto wt network with combination in analysis higher conformational developing are we end, this To catalysis. or inhibition perform and partners, signaling bind fold, natively to order in access proteins short discern to is biophysics molecular in challenge key A available IV. publically a of use the to be will projecthuman judgments applied of odor quality to testourthese hypotheses. of piece techniques critical A computationalcompounds. chemical ~3×10^7 of using (PubChem) database to space aim we odor space, odorant characterize of subsets densely idiosyncratic and small involving experiments through mapping methods accomplishto this. In contrast to traditional approaches,which have soughtstructure a – no still is labels thereodordescriptive assigningaccurately andsystematically industry,for means and science basic by problem this in invested decades many Despite percept. odorrelationshipstructureandchemicalbetweendescribetheto isolfactory researchchallengein key A III. PCD diagnosis. forpatterns motion ciliarycanonical establishing are we invariants,velocity image on stepsclustering of series a Using phenotypes. motion ciliary abnormal of for identification quantitativeapproaches and computationalrecognition novelvisual developing are We subjective. highly and prone error is approach manual high onusing biopsies nasal reliesof inspection standard diagnostic current The epithelia. respiratory the in motion ciliary dyskinetic to due disease sinopulmonary severe with associated disorder rare a is (PCD) dyskinesia ciliary Primary II. non into cancer of spread the quantify to images biomarker optical to techniques segmentation slide whole time large from images tissues multiplexed analyzing are We cancer. of development the into insight provides data longitudinal perform doctors medical proactively, cancer Barrett treat of to biopsies order regular In year. each cancer to progression of risk Barrett of group small the in However, sufferers. heartburn of 10% afflicts that condition benign the Barrett study (ii) of and progression biopsies esophageal in dysplasia of signs early detect (i) to algorithms learning machine and analysis image developing are We I. fewA of the on studies, pathology,molecular diagnostic imaging,genotype My group tackles big datachallenges emerging in the domains of digital analysis in the formof computational bioimaging and statistical inference. The research focus of my group is in translation science and big Assistant Professor Chakra Chennubhotla, Ph.D.

Computational Pathology o rirr ooat. e r dvlpn nw rp tertc n semi and theoretic graph new developing are We odorants. arbitrary to Computational Bioimaging Bioimaging Computational Computational Biophysics Biophysics Computational Computational Olfaction Olfaction Computational - canceroustissue.

molecular biophysics -

going projects thein lab are summarizedbelow: This This computational algorithm can be deployed for automated clinical diagnosis of PCD.

’ ’ - ptet wo eeo dslsa tee s 10% a is there dysplasia, develop who patients s eohgs o acr Barrett cancer. to esophagus s series patient data to stratify risk of cancer in Barrett in cancer of risk stratify to data patient series

’ ptet, okn fr is sgs f ypai. o mdcl eerhr, this researchers, medical For dysplasia. of signs first for looking patients, s

and more recently

o rti fnto, nldn ezm ctlss molecular catalysis, enzyme including function, recognition and signaling. protein to relevantintermediateconformationsare that thediscern and states sub conformational characterize to techniques single these applying are and (SANS) techniquesWe trajectories. simulation dynamics moleculartimescale long (smFRET) with scattering transfer energy neutron resonance Forster molecule angle relaxation small (NMR) resonancedispersion, magnetic nuclear from observations - re saitcl rjcoy nlss olo, ae anharmonic named toolbox, analysis trajectory statistical order - speed videomicroscopy to identify abnormal ciliary motion. ciliary abnormal identify tovideomicroscopy speed

public health dynamics. - phenotype

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- lived, rare intermediate conformations that conformations intermediate rare lived,

- correlative data Department of Computational and Systems Biology –

“ minty ’ s patients. We are applying are We patients. s - uevsd erig (SSL) learning supervised ”, “ ”, oily ”, “ ”, Annual Report, FY16 woody, - percept ”

This etc

-

Section II : Research and Other Scholarly Activities . 19

”

evolution evolution within genetic networks - - protein protein -

evolution : evolution functionality functionality between - -

wide wide datasets of ERC to provide co -

Male Interactions in the Cabbage White Butterfly: White the Cabbage in Interactions Male - evolution evolution maintain these crucial protein -

insight into the workings of biological pathways. of biological workings the into insight In the near future, a researcher studying a particular a particular studying researcher a In the near future, copulatory copulatory interactions between males and females have been shaped by a complex set of “ evolution within the Nuclear Pore: Nuclear the within evolution - - find other genes with parallel histories. This will provide provide will This histories. findwith parallel other genes genes into a database of evolutionary rate covariation to to covariation rate of evolutionary a database genes into disease or pathway will be able to plug a couple of known of known plug a couple to be able will disease pathway or Evolution of Female of Evolution Yeast Mating: Reproductive Isolation and Co and Isolation Reproductive YeastMating: Co Evolutionary Rate Covariation (ERC): Covariation Rate Evolutionary

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

divergence for reproductive isolation and speciation? speciation? and isolation reproductive for divergence · Post traits. female and male in specialized resulting forces, conflictive and competitive Yeast Yeast genetics shows that molecular incompatibilities between arise quickly nuclear compensatory pore co proteins. interactions? How · does the What is driving the evolution of gamete recognition proteins in process yeast? What are the implications of this of · ERC is a phylogenetic signature that reflects co genes. We develop genome evolutionary predictions and evolution. during to interpret major · functional shifts change change over time. We are particularly focused on the process of and co the ways by which proteins influence each other during evolution. We study a variety of organisms ranging from yeast to primates proteins. transport cellular and reproduction and sexual of evolution the in interested particularly use both computational and experimental approaches. We are Topics: Research Specific Nathan Clark, Ph.D. Clark, Nathan Professor Assistant Our overarching goal is to understand how the functions of proteins 20 Section II : Research and Other Scholarly Activities

Associate Professor Faeder,James R. Ph.D. experimental collaborators. by out carried be may whichexperiments, novel of results the predict to used then is ensembleThe resulting data. experimental of set given a with consistent are that model a of behaviors possible the all find qualitative gaining Faeder on Dr. is focus end, this the Toward discoveries. therapeutic ultimately systemsand experiments guide to biologicalused be can that of insights models developing In parameters. model of rule of simulationand formulationefficient Besides complex the how of models accurate develop to order geometriesof cellsaffect signaling dynamics. in simulations resolved spatially performing rule of integrate Modeling to working Multiscale also Center is group the the (MMBioS), of Systems part Biological As models. of analysis comparative perform to visualization model improved of usingandnetworkmodels, reaction frombiochemical development rules automaticinferenceoftools, algorithmsand the are rule group for the capabilities within visualization research active side development the On range of scientists, including those with limited mathematical training. rule the Faeder making at geared Dr. is which BioNetGen, package systems. chemical signaling standard of using representation formulated concise are models and tractable a such provide and networks, such for functions generating when as serve Rules approaches. kinetics species signaling of intracellular explosion of combinatorial models the detailed limit to of requiredassumptions hoc ad making constructionwithout data and knowledgeavailable on based allowspathways approach This processes. that decision rules of form the in models describe basic biochemical formulating interactions and applying these methods to gain greater understanding by of cell biochemistry molecular simulating and representing Faeder Dr. ’ ’ s lab has been developing parameter estimation tools that use ensemble use that tools estimation parameter developing been has lab s rsac ivle te eeomn o nw ehd for methods new of development the involves research s fromsite regulatethe offlow information using intertwined feedback and feedforward loops, which arise mustprocessand integrate multiple downinputs, to thelevel of (B) signaling networks that Cellularinformation processing at multiplelevelsresolution of from (A) thewhole cell,which - basedmolecular biochemistry (inset).

- ae mdl, h itgain f rule of integration the models, based

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based models, another major issue is the estimationthe is issue majoranother models, based - based modeling approach accessible to a wide a to accessible approach modeling based ’ lb anan ad eeos h software the develops and maintains lab s Department of Computational and Systems Biology

- - based modeling with tools for tools with modeling based ae ad pta simulation spatial and based - based methods to methods based Annual Report, FY16

Section II : Research and Other Scholarly Activities . 21

bio -

Juan Tapia, Sanjana Gupta, John Sekar, Sekar, John Gupta, Sanjana Tapia, Juan -

Dr. Faeder and four members of the lab en route from q route of recent en a four lab members and the Dr. Faeder Jose right: to Left conference. Robert and Sheehan. Dr. Faeder, s s efforts in method development are tightly coupled to applications of the methods to specific ’

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual novel mechanism for regulation of PTEN for regulation novel in mechanism T that The model cells. a also feedback positive demonstrates loop involving PTEN sets a threshold for the signaling strength and duration required for differentiation to form T helper cells, and experiments have confirmed the operation of this mechanism. Other systems being actively activation. cell in T aggregation receptor investigated multivalent and spores, bacterial of germination include cytokine signaling in the immune system, heterogeneity in biological biological systems of interest and driven by data from involves experimental developing collaborations. models One of collaboration the biochemical signaling pathways involved process that is in dysregulated opposite ways and response) in immune in autoimmune cancer (suppressed T cell differentiation, a disorders (hyperactive immune response). The model demonstrated how the structure of the network could signaling lead to differential responses depending on the duration of T cell stimulation through its antigen receptor. Development of a detailed model of the network lipid led phosphatase, to as the a identification of critical regulator PTEN, of a the differentiation process, which led to the discovery of a The lab 22 Section II : Research and Other Scholarly Activities

Assistant Professor RobinE.C. Lee, Ph.D. understand how the microenvironment affects signal culturesembedded ECM.in in affects epithelial mesenchymal between fate and transdifferentiate To cell to microenvironment and induced cells drugs. dynamics single signal the quantify to we transduction, resistance how and understand sensitivity responsiveness, different display frequently cells cultures, 3D and 2D in grown cells comparing When (ECM). matrix other extracellular of or vicinitytypes cell the in and another one with contact close in frequently are cells which in tissue, a of microenvironment the bridge dimension. third the in transduction Signal fine sequences promoter different of responses. features how determine to assays gene mediate and signals dynamic decode to proteins repressor type incoherent an NF use with work Our molecules. same the by regulated are they though even signal same the to distinctly respond can genes different and genes, nearby of transcription repress or activate competition. through diversity Transcriptional how of models cellular predicting inform of relationships goal the These responses to stimuli. with cells, fate. single cell in information process 2) and andtransmit circuits signals,molecular dynamic other 1) with correlate localizationproteindynamic abundancecellsignals cells,samehow andand livingobserve theinin then of dynamics monitor to biosensorsfluorescent use We cell. each within localizationssubcellular single distinct from emerge transduction. signal of Dynamics populations cell of Specific disease. responses in decisions how fate cell projects in the lab include: understand manipulate rationally to to strategies develop is and heterogeneity, goal ultimate The single with process decision the of models mathematical develop and signals molecular single input observing By cytokines. inflammatory to responses on emphasisparticular with circuits, molecular through flows information how understand Lee Dr. decisions are made.

- oeue irsoy ih irfudc, h Le a am t dcd te enn o dynamic of meaning the decode to aims lab Lee the microfluidics, with microscopy molecule in vitro in ’ rsac cmie picpe o sses n snhtc ilg wt large with biology synthetic and systems of principles combines research s

and - ie tts oprn 2 clue wt 3D with cultures 2D comparing states like in vivo vivo in

- fe frad (I1 forward feed 1 tracked within single cells to gain a predictive understanding of how cell fate cell how of understanding predictive a gain to cells single within be tracked must and use, cells that language molecular the of component semantic a cell eachcriticalto is that code temporal a provide localization, or abundance protein in changes as such circuits, transduction signal within molecules of behaviors isdynamic principle that emerging an surface, the scratch to begun only have we Although environmental or drugs, ‘ to response their compute to molecules of interacting language a use effectively cells this, By response. and adaptive stimulus, survive an mounts of then intensity or transduction type To the signal about information molecular process decisions? that engagescircuits cell make each challenges, cells environmental do How cues

systems by more closely mimicking closely more by systems

’, and make decisions such as whether to differentiate, proliferate, or die. or proliferate, differentiate, to whether as such decisions make and

In response to inflammatory cues, proteins rapidly reorganize to to reorganize rapidly proteins cues, inflammatory to response In

- F) ewr mtf ossig f optn atvtr and activator competing of consisting motif network FFL) - upt eainhp i te ae el sn live using cell same the in relationships output

’ 3D cell cultures cultures cell 3D s response. Dynamics of signalingresponse.thereforemoleculessDynamicsareof

Proteins bound to DNA promoters can either either can promoters DNA to bound Proteins -

- specific responses. We use single use We responses. specific Department of Computational and Systems Biology - ue ies transcriptional diverse tune - ĸB suggests that cells that suggests ĸB - - cell resolution. cell cl dt to data scale Annual Report, FY16 - el and cell - - cell cell

Section II : Research and Other Scholarly Activities . 23 , high - s antitumor s effect and antitumor

’ identify identify drug targetable

and

This project is in collaboration like like transcriptome profiles and

- lowering lowering agents, selectively inhibit the -

mechanisms cadherin cadherin expression, a signature that is -

or or neurodegenerative diseases.

-

mesenchymal mesenchymal transition during initiation of the metastatic cascade.

- to

cells in the presence of various signaling molecules and drug gradients or or in cells and the of gradients molecules presence drug signaling various - Joseph Joseph (Machine Learning, Carnegie Mellon University) and Hanna Salman - E. coli level level understanding of the underlying

- approved approved drugs and other com­pounds that enhance the cancer inhibitory effect of and metabolic enzyme targeted approaches. This project is in collaboration with -

- sensitive sensitive cancer cell lines. This goal will be achieved by using computational -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual activity activity of complex molecular interaction networks of the cell which are often dysregulated in diseases. To obtain a system interven­tion points we build autophagy computational to models assess of their cellular approaches processes differential could such dynamics contribute as in to diseases, apoptosis response including the and cancer, to radiation development various of Bahar. Ivet colleague, CSB with cytotoxic (poly)pharmacological stresses. strategies These for diverse aim to identify FDA statins on statin content screening (Pathology). Wells Alan and [CSB]) Biology & Systems (Computational Benos Takis modulation pharmacological their for modules cellular coupled Modeling Cell fate decisions are made in the context of signals environmental and internal cues by the combined Cancer metastasis inhibition with statins with inhibition metastasis Cancer Metabolic reprogramming is essential for tumor growth and the formation of metastases. Most primary tumor cells undergo epithelial We previously observed that statins, widely used growth cholesterol of slowly dividing abundant cancer cell cyto­solic lines vimentin with and of cancer characteristic cells. metastatic We study the of mechanistic basis mesenchymal statin absent cell cell surface E combine combine modeling with experiments to identify basic principles by which bacterial cells communicate platforms microfluidic using environments defined and chemically physically create We other. each with and test of the behavior when interacting with chemically coupled but spatially separated bacterial swarms. collaboration This with project Ziv is Bar in Pittsburgh). of University (Physics, understand understand disease pathologies and below. described are develop projects research active Currently them. improved diagnostic and therapeutic approaches for communication cell of bacterial principles Design Communication and play coordination a major role in the ability of bacterial cells to adapt to changing environments. Recent work has shown that such responses coordination including underlies their ability several to aspects form of biofilms and bacterial develop antibiotic resistance. In this project we Associate Professor, Pathology and Computational & Computational and Pathology Professor, Associate Biology Systems My overall research interest is on using system biology approaches to Zoltán N. Oltvai, M.D. M.D. N. Oltvai, Zoltán 24 Section II : Research and Other Scholarly Activities

laboratories. post faculty, multiple involve programs These programs. the drive will supportfoundation collaborationsand company pharmaceuticalgrants, ofcombination A lead what on decisions alter could process discovery the compounds in toselect for further development. potency as well as heterogeneity, the of level the analyze to ability The developingdata. screeningdiscovery drug on in heterogeneityquantify to workingand identify been also have I cancer. breast non including disease human of models as well as studies,toxicologyperform to used be will system chip a on liver The hepatocytes. studies.PDand Presently,useprimary we humanhepatocytes, butareworking convert to toiPS Database physiologicalone a the stateover microfluidicmodelsystem, are we developing fluorescence cell 4 human, 3 a developing in effort major a have I medicine. personalized advancing and todiscovery drug (QSP) pharmacology systems quantitative applying plans development andthe research of UPDDI part overall as program research personal my both develop to continue I Professor D.Lansing Taylor, Ph.D. - ” D, microfluidic, liver model chipson with funding from the NIH andthe EPA. partAs of the liver that will be used to capturemultiplexedPKmodels dataandused to thefromforbe the datamodel will sets that

- alcoholic fatty liver disease, hepatocarcinoma and a metastatic site for site metastatic a and hepatocarcinoma disease, liver fatty alcoholic - month time period. In addition, we are creatingarea addition,we In period. monthtime

“ - - os n suet wrig cos multiple across working students and docs eeoeet Indices Heterogeneity basedbiosensors for real Department of Computational and Systems Biology ” ht il e sd to used be will that “ - Microphysiological monitoringtime of Annual Report, FY16 - derived - type

Section II : Research and Other Scholarly Activities . 25

311+G(2d,p) 311+G(2d,p) - H H in ubiquitin using AMBER 1 with the FF14SB force field. force FF14SB the with These studies were carried out in collaboration with Gronenborn from the Department Dr. of Angela methodology Structural paper prepared for is publication. additional Biology. Three being studies designed to of take advantage this have A approach. been The comparing the first chemical differences shifts involves differentmutations in caused protein. by thein OAA The the comparing errors in calculated 9 second chemical involves shiftsassociated different force with fields. involves evaluation The of third polarizing chemical on calculated fields force shift errors. The Figurebelow from our paper methodology shows the large systematic error associated shifts with chemical the calculated for N H

1 15 H H atoms of the peptide bonds. Smaller 1 H H and 1 systematic systematic

- , a C 13 localized regions localized to a library representative of conformers N N atoms were also detected. We believe these errors could 15

N atoms. The calculated values for 15 Ca Ca and 13 and and

a C 13 H H error. There were also localized non 1

Comparison of the calculated chemical shifts for

length and contained 100,000 samples. The differencesbetween calculated and observed values were small for were consistently low by an average of 1.388 ppm. There appears to be systematic component to a the large conformations. rotameric incorrect tobe related may that differences Figure Figure 12. atoms with experimental observations (BMRB 6457). The averaged from 20 chemical separate ubiquitin simulations. shifts Each simulation was 100 were ns in

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual serve as metrics for guiding the development of new force fields. force new of development the guiding for metrics as serve of an MD The simulation. matched approach with calculated chemical shifts. Thechemical shiftsfor the librarywere obtainedfrom QM calculations based on the GIAO approach using density functional theory at basis the B3LYP set. level with a The 6 library contained proteins. 168,499 Chemical shifts conformers calculated from sampled ensembleaverages were from compared with observed the values in simulations MD trajectories of of 6 these model interatomic model distances proteins. was A identified systematic large errors affecting that the systematic affected error the correlated with abnormalities in (MD) (MD) simulation is determined from calculations these coordinates field. If force underlying the and based package of simulation the function a are coordinates on classical mechanics. The values of are these chemically realistic, coordinates should NMR match experimental observations. chemical Unrealistic atomic coordinates shifts should chemical shiftsdeviate that from values. The observed errorchemical in shifts provide might calculated lead to calculated from a To developed quantum we this test hypothesis, force a field for theperformance. direct means evaluating ensemble averages mechanical (QM) approach for measuring the chemical shiftsof peptide atoms backbone in eachframe these of Associate Professor Associate dynamics molecular a of frame each in atoms the of orientation spatial The John K. Vries, M.D. John K. Vries, 26 Section II : Research and Other Scholarly Activities

Associate Professor Jianhua Ph.D.Xing, ofre svrl oe peitos Crety e r wrig n h floig projects: following the on working are we Currently predictions. cell model different and explain to several experimental modelexperimentally we mathematical paper, integrated (2014) confirmed Signaling a Science subsequent proposed uses a we In EMT. article, during lab appeared Journalphenotypes Biophysical The 2013 metastasis. a In cancer and kidney, and computational approaches liver, to study the coupled gene expression andepigenetic dynamics EMT.of lung, in fibrosisas pathologicalprocessessuch and healingregeneration,wounddevelopment, tissueembryonic cell of loss by Epithelial on characterized focuses currently lab species the direct molecules and Specifically, regulate of phenotype? one can thousands cell How do phenotype? Howcell a determine to questions.spatially and fundamentaltemporally orchestrate following the in interested are We Research “ Wedevelopingat aim cell transition phenotypic studies as 4.

roles EMT.in its and expression, gene and modification epigenetic betweenquestion intriguing the exploring a Nobel developed have We regulation. phenotype physical of model for histone modification dynamics, and used the model layer to explain a long important are modifications Epigenetic TGF : Physical biology of cell phenotypic transitions phenotypic cell of biology Physical : β

strength and duration and respondaccordingly. - rz wnig uze n oolei ofcoy eetr ciain Crety e are we Currently activation. receptor olfactory monoallelic on puzzle winning Prize

a field physicala of sciences. - el dein n icesd el oiiy ET ly iprat oe in roles important plays EMT motility. cell increased and adhesion cell ontem lmns atfly W ae sn qatttv snl cell single sensecells how decipher quantitativeto mathematicalmodeling and measurements using are We faithfully. elements downstream sensethe externalto them environmenttransmitandinformation the to 3. cancercells? in dysregulated becomes What principles? decision? design the the make are cell What a does How EMT. and senescence, apoptosis, 2. E process and cell with cells (see time cell single network performing regulatory EMT the in 1.

While under stress, cells choose different cell fates such as ever under live Cells such fates cell different choose cells stress, under While CRISPR the Using

- to - cell heterogeneity. - Cas9 technique, we are labeling different players different labeling are we technique, Cas9 - changing environment, thus it is important for important is it thus environment, changing

- lapse imaging to study the dynamics of the of dynamics the study to imaging lapse

- Department of Computational and Systems Biology to

- eecya Tasto (EMT), Transition Mesenchymal ”

- cadherin Annual Report, FY16 - F) and GFP), - standing

Section II : Research and Other Scholarly Activities . 27 - state state and - structural models of of models structural - explaining the physics physics the explaining

(WE) software, which is now in ” Statistical Physics of Biomolecules: An are are only the beginning of the story. We — PhysicalLensOnTheCell.org, without without motion. Beautiful but static experimental weighted weighted ensemble atom protein binding and to highly complex systems systems highly and to complex binding protein atom

“ — -

free free resolution resolution models of proteins for application to small - -

informed kinetic informed models. A current focus is the rotary of mechanism -

binding domain of the estrogen receptor. estrogen the of domain binding scale biology are inherently dynamic. No signal can pass, no ligand can can ligand no pass, can signal No dynamic. inherently are biology scale - -

scale models. scale -

: Computational Study of Protein Binding, Allostery, level and systems and level -

and is currently developing on online book, book, online on developing currently is and

type ATPases. type which which indeed explain substantial biochemistry - complex and cell and complex — - and V and

-

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual the the remainder of the flexibility. protein represented in The a approach ligand the in occur that mutations simplified is form being to applied save to resources designing while drugs preserving suitable for Pedagogically, Professor Zuckerman is metastatically the author of a enriched textbook, Introduction, undergraduates. advanced for processes, of cellular use around the world. Zuckerman and colleagues developed WE methods for sampling use the steady WE for sampling world. around methods developed and colleagues Zuckerman to all have applied that been conditions equilibrium signaling. cell of models biology Another focus is the development with detail, in atomistic site is binding the modeled design. Methodologically, and drug binding molecule of mixed molecular To start to of the studies structural bridge and systems biology, the mechanisms now between gap group using structure molecular machines F the The group has been active in developing scale and Molecular Machines Machines and Molecular molecular Both bind, no motor can walk, no enzyme can structures catalyze play change that a key of pathways conformational and structures of protein ensembles pursue therefore but also non We machines. dynamical study and molecular role allostery in binding, Associate Professor Associate Activities of Educational Chair Vice Interests Research Daniel M. Zuckerman, Ph.D. Zuckerman, M. Daniel 28 Section II : Research and Other Scholarly Activities

and science in didactic careers successful plus for experience them research prepare to mentored medicine. activities primary, enrichment a additional and with training students provide Academies Drug present) Pittsburgh of University 2011 the (CoSBBI; and Informatics, Biomedical of DiscoveryInstitute. These programs, theComputational Systemsand BiologyandBiomedical Informatics Department the Institute, Cancer Pittsburgh of University the have with collaboration I in programs level, research summer schooltwo directed highand founded the at Lastly, backgrounds. racial and in geographic, academic, different Experimentation of host and TrainingProgram and graduate challenging PI a as calledprovides serve Director, I which for program program, Pitt at REU (REU) TECBio The (TECBio). Biology Undergraduates Computational for Experiences Research for DOD and students NSF an hosts graduate also department Our careers. our future their prepareand schoolgraduate in helps success that Co series development professional the a is program), which graduate,MetaSchool, the the on in focused course have lines Co the these am make I program, along to continue efforts to My are goals. jointCMU wethe In level.school high even undergraduate,and if research necessity our a towards and community progress scientific the of responsibility important fly Teaching/Mentoring/Outreach fruit the use We cells. vivo and in vitro, and the ability to easily observe developmental phenomenon in the living, intact animal. apoptotic molecular of of array large the humans, with function phagocytosis proper the melanogaster for required genes from geneticallynovel,far a using ifare we processend this To this complete. of grasp full our discovered, been have cues these of some While phagocytosis? efferocytosis and of progression betweenapoptosisthelink interested inare Inwe particular,cells. dyingelimination effeteandproper of the in result proper can a pathways signaling maintaining auto theseand for cancer in adults Errors body. in the important in also balance homeostatic are processes These to (efferocytosis) development.engulfment cell proper and (apoptosis) ensure death cell of processes orderly an by eliminated are Research Associate Professor Joesph C. Ayoob, Ph.D. Non

- Tenure Track Faculty –

As we develop, we make too many cells. Thesecells.manydevelop,wetooextraneousmake we cells As

as our modeloursystem as theseconservationforstudiesdegree ofhigh accounton its ofgene of - 02 ad h Du Dsoey Sses n Cmuainl ilg (iCBo 2013 (DiSCoBio; Biology Computational and Systems Discovery, Drug the and 2012) - immune diseases. My research investigates the molecular mechanisms that ensure the ensure that mechanisms molecular the investigates research My diseases. immune –

o d cls ht r tigrd o i sga ta te ne t b eiiae by eliminated be to need they that signal die to triggered are that cells do how - ietro teLbrtr Mtosfr opttoa Booit Cus ( core (a Course Biologists Computational for Methods Laboratory the of director

–

ecig u t ad riig h nx gnrto o sinit i an is scientists of generation next the training and to out Reaching - hi o te diig omte, n Co and Committee, Advising the of Chair

- level experience in computational biology to students from a from students to biology computational in experience level

- genetic, biochemical, and imaging tools available in available tools imaging and biochemical, genetic, - encoded, cell engulfment reporter to identify the identify to reporterengulfment cell encoded, - Pitt Computational Biology (CPCB) graduate(CPCB)ComputationalBiology Pitt Department of Computational and Systems Biology - ietr f h CPCB the of Director Annual Report, FY16 Drosophila Drosophila - funded -

Section II : Research and Other Scholarly Activities . 29

state, state,

gated gated ion - facing closed closed facing - occluded - facing) facing) changes in - holo (Cheng and Bahar, and and inward

- 2. My research interest and expertise lie -

lipid lipid interactions. I am particularly interested in -

facing open (IFo*/IFo) state. The study confirms the conserved conserved the confirms study state. The (IFo*/IFo) facing open -

lipid lipid and lipid scale scale modeling technology to investigate biological systems at both - -

fold fold family, while providing insights into the mechanistic role of specific residues in 81;2015) - -

Dopamine translocation through hDAT, captured by MD simulations. MD simulations. by captured hDAT, through translocation Dopamine facing open (OFo) state, to closure of the extracellular gate leading the to leading outward state, togate open facing closure of extracellular (OFo) the

: -

Research Assistant Professor Assistant Research Mary Hongying Cheng,Mary Hongying Ph.D.

Department of Computational and Systems Biology Systems and Computational of Department Figure 1 Figure 2171 23: Structure FY16 Report, Annual release of substrate and ions in the in inward the ions and substrate of release dynamics of LeuT hDAT. microseconds microseconds or slower for methods. global). We have implemented multiscale approaches that combine Their conventional molecular dynamics examination thus (MD), necessitates targeted MD, to accelerated adoption MD, and of properties anisotropic and multiscale network transport cycle model of NSS (ANM) family members to LeuT investigate and human the Our dopamine transporter binding (hDAT). simulations reveal at atomic outward resolution all successive stages to a to proceed of TM helices rearrangements accompanying with (OFc*), along state from substrate recognition in the methodology that can be applied to investigate ion permeation through membrane protein channels. protein membrane through permeation ion investigate to applied be can that methodology My current research focuses on the functional mechanism of neurotransmitter:sodium symporter (NSS) family and its interactions with drugs and substrate proteins. function of Unraveling NSS family members has been a the challenge due to the involvement of both molecular local (extracellular mechanism of or intracellular gate opening/closure) and structure. global These (between events outward usually occur at different time scales (e.g. tens of nanoseconds for local, including including several types of sodium coupled channels, neurotransmitter CLC channels/transporters, and transporters, gap junction neurotransmitter Claudin in protein modeling and functions, medicinal i.e., chemistry, transporter focusing cycle; on sites and i.e. binding ligand ii) receptors, of protein drug modulation iii) selectivity; and charge conductance molecular ion mechanisms transport of through i) membrane binding transporter protein channels, affinity; i.e. channel and development/implement of iv) multi protein molecular and cellular levels. I developed a hybrid molecular dynamics/Brownian dynamics I have modeling experience in a wide range of ion channels/transporters, 30 Section II : Research and Other Scholarly Activities

h ga o m rsac i t ue eoe cl dt t avne our advance to data scale genome use to is research my of goal The Assistant Professor Maria Chikina, Ph.D.

dvlp e mcie erig ehius ht a fully can that several techniques learning have existingdatapredictnuanced,toallutilize context machine new may develop genesI that a different in functionsarethat is contextresearch my In dependent. function organism gene predicting multicellular the in challenge important understand One and function, gene disease. molecular basis of disease. predict and networks, health gene build to datasets in large use that methodscomputational organism, a of complex function the to contribute genes how of understanding te fcs f y eerh s h dvlpet f new of molecular perturbations anddisease states. development datasets the clinical targetedrelationshipsbetweensubtleelucidatingcapable are of which is analyzing research for methods my statistical of the and focus diseases, human other of studies in difficult particularly context Inferring function. gene

Department of Computational and Systems Biology

- eedn gn fnto is function gene dependent

Annual Report, FY16 -

dependent develop I

Section II : Research and Other Scholarly Activities . 31 cell basis cell basis - investigator investigator by - - picked picked cells to -

and and improved methods for the segmentation of subpopulations ’, ’,

DB) for associating data generated by the module with human safety - hyperplexing ‘ based based cell analysis, going from a few measurements on a few hand -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual on an NCATS funded project to a develop human liver module microfluidic for safety and efficacytesting, I am developing High Content imaging methods for the analysis of cellular function in the module, and a Microphysiology Database (MPS and efficacy datafrom a variety of sources. Thiscombined datawill beused to construct computational efficacy. and safety human predict that models application application have been implemented in R, published, integrated into a commercial software collaboration. another in Pfizer application at tested being are and collaboration, a through HCS has been principally focused on the demonstrated use significant of differences 2D cellular in models cellular relevant in 3D cellular models based microplates. on functions human cells, is expected to in have Recent major impact on development data of 3D has models.drugs for and testing compounds effects, buthuman also topresentmany challenges. As co The development more of of cells using highly multiplexed of data. cells multiplexed using is highly The goal to use of profiles function on cellular a cell networks. signaling underlying the in activation pathway variable the identify to from of vary widely lab heterogeneity Methods characterizing to of lab, and measuring the limiting ability the and proposed developed we have limitation, this To address toheterogeneity. and interpret compare adoption of three indices of heterogeneity that distribution characterize the contains overall diversity, subpopulations whether or and not the the degree of outliers. These three indices and their whole whole story with regards of differences these An understanding discovery. and drug disease, biology, in cell implications important to cellular function. The at differential the response level of system. whole the target to therapies of combination developing a the minority of cellular cells pathways has will be important To optimally in assess heterogeneity understanding at the polypharmacology, cellular developing level, methods for calibrating imaging and cytometry systems, methods across to enhance the multiplexing of a wide range of cellular cellular function, measurements, I am development development High Screening Content (HCS) as a method for high throughput cell biology. HCS sparked a revolution in image multiplexed measurements on large objectively populations chosen populations of of cells. cells, Meanwhile, genomics providing and resulted in maps inference of many signaling pathways. In interconnected both cases, the proteomics principle focus statistically profiling methods significant have data on has been on understanding the activity of populations of cellular is activity and the therefore heterogeneous, cells. is important, average while activity, certainly not the However, it has long been known that Research Associate Professor Associate Research Prior to coming to the University of Pittsburgh, my focus was on the Albert Gough, Ph.D. Albert Gough, 32 Section II : Research and Other Scholarly Activities

*Joint work with CarlosCamacho. projects,including those targeting the metabolism of cancer (SHMT, PGAM1) and stroke (CYP4F2). Drug Discovery not the size of the library being searched.(http://pharmer.sf.net) in structures query,chemicalthe of complexitythe with performancescalesPharmertechnologies, other theof Unlikeofseconds. millions search to structure data index spatial custom a uses that technology Pharmer* 3D general a with compounds http://zincpharmer.csb.pitt.edu purchasable of database hydrogen bonds). ZINCPharmer uses the Pharmer search technology. ZINC the searching pharmacophore model(a spatial arrangement essential of interaction features, such hydrophobesas and for interface online ZINCPharmer* optimizer. (http://anchorquery.csb.pitt.edu and http://nucleoquery.csb.pitt.edu) lead and engine pharmacophoresearch novel a be exploited is proteinchemistry This acid. nucleic or amino an target to biased ofmimic a is thatanchor fragment an includedesignedto arechemistrydatabase MCR an compoundsin is that space chemical AnchorQuery/NucleoQuery* small promising AnchorQuery and ZINCPharmer. most(http://pocketquery.csb.pitt.edu) a the at residues of identifies clusters key identifying and exploring interactively protein for service web a is PocketQuery PocketQuery* a in matter of seconds.(https://github.com/dkoes/shapedb) precise a sculpts matches for screeneduser be can shapesThe molecular of millionsand shape moleculardesired the scheme. of definition indexing novel a using shapes molecular specific for searching for ShapeDB* small high of general, a estimation developed affinity have and We site. minimization empirical scoring forked fromAutoDock Vina called binding smina. (http://smina.sf.net) energy a for in posed function molecules scoring empirical an designing Ligand essential an is 3Dmol.js browser. web a component for supporting online collaboration in andworkflows. (http://3dmol.csb.pitt.edu)graphics 3D accelerated hardware using data molecular 3D 3Dmol.js chemical of libraries screen and create to technologies compounds.(http://pharmit.csb.pitt.edu) smina and 3Dmol.js, Pharmer, ShapeDB, our Pharmit drug computational to the accuracy of virtualenhancing screening. Specificand barriers projects include: discovery of pace remove the accelerating for to methods computational novel create is I discovery. research my of goal The Research Assistant Professor DavidKoes, Ph.D.

- - Protein Scoring Protein protein interface. A consensus score derived using machine learning immediately and effectivelyand immediately learning machine using derived scoreconsensus A interface. protein

Online interactive exploration of chemical space. Pharmit is a web interface for exploiting exploiting for interface web a is Pharmit space. chemical of exploration interactive Online

Modern molecular visualization for the web. 3Dmol.js is a JavaScript library for viewing viewing for library JavaScript a is 3Dmol.js web. the for visualization molecular Modern Fast, efficient search of molecular shapes. We have developed a computational method method computational a developed have We shapes. molecular of search efficient Fast, Open

Online interactive pharmacophore search of the ZINC database. ZINCPharmer is an an is ZINCPharmer database. ZINC the of search pharmacophore interactive Online We are applying our methods in a number of ongoing collaborative drug discovery discovery drug collaborative ongoing of number a in methods our applying are We Rapid identification of small of identification Rapid - ore hraohr sac sfwr. hre i a hraohr search pharmacophore a is Pharmer software. search pharmacophore source Affinity prediction and hit identification of small of identification hit and prediction Affinity

Specialized pharmacophore search of an easy to synthesize virtual virtual synthesize to easy an of search pharmacophore Specialized

- oeue trig ons PceQey s nertd with integrated is PocketQuery points. starting molecule

- molecule starting points in protein in points starting molecule - rti ad protein and protein

Department of Computational and Systems Biology

- uli ai interactions. acid nucleic - efrac, rmwr for framework performance, - molecule inhibitors. We are We inhibitors.molecule

- protein interactions. protein

Annual Report, FY16

All -

Section II : Research and Other Scholarly Activities . 33

-

based based view in which drugs are used in -

target target view of drug discovery is yielding to a systems -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual heterogeneity heterogeneity and extracting distributions. Working useful in pathology. assist to organization biological collaboration spatial their and phenotypes cellular of distributions exploit to methods with information a team from at analysis GE, we of are cellular developing new phenotype computational depend depend on their local environments. Similarly, seemingly identical cells within a clonal population may express a range of phenotypes in response to external stimulation. major This obstacle cellular to heterogeneity developing effective poses personalized therapies. a In cancer,for example, slight differences determine whether or not a cell will go on to produce a tumor. genetic Our lab investigates the cellular origins of non heterogeneity and University its of Pittsburgh potential Drug Discovery for Institute, based we improving are diagnosis forging on new of methods for high cancer. lead development As content part analysis of the (HCA). We are refining methods for automatically quantifying Systems Systems Pharmacology (QSP) experiments. Among requires the computational challenges of QSP are the identifying pathways relevant to disease seamless and outlining the integration mechanistic details of of those disease pathways. computational pathways Our from lab modeling screening is data, developing and methods and is for central constructing inferring to mechanistic models the of progression pathways drugs. more or two of combinations to of that respond will cells how including screens, phenotypic are of cancer outcomes and other diseases. We are developing methods to predict the The cells within a tissue are not all identical, but exhibit a variety of behaviors and characteristics that interactions, interactions, the details of which remain quantitative understanding of these largely fundamental processes and how they unknown. can be safely Developing manipulated to effectivetherapies thwart requires a disease. Our Disease. Huntington's and lab as cancer such diseases of treatment focuses on constructing mathematical models to The assist single in diagnosis and combinations to alter the behavior of specific and multiple pathways. This approach, called Quantitative Timothy Lezon, Ph.D. Lezon, Timothy Professor Assistant Cellular behavior emerges from a complex network of chemical 34 Section II : Research and Other Scholarly Activities

Pittsburgh. Finally, my research efforts extend to developing approaches to detect and quantify and detect to approaches developing to extend efforts research heterogeneity in cell based model systems. my of University Finally, the across investigators support to Pittsburgh. projects other several as well as diseases, infectious iPSC an development of the ininvolved also am I drugs. molecule small by neurotoxicity of protection the in and progression disease the in involved pathways molecularcritical infer to chemogenomicdata with data transcriptomicaligning are we where program phen and computational to (QSP) Pharmacology My

r e s e a r ch

focus is in the application of Quantitative Systems Systems Quantitative of application the in is focus Visiting Research Associate Professor o d t e y v pe e l / o Mark Schurdak, Ph.D. f p unc m t o i o r n e -

ba e

ff s e ed c ti

v an e

a d l y r s g d ug i s - I m edn te Huntington the leading am I . based platform for neuronalprotectionagainstforplatform based i s c o v e r s y Department of Computational and Systems Biology tr a

t e g i e s

t h a t

u t i l ’ i Dsae QSP Disease s z Annual Report, FY16 e

i n t e g r a t e d

Section II : Research and Other Scholarly Activities . 35 PE. PE.

-

trivial trivial - lipid lipid interactions. -

1 - . . This work is contributing towards a picture of protein dynamics, which aids in in aids which dynamics, protein of picture ’ dimensional dimensional structure by employing multiple -

complete ‘ function function paradigm which -

is a key enzyme (lipoxygenase) involved in fatty acid 1 kinase inhibitory protein (RKIP), is involved in several -

This work has been submitted to observed observed to inhibit signaling pathways involving GPCRs. I modelled interactions of lipoxygensase with PEBP1 in an effort to understand the mechanism of inhibition P01 grant being submitted 2016. September by Wenzel and other collaborators in 15LO1 metabolism that arachidonic oxidizes acid at position unsaturated Phosphatidylethanolamine 15, binding protein to 1 fatty (PEBP1), generate also active known 15 acids, as HETE Raf including physiological functions and processes bovine rat in and mouse. living The interaction cells of PEBP1 of with 15LO1 was human, 1. This leads to subsequent

-

review.

protein interactions protein of interactions systems several with in different labs collaboration at the - grained network models, such as Gaussian Network Model (GNM) and Anisotropic (GNM) and Anisotropic Model as Network such Gaussian models, grained network - protein interactions are of particular interest, when one of the proteins functions as as functions proteins the of one when interest, of particular are interactions protein - protein protein or protein ligand interactions. The interactions proposed by such docking immune immune cell types. - -

based based coarse - s lab at the Department of Environmental and Occupational Health, University of Pittsburgh. of University Health, Occupational and of Environmental Department the at lab s ’ HMGB1(Melanie HMGB1(Melanie Scott, Dept. of Surgery Labs, University of - DNA binding site to facilitate inflammasome and caspase -

Department of Computational and Systems Biology Systems and Computational of Department non activation in hepatocytes. Modeling the interaction between these two proteins helped regulation in non understand under is currently and Hepatology the mechanism of FY16 Report, inflammasome Annual AIM2 Pittsburgh) with associates HMGB1 cytosolic liver, in mice shock hemorrhagic During AIM2 a at AIM2 to binds HMGB1 which autophagy. such as responses heptoprotective activates caspase I have modelled protein following: the include These Pittsburgh. of University of Pittsburgh) Univ, Care, Critical and Allergy Pulmonary, Div. Wenzel: Sally (Prof 15LO1_PEBP1 More More recently, large research Experimentally, efforts NMR is the technique are used to measure directed these interactions. However the towards molecular level detail is understanding not captured protein by these methods. Modeling the interactions between field protein I and am venturing lipids into. is The interactions my will a be modelled using standard docking alogrithms, and proposed the interactions will be validated by NMR experiments. Valerian This work will be performed in Prof. In the absence of Xray structures, protein The problem. understanding the mechanism of protein function, an of inhibitor such process and/or differentiation. a important as biologically proliferation cell signaling, is a non level molecular understand to employed, are methods docking and modeling molecular For this purpose, details of protein techniques. experimental biochemical by invalidated) (or validated then are studies equates equates the function computational modeling of approaches. At one the end structure of protein the computational to approach Network spectrum, Model its lies (ANM) used the to analyze three the global dynamics. simulations is Fully atomistic molecular used dynamics (MD) to or less more a provides MD and ANM of analyze combination the structural dynamics mechanism. at functional its understanding the residue/atomic level interactions. A Research Assistant Professor Assistant Research I am interested in exploring the structure Indira Shrivastava, Ph.D. Shrivastava, Indira 36 Section II : Research and Other Scholarly Activities

is currently in progress in Yong lab, to validate modelthe predictions. association.thecriticalfor areresidueswhich suggested somekey proteinsandtwo thesebetween PARP1 between interaction an of existence showed lab Yong The expression. gene regulate and DNA to bind also which Kruppelare KLF4treatment. cancer effectivenessofthe chemotherapy, diminish theywhere poly(ADP ADP of of Formation familyresponses. a are PARP University of Pittsburgh) PARP1 submitted to Mol. Cell and is currently under review. proteins. two these between progression.interaction the for critical residues the revealed which performed, and USP11 of modeling Molecular USP11. protein target mass initiation with using coupled the of tumor determinant Furthermore, to critical led a as mammary assay USP11 transformation of identification cell mammary a non A USP11 - biased screening of 67 deubiquitinases (DUBs) ondeubiquitinases(DUBs) 67 screeningof biased - - XIAP (Prof. Yong Wan, Dept. of Cell Biology, University of Pittsburgh) L4 Po. og a, et o Cl Biology, Cell of Dept. Wan, Yong (Prof. KLF4 - L4 Uig uainl xeietl aa rm og a, mdld h interaction the modeled I lab, Yong from data experimental mutational Using KLF4. - pcrmty XA ws on to found was XIAP spectrometry,

- ribose polymers is crucial in repair of DNA damaged by radiation and/or radiation by damaged DNA of repair in crucial is polymers ribose - ope purification complex - ioe plmrs poen wih r ivle i clua stress cellular in involved are which proteins polymerase ribose) This work is is work This - IP was XIAP

Department of Computational and Systems Biology

Annual Report, FY16 - like factorslike Work Work

Section II : Research and Other Scholarly Activities . 37 - s ’

based based - driven driven programs for -

institutional, highly cooperative efforts efforts cooperative highly institutional, - the QSP platform is its ability to embrace embrace to is its ability platform the QSP heterogeneity (PlosOne, 2014) anticipate and the to evolution mechanisms, of both major resistance challenges in drug discovery and development grams. The pro- full implementation of platform involves mul- multidisciplinary, this ti dedicated to gaining a standing of disease biology. Accordingly, holistic under- we have built QSP metastatic breast cancer, Huntington of diseases and disease, the several liver. The platform is helping to development drive these programs creating novel both therapies and repurpose existing opportunities drugs. For to example, in a study that helped inspire an (Cell, 2012) platform we implemented the QSP integrated target identification platform validation validation populations of patients. A key feature of for appropriate sub based based medicine to proactive personalized -

scale scale experimental and computational models to identify - molecule molecule screening that identified AURKA as a regulator negative -

defined defined pharmacokinetic and pharmacodynamics profiles fail to centric, centric, quantitative systems pharmacology (QSP) drug discovery - -

based based models in conjunction with chemogenomic and systems biology ap- - concept concept in Phase II and III trials. To address this challenge, we have developed a - of -

derived derived iPSC s disease program in collaboration with the Friedlander laboratory, the Brain Institute, and - ’ causing mutation. In focusing on the patient as the starting and the end as point, on the starting will the the In patient QSP focusing platform mutation. causing -

Department of Computational and Systems Biology Systems and Computational of Department help help promote the paradigm shift from medicine. reactive Furthermore, population by analogy to our recent studies (Nature Chemical Biology, 2013; Cell 2015) we Reports, have a taken QSP approach to model the tumor in microenvironment breast metastatic cancer (Journal of Pathology Informatics, 2016) Center. Microenvironment and Virus in Tumor the and Ferris Robert collaboration Dr. with with collaboration in cancer neck Chakra Chennubhotla and in head and FY16 Report, Annual gen receptor gen receptor mutations acquired during estrogen deprivation therapy to enable the identification of tar- our multidisciplinary 2016). Similarly, Research, Cancer (Clinical dependencies disease metastatic getable Huntington cell isogenic mammalian is using (NCATS) Sciences Translational Advancing for Center the National and patient proaches to identify optimal that therapeutic strategies address the of pleiotropic consequences the dis- ease involving proteomic, involving proteomic, shRNA, and small of differentiation in megakaryocyte the rare disease acute leukemia megakaryocyte (AMKL). Based upon these mechanistic studies and additional computational, and cellular, in vivo model studies, the clinical Alisertib candidate is being repurposed as for a therapy this specific innovel differentiation indication an ongoing Phase I trial. Our metastatic breast cancer program involving the estro- of Lee, relevance Oesterreich, clinical the and determining upon Puhalla focused is AstraZeneca with in collaboration laboratories Intrinsic Intrinsic to QSP is its integrated use of multi mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical in human complex disease progression and use this knowledge to develop novel optimized therapies for individual patients. All too often, even amidst the growth of genomic medicine, drug candidates with no limiting toxicity and exhibiting well demonstrate proof comprehensive, unbiased, pathway 2016). Screening, Biomolecular of (Journal platform development and Visiting Research Associate Professor Associate Research Visiting The overarching goal of our research is to identify mechanisms involved Andrew Stern, Ph.D. Stern, Andrew 38 Section II : Research and Other Scholarly Activities

Drug Drug Discovery Institute to createzonation a Pittsburgh model will vital be of in futureUniversity research. the at work recent the so modelzonation vitro in based human robust a of lack the by hampered been has zonation and disease liver between link the studying investigators However, re to diseaseprogression.etiologyandinplayer key a developed be thoughtandto injury induceddrugliver importantin the been between has collaboration liver a the of part of as compounds EPA screening use in University Pittsburghof Drug DiscoveryInstitute Vanderbiltand advancedUniversity. Recently, an model now is model This days. 28 for albumin of outputsecretion normal withmetabolism drug 3 and 2 1, phasedemonstrated has model a real quantitative, of provide deploymentto and validation flow re development, tissue by a surrounded cords is hepatic the recognizable model into organized are The which into model. organoid 2012 liver sinusoidal human the in of type construction cell participate 4 3D, microfluidic active relevant physiologically an de became to I information crucial possible and immediate provide experimental data entered preinto the database. only to not prior assays compounds in assay These zERG cardiotoxicity vivo in automated Zebrafish. an and clearance drug cell intrinsic drug safety for screen microsomal early Iimplemented of Pittsburgh, rapid hepatotoxicity,a on focus a University with toxicity of mechanismsrelevantclinically identify to assays based the at role research my accepting After toxicology from human cell crucialpharmacokinetic are These and findings. safety drug human and rodent of assessment quantitative a with database a into entryresultsfor clinical trial and studiescuratedpreclinical animal hundreds haveof I 2008, in Beginning of part the on desire human3D cell based experimental a model systems and computationalwith predictivetrial algorithms. animal safety traditional is the replace to goal forceeventual The testing. safety drivingdrug in used animals of Thenumber the reduce molecules.to industry drug the small and regulators European to and and Federal researchers, methods academic biologics analysis to and models risk vivo human vitro/in evaluate in develop to is focus My - through device. A subset of the integrated cells are genetically encoded with functional biosensorsfunctional with encodedgenetically are cells integrated the of subset A device. through - lncl addt slcin o ogig rjcs bt r icue i the in included are but projects, ongoing for selection candidate clinical - ‘ Lawrence Vernetti, Ph.D. based vitroin toxicology assays. truth - ie edus f el elh n mlclr ehnss f oiiy The toxicity. of mechanisms molecular and health cell of readouts time - acinus, the smallest function unit of the liver, using 4 key liver cell types cell liver key 4 using liver, the of unit function smallest the acinus, ’ data needed to build a computational model to predict human in vivo in human predict to modelcomputational a build to needed data Research Associate Professor - rae h mtblc oain on i te ie. oain is Zonation liver. the in found zonation metabolic the create

Department of Computational and Systems Biology

- like support cells in a media a in cells support like

Annual Report, FY16

- risk -

Section II : Research and Other Scholarly Activities . 39 beta beta signaling as - based based from analysis cell culture to -

content content analysis, transgenic zebrafish, and computational -

activated activated kinases, and have been implicated in cancer, immune -

content content analysis (HCA) capabilities. Collaborative drug discovery projects activated activated protein kinase phosphatases or MKPs. MKPs play important roles - based based method termed Cognition Network Technology (CNT) that emulates - -

based based methods to analyze fluorescent transgenic zebrafish embryos. Of particular interest -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual human human cognitive processes. Using this genetics approach, we screens are for currently performing activators automated chemical potential of regenerative Fibroblast therapies, and growth have factor discovered injury. of models mouse in activity (FGF) a number and of Nodal/TGF promising candidates that have using HCA include kidney and heart regeneration, breast cancer metastasis, and, recently, immunogenic immunogenic and, recently, metastasis, cancer breast regeneration, kidney and heart include using HCA cell death. A natural extension of my work is the of expansion image zebrafish. The zebrafish embryo is a particularly attractive candidate for contemporary drug discovery because it is small, easily kept and obtained, and optically transparent. The NIH is supporting currently collaborations with Neil Hukriede and Michael novel image Tsang (Developmental Biology) aimed at is developing an artificial intelligence Developmental Developmental Biology, Computational and currently Systems using Biology, a and inhibition. MKP to approaches novel engineer to combination modeling Pharmaceutical of Sciences, I high am My second focus is information the content continued of development biological establishment of the University of Pittsburgh of Drug Discovery Institute (UPDDI) as a assays. nationally respected novel Over drug the academic discovery center past with tools high ten that years, enhance I the have been involved in the affecting proliferative diseasesusing functional cellular andwhole organismmodels. My major targets of interest are the mitogen regulating the activities of mitogen response, and myocardial infarction. challenging due to a lack of structural The guidance for inhibitor design, the abundant use of uninformative search for small in vitro assays for molecule phosphatase activity, and the absence of definitive assays inhibitors to probe MKP inhibition in of MKPs the context of has the living cell or a whole been organism. With the help of collaborators in the Departments of Associate Professor Associate My research interests center around the discovery of small molecules Andreas Vogt, Ph.D. Vogt, Andreas 40 Section II : Research and Other Scholarly Activities

Collaborative Research Activities Peer Ivet Bahar Presentations at National and International Meetings Collaborative Activities Research Peer Review Activities - Review Activities University Reviewer: EditorialBoard Membership

13. Dr.Andreas Vogt and UvrB 12. Dr.Ben vanHouten,at UPCI, Molecular Biology on the mechanism of function of UvrA MAP kinase phosphatases 11. Dr.Michael Tsang (Biochemistry and Molecular Genetics) on developing inhibitors for 10. Dr.Lans Taylor andcoworkers at the Drug Discovery Institute mechanisms of matrix metalloprotein 8. Drs.9. Steve Shapiro and MuratKaynar on modeling the activation and deactivation Dr.8. Ed Prochownik,UPMC, on the development of c Dr.7. Oltvai (Pathology) on elucidating transcription/regulation pathways the scientific community Sciences) in the developmentof an online database of molecular motions,available to H.6. Karimi (Information Science and Telecommunications, School of Information Dr.5. Valerian Kagan on the inhibition of the peroxidase activity cytochromeof c. determined NMRby and X Dr.4. GronenbornA. on comparing the fluctuation dynamics of proteins structurally Dr.3 Joel Greenberger, UPMC, theon development of PUMA inhibitors apoptotic pathways Dr.2. Bard Ermentrout(Mathematics) andYoram Vodovotz (Surgery) modelingon necrotizing enterocolitis. Gary1. Silverman (U Pitt) on preclinical drug discovery for the prevention of neonatal and others Proceedings of Nationalthe Academy of Sciences Macromolecules PhysicalReviews E Phys Review Letters Structure Journal of Structural Biology Biochemistry: Polymer Bioinformatics BiophysicalJournal; Journal ofMolecular Biology Journal ofChemical Physics Protein Science Proteins: Structure, Function, Bioinformatics PLoS Computational Biology Molecular Systems Biology Cell Nature Science Protein Science, Proteins: Structure, Function and Bioinformatics, Structure, -

wide Collaboration with

2015

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2011

present

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present

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June 2008

(Alphabetized, Chair first) - Department of Computational and Systems Biology Myc - – Max inhibitors

present

Annual Report, FY16

Section II : Research and Other Scholarly Activities . 41

.

External Advisory Board Meeting. Meeting. Board Advisory External

protein interaction interaction protein - Pittsburgh, PA. Principal Investigator. Principal PA. Pittsburgh,

. Invited Seminar Speaker Seminar Invited .

Qun (Sean) Xie (Pitt, Pharmaceutical Science) and Dr. Eric Xing Xing Eric Dr. and Science) Pharmaceutical (Pitt, Xie (Sean) Qun -

NINDS Board of Scientific Counselors meeting. Bethesda, MD. Ad MD. Ad Bethesda, meeting. Counselors Scientific of Board NINDS

MMBioS National Center for Multiscale Modeling of Biological Biological of Modeling Multiscale for Center National MMBioS P30, NIDA Center of Excellence for Computational Drug Abuse Abuse Drug Computational for Excellence of Center NIDA P30,

NIH P01 Program Project application "Molecular structure, structure, "Molecular application Project Program P01 NIH - : :

:

University of Pittsburgh Drug Discovery Institute (UPDDI) External External (UPDDI) Institute Discovery Drug Pittsburgh of University Collaborative book writing meeting in Stony Brook University. Stony Stony University. Brook in Stony meeting writing book Collaborative : CATER Seminar Series Seminar CATER Center for Causal Discovery (CCD) Discovery Causal for Center P01DK096990 External Advisory Board Meeting, Pittsburgh, PA. PA. Pittsburgh, Meeting, Board Advisory External P01DK096990 :

:

: : NIDA NIDA

- 18, 2015 18, 2015 22,

18, 2015 18, - -

- 2, 2015 2, 19, 2015 19, - - 10, 2015 10, -

Brook, NY Brook, Meeting. Board Advisory External Systems Reviewer. Hoc Investigator. Principal PA. Pittsburgh, of University Attendee. meeting. (EAB) Board Advisory in cellular enzymes and transporters membrane key of mechanism and dynamics, Chair. Panel Review Online metabolism". 6. Dr. David Perlmutter (UPMC) on mitochondrial targeting against radiation damage. damage. radiation against targeting mitochondrial on (UPMC) Perlmutter David 6. Dr. glutamate of functioning of mechanism molecular the on (NIH/NIMH) Amara Susan 7. Dr. and of Chicago, University the at Roux Benoit and Perozo Eduardo Drs. transporters8. Structural Protein Membrane on The School Medical Weill Cornell at Weinstein Harel Consortium Dynamics Urbana Illinois, of (University Tajkhorshid and Emad Schulten Klaus with 9. Collaboration that method simulation dynamics molecular an efficient on developing Champaign) models. atomic full with models network elastic combines PI. Core Pharmacology Computational (CMU) on NIH on (CMU) (CDAR) Research protein a regarding (UPMC) Wenzel Sally Dr. 17. of development the on U Stanford at Schmidt Jeanette and Altman Russ 1. Drs. systems supramolecular examining for methods and models molecular of Prediction Computational the on project R33 on a Austin), (UT Elber R. Prof. 2. Bahar). (PI: Dynamics Biomolecular mechanisms allosteric the elucidating on Mass) (U Gierasch Lila with 3. Collaboration chaperones of activity the underlying of dynamics allosteric the of examination the on UK) (Cambridge, Greger 4.Ingo Dr. receptors. glutamate correlated of identification the on (Weizmann) Pietrokovski Dr. with 5. Collaboration information sequence from mutations 14. Dr. Alan Wells (Pathology) on ligand binding affinities of epidermal growth factor factor growth epidermal of affinities binding on ligand (Pathology) Wells Alan Dr. 14. Receptors Wipf Peter Dr. 15. Xiang Dr. with PI Joint 16.

December 4, 2015 4, December August 12 August 17 September 20 September 1 October 2015 11, November 17 November August 9 August

National Meetings: National External External

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

Presentations at National or International Meetings or International National at Presentations 42 Section II : Research and Other Scholarly Activities

Peer Panayiotis Benos Presentations at National or International Meetings Collaborative Research Activities Peer CAyoob Joseph

- - Review Activities Review Activities 2. Collaboration2. with Dan Zuckerman, Carlos Camacho,Nathan Clark,Timothy Lezon, Jianhua Collaboration1. with Chakra Chennubhotla Computationalin andSystems Biology; funded by Review1. Panelist, NSF Graduate Research Fellowship Program (GRFP) (Jan. 28; Feb.1, 2016) 1.A. Grants Invited Speaker, Discover Your Future STEM Career Tour, Grow a Generation (July 6, 2015). Selected Presenter,Academically Based Community Engagement Meeting, and network levels. and (ii) identify novel cancer therapeutic targets using computational analyses at the molecular mechanisms of thestress will:(i) identify novel apoptosissignaling pathway regulatorsand dissect the regulation pathways and molecules important for the progression cancer.of Our multifaceted approach experimentsto study the molecular mechanisms,networks, andtherapeutic targetsof signaling cancer signaling networks Department of Health CURE funds: Xing,and Maria Chikina Computationalin and Systems Biology; funded Commonwealthby of PA signals from dying cellsthat triggers their engulfment. project studies the molecular basis of cellular elimination by identifying andcharacterizing novel NIH International: - June 21, 2016: SBGrid June 6 May 26 March 15 March 13 September 7 June 29 February 10, 2016: Arizona State University in the Center for Biological Physics Seminar December 7,2015 NIAID Genetics(GGG), panel member National Institutes of Health(NIH) (06/2015),ZRG1 F08 Genetics(GGG), panel member National Institutes of Health(NIH) (03/2015),ZRG1 F08 Genetics(GGG), panel member National Institutes of Health(NIH) (11/2014),ZRG1 F08 Systems, (MABS), panel member National Institutes of Health(NIH) (10/2014), Modeling and Analysis of Biological (September 25, 2015). and Training Computationalin Biology Research, University of PittsburghHonors College Symposium Committee, Strasbourg,FR. Selection Committee Member. Supercomputing Center, Pennsylvania. Instructor. Speaker. Speaker. Symposium, 251st ACS National Meeting & Exposition. San Diego,California. Invited Series, Tempe, AZ.Invited Speaker. Seminar Speaker. -

-

R21AI101593: - 10, 10, 2016:Hands 27, 27, 2016: DataBig BioinformaticsConference. Boston, MA.Invited Distinguished -

July 1,2015 : The International HumanFrontier Science Program Selection - -

17, 17, 2016:"Landscapes, Pathways,and Kinetics in Biomolecular Simulations" 15, 2016:

-

9, 9, 2015

, Vienna,, Austria.Invited Speaker.

: NIH BTRR P41 Directors Meeting Universityof Maryland Biophysics Program,Seminar Series.Invited

“ - : Eat

response protein, Hsp27, which plays an important rolein this process ”. -

8th SFB35 Transmembrane Transporters Healthin andDisease

- ProDy on on Workshop Computationalon Biophysics. Pittsburgh Thisproject leverages strengths of modern biocomputation and - Me Signaling Cascade in the Apoptotic Cell Engulfment

Webinar “ Identifying andcharacterizing regulators and therapeuticsof

. Invited. Host.

, Bethesda,MD. Invited Speaker.

Department of Computational and Systems Biology - - - B (20)B L,Genes, Genomes and (20)B L,Genes, Genomes and (20)B L,Genes, Genomes and

“ TieredMentoring Annual Report, FY16 ”. This

Section II : Research and Other Scholarly Activities . 43 ), - In

), In

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), BMC ), BMC - ), ), - - We are now now We are enriched cell cell enriched

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developing various various developing

that will develop and and develop will that ), J Theor Biol (2009 Biol ), J Theor

- clinical data. Drs Benos Benos Drs data. clinical

and clinical data. clinical and

), Molecular Systems Systems ), Molecular identify mechanisms and mechanisms identify -

Modal Biomedical Data Biomedical Modal

- ), Cancer Letters (2012 Letters ), Cancer comics

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coding RNAs in in RNAs coding

), Gene (2008 Gene ), ), BMC Bioinformatics (2003 Bioinformatics BMC ), - - - ).

heterogeneous data streams. This This streams. data heterogeneous -

with Dr. Bahar (Comput & Systems Systems & (Comput Bahar Dr.with

), PLoS Computational Biology (2005 Biology Computational ), PLoS - ), Genomics (2007 ), Genomics analyze

- ), RNA (2009 ), RNA (Dept of Philosophy, CMU) we develop causal causal develop we CMU) Philosophy, of (Dept

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), Biochemistry (2003 ), Biochemistry -

), FEBS Letters (2004 Letters ), FEBS -

Glymour ), Bioinformatics (1998 Bioinformatics ),

throughput data associated with terminal diseases. terminal with associated data throughput - different compounds in order to order in compounds different

- Clark

the establishment of a Center of Excellence of Center a of establishment the

for

) and

We recently established a collaboration with Dr. Friedlander to Friedlander Dr. with a collaboration established recently We -

), Genome Research (2007 Research ), Genome

) Proceedings of the National Academy of Sciences (USA) (2003 (USA) Sciences of Academy National the of Proceedings ) . - Dr. Benos laboratory collaborates laboratory Dr. Benos -

), Nucleic Acids Research (2005 Research Acids ), Nucleic with Dr. Naftali Kaminski (Dept of Medicine, Yale School of Medicine) we Medicine) of School Yale Medicine, of (Dept Kaminski Naftali Dr. with with Prof. with

- Drs Benos and Duerr have one NIH grant funded together. funded grant NIH one have Duerr and Benos Drs Analysis of high of Analysis Investigating the role of coding and non and coding of role the Investigating Multi Large For Models Graphical Integrative Center for Causal Modeling and Discovery of Biomedical Knowledge Knowledge Biomedical of Discovery and Modeling for Causal Center with features image tissue of integration

Investigating molecular mechanisms involved in inflammatory bowel disease disease bowel inflammatory in involved mechanisms molecular Investigating

In collaboration with Dr. Duerr we have analyzed data from Th17 from data analyzed have we Duerr Dr. with collaboration In

wide ECCB 2015 ECCB - - ).

departmental - Acta Pharmacologica Sinica (2003 Sinica Pharmacologica Acta 2. Naftali Kaminski (Yale University, School of Medicine): of School University, (Yale Kaminski 2. Naftali title: Project collaboration and expression gene analyze to methods computational develop together. funded grants NIH two have and Kaminski populations stimulated with stimulated populations for work this of results the preparing now are We IBD. to relevant be might that SNPs publication. title: Project atherosclerosis formation. plaque in miRNAs and mRNAs of role possible the investigate publication. for work this of results the preparing University): Mellon (Carnegie Glymour 1. Clark title: Project collaboration and integrate to algorithms learning graph award. R01 NIH by an funded is work Project title: Project Data. Big from grant in aU54 Biol), NIH the of part is Center This Data. Biomedical Big to algorithms modeling causal apply initiative. BD2K title: Project are we Biol), Systems & (Comput Chenubhotla Dr. with collaboration streams. data biomedical multiple combine to methods tile: Project (IBD Applied Bioinformatics (2004 Bioinformatics Applied (2009 Genomics (2007 Biology (2011 One PLoS (2014 OncoTarget (2007 Physiol Biochem Compar (2007 Evolution and Biology Molecular 2015 RECOMB ISMB (**) Due to substantial service to peer review at the National Institutes of Health I Health of Institutes National the at review peer to service substantial to Due (**) 2016. September to 2014 August from Submission Continuous for eligible became

External

2. Robert Friedlander (Neurosurgery): Friedlander 2. Robert 1. Richard Duerr (Gastroenterology): Duerr 1. Richard 2. Chakra Chennubhotla (Computational and Systems Biology) Systems and (Computational Chennubhotla 2. Chakra 1. Ivet Bahar (Computational and Systems Biology). Systems and (Computational Bahar 1. Ivet 2.A. Intra 2.A. National Academy of Sciences Sciences of Academy National Manuscript reviewer for: Science, Journal of the American Chemical Society, Proceedings of the the of Proceedings Society, Chemical American the of Journal Science, for: reviewer Manuscript

2.C. 2.C. 2.B. University 2.B.

papers Conference 1.C 1.B Papers 1.B Review Activities Review - Department of Computational and Systems Biology Systems and Computational of Department FY16 Report, Annual BergJeremy Peer Activities Research Collaborative 44 Section II : Research and Other Scholarly Activities

Collaborative Research Activities Peer Mary Cheng Presentations at National or International Meetings Collaborative Research Activities Peer Carlos Camacho - - Review Activities Review Activities Prof. Susan Amara,NIH Prof. Gonzalo,Dept. of Neurology, University of Pittsburgh Prof. Kunhong(Kevin) Xiao, Prof. Sorkin, Dept. of Cell Biology, University of Pittsburgh Reviewer: reviewer two times for Invited speaker and topic leader:CCBC GLBIO/ 2016 (May15 (May 13, 2016, Kraków, Poland). Keynote Speaker Smoluchowski Symposium:Department of Chemistry, Jagiellonian University Berlin, Germany). Invited Speaker: 10 Groningen, The Netherlands). Invited Speaker: Seminar SeriesSchool of Pharmacy, University of Groningen (May 5, 2016, NIH, 2009 NSF,2008 Reviewer for grants of And sporadic reviewer for several other journals. JMB PNAS Bioinformatics BiophysicalJournal Proteins Regular Reviewer: Biology Editorial Boardmember,

, 2000 , 2000

, 1992 protein GPCR signaling complexes. of changes spatiotemporal multi as of mechanismsregulatory understand to wellii) and complexes;signaling GPCR as these dynamics, their multi and order complexes higher signaling resolve GPCR to i) approaches: computational and experimental b2 of dynamics kinetic schemes for microphysiology ( used to reconstructa realistic simulation environment and adopt physically mouse that expresses hemagglutinin epitope of electron microscopy and immunofluorescence labeling (using transgenic knock transitions between these substates. These data, along with images from a combination homology simulations helped us identify the most probable functional states accessible to usingcombination a of molecular andcellular structure We modeledand simulated the dynamics of DA release and reuptake DAby neurons Neuroscience 2016 annual meeting gating mechanism. An abstractwas submitted for presentation in the EAAT1 (Amara lab), to explore residues lining the anion channel and the anion channel molecular modeling together with the substitution cysteine accessibility method in mechanism. They also work as substrate homeostasisin the brain by clearing glutamate throughsecondary a active transport Excitatory amino acid transporters(EAATs) maintain extracellular glutamate Chemistry e ae ntae nw olbrto wt io a oivsiaete tutrs and structures the investigate to lab Xiao with collaboration new a initiated have We - - - Present Study Section PermanentMember To present 2011. Panel Member -

To presentTo - , 1992 To present

- modeled human DAT (hDAT), their populations as well as relative rates of th , 1992 -

To present Drug Design & Medicinal Chemistry Conference,Berlin (May 11

- deegc eetr ( receptor adrenergic -

To presentTo

Dept. of Pharmacology and Chemical Biology, University of Pittsburgh

Journal of Chemical Physics Chemical of Journal International Journal of Molecular Science Science Molecular of Journal International

.

MCell

β 2

- AR) gated anion channels.We are using advanced ) ) simulations. -

retnsgaig ope.W lnt use to plan We complex. signaling arrestin - taggedDAT performedin Sorkin lab) were

- 19 19 2016, Toronto, Canada). ; Department of Computational and Systems Biology one time for Journal ofBiological - based tools.Molecular

(2009 Society of of Society - Present) - Annual Report, FY16 12,2016, - based

- protein - in in

-

Section II : Research and Other Scholarly Activities . 45

types and and types

-

was submitted. was ”

s Research Institute, and General General and Institute, Research s ’

Investigation of the functional mechanism mechanism functional the of Investigation “ Me Signaling Cascase in the Apoptotic Cell Engulfment Engulfment Cell Apoptotic the in Cascase Me Signaling Prof. Peter Wipf (Chemistry), University of Pittsburgh of University (Chemistry), Wipf Peter Prof.

- gated calcium channels calcium gated - bridging experimental timescales with atomistic details atomistic with timescales experimental bridging

–

selectivity in anion channels. A joint paper was submitted. was paper A joint channels. in anion selectivity -

A joint manuscript is under preparation. preparation. under is manuscript A joint

protein docking analysis, molecular dynamics simulations, together with with together simulations, dynamics molecular analysis, docking protein -

analysis of Imaging Phenotypes and Genomic Signatures in the Lung DBP for the the for DBP Lung in the Signatures Genomic and Phenotypes Imaging of analysis -

wide

-

Mark Shlomchik's group: data analysis for a project investigating B cell sub cell B investigating project a for analysis data group: Shlomchik's Mark dynamics. center germinal Doucet, INRS, Quebec on computational identification of functionally relevant relevant functionally of identification computational on Quebec INRS, Doucet, intermediates biomolecular molecular and traits physical correlating group: Clarks's Nathan with Collaboration mammals in evolution 3. Computational Pathology: Ongoing collaboration with Dr. Lans Taylor (DCB) and Drug Drug and (DCB) Taylor Lans Dr. with collaboration Ongoing Pathology: 3. Computational Women Magee Lee, Adrian Dr. Institute, Discovery cancer on breast team Center Research Electric (Developmental Lo Cecilia Dr. with collaboration Ongoing Bioimaging: 4. Computational other and disease heart in congenital dysfunction ciliary of role the on Biology) ciliopathies Bates College, Castro, Jason Dr. with collaboration Ongoing Olfaction: 5. Computational Quinn, Shannon Dr. and Laboratory National Ridge Oak Ramanathan, Arvind Dr. perception odor of determinants physicochemical on Georgia of University Arvind Agarwal, Pratul Drs. with Collaboration Ongoing Biophysics: 6. Computational Nicolas Dr. and Laboratory National Ridge Oak Stanley, Christopher Ramanathan, 1. Co (Yale). Kaminski Naftali Dr. and (DCB) Benos Takis Dr. with grant BD2K Eat Bioimaging: 2. Computational (DCB) Ayoob Joseph Dr. with function of hDAT. of function hDAT. through dopamine substrate of path efflux the Exploring Chemistry, lab, (Wipf synthesis chemical strategic of combination novel a using are we (i) (Meriney function VGCC of measurements biophysical and mutagenesis VGCC U PITT), of model a develop to lab), (Bahar modeling computational and U PITT) neurology, lab, gating. Cav2 VGCC Wipf) and Meriney Bahar, (PIs grant A joint (ii) voltage of modulation drug and the studied we dynamics, molecular and studies experimental combined Using anion for basis molecular Using protein Using are we measurement, experimental functional and biochemical of combination the on effects modulation its and hDAT to sites binding protein Gbg the investigating

departmental departmental - -

Intra Wide University PlosOne Hepatology External University Biophysical Journal; Biophysical Bioinformatics; One PLoS RECOMB, Intra Reviewer: Reviewer: Korean Medicine, of College University Yonsei Lee, MinGoo Prof. and (Neuroscience) Meriney Stephen Prof. Review Activities Review Review Activities Review - -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Maria ChikinaMaria Peer Activities Research Collaborative

Activities Research Collaborative Chennubhotla Chakra Peer 46 Section II : Research and Other Scholarly Activities

Peer James Faeder Presentations at National or International Meetings Collaborative Research Activities Peer Nathan Clark - - Review Activities Review Activities Grant proposal reviewing Conference organization andreviewing Journal reviewing Editorial board member spermatophore “ Presentation Evolutionat Conference (Society for the Study of Evolution) Austin,Texas. "Exploiting Evolutionary Rates to Reveal Novel Protein Functions" Oral presentation atSociety for Molecular Biology andEvolution 2015 Vienna,in Austria on University Intra Genetics G3 PLOS One Molecular Biology andEvolution Genome Biology andEvolution ManuscriptReviewer for: External Understanding sexual conflictthrough male

- departmental

Cancer Institute), April 13 Member, Special Emphasis Panel for Physical Sciences Oncology Centers (National Visualization, BioVis@ISMB),July 8, 2016, Orlando, FL, USA. Program Committee,BioVis 2016 (the 6th IEEE Symposium on Biological Data Visualization),July 10 Program Committee,BioVis 2015 (the 5th IEEE Symposium on Biological Data Information Processing, Vanderbilt University, Nashville,TN, July 27 Program Committee Co Information Processing, Blacksburg, VA, August 5 Program Committee Co Simulation Academy of Science (USA), Science Signaling,Transactions on Modeling andComputer Physical Review E, PLoS Computational Biology, PLoS One, Proceedingsof the National Biology, Journal of Chemical Physics,Molecular Biosystems, Bioinformatics, Biology Direct, Biophysical Journal,BMC Bioinformatics, BMC Systems Board of Reviewing Editors, Science Signaling, May,2010 Mathematical Biology section of Biology Direct, 2007 and mitochondria Dr.5. Kiril Kiselyov involved in trafficking Dr.4. Marijn Ford yeastin and human renal proteins. Dr.3. Allyson O'Donnell in Dr.2. Hong Nguyen andDr. ClancyNeil (Division of Infectious Disease) geneon regulation Work1. with Joe Ayoob to predictnovel regulators of cellular engulfment in disease. Ichan School of Medicine at Mount Sinai: investigating biomarkersfor neurological regulatory cells cancerin and autoimmunity. DarioVignali's group: data analysis for various projects investigating the role ofT

-

wide Candida albicans

”.

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evolutionary correlations between Vps1 and yeast protein complexes -

infections samples from human infection sites. determining novel proteins acting theat interface of the lysosome

- 11, 2015, Dublin,Ireland.

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Chair. Chair.

Matching alpha - 14, 14, 2016.

10th International q 9th International q

- female interactions in the lepidopteran

- arrestin trafficking proteins with their cargoes

- 9, 2015. - bio Conference Cellularon - bio Conference Cellularon - present. Department of Computational and Systems Biology -

present.

Nature, Physical Biology,

- 30, 2016. Annual Report, FY16

Drosophila.

Section II : Research and Other Scholarly Activities . 47

source source - 16, 2015. 16, - Introduction Introduction

, Knoxville, , Knoxville, , Knoxville, ”, “ ”, leader of Project Project of leader - events. This is a a is This events. - bio Summer School on on School Summer bio William Dunn School School Dunn William -

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”, ”, Advances in numerical numerical in Advances

th ”, ”, William Dunn School of School Dunn William , and we are currently currently are we and , ”, The 9 The funded center to develop novel novel develop to center funded Based Modeling Based - ”, ”, -

. mentor Sanjana Gupta, a graduate graduate a Gupta, Sanjana mentor - Cancer Research UK, July, 2016. July, UK, Research Cancer

- An NIH P41 An NIH

– Based Models Based mediated inflammation. mediated - spatial stochastic dynamical systems in in systems dynamical stochastic spatial - - 17

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Whole Cell Modeling Course, Center for Regulatory Regulatory for Center Course, Modeling Cell Whole

” Science Signaling Introduction to Rule to Introduction “

17 receptor signaling is inhibited, and the biological biological the and inhibited, is signaling receptor 17 Based Modeling with BioNetGen with Modeling Based , University of New Mexico, Albuquerque, NM, July 13 July NM, Albuquerque, Mexico, New of University , - - NIMBioS Working Group for Spatial Cell Simulation NIMBioS Working Group for Spatial Cell Simulation

Pitt PhD Program in Computation Biology. Computation in Program PhD Pitt

-

18, 2016. 18,

- The Journal of Immunology (Cutting Edge) (Cutting of Immunology The Journal 17R Signaling: Implications for Fungal Immunity. Immunity. Fungal for Implications Signaling: 17R Computational modeling of cell decision processes decision of cell modeling Computational and and - Methods for Simulating Rule Simulating for Methods 8, 2016. 8,

“ 3, 2015. 2015. 3, Third International Mathematical and Computational Medicine Conference, Conference, Medicine Computational and Mathematical International Third - “ - ”, ”, 23, 2016. 2016. 23, Towards large scale models of biochemical networks biochemical of models scale large Towards T cell in strength signaling receptor antigen T cell of role the Determining - “ “ Introduction to Rule to Introduction “ funded collaboration with Dan Zuckerman in the Department of Computational and and Computational of Department the in Zuckerman Dan with collaboration funded - based modeling with BioNetGen, with modeling based

- Reviewer for Cancer Immunology Project Award Award Project Immunology Cancer for Reviewer organizer and presenter, presenter, and organizer organizer and presenter, presenter, and organizer - -

performance weighted ensemble software for simulation of complex bio complex of simulation for software ensemble weighted performance - differentiation 16 May OH, Columbus, Co 21 TN, March Tutorial, 2016. 1, April Kingdom, United Oxford, University, Oxford Pharmacy, seminar, Invited 2016. 1, April Kingdom, United Oxford, University, Oxford Pharmacy, of talk, Invited non of study the for approaches and analytic United Cambridge, Sciences, Mathematical for Institute Newton Isaac biology, molecular 4 April Kingdom, presenter, tutorial and lecturer Invited rule to 2016. 6, April Spain, Barcelona, Genomics, talk, Invited efforts. statistical develop to Medicine Care in Critical Bayir Huyla and Health Public of School in the function. system immune to species cardiolipin of profile the relate that models that biology Systems and of Computational Department the in Lee Robin with collaboration on focus a with heterogeneity cellular of understanding a mechanistic development to aims co Faeder and Lee Drs. cells. cancer in signaling cytokine CMU Joint in the student lecturer, Invited Processing Information Cellular Co 1 TN, December Li at East Carolina University to develop and test mathematical and computational models of of models computational and mathematical test and develop to University Carolina LiEast at germination. spore bacterial IL how detail in understanding immunity also but inflammation baseline to respect with both inhibition, its of consequences to used be may that information provide ultimately will findings These albicans. to Candida IL treat and/or diagnose predict, enhance, recently open provide to Chemistry of Department the in Chong Lillian and Biology Systems a for cellular) molecular, (e.g. scale any at simulations of power the enhance to software the of segments key facilitate be to will impact primary the Thus, base. user large potentially may yield effort this Additionally, research. biomedical computational of field burgeoning design drug enhance to potential with processes neurological and cancer into insights methods and software for simulation of biological systems. Dr. Faeder is co is Faeder Dr. systems. biological of simulation for software and methods This complexity. molecular and spatial handle to tools simulation of development 2, the Pittsburgh, of University the at researchers with collaboration close involves project and Institute Salk the and University, Mellon Carnegie Center, Supercomputing Pittsburgh publications. joint of a number to has led of Department in the Lee Robin and Immunology of Department the in Kane Lawrence to response in differentiation T cell peripheral model to Biology Systems and Computaitonal in published been have that publications joint to was led project This stimulation. antigen Signaling Science T cell helper for threshold the setting for a mechanism describing manuscript a preparing to submitted be will that differentiation Yongqing and Center Health of Connecticut University the at Setlow Peter with collaboration

8. 5. 6. 7. 1. 2. 3. 4. Quantitative measurements and models of signal transduction in single cells. This is a is This cells. single in transduction signal of models and measurements Quantitative Cardiolipin as a Novel Mediator of Acute Lung Injury. This is a collaboration with Valerian Kagan Kagan Valerian with a collaboration is This Injury. Lung Acute of Mediator Novel a as Cardiolipin High Mechanisms of Bacterial Spore Germination and its Heterogeneity. This is an ongoing ongoing an is This Heterogeneity. its and Germination Spore Bacterial of Mechanisms IL of Control Negative of goal the with Immunology of Department the in Gaffen Sarah with a collaboration is This Modeling of T cell differentiation. This is an ongoing collaboration with Penelope Morel and and Morel Penelope with collaboration ongoing is an This differentiation. T cell of Modeling (MMBioS) Systems Biological of Modeling Multiscale Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Presentations at National or International Meetings or International National at Presentations Activities Research Collaborative 48 Section II : Research and Other Scholarly Activities

Peer David Koes Presentations at National or International Meetings Collaborative Research Activities Albert Gough - Review Activities Journal Computerof Journal Chemicalof Information and Modeling NIH Gough A. Identifying andQuantifying Heterogeneityin HighContent Analysis: Gough A. Mining Cellular Heterogeneity for Mechanistic Insights in Phenotypic Profiling and Drug Gough A. Mining Cellular Heterogeneity for Insights into Compound Activity in Drug Discovery and Gough A. Mining Cellular Heterogeneity for Mechanistic Insights in Phenotypic Profiling and Drug Mining Cellular Heterogeneityfor Mechanistic Insights in Phenotypic Profiling and Drug Collaboration with Perkin Elmer and Pfizer on the application of three heterogeneity indices in Collaboration with GE Global research theon application of the MultiOmyx platformto cancer Research on heterogeneity in cellular responsesto cytokines and drug candidatesin head and Development of a breastcancer niche using the microfluidic liver model, to evaluate the Collaboration with Vanderbilton an EPA project to develop a broad tox panel for drug and Development of a 3D, microfluidic, microphysiology liver model and integration of the liver 13. 12. 11. 10. 9.

2015; Cambridge, MA. Heterogeneity Indices toDrug Discovery. 2015New England Cytometry Meeting; October 6, Discovery.ThermoFisher Regional User Group Symposium;October 9,2015; Cambridge, MA. 2015; Nemacolin WoodlandsResort, Nemacolin, PA. Phenotypic Profiling.5th Annual Women 2015; San Diego, CA. Discovery.ASCB International Forum for Cell Biology, Annual Meeting;December 12 San Diego,CA. Discovery. 5th Annual Society for Laboratory Automation andScreening; Jan 26 drug discovery. include Chakra Chennubhotla, Lans Taylor andAdrian Lee. research anddiagnostics with a starting pointin breast cancer. preparation. Stern, TimLezon andMark Schurdak. neck andbreast cancer models. with Alan Wells, LansTaylor, Lawrence Vernetti and Nina Senutovitch. influence of the liver microenvironment theon proliferation of cancer cells. Collaboration Vanderbiltare Shane Hutson, Lisa McCawley, Kevin Osteen and John Wikswo. Taylor,Rocky Tuan, LawrenceVernetti and Nina Senutovitch.External collaboratorsat environmental compounds, using microphysiology organ models. Pitt collaboratorsare Lans model). (kidney model),Mark Donovitz atJohns Hopkins (gut model) and Mary Estesat Baylor (gut and Nina Senutovitch. External collaborations withJonathan Himmelfarb at U. Washington model with kidney and gut models. collaborationPitt with Lans Taylor, Lawrence Vernetti, differentiation Invited talk, Vanderbilt, TN, July 27, 2016. BioNetGen and MCell, Contributedtutorial, 2016. on Cellular Information Processing Invited lecturer, China, June 10 University of Pittsburgh Invited speaker, - Microphysiological Simulations 2015 Workshop organizer and presenter, 3, 2016.3,

“

Determining the role of T cell antigen receptor signaling strength in T cell Grants being plannedto apply the new analyses to breastcancer.

”, -

11, 2016. “ “ A Symposium on Cell Signaling - Aided Molecular Design Methods for Simulating Rule Computational modeling cellof decision processes

“

” Modeling Complex Biochemical Systems in Time and Space Using

10 –

th Tsinghua UniversityJoint Symposium, Tsinghua University, Beijing,

Internationalq

Major collaboration within the UPDDI with LansTaylor, Andy , University of New Mexico, Albuquerque, NM, July 11 , Pittsburgh Supercomputing Center, Pittsburgh, PA, June 1 Computational Methods for Spatially Realistic Realistic Spatially for Methods Computational

Two publicationsand a manuscript in

’ s Cancer Research Center Retreat; November 13 -

bio Conference on Cellular Information Processing

- , Santa Fe, New Mexico, August 28 Based Models

Department of Computational and Systems Biology

”, The 10

Collaboratorsfrom Pitt ”, th

q Fifth Annual - bio Summer School

Application of

Annual Report, FY16 - 31, 2016. –

30, 2016; - - 16, 16, 14, 14,

- 14, ,

Section II : Research and Other Scholarly Activities . 49

- ”. ness ness

-

based virtual screening screening virtual based cells - -

cells -

energy estimation (poster) estimation energy -

ligand scoring (poster) scoring ligand - Oncogenic factors that alter mitotic protein synthesis in synthesis protein mitotic alter that factors Oncogenic

Cell fate decisions in response to a short pulse of TNF pulse short a to response in decisions fate Cell – “

enabled tracking of chromosome missegregation and stem and missegregation chromosome of tracking enabled -

Rational selection of small molecules to target upstream regulators of of regulators upstream target to molecules small of selection Rational

Identifying inhibitory receptors that mediate exhaustion in single T single in exhaustion mediate that receptors inhibitory Identifying

Monitoring fate decisions in live single T single live in decisions fate Monitoring

– Computational identification of serine hydroxymethyltransferase – -

Inhibiting cancer metastases with statin therapy statin with metastases cancer Inhibiting CRISPR

(Computational modeling of CYP4F2 inhibition) - - Monitoring fate decisions in live single T single live in decisions fate Monitoring

Computational modeling of profilin)

–

bio Conference, Aug 2015, 2015, Aug Conference, bio - wide: - in human glioblastoma in human Oltvai Zoltan Jim Faeder Camacho Carlos pathway. NFkB the Morel Penelope Chang Yuan and Moore Patrick cells single Zarour Hassane cells Jianhua Xing Xing Jianhua Partha Roy ( Roy Partha Poloyac Sam Alessandro Paiardini, Roberto Contestabile, and Francesca Cutruzzolà (Sapienza (Sapienza Cutruzzolà Francesca and Contestabile, Roberto Paiardini, Alessandro Rome) of University inhibitors hydroxymethyltransferase serine potential of Identification University) (Duquesne Madura Jeffry transporters/receptors monamine of modeling Computational Clinic) (Mayo Hitosugi Taro 1 mutase of phosphoglycerate screening Virtual ( McDermott Lee computational inhibitors; glutaminase of prioritization computational inhibitors; modeling computational inhibitors; 1 mutase phosphoglycerate of potential identification inhibition) CYP4F2 of

departmental: - Ninth q Ninth

University Intra Journal of Biological Chemistry Chemistry Biological of Journal Acta Biophysica et Biochimica (1x) Systems Cell (3x) Communications Nature (2x) Biology Computational PLoS (1x) One PLoS (1x) Biology Molecular of Journal (2x) Signaling Science Molecular Systems Biology Systems Molecular Proteomics Cellular & Molecular Pharmit: Interactive exploration of chemical space chemical of exploration Interactive Pharmit: protein for networks neural Convolutional (poster) web modern the for visualization structure Chemical 3Dmol.js: (poster) screening virtual for minimization energy of implementation GPU free binding for methods computational Benchmarking shape novel A search: (FOMS) shape molecular oriented Fragment (poster) method

External Collaborations: External Collaborations: Internal International Collaborations: International Review Activities Review -

Department of Computational and Systems Biology Systems and Computational of Department Meetings or International National at Presentations FY16 Report, Annual

Activities Research Collaborative RobinLee E C Peer

Meetings or International National at Presentations Activities Research Collaborative 50 Section II : Research and Other Scholarly Activities

Collaborative Research Activities Peer Zoltan Oltvai Presentations at National or International Meetings Collaborative Research Activities Peer Timothy Lezon - - Review Activities Review Activities External University Intra Professional Committees: Reviewer for: Editorial Board Membership: Meeting, Los Angeles,March 2016. Global changesinduced by local perturbations to the HIV External collaborations Internal collaborations Program Committee,GLBIO 2016 Journal of Molecular Graphics andModelling Proteins BMC Biophysics Reviewer: TNF are UPCI Basic & Translational Research Seminar Series,April 2016, pulse of TNF Pittsburgh Biophysical Theory Club, March 2016, March 2016, ColdSpring Harbor Laboratory,Systems Biology: Global Regulation of Gene Expression, fate Science 2015, University of Pittsburgh, Oct 2015, - departmental

”. : effect of macromolecular crowding on cell metabolism Alexei Vazquez (Beatson Institute for cancer Research, Glasgow, UK) communication Ziv Bar Joseph (Carnegie Mellon) communication Hanna Salman (Physics) Alan Wells (Pathology) Takis Benos NIH,Study Section NIH,Study Section Scientific Reports BMC Systems Biology Scientific Reports BMC Biology GE GRC,along with UPDDI, developingon novel technologiesfor imaging Adrian Lee, Magee Jean Andy Stern,Mark Schurdak, Lans Taylor, on application of QSP to Huntington development Lans Taylor, BertGough andChakra Chennubhotla, on the analysis of HCS datafor drug

-

Wide ‘ fine -

Pierre Vilardaga, Department of Pharmacology, on GPCR pathway kinetics :

”. “ - tuned Cell fate decisions in response toshort a pulse of TNF

:

–

inhibition of mesenchymal

’”.

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Womens Research Institute, IGFon signaling in breastcancer ‘ ‘ Cancer SystemsBiology consortium NIH Director

–

inhibition of mesenchymal –

modeling and experimentally testing bacterial cell ’ s Earlys Independence Awards, ad hoc member, 2016 –

modeling and experimentally testing bacterial

- like cancerlike cells with statins “ “ Molecularcircuits at thecrossroads of cell Cell fate decisions in response toshort a - 1 capsid.1 Biophysical Society Annual - like cancer cells withstatins Department of Computational and Systems Biology

’, ad hoc member, 2016 “ How cellular responses to ”.

–

assessingthe

Annual Report, FY16 - cell ’ s disease

Section II : Research and Other Scholarly Activities . 51

Collaborators: Collaborators:

s disease. s

’

Funded by the Brain Institute. Brain the by Funded by DOD.Funded

1. Collaborator Vish Vish Collaborator 1. -

Collaborator: Jeff Brodsky.

Content Analysis & Phenotypic Screening Screening Phenotypic & Analysis Content -

Collaborators: Nathan Yates, Andy Stern, Lans Lans Stern, Andy Yates, Nathan Collaborators:

s High ’

Novel Treatment Targets in Ovarian Cancer. Collaborator: Collaborator: Cancer. Ovarian in Targets Treatment Novel

-

Collaborator: David Whitcomb.

(Department of Surgery). Modeling interactions of AIM2 and HMGB1 and and HMGB1 and AIM2 of interactions Modeling Surgery). of (Department

(Dept. of Critical Care Medicine). Structure and functional dynamics of of dynamics functional and Structure Medicine). Care Critical of (Dept. (Dept. of Cell Biology & Univ. of Pittsburgh Cancer Institute). Understanding Understanding Institute). Cancer Pittsburgh of Univ. & Biology Cell of (Dept. 12, 2016 in San Diego, CA San Diego, in 2016 12, Funded by DOD. Funded (Director, Division of Pulmonary, Allergy and Critical Care Medicine). Modeling Modeling Medicine). Care Critical and Allergy Pulmonary, of Division (Director,

-

(Chair, Dept. of Environmental Sciences and Occupational Health) Structure Structure Health) Occupational and Sciences Environmental of Dept. (Chair,

Funded by the Stanley Foundation. Stanley the by Funded

Structural Biology Related to HIV/AIDS, NIH, Bethesda, 2012. 2012. Bethesda, NIH, HIV/AIDS, to Related Biology Structural Society of Toxicology, San Antonio, Texas, 2013 2013 Texas, Antonio, San Toxicology, of Society

. Cambridge Healthtech Institute Healthtech Cambridge .

Internally Funded Internally

Ion Channel meeting, Bordeau, France 2013. 2013. France Bordeau, meeting, Channel Ion

Prof. Melanie Scott Melanie Prof. Wan Yong Prof. Dr Sally Wenzel Wenzel Sally Dr Prof. V. Kagan V. Kagan Prof. Hulya Bayir, MD MD Bayir, Hulya

3. propose and proteins binding phosphatidylethanolamine and lipoxygenase of interactions interaction. the inhibit to sites these targeting molecules drug and sites binding 4. interaction. this inhibit to mutations propose 5. of effect understand to effort an in Klf4, and PARP1 between interactions modeling and interaction. on this Klf4 on residues critical of mutation 1. proteins. membrane mitochondrial of dynamics functional and 2. proteins. membrane mitochondrial dynamics study. study. dynamics Poster: Talk: Poster: molecular A implications: c and cytochrome with acrolein and of cardiolipin Interaction Taylor. proteostasisThe network in cancerbreast lines. cell pancreatitisAcute as modela to predict transition systemic of inflammation to organ failure in trauma and critical illness. Launching the Neurodegenerative Disease program, Huntington program, Disease Neurodegenerative the Launching Taylor. Lans Stern, Andy Bahar, Ivet Friedlander, Robert Receptors Orphan Understudied Oesterreich. Steffi HSV with infection latent treat to drugs for search HT Nimgaonkar. platform. Proteomics Chemical a Developing

Welcome Trust Grant Trust Welcome Modeling and Information Chemical Journal Biology Computational PLoS One PLoS Internal Reviewer Reviewer Comp. and Theory Chemical J. J. Biophys European Proteins Proteomes Genetics in Frontiers Science. Mol. J. International Compounds 11 February meeting. Analysis of Cellular Heterogeneity in Phenotypic Assays Is Critical For Optimal Development Of Lead Lead Of Development Optimal For Critical Is Assays in Phenotypic Heterogeneity Cellular of Analysis Reviewer on the Research Review Committee for the Dept. of Psychiatry of Dept. the for Committee Review Research the on Reviewer journal One PLOS          

Review Activities Review Review Activities Review - -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Meetings or International National at Presentations Activities Research Collaborative Shrivastava Indira Peer Meetings or International National at Presentations Mark Schurdak Mark Peer Activities Research Collaborative 52 Section II : Research and Other Scholarly Activities

Collaborative Research Activities Peer LansingD. Taylor Presentations at National or International Meetings Collaborative Research Activities Peer Andrew Stern

- - Review Activities Review Activities

1. Determine the clinical relevance of estrogen receptor mutations engendering resistance to        Reviewer for Nature Reviews Member/Reviewer, NCI Working group on biomarkers Reviewer for European Pharmaceutical Review Editor for Experimental Biology andMedicine Some Guiding Principles for Identifying andtargeting Cancer's Vulnerabilities; UPCI Keynote (AMS, PI; 01/01/2015 Department of Neurosurgery and funded through the PADepartment Health:of $1.36 million Freidlander, Peter Strick, andLans Taylor from the Drug Discovery Institute, Brain Institute, and disease; Principle Investigator working withDrs. Mark Schurdak, Diane Carlisle, Robert mechanisms of disease progression and identify novel therapeutic strategies to treatthis fatal Circulating Tumor DNA. with Fulvestrantand Tamoxifen: A Randomized PhaseII Trial with ESR1 mutation tested in and funding of $1.2 million to conduct the following trial:Treatment of metastatic breast cancer Footwear Association of New York.Initial studies has resulted collaborationin with AstraZeneca Department of Health, Womens ResearchInstitute, University of PittsburghCancer Institute; funded throughthe PA co estrogen deprivation therapyand identify novel therapeutic strategiesto address this resistance; PlosOne Reviewed two 2. 2.

-

Apply Quantitative Systems Pharmacology (QSP) to Huntington's disease to determine Principle Investigator with Drs.Adrian Lee, Steffi Oesterreich and Shannon Puhalla,Magee Perspective. Transport of Substrate Glutamatein Transporters: A molecular Dynamics Simulation Poster: Biophysical Society, SanFrancisco,California, 2010 transporter from molecular dynamics simulations. Insights into the Mechanismof Release of Transport in the Inward facing Glutamate Poster: rearrangement HIV Collaborations within Pitt (Bert Gough and Larry Vernetti and two post two year. and Vernetti Larry and Gough (Bert Pitt within more Collaborations one for continues entities. chemical new of testing contain that models liver engineered fluorescence tissue human build to program research Continued of analyses include willheterogeneity and of responses submittedto biological and chemical perturbagens. be biology will and in submitted been heterogeneityhave that Grants of medicine. measurement and detection the on review/persepective generating Schurdak, including Mark and publications. two Lezon perturbagens Tim Stern, Andy to Gough, models. Bert cancerwith UPDDI responses breast the within metastatic cellular in candidates in drug and heterogeneity cytokines on research Continued Lecture July 8, 2015 - 1 glycoprotein,1 gp120: Molecular Dynamicssimulations of spontaneousbridging

Biophysical Society,Baltimore, Maryland,2011.

manuscripts

- based biosensors to create a platform for better in vitro efficacy and safety and efficacy vitro in better for platform a create to biosensors based

- 12/31/2018).

Breast CancerBreast Research Foundation and donor support from Fashion Led a team of Pitt scientists and one researcher from Eli Lilly in writing a writing in Lilly Eli from researcher one and scientists Pitt of team a Led

e il opt fr nw F i te al f 2016. of Fall the in RFA new a for compete will We

I Mcohsooy rn ta spot te program the supports that grant Microphysiology NIH

Department of Computational and Systems Biology

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docs) ao collaboration Major

generated two generated Annual Report, FY16 - sheet

Section II : Research and Other Scholarly Activities . 53 - Co

types. - submitting submitting a -

Guterriez Guterriez and Ed Collaborators Collaborators from -

San Diego, CA Diego, San

- Pittsburgh, PA Pittsburgh, We are re

mentor mentor Dan Spagnolo, a -

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Pittsburgh, PA PA Pittsburgh,

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present)

– Arlington, VA Arlington,

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Pittsburgh, PA Pittsburgh, -

Bethesda, MD Bethesda,

- Boston, MA Boston, 2004; 2010 2004;

- –

The first paper on applying spatial statistics to map

mentoring mentoring of Cemal Erdem, a C&SB graduate student,

- Boston, MA Boston,

- Combining Combining genomics data analyses with image data sets. Collegeville, PA Collegeville,

-

PI grant was submitted. A publication is still in preparation. College Park, MD Park, College -

-

A multi

alcoholic alcoholic fatty liver disease with Jai Behari.

s Disease with Robert Friedlander, Mark Schurdak and Ivet Bahar; and - ’ MPS Investigator Review meeting Review Investigator MPS MPS Investigators Meeting Investigators MPS

th th Content Analysis & Phenotypic Screening conference Screening Phenotypic & Analysis Content -

Adrian Adrian Lee is a key collaborator. Published the first paper on translation time

QSP Congress 2015 Congress QSP oriented oriented grant on cancer systems biology with Adrian Lee, Andy Stern and

Associate Membership (1993 (1993 Membership Associate

We are extending the program to integrate models of liver, kidney and gut with with gut and kidney of models liver, to integrate program the We extending are -

-

- mentor Fen Pei, a C&SB graduate student with input from Tim Lezon, Andy Stern Stern Andy Lezon, Tim from input with student graduate a C&SB Pei, Fen mentor Presentation to GSK to Presentation High 8 NIH meeting DILI FDA NIH 7 NIH conference Target on Discovery meeting Visitors of Board Medicine of School Pitt s Disease. -

Atlantic Directors and Staff of Scientific Cores conference Cores Scientific of Staff and Directors Atlantic ’

------

Mid Fundamentals of Bench Research Course Research Bench of Fundamentals

PI, QSP - - - 2006

– 10/22/2015

- 10/7/2015 2/12/2016 2/25/2016 3/24/2016 8/28/2015 9/25/2015 9/29/2015 ------Pitt Pitt include Chakra Bert Chennubhotla, Gough and Adrian Lee. C&SB graduate student, with Chakra. heterogeneity in tumor sections labeled with multiple fluorescent probes was published. A continues. GE with collaboration the and awarded was grant U01 We initiated a Brain traumatic with Pat Injury and collaboration Kochanek a submitted CURE proposal. Grant support support from Peter Strick and the computational Brain Institute, to biology combine experimental to profiling and Huntington identify compounds that co and I Ivet will provide and neural other protection members preparation. in is paper genomics in chemical a and progressed of the UPDDI. A Collaboration with Adrian Lee, JeffreyFine and Andy research Stern on Breast Cancer sub collaboration with mentoring Luong Nguyen, a C&SB NCATS graduate student with at Chakra Chennubhotla and strong support NIH from Andy and has Adrian. computational initial This in preparation. others two and accepted been has publication One pathology project led to support from UPMC to extend the softwareto function as aguide to pathology. computational on based a company launch to like would UPMC pathologists. Collaboration with GE Global research cancer on research and diagnostics the with a starting application point in of breast cancer. the MultiOmyx platform to use the liver MPS for metastatic breast cancer and for rare childhood liver diseases. liver childhood rare for and cancer breast metastatic for MPS liver the use Quantitative systems pharmacology (QSP) has become the driving force published a of perspective on the the application of QSP UPDDI. to drug discovery We and the addvancement of personalized medicine. Three disease areas serve UPDDI as has a the leadership biomedical role: drivers a) metastatic where breast the cancer Stern; b) (MBC) Huntington with Adrian Lee and Andy c)two liver diseases, Prochownik and non heptocarcinoma with multi Paul Monga, Lu. Xinghua Alex Soto Collaboration with Tim Lezon and co on developing a combined experimental and cellular breast cancer. model of IGF signal transduction in preparation. in is paper second a and model with some Friedlander, and Robert Mark Schurdak Andy Bahar, Stern, Ivet with Collaboration publications. collaborations extending to Johns Hopkins, Vanderbilt and University of microphysiology Washington. database has been The published. Received a supplement to the UH3 grant to

6/10/2016 9/22 9/28 10/20 10/6 2/11 2/24 3/22  7/29/2015 8/26      Review Activities Review - Invited Journal Referee, PLoS One, In Vitro Toxicology, Experimental Biology and Medicine, Medicine, and Biology Experimental Toxicology, Vitro In One, PLoS Referee, Journal Invited Nanobiomedicine Toxicology of Society 1993 DABT, Department of Computational and Systems Biology Systems and Computational of Department Activities Research Collaborative FY16 Report, Annual Lawrence Vernetti Peer Meetings or International National at Presentations 54 Section II : Research and Other Scholarly Activities

Collaborative Research Activities Peer Andreas Vogt - Review Activities     Natural Products, Molecular Cancer Therapeutics Cell Research,Journal of Biomolecular Screening, Journal of Medicinal Chemistry, Journal of Medicinal Chemistry, ChemBioChem, European Journal of Medicinal Chemistry, Experimental ACS Med Chem Letters, Analytical Chemistry, Biochemical Pharmacology, Bioorganic and     

R835736. for environmental toxin testing.This is funded through the EPA as cell based,in vitro liver organoid for toxicology andpharmacology testing as one of 4 organs Shane Hutson, Department of Physics,Vanderbilt University. We are modifying our human September 2018. tissue integrateddevice for environmental toxin testing. This collaboration is active until VPROMPTEPA Center grant R835736 to incorporate the liver organoidas part of the four DMPK modeling Vanderbilt University John Wikswo Department of Biomedical Engineering,Molecular Physiology & Biophysics, collaboration isactive until July 2017. database and integration of Transwell Mark Donowitz (Hopkins) and Mary Estes (Baylor) July 2017. integration of Kidney module for NIH # 1UH2TR000503 of UW Jonathan Himmelfarb,Department of Nephrology, University of Washington supported by NIH 1UH2TR000503# Mass Spec analysis.The projecthas been re of Pittsburgh.We have previously developed and deployed a metabolic stabilityscreen using Samuel Poloyak and MargaretMinnigh, Department of Pharmaceutical Sciences,University project is an integrated program acrossmultiple Departments at Pittand UPMC with the estrogen receptor mutationsthat confer ligand independence. The ultimate goal of this Shannon Puhalla (Departmentof Medicine) Institute), DanielZuckerman (Departmentof Computational and Systems Biology), and Andrew Stern (UPDDI), SteffiOesterreich, AdrianLee (Magee Women R21CA147985. a new research direction emanating from my prior NIH grants R44GM090386and validation of novel drug targets and pathways identifiedfrom clinical samples. The project is Drug Discovery atthe UPDDI, namely discovery ofnovel therapeutics based on genetic aggressiveness.This is an ongoing exploratory projectthat supports the new paradigm of developing methodology to exploit profilin that hasbeen found underexpressed in metastatic breast cancer; we are currently Profilin Partha Roy (Department of Bioengineering), Andrew Stern, Lans Taylor (UPDDI). Targeting NIH grantR01HD053287. evaluatedfor heart regeneration andcancer cell specific toxicity. This project is supportedby sites on mitogen activated protein kinase phosphatases. and a number of hitsare being assays,transgenic zebrafish,and computational modeling to explore novel allosteric binding agents. We are using a combination of high redox active inhibitors of MAPK phosphatases as potential anticancer and cardioprotective cancer and myocardial infarction. The goal is to discover novel, potent, selective, and non Biology. Michael Tsang, Department ofDevelopmental Biology, David Koes, Computationaland Systems grant R01HD053287and an R01 application has scored thein 6 affectingkidney repair andregeneration using zebrafish. This projectis supportedby NIH quantitative analysisof zebrafish phenotypesand to discover novel small molecules regeneration. The goal of this collaboration is to develop methodology for automated, Systems Biology, University ofPittsburgh. Neil Hukriede, Departmentof Developmental Biology, Carlos Camacho, Computational and

- Nortis kidney model, supply of biosensorsfor kidney, use of the MPS database and - 1 as amediator ofbreast cancer metastasis.

Mitogen

The collaboration is active until September 2018.

for NIH 1UH2TR000503#

- activated protein kinase (MAPK) phosphatases as pharmacologic targets in - use of MPS database, pumps and electronic sensors for O

- 01 01 until July 2017. -

gut module for

- Small molecule mediated augmentation of kidney 01 01 with funding through 2017.

to overcome breast cancer resistance due to - - - 1 expression to modulate breast cancer deployed for the liver MPS projectand will be contentanalysis, automated cell migration

- supply of biosensors for gut, use of MPS Profilin NIH # 1UH2TR000503 - 01.The collaboration is activeuntil Department of Computational and Systems Biology -

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2 Annual Report, FY16

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Section II : Research and Other Scholarly Activities . 55

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class drugs drugs class - parallel parallel in enabled enabled ’ - -

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Angela Gronenborn Angela Diebec Karl David Koes David

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th th department - renaissance since it has been reported that the majority of FDA approved first approved FDA of majority the that reported been has it since renaissance to needs however, project, discovery drug phenotypic Every screens. phenotypic from came validation target enable to (MMOA) action of mechanisms molecular elucidate eventually beta a currently are We candidates. drug new of optimization efficient and Persomics of potential transformative and utility, feasibility, the demonstrate enabling broadly tool discovery affordable an as arrays (RNAi) interference RNA miniaturized Innovative R33 NIH An discovery. drug phenotypic in studies action of mechanism molecular review. under currently is : Vogt) (PI proposal grant (IMAT) Technologies Molecular ultimate aim to develop personalized medicines for breast cancer metastasis. We are are We metastasis. cancer breast for medicines personalized develop to aim ultimate effort. collaborative scale large multidisciplinary, this for funding seeking currently Inc Persomics, Sjaastad, Mike Emans, Neil Hong Zhou (UN Irvine), Thoru Pederson (University of Massachussetts), of (University Pederson Thoru Irvine), (UN Zhou Hong -

functional roles of pathway crosstalks in TGF in crosstalks pathway of roles functional 2015, Oct., Pittsburgh, of University 2015, Science 2016 2016 International Workshop on Interdisciplinary Research between Mathematics and 2016. Biology, July, Beijing, The 11 2016. July, FL, Orlando, Applications, and Equations Differential on Conference International Systems dynamic and regulation biology: design principles, disease, 2015. August, Beijing, University, Peking The 7 Guang Yao, University of Arizona, Coupled dynamics between epigenetic modificaiton and and modificaiton epigenetic between dynamics Coupled Arizona, of University Yao, Guang transcription cells tumor circulating of variation number copy of analysis China, University, Peking Bai, Fan Yi stem and missegregation chromosome of tracking wide: University diseases. kidney chronic and injury kidney acute of treatment and mechanism Liu, Youhua migration. microtumor on microenvironment of effect Saint, Shilpa TGF of transduction Signal Watkins, Simon External Current Biology Current Signaling Science Target Drug Cancer Current Intra Lee, Robin glioblastoma Reviewer for: Reviewer Research Acid Nuclear Oncotarget Oncogene Departmental Wide University  Review Activities Review - Department of Computational and Systems Biology Systems and Computational of Department FY16 Report, Annual Meetings or International National at Presentations Activities Research Collaborative Jianhua XingJianhua Peer Collaborative Research Activities Research Collaborative John K Vries 56 Section II : Research and Other Scholarly Activities

Presentations at National or International Meetings Collaborative Research Activities Peer MZuckerman Daniel - Review Activities machine Wong. Seminar given: American Chemical Society national meeting organized DMZ, by Ron Elber,Ron Levy & Chung Landscapes, Pathways, and Kinetics in Biomolecular Simulations, 2015). perspective Trajectories and kinetics: Non Colorado organized by Ren, P. W.Yang, McCammon,A. Roux,B. G.Voth(July, 2015). calculations:Three decades of adventure in chemistry and biophysics Trajectory analysis using history information: Beyond Markov modeling Dept Computationalof Biology, CMU, collaboration withRobert Murphy External: Structural Biology,collaboration withRieko Ishima Chemistry Dept, U. Pittsburgh,collaborations withLillian Chong Extra Drug Discovery Institute, collaboration with Andy Stern Computational & Systems Biology,collaboration with JamesFaeder Inter Journal Computationalof Chemistry PLOS Computational Biology J Chem Phys J. Chem. Theory Comp. J PhysChem Nature Structural & Molecular Biology - - departmental: departmental:

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workshopin BIRS/Oaxaca,Mexico organized C.by Chipot, T. Lelievre, R. Skeel (July,

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“ Free energy Annual Report, FY16

Section II : Research and Other Scholarly Activities . 57

$20,205 $77,963 $13,306 $13,605 $403,021 $208,484 $235,686 $236,261 $171,602 $293,261 $106,036 $109,351 $110,452 $352,750 $242,298 Annual TC Annual $1,201,314 $2,346,608

$6,544 $4,666 $4,771 $9,396 $85,242 $26,588 $72,809 $77,670 $85,441 $80,937 $59,991 $98,033 $26,823 $18,409 $64,065 $45,656 $474,881 Annual IDC Annual

$8,640 $8,834 $13,661 $51,375 $79,213 $99,955 $92,043 $317,779 $135,675 $158,016 $155,324 $111,611 $195,228 $288,685 $196,642 $1,115,873 Annual DC Annual $1,871,727

AT Deficiency

-

GRANTS AWARDED GRANTS CMCR renewal CMCR

Project Title Project -

federal agencies) federal - TECBio

Specific Transcription in Cancer Mi- Cancer in Transcription Specific - Me Signaling Cascade in the Apop- the in Cascade Signaling Me - -

Genomic Analysis of Tissue and Cellular Cellular and Tissue of Analysis Genomic in IPF Heterogeneity perproliferation in Alpha 1 Alpha in perproliferation Computa- for of Excellence NIDA Center (CDAR) Research Abuse Drug tional and Discov- Modeling Causal for Center Big from Knowledge Biomedical of ery Data Cell vivo in and vitro in croenvironment Continued Development of Protein Dy- Protein of Development Continued ProDy Software namics Mul- for Computing Performance High Systems Biological of Modeling tiscale Radia- Against Targeting Mitochondrial Damage tion Hy- and Fibrosis Liver for Therapies New of Characterization and Identification Cancer of Therapeutics and Regulators Networks Signaling and Discov- Modeling Causal for Center Big from Knowledge Biomedical of ery Data Center for Causal Modeling and Discov- Modeling Causal for Center Big from Knowledge Biomedical of ery Data Eat Engulfment totic Cell Computa- for of Excellence NIDA Center (CDAR) Research Abuse Drug tional REU

NCI NSF NIDA NIDA Abbr NIAID NIAID NHLBI NIDDK NHGRI NHGRI NHGRI NIGMS NIGMS Health

Agency Agency PA Dept PA Dept

federal federal

-

-

Total Bahar (Federal and non and (Federal Bahar Total Total Ayoob (Federal (Federal Ayoob Total non and agencies) Subtotal Subtotal Ayoob Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Benos Benos Benos Bahar Bahar Bahar Bahar Bahar Bahar Ayoob Ayoob Non Federal: Ayoob Ayoob Last Name Last Ayoob 58 Section II : Research and Other Scholarly Activities

Camacho Non Camacho Camacho Camacho Berg Non Berg Benos Non Benos Benos Benos Benos Benos Benos Total Camacho (Federal and non Subtotal Camacho Total Berg (Federal and non Subtotal Berg Total Benos (Federal and non Subtotal Benos

- - -

Federal: Federal: Federal:

Nantom-

PA Dept PA Dept PA ics, ics, LLC Health NIGMS NIGMS Health NHGRI NIDDK NIBIB NLM NSF NSF NSF NIH

resource of Novel PPI (Ant) ANCHOR: A PDB Peptide Interactions Thermodynamic Studies of Protein of Biomedical Knowledge from Big Data Center for Causal Modeling and Discovery DIGM Software and Pathways medical and Clinical Data Using the PARA- Methods for Analysis of Hetergeneous Bio- lution of development The role of microRNA regulation thein evo- matics Center Sarcoidosis and A1AT Genomics & Infor- Research Center NIDDKIBD Genetics ConsortiumGenetic ti Integrative Graphical Modelsfor Large Mul- Biology (T32) university PhD ProgramComputational Integrated Interdisciplinary, inter I ing Networks u Identification and Characterization of Reg- droidallorecognition proteins Structural basis of self during Motility Spatial Segregation Cellof Functioning

- l Corps Project iCAHD - a Modal Biomedical Data t - o federal agencies) - r federal agencies) s

a - n federal agencies) d

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a Wide Real time Discovery p e u

t

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recognition in hy- c

s

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agonists

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Department of Computational and Systems Biology

$726,939 $162,648 $770,450 $198,285 $133,030 $79,849 $99,871 $45,129 $82,799 $24,451 $53,010 $54,742 $45,129 $43,511 $3,042 $6,011 $3,001 $2,388 $755

$176,266 $184,968 $37,327 $53,887 $24,370 $24,370 $46,647 $16,560 $10,111 $25,927 $24,370 $1,567 $2,486 $6,803 $1,289 $8,702 $389 $0 $0 Annual Report, FY16

$216,535 $117,176 $903,205 $124,241 $955,418 $244,932 $139,833 $69,499 $99,359 $34,562 $78,937 $54,742 $69,499 $52,213 $1,144 $4,609 $8,497 $3,001 $3,677

Section II : Research and Other Scholarly Activities . 59

$2,064 $2,342 $1,757 $67,802 $24,001 $20,659 $47,287 $69,134 $12,996 $94,370 $28,100 $67,946 $159,409 $126,118 $387,399 $156,754 $846,771 $187,509 $690,017

$344 $821 $616 $6,164 $8,416 $7,244 $4,557 $9,853 $16,581 $24,242 $44,188 $41,774 $27,056 $26,140 $26,568 $36,421 $23,825 $167,957 $141,817

$1,720 $1,521 $1,141 $8,439 $61,638 $15,585 $13,415 $30,706 $44,892 $81,930 $67,314 $18,247 $44,121 $345,625 $130,614 $678,814 $132,841 $151,088 $548,200

states in enzyme ca- enzyme in states - Right Patterning Right - tumor hetogeneity (ITH) (ITH) hetogeneity tumor - catenin interactions in liver: in liver: interactions catenin

-

scale Analysis of the Evolution of of Evolution the of Analysis scale

- evolutionary Signatures as a Novel Ap- Novel as a Signatures evolutionary -

proach to Gene Discovery Gene to proach Center for Causal Modeling and Discovery and Discovery Modeling Causal for Center Data Big from Knowledge Biomedical of Research Kidney for Center Pittsburgh intra of Impact response and prognosis cancer on breast therapy to Co tiation beta Yap and Pathophysiology in Implications Reg- of Characterization and Identification Signal- Cancer of Therapeutics and ulators Networks ing Accurate for Pathology Computational (ComPACD) Diagnoses Cancer and Discovery Modeling Causal for Center Data Big from Knowledge Biomedical of Differen- Bcell on Elongation Transcription Bayesian Inference Toolkit Development Toolkit Inference Bayesian and Discovery Modeling Causal for Center Data Big from Knowledge Biomedical of sub Conformational ribonuclease the to Applications talysis: family Large Networks Social Organellar Cilia in Genes Patient Heterotaxy Assaying Left and Motility

NCI NIH Fdn NSF BCRF ORNL UPMC

NIDDK NHGRI NHGRI NHGRI NIGMS NIGMS Health PA Dept PA Dept Kaufman Kaufman Subtotal Subtotal Clark

Federal: Federal: Federal: - - - Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

Total Chikina Total Subtotal Chikina Subtotal Total Chennubhotla Total Subtotal Chennubhotla Subtotal Clark Clark Subtotal Clark Non Chikina Clark Chikina Non Chikina Chikina Non Chennubhotla Chennubhotla Chennubhotla Chennubhotla Chennubhotla Chennubhotla 60 Section II : Research and Other Scholarly Activities

Xing Xing Pullara Lezon Koes Faeder Faeder ` Faeder Faeder Faeder Clark Total Pullara Total Lezon Total Koes Total Faeder Total Clark (Federal and non

PA DeptPA Dept PA Health Health NIGMS NIGMS RiMED NHLBI NIAID NIAID NSF NSF NIH

RimedFoundation Fellowship Cancer Signaling Networks of Regulators and Therapeutics of Identification and Characterization al Screening tive andCollaborative On BIGDATA:Small: DA: ESCE: Interac- enza Infection Age Omics Complex Bio semble Softwarefor Simulation of High munity ing: Implications for Fungal Im- Negative Control of IL Systems Multiscale Modeling of Biological High Performance Computing for Acute Lung Injury Cardiolipin as a Novel Mediator of Cancer Signaling Networks of Regulators and Therapeutics of Identification and Characterization method ology: the Mori Modeling Reduction Systemsin Bi- scriptional regulation the coupling of epigenetic and tran- Collaborative Research:Modeling

- federal agencies) - Dependent Outcome to Influ- - performance WeightedEn- - Based Predictive Modeling of

- events

- Zwanzig projection

-

17R 17R Signal- - line Virtu-line

$164,186 $109,240 $154,764 $142,211 $109,240 $44,224 $86,924 $13,293 $31,033 $86,233 $17,168 $56,707 $86,984 $44,224 $86,924 Department of Computational and Systems Biology $7,037

$41,674 $58,989 $45,427 $67,476 $14,328 $34,369 $11,341 $74,972 $46,971 $58,989 $45,427 $8,845 $6,923 $9,271 $8,845 $2,585 Annual Report, FY16

$168,229 $205,860 $132,351 $222,240 $120,602 $217,183 $133,955 $168,229 $132,351 $53,069 $20,216 $45,361 $26,439 $68,048 $53,069 $9,622

Section II : Research and Other Scholarly Activities . 61

$100,309 $143,608 $221,528 $100,397 $146,931 $569,165 $351,138 $494,746 $468,856 $6,875,492

$16,718 $23,935 $73,634 $32,906 $50,907 $174,165 $121,943 $145,878 $157,447

$1,549,003

NSF 11% 6% NIAID $83,591 $67,491 $96,024 $119,673 $147,894 $395,000 $229,195 $348,868 $311,409 4% $5,326,489

NIDA

12% l- l- a a - n n PA DeptHlth

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tiscale Modeling of Biological Systems Biological of Modeling tiscale High Bio Complex of Simulation for Software events Reg- of Characterization and Identification u Networks ing Reg- of Characterization and Identification u Networks ing equilibrium for methods Complementary biomolecules of sampling Mul- for Computing Performance High

4% NLM

NIH NSF NIGMS Health Health PA Dept PA Dept PA Dept PA Dept

Federal: Federal:

- -

TOTAL DEPARTMENT FUNDING DEPARTMENT TOTAL Total Zuckerman (Federal and non and (Federal Zuckerman Total Subtotal Zuckerman Subtotal Total Xing Total Subtotal Subtotal Xing Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Zuckerman Zuckerman Non Zuckerman Zuckerman Xing Non 62 Section II : Research and Other Scholarly Activities

Federal Non-Federal $2,000,000 $3,000,000 $4,000,000 $5,000,000 $6,000,000 $1,000,000 Total Costs Total Costs Indirect Costs Direct $1,000,000 $2,000,000 $3,000,000 $4,000,000 $5,000,000 $6,000,000 $7,000,000 $8,000,000 $0 $0 $3,759,130 $3,759,130 $353,571 FY10 $4,112,701 $1,120,793 $2,991,908 $353,571 Non-Federal Federal FY10 $3,824,448 $3,824,448 $486,510 FY11 $4,310,958 $1,170,628 $3,140,330 $486,510 FY11 Research Research Fundingby SponsorType $3,217,297 $3,217,297 $541,692 FY12 $3,758,989 $2,780,762 $978,227 $541,692 Grant SpendingHistoryGrant FY12 $4,432,369 $4,432,369 $789,010 FY13 $5,221,380 $1,041,896 $4,179,484 $789,010 FY13 $5,591,188 $1,155,602 $5,591,188 FY14 $1,155,602 $6,746,792 $1,105,197 $5,641,595 Department of Computational and Systems Biology FY14 $5,174,163 $1,059,565 $5,174,163 FY15 $1,059,565 $6,233,728 $1,266,738 $4,966,990 FY15 $5,626,438 $1,249,054 $5,626,438 Annual Report, FY16 FY16 $1,249,054 $6,875,492 $1,549,003 $5,326,489 FY16

Section II : Research and Other Scholarly Activities . 63

$65,000 $41,900 $75,863 $440,162 $362,311 $681,365 $1,602,115 $1,947,500 $5,216,216 Total Award Total

NCI NCI NIH NIH NIHLB NIGMS NIGMS NIGMS Agency

0% 0% 0%

90% 10% 98% 46% 84% 77% 95% 54% 40% 67% 98% 94% 98% 57% 37% -

Events - Computational Biology Computational Project title Project

FY16 - Performance Weighted Ensemble Software Software Ensemble Weighted Performance

- Recently Awarded Grants since 7/1/16 7/1/16 since Grants Awarded Recently

35 and the tumor microenvironment tumor the 35 and -

scale Analysis of the Evolution of Organellar Social Net- Social Organellar of Evolution the of Analysis scale -

Interleukin Large Improved Methods and Applications for Interactive Online Virtu- Online Interactive for Applications and Methods Improved Optimization Lead and Screening al Decisions Fate Cell of in Control Signals Dynamic Deciphering High Supplement: Bio Complex of Simulation for Total Department Immunoprevention and Immunosurveillance of Human Non Human of Immunosurveillance and Immunoprevention viral Cancers Disease Lung of Subphenotypes Vascular Function and Development B Cell Memory Murine

Percentage Grant Effort Grant Percentage

PI

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

Indira Shrivastava Indira K. Vries John Xing Jianhua Zuckerman M. Daniel James R. Faeder R. James Ferreira Antonio Koes David Lee E.C. Robin Oltvai Zoltan Carlos J. Camacho Carlos H. Cheng Mary Chennubhotla Chakra Chikina Maria Clark Nathan Ayoob Joseph Bahar Ivet Benos Panagiotis Berg Jeremy Chennubhot- Chikina

Lee Zuckerman Koes Benos Chikina Benos

Annual Report, FY16 64 Department of Computational and Systems Biology Teaching Activities………………………………..…………..65 CPCB Seminar Series……………………………...………....68 Section III Administrative Duties…………………………………...…..70 Postdoctoral Fellows………………………………….………72 Teaching Activities Graduate Students…………………………………….………73 Training Grant Publications……………………… ……….74 Department Seminar Series……………………………….75

TEACHING ACTIVITIES

The educational mission of the CSB is to offer first-class research and training programs in computational biology at the graduate, undergraduate, and high school levels, all of which serve multiple purposes: (i) to introduce computational biology problems and methods to students from chemistry, physics, engineering, mathematics, and computer sciences, (ii) to teach fundamental concepts of the quantitative and physical sciences to biology and biomedical sciences students, and (iii) to prepare and encourage students at all levels for a career in computational biology research. In all cases, the aim is to train students and postdoctoral researchers to identify and tackle complex biological problems on a computer—managing and integrating databases, simulating biological phenomena at multiple scales, and other related research problems.

These objectives are in concert with those of the School of Medicine, which aims to integrate the diverse research and educational activities being supported at the University, and to further develop our expertise in using computational and quantitative approaches to elucidate biological phenomena.

Graduate Training Programs

1. Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) The Joint CMU-Pitt Ph.D. Program in Computational Biology (CPCB) is offered jointly by the University of Pittsburgh and Carnegie Mellon University. (The program admitted the first class of students in Fall 2005, and has to date 55 graduates.) The development of this Ph.D. program in computational biology was a natural consequence of the foreseen educational goals and research progress of the erstwhile Center for Computational Biology and Bioinformatics (CCBB), and the subsequent creation of the new Department of Computational Biology.

In this program, students, who come from a wide variety of academic backgrounds (see chart below), receive their doctoral degree from the university in which their thesis advisor holds their primary appointment. Within the University of Pittsburgh, the degree-granting schools are the School of Medicine and the School of Arts & Sciences. The Program institutes a curriculum that is designed to train students who will shape the next generation of discovery in computational biology in academia and industry, and offers students the opportunity to participate in research studies in one of four areas of specialization: Computational Genomics, Computational Structural Biology, Cellular and Systems Modeling, and Bioimage Informatics. Students have access to a community of faculty mentors from both institutions thereby providing a breadth of research areas for investigation.

The Program enhances the use of faculty resources and promotes scientific interactions between the two universities. It serves as a catalyst to generate more advanced topics courses that exploit the research

Annual Report, FY16 Department of Computational and Systems Biology 65 66 Section III : Teaching Activities

across the 50 states and Puerto Rico. Again, our applicants were a very diverse group who came from came who group diverse very a were applicants our Again, Rico. Puerto and states 50 the across from students 300 over well comprised pool applicant our again Once Pittsburgh. in available culturalactivities and social various the experiencing while discussions, and seminars, classes, as such activities, cutting performing to addition graduate challenging a provide to continuing by previoussummercontinuedgreatoursuccessprogramshaveofwethe Pitt,on atTECBio REU the of year seventhour In started.initiativeInstituteBioengineeringBioinformatics(BBSI) Summerpreviousand our (2010 students of cohort seventh its taken has Foundation(NSF) Science National Our Training and Experimentation in Computational Biology (TECBio) 4. Summer Training Programs for Undergraduates and High School Students in functionone of nine degree granting programstissue at the School of Medicine. and cell controlling mechanisms molecular the of knowledge convey that techniques This program introducesstudents tovariety a of fields throughinterdisciplinary courses and experimental their complete to 3. Interdisciplinary Biomedical Graduate Program choose can program the to admitted students under wherein dissertation under the mentorship of faculty in the Department of Computational program Biology. areas the research Training for as Serve important (ii) Faculty and I); and the I Biophysics (Molecular biophysics Activelymolecular of in (i) courses ways: core two the one teach in program this is in contribute department this biology from faculty biophysics, Computationalmolecular fundamental Since of function processes. and form molecular biological elucidate to principles physical basic and spectroscopy) X as (such methodologies and techniques experimentalthe emphasis CPCB, program the unlike However, backgrounds. divergent from fac­ulty of viewpoints the re­quiring ­nary, campusThe 2. Molecular Biophysics + Structural Biology (MB+SB) Graduate Program Some additional undertakingsin the program during FY2016 were: Kyle Xiong, Yifeng Tao, Eswaran Ramamurthy,B.E., Amir Alavi, (CMU) Yan Zhang, Jennifer Williams, B.S., Chaitanya Mokashi, Haoyun Lei, AmandaE. Kowalczyk, Emilee Holtzapple, Daniel Bork, Technology Aggarwal, Kunal (Pitt The following is alisting of the new CPCBstudents on both sides (Pittand CMU) of the program. 87 the in scoresaveraged 2016 which of students; to extended 2016 the during of total a were there FY2016 in program, CPCB the to applications of terms In training for foundation strong a provides focus, thematic tight grant applications. its with coupled program, new the via the participatingSchools. Documented evidence cross of membersbetweenfacultyadditionalinteractiontheconsequenceenhanced positiveof association. An is program of regardless students, graduate all benefits courses new Creating University. Mellon Carnegie ComputerScienceSchoolatSciencesCollegetheandof Mellon Pittsburgh,andtheof University of the at Medicine of School the and Sciences & Arts of School the with associated faculty of talents teaching and

h gaut porm ietr cnutd h ana Drcos n Suet meig o solicit newA group to of studentswas recruited for 2016 meeting Students and Directors annual the conducted directors program graduate The ) ,

including two students who deferred from 2015 from deferred who students two including comments and evaluations theon programfrom the students.

B.S.E, University of Michigan, Ann Arbor B.S., B.E.,Tsinghua University B.S., Nanjing University of Chinese Medicine,M.S. University of Pittsburgh , -

B.S., Huazhong University of Science andTechnology B.S.,Harvey Mudd College M.Sc. wide Ph.D. pro­gram Molecularin Biophysics and Structural Biology is inherently interdiscipli

JohnsHopkins University -

07 eriig season. recruiting 2017 B.TECH. (TECH), Aalto University Aalto (TECH),

B.S.,Ohio University,

B.TECH.,Indian Institute Technology,of Bombay

B.S., CanisiusCollege, M.S.,SUNY at Buffalo, M. TECH, Universityof Luxembourg rd

and 94 and

Westminster College Delhi College of Engineering,M.S., Carnegie Mellon University ainl nttt o Tcnlg, Warangal Technology, of Institute National

10 - de eerh TCi suet as priiae n te academic other in participate also students TECBio research, edge th

students accepted the offer the accepted students percentiles on the GRE quantitative and verbal sections, respectively.sections,verbal andquantitative GRE the percentileson

- funded Research Experiences for Undergraduates (REU) program(REU)Undergraduates for ExperiencesResearch funded

olwn etnie review extensive Following - 2016), and continues to build on the solid foundation thatfoundation solid the on build continuesto and 2016),

- level research experience in computational biology. In biology. computational in experience research level

- 2017 school year.

. Our new cohort has an average GPA of 3.60 and 3.60 of GPA average an has cohort new Our - campus interactionamongfaculty and students

in program the join will students 12 ;

Department of Computational and Systems Biology

and discussion, 41 offers were offers 41 discussion, and

- ray diffraction, NMR, optical NMR, diffraction, ray ,

M.S.

148applications received

,

oa Isiue of Institute Royal

Annual Report, FY16

Section III : Teaching Activities . 67 s (UPCI) High ’ on on and sessions, demonstration tours

-

PhD PhD granting 3 institutions), students from Pitt (2 - supported supported by our NSF award (100% of whom are from 10

–

week week summer research experience. The mentored research component was -

PI (NSF 1458766)), and 1 student from the University of Puerto Rico via their R25 and our -

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual to prepare oneself to go about the application processes for both. both. for processes application the about go to oneself prepare to complemented complemented by training didactic in the major disciplines, hands of labs and opportunities for research the students to present their facilities work and hone their on of hosts communication students and/or lecturers, skills. asmentors, efforts their through program in this involved were faculty Many of campus, our educational and on various social tours and other field events. trips, graduate and students undergraduate in the Students CSB to provide them with sources important of in information and the DiSCoBio program a were also on how to number integrated conduct their research, and with what it is the like of to be an student undergraduate/graduate and how Drug Discovery, Systems and Computational Biology (DiSCoBio) Biology Computational and Systems Drug Discovery, Program Summer School High This past summer saw the continuation of the CSB initiative that reached out to high school students in the Pittsburgh area. We are a component of the University of Pittsburgh Biology (DiSCoBio). which and we Systems Computational Academy, call Drug Discovery, School Summer Cancer Institute In our fourth summer, we accepted 8 students in mentor our program and for matched them up with an a research 8

training to inspire the next great generation of scientists. scientists. of generation great next the inspire to training many many academic and ethnic backgrounds, including many students from groups in underrepresented the sciences and those that are at universities consisted that have of limited research opportunities. 14 Our 2016 and/or backgrounds from underrepresented small non students cohort in total supported by departmental funds granted by the Deans of the medical school from and the 1 via Co an NSF award pending Supplement. We look forward to continuing our traditions of undergraduate mentoring and 68 Section III : Teaching Activities

6014 BST3 2015 October 9, S120 BST 2015 October 2, 6014 BST3 2015 25, September (to berescheduled) 2015 18, September 2015 11, September Date NSH 3305 11, 2015 December 6014 BST3 2015 20, November 6115 GHC 2015 13, November S120 BST 2015 6, November 6115 GHC 2015 October 30, 6014 BST3 2015 October 16,

2015

-

2016 CPCB Seminar Series Seminar |Fridays2016 CPCB at 11am

Brown University Brown Ph.D. Ben Raphael, of Pittsburgh University Ph.D. Vaughn Cooper, University Vanderbilt Ph.D. Lopez, Carlos of Pittsburgh University Ph.D. Jason Shoemaker, Dr. University WestVirginia Ph.D. Klinke, David ofMedicine School of Pittsburgh University Wan, Ph.D. Yong State University Ohio Da Speaker Institute for Systems Biology Systems Institute for Ph.D. MD, Huang, Sui of California University Ph.D. Wollman, Roy UT Southwestern Ph.D. Gaudenz Danuser, University Stanford KC Huang - Wei Li,Wei Ph.D.

, Ph.D. ,

–

SanDiego

Department of Computational and Systems Biology

Pitt Lezon, Ph.D. Tim Pitt Ph.D. Bahar, Ivet Pitt Ph.D. Koes, David Host CMU Ph.D. Philip LeDuc, Pitt Ph.D. Lee, C. Robin E. CMU Ph.D. Ge Yang, Pitt Ph.D. Hanna Salman, CMU Pitt Ph.D. Faeder, James Pitt Russell Schwartz, Ph.D. Schwartz, Russell Pitt

Annual Report, FY16

Section III : Teaching Activities . 69

C. Joel McManus, Ph.D. McManus, C. Joel Jim Faeder, Ph.D. Faeder, Jim Jianhua Xing, Ph.D. Xing, Jianhua Pitt Nominated Student Sedgewick AJ Host: Pitt Ph.D. Kim, Seyoung CMU CMU CMU CMU Jim Faeder, Ph.D. Jim Faeder, Pitt Ph.D. Jim Faeder, Pitt CMU Ph.D. Xing, Jianhua Pitt Pitt

Professor Professor ’

Zivanov, Ph.D. Zivanov,

-

Jian Ma, Ph.D. Ma, Jian Tim Hughes, Ph.D. Tim Hughes, Toronto of University Ph.D. Igoshin, Oleg University Rice Ph.D. Pritchard, Jonathan University Stanford Jennifer Listgarten, Ph.D. Listgarten, Jennifer Research Microsoft University Mellon Carnegie Ph.D. Pfenning, Andreas University Mellon Carnegie Stanford University Stanford Nersessian J. Nancy Associate Research University Harvard PsychologyRegents of Department (Emerita) Science of Cognitive Technology of Institute Georgia William Hlavacek, Ph.D. Hlavacek, William Labratory National Alamos Los Ph.D. Ramanathan, Arvind Laboratory National Ridge Oak Ph.D. Liphardt, Jan Natasa Miskov Natasa Pittsburgh, of University Engineering Computer and Electrical

April 22, 2016 22, April GHC 4405 GHC 2016 8, April March 25, 2016 25, March 4307 GHC 2016 1, April March 18, 2016 18, March 4307 GHC February 19, 2016 19, February BST3 6014 2016 26, February NSH 3305 February 12, 2016 12, February February 5, 2016 5, February BST3 6014 January 22, 2016 22, January 2016 29, January 6115 GHC January 15, 2016 15, January BST3 6014 Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

70 Section III : Teaching Activities

personalizedDA therapeutics. goal of translating the advances in computational chemistry,biology andgenomics to developefficient Center of Excellence isheaded by PIs Sean Xie, Ivet Bahar, and Eric Xing (CMU),and has the long term Enrichment ProgramCoordinator, Computational Drug Abuse Research (CDAR) Center. This P30Center generatedby the MMBioS investigators. September 2012 outreach component to helptrain scientistsin the tools,methodologies and expertise housed andin Biomedical Technology Research Center headedby PI,Ivet Bahar hasa substantial education and Training Coordinator, Center for Multiscale Modeling of Biological Systems (MMBioS).This P41 and Systems Biology and Biomedical Informatics(CoSBBI) Academy. schools bothlocally and nationwide. Summer 2011 didactic Academies.This program, whichhighlights a mix of experimental andcomputational research projects, training programthat is one (DiSCoBio) Summer Academy.The DiSCoBio summer academy is an 8 Founding Program Director and Instructor,Drug Discovery,Systems andComputational Biology Summer 2010 an immersive, 10 support by the NSF andthe Department of Defense (NSF DBI funded during the first three years NSFby grant: DBI Biology (TECBio) Research Experiences for Undergraduates(REU) summer program Founding Program Director and Principal Investigator, Training and Experimentation Computationalin how to performexperiments and better understand the data that Graduate Program that usesexperiential teaching approaches to educate computational scientists on course(MSCBIO/CMPBIO 2050). This is acore course in the joint CMU Course Co Joseph C. Ayoob, Ph.D. Faculty; Pittsburgh Medical InformaticsTraining Program(2001 Member, Center for Molecular & Materials Simulations (CMMS), (2002 Member, Molecular Medicine Institute, University of Pittsburgh (2002 Member, McGowan Institute for Regenerative Medicine (2002 Faculty,Interdisciplinary Biomedical Graduate Program,School of Medicine ( 2002 Biology (2005 Founding Director,Carnegie Mellon University and U of Pittsburgh,Joint PhD Program in Computational Professor,Department of Computational Biology (2005 Faculty, Peterson Institute of Nanoscience and Nanotechnology (2005 Faculty, PhD Program Structuralin Biology and Molecular Biophysics (CMU Faculty, Program Integrative in Molecular Biology (PIMB) (2005 Computational Biology (2005 Facultyand Executive Committee Member,Carnegie Mellon University/Pitt PhD Program in Member, Center for Simulationsand Modeling (SAM) (2008 PhD Program in Comp Biology (CPCB) (2009 Joint withPI Drs. Murphy,Russell & Benos,NIH Professor,Department of Computational and Systems Biology (2010 (2010 Director,ChemoInformatics Core, Center for Medical CountermeasuresAgainst Radiation Pitt UPCI& John VriesK. Chair,Department Compof Systems& Biology, SOM, Pitt,2010 Associate Director,University of Pittsburgh Drug Discovery Institute (2010 Hyperproliferation in Director, NIH present) Director/PI, Director/PI, NIGMS Biomedical Technology Research& Center (2012 JointCenter between Pitt, Carnegie Mellon, PSC & Salk Inst, 2012 Dickson Prize Committee Member (2013 (2012 DistinguishedProfessor, and John VriesK. Chair, Department of Computational andSystems Biology Biomedical Knowledge fromBig Data (CCD) (2014 Joint withPI, Drs. Cooper & Berg, NIH Abuse Research (CDAR) (2014 Joint withPI Drs.Xiang Ivet Bahar, Ph.D.

- – present)

present)

training,and enrichment activities, reaches out to - Director, Lecturer,and Laboratory Head, Laboratory Methodsfor Computational Biologists

NIH - - NIDDK - 2009) present.

-

NIGMS - week, graduate

– α1

ADMINISTRATIVE DUTIES RELATED TO EDUCATION 2015 P01, Computational Pharmacology Core, New Therapies for Liver Fibrosisand -

- Qun and Xing, NIH -

AT Deficiency R01, Continued Development of Protein Dynamics Software of the six University of PittsburghCancer Institute (UPCI) Summer - - present)

present) - level, summer research experience for 12 students each summer. - NHGRI

-

present) - NIDA - present) -

U54, Center for Causal Modeling and Discovery of - NIBIB -

P30, NIDA Center of Excellence for Computational Drug -

present) - present; Summers 2011 -

1004555 and was renewedfor 4 additional years of

-

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Akif Burak Tosun, Ph.D. Computerin Engineering, BilkentUniversity, Turkey, 2012 OmPrakash Choudhary, Ph.D.in Computational Biology, University of Pittsburgh, 2013 S. Chakra Chennubhotla, Ph.D. Timothy Travers, Ph.D.in Computational Biology, University of Pittsburgh, 2014 Carlos Camacho, Ph.D. Dimitrios Manatakis, Ph.D. in Computer Science, University ofAthens, 2014 Klea Panayidou, Ph.D. in Statistics, University of Oxford, UK, 2010 Hyokyeong Lee, Ph.D. Computerin Science, University of Southern California, 2010 Panagiotis Benos, Ph.D. AmitSrivastava, Ph.D. in Physics,Banaras Hindu University,India, 2008 Filippo Pullara, Ph.D.in Physics, University of Palermo, Italy, 2013 Mikulska Karolina Bing Liu, Ph.D. Computerin Science, National University of Singapore, 2011 Hongchun Li, Ph.D. in JiYoung Lee, Ph.D. in Physics,Pohang University of Science andTechnology, Korea, 2008 Ivet Bahar, Ph.D. Emily Furbee, Ph.D.in Biology, Carnegie Mellon University, 2014 Joseph Ayoob, Ph.D. Member Executive Committee Member, CurriculumCommittee, Molecular Biophysics and Structural Biology Graduate Program Co Course Head, Intro to Computational Structural Biology (MSCBIO 2030)/Mol. Biophysics II(MSMBPH 2002) Co Daniel M. Zuckerman, Ph.D. CPCB Member of the Admissions Committee CPCB Member of the CurriculumCommittee Supervisor of one Supervisor of one REU student, Summer 2016 Jianhua Xing, Ph.D. Shana Bergman, TECBio student(2015) Stephen Provencher,chemical and petroleum engineering undergraduate Cemal Erdem,CPCB graduate student Students Mentored: Co Course Instructor,MSCBIO/CMPBIO 2060: Current Topics in Computational Biology, Fall 2015 Course Co Timothy R. Lezon, Ph.D. Mentor for DiSCoBio StudentOluremi Akindele Mentor for StudentSummer Researcher Ryan Hausler Mentor for UndergradBioinformatics Student Gabriel Kowalczyk Mentor for StudentResearcher Allison Mancini Mentor for Graduate StudentSanjana Gupta CSB FacultyRecruitment Committee (?Isthis relevant?) CPCB Admissions Committee Member of the University of Pittsburgh Ph.D. program in IntegratedSystems Biology Member of Joint Carnegie Mellon Robin E.C. Ph.D. Lee, Course Director,MSCBIO 2025: Introduction to Bioinformatics Programming in Python, Fall 2013,2015 2011,2012,2013 Guest Lecturer, MSCBIO/CMPBIO 2030:Introduction to Computational Structural Biology,Fall CourseLecturer, DiSCOBio, Summer 2013,2014,2015,2016 - - - organizer,Pittsburgh Biophysical Theory Club, a monthly seminar series Director, JointCMU Director, Drug Discovery,Computational andSystems Biology (DiSCoBio) Academy, Summer 2014 - Instructor, MSCBIO/CMPBIO 2030: Introduction to Computational Structural Biology, Fall 2015

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- Jiun Chen, Ph.D. in Molecular Biology, National Taiwan University, Taiwan, 2009 Taiwan, University, Taiwan National Biology, Molecular in Ph.D. Jiun Chen, -

Department of Computational and Systems Biology Systems and Computational of Department David Koes, Ph.D. Koes, David CMU Sunseri, Jocelyn Lee, Ph.D. E.C. Robin CMU Gupta, Sanjana FY16 Report, Annual Maria Chikina, Ph.D. Chikina, Maria CMU Mao, Weiguang Ph.D. Faeder, James Jose CMU Sheehan, Robert CMU Kaya, Cihan Raghavendran Partha, CMU Partha, Raghavendran Ph.D. Chennubhotla, Chakra CMU Mao, Weiguang CMU Spagnolo, Dan CMU Nguyen, Luong Carlos Camacho, Ph.D. Camacho, Carlos CMU Baumgartner, Matthew CMU Pabon, Nicolas Tsinghua of University Xu, Zhiyu Tsinghua of University Ye, Zhaofeng Ph.D. Clark, Nathan Fen Pei, CMU Pei, Fen CMU Zhang, She Ph.D. Benos, Panagiotis CMU Buschur, Kristina CMU J. Sedgewick, Andrew DBMI MSc student, Santos, dos Santana Lucas Ramamoorthi Anandakrishnan, Ph.D. in Computer Science, Virginia Tech, 2011 Tech, Virginia Science, Computer in Ph.D. Anandakrishnan, Ramamoorthi 2011 University, State Arizona Chemistry, in Ph.D. Spiriti, Justin Ph.D. Bahar, Ivet CMU Kaya, Cihan Yi 2012 China, University, Nanjing Biophysics, in Ph.D. Tian, Xiaojun 2015 University, Peking Physics, in Ph.D. Wang, Weikang Ph.D. Zuckerman, M. Daniel Spain, Oviedo, of University Chemistry, Computational and Theoretical in Ph.D. Alvarez, Suarez Ernesto 2011 Ning Chen, Ph.D. in Cellular, Molecular and Biophysical Studies, Columbia University, 2004 2004 University, Columbia Studies, Biophysical and Molecular Cellular, in Ph.D. Chen, Ning 2014 Arlington, Texas, of University Engineering, Mechanical in Ph.D. George, Subin in Ph.D. Miedel, Mark Lee Felipe 2011 University, Mellon Carnegie Sciences, Biological in Ph.D. Senutovitch, Nina 2011 University, State Iowa Engineering, Biological and Chemical in Ph.D. Sharma, Dutt Anup Ph.D. Xing, Jianhua Nathan Clark, Ph.D. Clark, Nathan 2011 France, Tours, of University Health, and Science Life in Ph.D. Meslin, Camille Ph.D. Lee, Robin 2005 China, University, Medical Hunan Pathophysiology, and Pathology in Ph.D. M.D., Zhang, Qiuhong Ph.D. Taylor, Lansing D. 74 Section III : Teaching Activities

Zining Zhang, University of Tsinghua Rory Donovan, CMU Daniel Zuckerman, Ph.D. Jingyu Zhang,CMU Jianhua Xing, Ph.D. Cemel Erdem,CMU Luong Nguyen,CMU Dan Spagnolo,CMU D. Lansing Taylor, Ph.D.

Mukherjee,S., Zheng, H.,Derebe, M.,C Gardner CL,Hritz Sun J, C,Vanlandingham DL, Moroco JA, YP Yu, Mochan E Kumar RaoA, A, Bhavani S, Newberg JY,and Murhpy RF.(2014) J Grover P,Shi H, Matthew Patrick Baumgartner Price I, Mochan Wells M Z - - H Luo, H Zuo, Y intestinal C J., infectivity: a model for rational arboviral vaccine design (2014) immunohistochemistry images identifiescandidate location biomarkers for cancers Pathology transcripts in prostate cancer and normal prostate tissue. e0133590. doi: 10.1371/journal.pone.0133590 Screening Assays for Small Molecule Allosteric Modulatorsof ABLKinase Function. PLoS ONE 10(7): Modeling for Docking and Scoring in the CSAR 2013/2014Experiment Journal of Chemical Information and study (2015) Murine Intrahost Immune Response to Multiple Pneumococcal Strains. of Medicinal Chemistry. 58(7):2958 cannabinoid Journal ofPathology. 650in prostate cancer ismediated by suppression of CSR1 expression. PMID: 25376742 SH2 Discovery Strategy for Selective Inhibitors of c PMCID: PMC4188871 Associate with Progressive Prostate Cancer Jarrard,JB Nelson, GKMichalopoulos, – pneumococcal pneumonia infection dynamics in murine strains. press). 54.

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76 Section III : Teaching Activities

*ThePittsburgh Biophysical Theory Club PhD Sudhir Kumar, Rob Coalson, PhD Guerriero, PhD Christopher J Jennifer Boatz PhD Robin E.C. Lee, PhD Jeffry Madura, Patricia Campbell PhD Aaron R.Haeusler, Zhaofeng Ye dakrishnan, PhD Ramu Anan-

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* * * *

* *

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Section III : Teaching Activities . 77

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

Dr. Zheng Yang (Bahar Lab).

Annual Report, FY16 78 Department of Computational and Systems Biology Faculty Data Primary Faculty……………………………………………....79

Section IV Joint Appointments…………………………………...……80 U of Pitt School of Medicine Committee Service…….81 Faculty Data University of Pittsburgh Committee Service……..……85 Other Services Outside of University of Pittsburgh..86

Primary Faculty

Professor Ivet Bahar, Ph.D., Distinguished Professor and John K. Vries Chair, Istanbul Technical University, Turkey, 1986 Panayiotis V. Benos, Ph.D., University of Crete, Greece, 1997 Jeremy Berg, Ph.D., Professor, Harvard University, Cambridge, 1985 Hagai Meirovitch, Ph.D., Emeritus, The Weizmann Institute of Science, Rehobot, Israel, 1974 D. Lansing Taylor, Ph.D., Professor, State University of New York at Albany, 1973

Associate Professor Joseph Ayoob, Ph.D., John Hopkins University, Maryland, 2006 Carlos J. Camacho, Ph.D., University of Maryland, College Park, 1991 S. Chakra Chennubhotla, Ph.D., University of Toronto, 2004 James R. Faeder., Ph.D., University of Colorado at Boulder, 1998 Andreas Vogt, Ph.D., University of Hamburg, Germany, 1990 John K. Vries, M.D., University of California at San Francisco, 1966 Jianhua Xing, Ph.D., University of California, Berkeley, 2002 Daniel M. Zuckerman, Ph.D., University of Maryland, College Park, 1998

Research Associate Professor Albert Gough, Ph.D., Carnegie Mellon University, Pittsburgh, 1992 Andrew Stern, Ph.D., University of California at Los Angeles, 1977 Lawrence Vernetti, Ph.D., University of Arizona, 1991

Visiting Research Associate Professor Mark Schurdak, Ph.D., Baylor College of Medicine, Houston, 1987

Assistant Professor Maria Chikina, Ph.D., Princeton University, 2011 Nathan Clark, Ph.D., University of Washington, Seattle, 2007 David Koes, Ph.D., Carnegie Mellon University, 2009 Robin Lee, Ph.D., University of Ottawa, 2010 Timothy R. Lezon, Ph.D., The Pennsylvania State University, University Park, 2007

Research Assistant Professor Hongying (Mary) Cheng, Ph.D., Polytechnic Institute, 2002 Indira Shrivastava, Ph.D., University of Pune, 1993

Annual Report, FY16 Department of Computational and Systems Biology 79 80 Section IV: Faculty Data

Christopher Langmead MarkusDittrich, Ph.D., Carnegie Mellon University, 2009 Ziv Bar Adjunct Assistant Professor Eric Poe Xing Maria Kurnikova Elodie Ghedin, Ph.D., McGill University, Quebec, 1998 Adjunct Associate Professor Mahadev Satyanarayanan, Ph.D.,Carnegie Mellon University 1983 Gordon Rule, Ph.D., Ph.D.,Carnegie Mellon University, 1986 Ronald Rosenfeld Jaime Carbonell Visiting Adjunct Professor Michael Widom RobertSwendsen, Ph.D., University of Pennsylvania, 1971 Naftali Kaminski, M.D. The HebrewUniversity Jeffry Madura Adjunct Professor David Swigon Hanna Salman Elisabeta (Liz) Marai Dennis Kostka, Ph.D. Free University, Berlin, Germany, 2006 Kyle Bibby, Ph.D., Yale University, 2012 Assistant Professor Lance Davidson, Ph.D., Chien ZoltanOltvai Vanathi Gopalakrishnani Alexander Doemling Vaughn Cooper, Lillian Chong Associate Professor Xiang Alan Wells Yoram Vodovotz BennettVan Houten Pei Tang Alexander Sorkin, Ph.D.,Institute Cytology,of Academy of Sciences of the USSR, 1986 Jonathan Rubin John Rosenberg GrahamHatfull G.Bard Ermentrout Gregory Cooper Rob D. Coalson Professor Joint Appointments

- - Qun (Sean) Xie Cheng Tseng - Joseph , Ph.D.,, SUNY at Stony Brook, 1990

, M.D., Brown University, 1988 D.M.Sc., Karolinska Institute, Sweden, 1982 , Ph.D., University of California, Berkeley, 2004 , M.D.,Semmelweiss Medical University,Budapest, Hungary, 1984 , Ph.D., University of California atSan Francisco, 2002 , Ph.D.,Rutgers University, 1999 , Ph.D., Purdue University, 1985 , Ph.D., Weizmann Institute of Science,2002 , Ph.D.,MIT, 2003 , Ph.D.,Harvard University, 1984 , Ph.D.,, University of Chicago, 1983 , Ph.D.,, Edinburgh University, UK,1981 , M.D., Ph.D.,Stanford University, 1985 (Ph.D.) and 1986(M.D) , Ph.D., Brown University, 1996 , Ph.D., MIT,1973 , Ph.D., University of Pittsburgh,1998 , Ph.D.,, Yale University, 1979 , Ph.D., Cornell University,1993 Ph.D., Michigan State University, 2000 , Ph.D., Carnegie Mellon University, 1994

, Ph.D., University of California at San Francisco, 2002 , Ph.D., University of Chicago, 1979 , Ph.D., Brown University, 2007 , Ph.D., Technical University of Munich, 1994 , Ph.D., University of Tennessee, 1984

, MBA, University of Connecticut, 1990(Ph.D.) and2003 (E.M.B.A.)

, Ph.D.,Dartmouth, Christopher, 2003 Biophysics, University of Californiaat Berkeley, Berkeley, CA,1995 , Ph.D., University of Pittsburgh,1999

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HadassahMedical School,Jerusalem, 1989

Department of Computational and Systems Biology

Annual Report, FY16

Section IV: Faculty Data. 81

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efforts. Participant, Planning Group on University of Pittsburgh Member, Distinguished Facultycommittee. Member, SOM Graduate Faculty. Chair,Dickson Prize Committee. Member, National Research Council Committee on Women in Science, Medicine and Engineering. DistinguishedFaculty committee member. Review Committee Chair,RiMed Fellows. Scientific Director of theUniversity ofPittsburgh Founding Director of the Institute for Personalized Medicine. Jeremy Berg, Ph.D. Associate Director of the IntegratedSystems Biology (ISB) PhD Program, September 2014 – ChairVice for Academic Affairs, Departmentof Computational and Systems Biology, SOM, November 2014 Member, AdmissionsCommittee, Integrative Systems Biology (ISB) PhD Program, 2014 Member, CurriculumCommittee, Integrative Systems Biology (ISB) PhD Program, 2014 Member, Integrative Systems Biology (ISB) PhD Program, 2014 present. Member, CurriculumCommittee, Joint CMU Member, GPCLAdvisory Board, 2013 Co to serveJanuary 2014 Member (elected),Committee for Tenured Faculty Promotions and Appointments (TFPA), elected in 2013 Member, Task Force,Institute for Personalized Medicine, 2012 present). Contact andPI responsible for all the reports for the T32NIBIB grant thatfunds the CPCBprogram (2010 present. Secondary Appointment, Associate Professor,Clinical and Translational Science Institute (CTSI), 2010 StudentDissertation Committees. Member, Department Computationalof Biology Faculty Selection Committee, 2009 Member, Department Computationalof Biology Promotion Committee, 2009 Secondary Appointment, Associate Professor,Department of Biomedical Informatics,2008 Member, University of PittsburghGraduate Faculty, School of Medicine. Member, Advising Committee, CMU Medical Scientist Training Program (MD/PhD), University of Pittsburgh School of Medicine, 2007 Member, University of PittsburghGraduate Faculty, School of Medicine. Nathan Clark, Ph.D. Member, CurriculumCommittee, Integrative Systems Biology Program http://www.pimb.pitt.edu/ Member, Organizing Committee for a Masters Program in Computational andSystems Biology. Member, Faculty Recruiting Committee, Department of Computational and SystemsBiology. Core Member, University of Pittsburgh Department of Biomedical Informatics Training Program. Chair,Journal ClubCommittee. Membership, University of PittsburghGraduate Faculty, December 2009 JointCMU JointCMU Chakra Chennubhotla, Ph.D. CSB Computing Committee Member. Maria Chikina, Ph.D. Organizational Coordinator of the External Advisory Board (EAB) for MMBIOS and CDAR. Peer reviewer of RSC Advances. Mary Cheng, Ph.D. Chair Admission committee Pitt Member Faculty SearchCommittee Seminar SeriesDirector DC SB.& Member, University of PittsburghGraduate Faculty, School of Medicine. Member, Curriculum Committee(2004 Member, Executive Committee,Joint CMU Tenure Committee SOM. Carlos J. Camacho, Ph.D.

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ChairVice for Educational Activities. Director, Ph.D. Program in Computational Biology. Co Referee for numerous journals. Departmentcomputing committee. Curriculum Committee Member Graduate Council member (overseesgraduate education; advises Dr.Horn). School of Medicine Tenure and Promotion Committee, January 2010 Member, University of PittsburghGraduate Faculty, School of Medicine. Search Committee Chair,Department of Computational and Systems Biology. StudentAdvising Committee,Co Pitt Ph.D. Program Computationalin Biology. Member and Co Chair,Curriculum Committee, Molecular Biophysics andStructural Biology Graduate Program. Daniel M. Zuckerman, Ph.D. CPCB admission committee member. Department retreat organizer. Jianhua Xing CSB Computer Committee. Member of the tenure promotion committee. Member, University of PittsburghGraduate Faculty, School of Medicine. Member, Faculty Promotion Committeesand Faculty recruitmentcommittees. John Vries, M.D. MSMPHL 3360 MED5224 MED5133 Member, School of Medicine Ambassador present. Representative, School of Medicine,Chemical Hygiene Officers Committee,University ofPittsburgh, 2004 Reviewer,Clinical andTranslational Science Institute (CTSI) Basic to Clinical Collaborative. Member, SOM, AmbassadorsProgram, University of Pittsburgh. Member, UPDDI New Projects Group. Member, UPDDI Core Management Team. Mentor, Metastatic BreastCancer Program. Lecturer and Mentor, DiSCoBio Program. Lecturer,TecBio Joint Pitt/CMU Program. Andreas Vogt, Ph.D. Pittsburgh scientists interested in mitochondria in regards to basic researchand drug discovery. Member, Mitochondria, Metabolism and Disease Working Group (M Ms),& agroup of University of Member, Liver Research Forum. Mentor,DiSCoBio UPCI Summer Academy Program. Lawrence Vernetti, Ph.D. Developmental Biology, andWomen Computational & Systems Biology and Department Infectiousof Diseases. Delivered seminars on the plans of the UPDDI and how to collaborate to Cancer Institute Department of Co Chair of the pharmaceutical collaboration committee. Graduate Program. Served Thesison Committees for JointCMU/Pitt Ph.D. Program Computationalin and Systems Biology Member, SOM, Graduate Faculty. Faculty Search Committee. School of Medicine UPSOM Planning and Budgeting Committee. - - coordinator: Pittsburgh Biophysical Theory Club. chaired PittsburghWorkshop Quantitativeon Systems Pharmacology in Personalized Medicine.

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January 25, 2016: The International Human Frontier Science Program ResearchGrant Review Committee. Strasbourg,FR. Selection Committee Member. June 29 Organizing Committee Member. Dynamics, Allostery and Regulation: Bridging Experimentsand Computations." Istanbul,Turkey. Phys Review Letters,and J. Amer ChemSociety. Refereeing in a broad range of archival journals including Science, Nature,Cell, Proc Natl Acad Sci USA, Fellowships,NATO projects, Israel Science Foundation grants,Science Foundation Irelandof projects. submitted to international funding agencies suchas EMBO research grants and fellowships;TWAS Review of several national and international projects/awards, including NSF and DoD grants; proposal present. Member, The International Human Frontier Science ProgramResearch GrantReview Committee, 2016 Participant in Annual Meetingsin Strasbourg in January 2013, 2014, 2015). Human Frontiers in Science Program (HFSP) Scientific Review Committee member (in 2012 Systems), EUREKA,IMST, DP2. Chair and/or Reviewer in several NIH StudySections, including MABS (Modeling andAnalysis of Biological ElectedMember of EMBO (European Molecular Biology Organization) http://www.embo.org/ Signatures (LINCS). NIH Study Section Chair, Special Emphasis Panel for Library of IntegratedNetwork Jerusalem, most recently). Reviewer for promotions of faculty members from outside institutions (e.g the Hebrew University of Trieste (SISSA),chaired by Professor Guido. Member of the Hiring and Promotion Committee of the International School for AdvancedStudies of Council (ERC) proposals, TUBA (Turkish Academy ofSciences) fellowships. Serving as referee for EMBO fellowships,Israel Sciences Foundation (ISF) projects, European Research Several invited talks (including plenary lectures) in national andinternational meetings. Biophysical Society Council meetings. Quarterly meetings of the Biophysical SocietyExecutive Committee. Editorial Board Scientific Reports (Nature Publishing Group) 2014 Editorial Board,Structure, 2014 (2013 Human Frontier Science Program (HFSP http://www.hfsp.org/) Scientific Review Committee Member Pittsburgh Supercomputing Center andSalk Institute, 2012 Director/PI, NIGMS Biomedical Technology Research& Center (Joint between Pitt (lead),Carnegie Mellon, Editorial Board, Protein Science (Wiley Editorial Board, Proteins: Structure, Function and Bioinformatics(Wiley), 2008 Biophysical Journal (2004 Editorial Board.Scientific Reports (Nature publication); 2013 Editorial Board Member for Proteins, 2015 SocietInternational Society for Computational Biology (ISCB),2001 (*) currently serving as Member of the Publications Committee (2012 Biology, BMC Bioinformatics,Biophysical Society (*), 2001 Acta, Comput Theor& Polymer Science, Structure, Biochemistry, Protein Science, Phys Rev E,J Mol. Physical Review Letters, J. Chemical Physics,European Polymer J,Macromolecules, Biophys.Biochim. Cell,Proceedings of the National Academy of Science, Proteins: Structure,Function & Bioinformatics, PNAS, PLoS Computational Biology, Molecular Systems Biology, Biophysical Journal,Proteins and others Actedas referee for several journal papers, including Science, Nature, Structural andMolecular Biology, Member, Protein Society, 1999 American Association for the Advancementof Science (AAAS), 1995 American Chemical Society (ACS), 1987 Ivet Bahar, Ph.D. Mentor, MentorNet andNational Research Mentoring Network (2013 Symposium,Carnegie Mellon University. Judge,Sigma HonorXi Society Poster Competition at the Meeting of the Minds: Undergraduate Research Member, American Society for Cell Biology. Review Panelist, NSF Postdoctoral ResearchFellowship in Biology Review Panelist, NSF Research Experiences for Undergraduates(REU) Programs, 2010 Judge,Sigma HonorXi Society Poster Competition at the Meeting of the Minds: Undergraduate. Joseph Ayoob, Ph.D. Judge, 75th PittsburghRegional Science andEngineering Fair (PRSEF); Biology Member, American Association for the Advancement of Science. - 2017) http://www.hfsp.org/sites/www.hfsp.org/files/webfm/Grants/RC%20list.pdf. -

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PA PA Board of Directors,Celsense, Inc., Pittsburgh. PA Scientific Advisory Board,Reaction Biology Inc., Pittsburgh. Chairman of the Board,Pittsburgh Life Sciences Greenhouse,Pittsburgh. Consultant for Light Optical Methods for NIH and NSF grants. Corporation Member, Marine Biological Laboratory, Woods Hole,MA. Served theon NCI NeXT program advisory committee for the chemical biology consortium Editor for Experimental Biology andMedicine. Engineering. Reviewed candidates for election as a fellow of the American Insitute of Medical and Biological Served on special NIH review committeefor technologies. Delivered symposium talk atthe annual RiMED meeting in Sicily. TechnologiesLife Thar Pharmaceutical Omnyx General Electric General Electric Worked on several life sciences company interactions for Pitt andUPMC. collaborations;working on all pharma companies for collaborations, securedcollaboration withJ&J. Created pharmaceutical company collaboration committee to recruit pharma industry to Pitt for Lans Taylor, PhD Reviewer for Nature and PLoS One. Andrew Stern, Ph.D. Organizing Committee Member: Biotechnology 2015Hyderabad, India. Web Development Member: Center for Drug and Abuse Research. Function, and Bioinformatics, Proteomes,and Nature. Reviewer: PLoS One, J. Biomolecular and Structural Dynamics,Frontiers Genetics,in Proteins: Structure, Editorial Board Member: Frontiers Geneticin Disorders. Indira Shrivastava, Ph.D. Member, Society for Laboratory Automation and Screening (SLAS). Member, American Chemical Society, (AAAS). Member, American Association for Cancer Research(AACR). Member, American Chemical Society, (ACS). Referee for Nature Scientific Reports. Member of the NCI Chemical Biology consortium steering committee. Mark Schurdak, Ph.D. Scientific Reports Journal of Molecular Graphics andModeling Biophysical Journal Chemical PhysicsLetters ACS Chemical Biology and Proteins Reviewer: Instructor for Timothy R. Lezon, Ph.D. Reviewer,Bioinformatics, Algorithms, Chemical Biology & Drug Design. Consultant for Carmolex. Science Fair Judge. Mentor for SciTech ExecutiveExperience. ACS Member. ACM/IEEE Member. David Koes, Ph.D. Project Manager,Chemical Biology Consortium (CBC) for the UPDDI. Active Member in the Society for Laboratory Automation and Screening (SLAS). Active Member in the American Association for the Advancement of Science (AAAS). Active Member in the American Chemical Society (ACS). Active Member in the American Association for Cancer Research (AACR). Referee for Nature Scientific Reports. Biology Consortium Steering Committee. Providedconsulting to the US Army Medcom automatingon safety analysisMember of the NCI Chemical Reviewer for PLoS ONE and the International Journal of Molecular Sciences. –

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Annual Report, FY16 92 Department of Computational and Systems Biology

Tenure Track Faculty…….………………………………….…………..93

Section V Non-Tenure Track Faculty……………………………………………..108 Bibliography

Ivet Bahar, PhD 2016 Liu B, Liu Q, Yang L, Palaniappan SK, Bahar I, Thiagarajan PS, Ding JL (2016) Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways Sci Signal 9: ra70 [JIF=5.03]

Stern AM, Schurdak ME, Bahar I, Berg JM, Taylor DL (2016) A perspective on implementing a quantitative systems pharmacology platform for drug discovery and the advancement of personalized medicine J of Biomolecular Screening 21: 521-34 [JIF=2.423]

Kurkcuoglu Z, Bahar I, Doruker P (2016) ClustENM: ENM-based sampling of essential conformational space at full atomic resolution J Chem Theory Comput. [Epub ahead of print]

Li H, Chang YY, Yang LW, Bahar I (2016) iGNM 2.0: the Gaussian network model database for bimolecular structural dynamics Nucleic Acids Res 44: D415-422 PMID: 26582920; PMC4702874 [JIF=9.202]

Jun I, Cheng MH, Sim E, Jung J, Suh BL, Kim Y, Son H, Park K, Kim CH, Yoon J-H, Whitcomb DC, Bahar I, Lee MG (2016) Pore Dilation Increases the Bicarbonate Permeability of CFTR, ANO1, and Glycine Receptor Anion Channels J of Physiology 594: 2929-55 PMID:26663196 [JIF=4.731]

2015 Gur M, Zomot E, Cheng MH, Bahar I (2015) Energy landscape of LeuT from molecular simulations J Chem Phys 143: 243134 PMID: 26723619; PMC4662675 [JIF=2.894]

Cheng MH, Bahar I (2015) Molecular Mechanism of Dopamine Transport by Human Dopamine Transporter Structure 23: 2171-81PMID: 26481814; PMC4635030 [JIF=5.237]

Hu D, Gur M, Zhou Z, Gamper A, Hung MC, Fujita N, Lan L, Bahar I, Wan Y (2015) Interplay between arginine methylation and ubiquitylation regulates KLF4-mediated genome stability and carcinogenesis Nat Commun 6: 8419PMID: 26420673; PMC4598737 [JIF=11.329]

Keskin O, Dyson HJ, Bahar I (2015) Biomolecular Systems Interactions, Dynamics, and Allostery: Reflections and New Directions Biophys J 109: E01-2PMID: 26377787; PMC4576319 [JIF=3.632]

Krieger J, Bahar I, Greger IH (2015) Structure, Dynamics, and Allosteric Potential of Ionotropic Glutamate Receptor N-Terminal Domains Biophys J 109: 1136-48PMID: 26255587; PMC4576161 [J-F=3.632]

Dutta A, Krieger J, Lee JY, Garcia-Nafria J, Greger IH, Bahar I (2015) Cooperative Dynamics of Intact AMPA and NMDA Glutamate Receptors: Similarities and Subfamily-Specific Differences. Structure 23: 1692- 1704PMID: 26256538; PMC4558295 [JIF=5.237]

Haliloglu T, Bahar I (2015) Adaptability of protein structures to enable functional interactions and evolutionary implications Current Opinion in Structural Biology 35: 17-23[Epub ahead of print] [JIF=7.021] Bahar I, Cheng MH, Lee JY, Kaya C, Zhang S (2015) Structure-Encoded Global Motions and Their Role in Mediating Protein-Substrate Interactions Biophys J pii: S0006-3495PMID: 26143655 [Epub ahead of print] [3.972]

Cooper GF, Bahar I, Becich MJ, Benos PV, Berg J, Espino JU, Glymour C, Jacobson RC, Kienholz M, Lee AV, Lu X, Scheines R, the Center for Causal Discovery team (2015) Center for Causal Discovery of Biomedical Knowledge from Big Data J Am Med Inform Assoc pii: ocv059PMID: 26138794 [Epub ahead of print] [JIF 3.504]

Annual Report, FY16 Department of Computational and Systems Biology 93 94 Section V : Bibliography

Residue during Na(+) Bahar I 6245 apoptosis in response to ionizing radiation, and polypharmacological strategies terminal Segment in Regulating TransportCycle Cheng MH, interactions Cobanoglu MC, Oltvai ZN,Taylor DL, PMC4281755[JIF=4.573] Mechanism of Sodium ZomotE, Gur M, 31 Eyal E, Lum G, PMC4481699[JIF=4.981] Currentof Methodsfor Detecting Sequence Coevolution Mao W, Kaya C,Dutta A,Horovitz A, 26078313 [Epub ahead of print] [JIF=4.128] Cytochrome c:De Novo Compound Discovery and Validation Bakan A,Kapralov AA,Bayir H,HuF, Kagan VE, 134PMID: 26106364 [JIF=3.184] dopamine transporter function by amphetamine, orphenadrine,and cocaine binding Cheng MH, Block E, HuF, Cobanoglu MC, Sorkin A, e1003521 biomolecular systemsusing simple a two Das A,Gur M,* Cheng MH,* Jo S, Allosteric Targeting of a Dual Specificity Phosphatase Bahar I Korotchenko VN,Saydmohammed VollmerM, LL, Bakan A, Sheetz K, Debiec KT, Greene KA, Agliori CS, interdomain allostery Hsp70in molecular chaperones General IJ, Liu Y, Blackburn M, Mao W, Gierasch L, (7):e1004376 permeation andincrease risk for pancreatitis but not for cystic fibrosis StudyGroup (2014) Aloe A, Kienholz ML, Yadav D, Barmada MM, D, Lawrence C, Romagnuolo J, Baillie J,Alkaade Cote'S, G, Gardner TB, Amann ST,Slivka A, Sandhu B, LaRuschJ, Jung General J, I,Lewis MD, Woo Park H, Brand RE,Gelrud A, Anderson MA, Banks PA,Conwell [JIF=6.677] elegans Silverman GA,Pak SC (2014) O'Reilly LP,* Long OS,* Cobanoglu MC,* Benson JA, LukeCJ, Miedel MT, Hale P, Perlmutter DH, print] [JIF=4.621] Protein Sequence Evolution and Structural Dynamics Bakan A,* Dutta A,*Mao W, Liu Y, Chennubhotla C,Lezon TR, Liu B, Bhatt D, Oltvai ZN,Greenberger JS, : 1487:

[JIF=5.228]

, Day BW, BW, Day ,

(2014) model of - 9 PMID:9 25568280; PMC4410662[JIF=4.981]

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Coupling between Neurotransmitter Translocation and Protonation State of a Titratable

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Complete Mapping of Substrate Translocation Highlightsthe Role of LeuT N

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A genomeA The Anisotropic Network Model web server at 2015 (ANM 2.0) :131 Microseconds Simulations Reveal a New Sodium

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, Designing inhibitors of cytochrome c/cardiolipin c/cardiolipin cytochrome of inhibitors Designing

Petersen OM, Kelly DR, Nicola T, T, Nicola DR, Kelly OM, Petersen

- 2014 2016 2015

Benos PV Benos

, Buch SC Buch

, (2016) Learning Mixed Graphical Models with Separate Separate with Models Graphical Mixed Learning (2016)

Proc Natl Acad Sci USA 111(3): 1114 111(3): USA Sci Acad Natl Proc , Berg J, Espino JU, Glymour C, Jacobson RC (2015) The Center Center The (2015) RC Jacobson C, Glymour JU, J, Espino Berg ,

(2015) MicroRNA expression profiling predicts clinical outcome of of outcome clinical predicts profiling expression MicroRNA (2015)

targeted imidazole targeted , Kagan VE (2014) VE Kagan , -

Kaminski N Kaminski

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87. [JIF=4.47] 87. based Model Selection. BMC Bioinformatics17 Suppl 5:175 [JIF=2.435] 5:175 Suppl Bioinformatics17 BMC Selection. Model based - Benos PV Benos - Santos L,Mao, P, Kim, SH, Minata M, Li J, J, Li M, Minata SH, Kim, P, L,Mao, Santos Kirkwood JM Kirkwood -

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, based therapy in metastatic melanoma treated on the ECOG the on treated melanoma metastatic in therapy based - Romkes M Romkes

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Benos PV Benos

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Frequent mutation of receptor protein tyrosine phosphatases provides a mechanism for STAT3 STAT3 for mechanism a provides phosphatases tyrosine protein receptor of mutation Frequent

230 Nathanson KL Nathanson

- , Huang GT Huang Jiang J, Bakan A, Kapralov AA, Ishara Silva K, Huang Z, Amoscato AA, Peterson J, Krishna Garapati V, V, Garapati Krishna J, Peterson AA, Amoscato Z, K, Huang Silva AA, Ishara Kapralov A, Bakan J, Jiang Department of Computational and Systems Biology Systems and Computational of Department Symp Pac Outcomes of Clinical Prediction to Application with Features of Groups Formed Dynamically 20:431 Biocomput FY16 Report, Annual for Causal Discovery of Biomedical Knowledge from Big Data. J Am Med Inform Assoc 6: 1132 6: Assoc Inform Med Am J Data. Big from Knowledge Biomedical of Discovery Causal for LC Villaruz MA carboplatin/paclitaxel [JIF=4.543] 7:58 Epigenetics Clinical Chandran UR, Luthra S, Santana S, Luthra UR, Chandran Stem Glioma Proneural Distinguishes Profiling Expression Gene (2015) RW Sobol I, Nakano SY, Cheng 333 Data5: Genom Cells. Stem Glioma Mesenchymal from Cells GF, Cooper Sedgewick AJ, Shi I, Donovan RM, RM, Donovan I, Shi AJ, Sedgewick Stability and Parameters Sparsity AC, Faye K, Cuna Pandit G, Halloran CV, Lal N, Olave MicroRNA by Septation Alveolar of Regulation N (2016) Ambalavanan N, Kaminisky L476 310 (5): Physiol Mol Cell Lung Ph.D. Benos, V. (Takis) Panayiotis (2014) cancer neck and head in hyperactivation J, Atkinson H, Bayir S, Saxena mitochondria complexes: peroxidase 221 IJ, General BR, L, Gilbert Wang H, Li Y, Lu Y, J, Zeng Hritz PK, Ng ND, Peyser VW, Lui JR Grandis DE, Johnson GB, Mills LA, Garraway PS, Hammerman JK, Vries Y, X, Du Xiao KP, Pendleton 96 Section V : Bibliography

Center. (2015) Geskin A MEDICINE Research academicin medical centers: Two threats to sustainable support Rappley,S; Marsha D; Reece AlbertE; Rothman Paul B; Schwinn Debra andA Spiegel Allen M (2015) Pamela FitzB; Gregory;Golden Robert N; Goldman Lee;Jameson J Larry; Lee Vivian S; Polonsky Kenneth Levine Arthur S; Alpern RobertJ; Andrews Nancy C; Antman Karen; Balser Jeffrey R; A. research enterprise:Finding consensus and implementing recommendations. Pickett CL, Corb BW,Matthews CR, Sundquist WI, Berg JM (2015) Toward a sustainable biomedical [JIF=38.138] Berg JM, Berg WA (2016) Biomol Screen. 2016Jul;21(6):521 SystemsPharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine. Stern AM, Schurdak ME, COMMUNITY. Preprints for the life sciences. M R Berg JM Jeremy M. Berg, Ph.D. Soc Nephrol. 2014Apr 17. [Epub ahead of print] [JIF=9.466] (2014) Edinger RS, Coronnello C,Bodnar AJ, Laframboise WA, Benos PV,J, Ho Johnson JP,Butterworth MB , ,

2015 Sep1;112(35):10832 King S Strasser C NeedsAssessment for Research Use Highof Aldosterone Regulates MicroRNAsin the Cortical Collecting Duct to Alter Sodium Transport

, Sum of the Times Cited: 9475 Published Items in Each Year PloS one ,

, Bhalla N

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, Bahar I Krumholz HM No myth: Benefits of breastscreening.

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, Camacho CJ Camacho PLoS One 10;10 One PLoS

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PLoS One Jul 29;10(7):e0133590. 29;10(7):e0133590. Jul One PLoS Watkins C, Alvarez RA, Miller MP, MP, Miller RA, Alvarez C, Watkins

(2015) (2015) - 87. [JIF=3.539] 87.

- Discover index: 62 index: - 401. [JIF=2.590] 401. - Sci Signal. 2015 May 26;8(378):ra51. 26;8(378):ra51. May 2015 Signal. Sci - h Fluorescence Polarization Screening Screening Polarization Fluorescence

Structure Guided Chemical Modifications of of Modifications Chemical Guided Structure 72. doi: 10.1074/jbc.M114.629964. Epub 2015 2015 Epub 10.1074/jbc.M114.629964. doi: 72. - A Teach 10(11):1179 Citations in Each Year Each in Citations Camacho CJ Camacho

(2015) (2015)

Watkins CE, Anthonymutu TS, Vlasova II, Vlasova TS, Anthonymutu CE, Watkins - 2015 2016

, Smithgall TE (2015) (2015) TE Smithgall , , Straub AC (2015) (2015) AC Straub ,

, Biondi RM, Dömling A (2015) Discoveryof a Potent Allosteric Camacho CJ Camacho

Fragment oriented molecular shapes Choosing the Optimal Rigid Receptor for Docking and Scoring in the the in Scoring and Docking for Receptor Rigid Optimal the Choosing Peroxidase activation of cytoglobin by anionic phospholipids: phospholipids: anionic by cytoglobin of activation Peroxidase

Biochim Biophys Acta 1861(5):391 Acta Biophys Biochim Expert Opin Drug Discov. Drug Opin Expert Camacho CJ Camacho

Camacho CJ Camacho (2015) (2015)

J Chem Inf Model. [Epub ahead of print] [JIF=3.738] print] of ahead [Epub Model. Inf Chem J

J Biol Chem. Jul 3;290(27):16861 Jul Chem. J Biol Camacho CJ Camacho

(2015) Indexing Volumetric Shapes with Matching and Packing Knowl Inf Syst Apr Apr Syst Inf Knowl Packing and Matching with Shapes Volumetric Indexing (2015)

, KoesDR (2016) Camacho CJ, Dömling A (2015) Focusing on shared subpockets subpockets shared on Focusing (2015) A CJ, Dömling Camacho Camacho CJ CJ Camacho

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180 180 - based drug discovery. discovery. drug based Camacho CJ Camacho - Sum of the Times Cited: 19116 Cited: Times the of Sum Published Items in Each Year Each in Items Published Camacho CJ Camacho 54. [JIF=1.674] 54.

-

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual Sparacino SM, Mutchler AT, Nguyen D, Koes FG, Reinders MM, Rahaman EK, Jackson BB, D, Chen Cheng Increase That 3 Reductase b5 Cytochrome of Inhibitors Molecule Small Novel Reveal Propylthiouracil Bioavailability. Oxide Nitric DR, Koes 157 1”43(1_: A, Wells DA, Lauffenburger BA, Joughin H, Shao T, Travers function ACTN4 regulates region disordered intrinsically an within [JIF=6.279] 10.1126/scisignal.aaa1977 doi: Baumgartner MP, MP, Baumgartner Experiment 2013/2014 CSAR M, Baumgartner H, Shi P, Grover Function Kinase ABL of Modulators Allosteric Molecule Small for Assays 2015.[JIF=3.234] eCollection 10.1371/journal.pone.0133590. doi: Abdelraheem EM, Abdelraheem fragment MA, Phillips X, Deng NA, Pabon DR, Koes Tool Screening Virtual and Modeling Pharmacophore Interactive An Online ZincPharmer: [JIF=3.234] eCollection 10.1371/journal.pone.0134697. doi: (8):e0134697. Kroon E, Schulze JO, Süß E, Süß JO, E, Schulze Kroon Nov Engl Ed Int Chem Angew Methods. Synthetic and Computational Combining by Modulator Kinase 16;54(47):13933 Carlos Camacho, Ph.D. Camacho, Carlos E, Hain 66:143 Sparacino MP, Baumgartner AA, Kapralov J, Tejero (2016) VE Kagan H, Bayir MT, Gladwin consequences. and Mechanisms

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ar400084s. Epub 2013 Aug 29. Review. [JIF=24.348] populationsand functionally relevant substates. Ramanathan A, Savol A,Burger V, ahead of print] [JIF=4.621] protein sequence evolution and structural dynamics. Bakan A, Dutta A, Mao W, Liu Y, 10.1126/scitranslmed.aaa1233.[JIF=15.843] abnormal respiratory ciliary motion nasalin biopsies. Quinn SP, Zahid MJ, Durkin JR,Francis RJ, Lo CW, [JIF Oak Ridge biosurveillance toolkit for public health dynamics. Ramanathan A, Pullum LL, Hobson TC,Steed CA, Quinn SP, Laplacian spectra. Savol AJ, Chakra Chennubhotla, Ph.D. Comput Chem 35(25):1824 KoesDR, JIF=2.485][ Region. Strategy for Selective Inhibitors c of Moroco JA, Baumgartner MP,Rust HL, Choi HG, Hur W, Gray NS, Camacho CJ, Smithgall TE JVI.00157 targeting2A by Merkel cell polyomavirus small T antigen. Kwun HJ, Shuda CamachoM, CJ,Gamper AM,Thant M, Chang Y,Moore PS May 22. [JIF=4.573]

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- Sum of the Times Cited: 872 Published Items in Each Year Faeder JR, Setlow P, Li YQ (2015) Memory of Germinant Stimuli in Bacterial Spore. bio conference: meeting reportand special issue preface. , Bernstein KA (2015) (2015) KA Bernstein , , Morel PA.(2016) PA.(2016) Morel , . PLoSGenet.

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[JIF=5.228] apoptosis in response to ionizing radiation, and polypharmacological strategies. Liu B, BhattD, Oltvai ZN, Greenberger JS, functional E mevalonate pathway inhibition attenuatesthe growth of mesenchymal Warita K, Warita T, Beckwitt CH,Schurdak ME,Vazquez WellsA, A, Oltvai ZN (2014) interactions Cobanoglu MC, Oltvai ZN,Taylor DL, Zoltan Oltvai, M.D. Makeus Sense of itAll? X,Xia Owen MS, Biology Metacaspases: Methodsand Protocols

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Department of Computational and Systems Biology Systems and Computational of Department Inform. Aug 31; 6:48. 31; Aug Inform. A, R, Gough, DeBiasio, R, Boltz, L*, Vernetti, N*, Senutovitch on Med, Biol Exp Systems. Microphysiological to Applied Biosensors Protein Fluorescent [JIF=2.542] FY16 Report, Annual Bendor L, Weyrich LS, Linz B, Rolin OY, B, Rolin Linz LS, L, Weyrich Bendor Limit Components Immune Adaptive and bronchiseptica Bordetella of System Secretion Six Type (2015) One PLoS Infection During Survival Intracellular T, J, Foxwell Li XM, Nguyen LM, Reese KA, Repa BB, Campbell JM, Davison JW, Prichard Critchley Is Positively Regulated by ToxR and Contributes to Bile Resistance Resistance Bile to Contributes and by ToxR Regulated Is Positively [JIF=2.688] DL Taylor 18;10(8):e0135676. Aug One PLoS Review A Systematic Cholera: Control to Interventions Hygiene [JIF=3.057] Green JL, Moon RW, Whalley D, Bowyer PW, Wallace C, Rochani A, Nageshan RK, Howell SA, Grainger M, Grainger SA, Howell RK, Nageshan A, Rochani C, Wallace PW, Bowyer D, Whalley RW, Moon JL, Green TM, Chapman KH, Ansell HM, Jones Calcium falciparum Plasmodium of Inhibitors Imidazopyridazine GMP Cyclic 28;60(3):1464 Dec Chemother. Agents Antimicrob Development. of Intracellular S, Sayeed MF, XR, Howard Bina VM, Ante A, Gough TY, Shun R, DeBiasio R, Boltz N, Senutovitch L, Vernetti Med Biol J Exp models. disease and safety drug physiology, investigating for platform microphysiology Jan;241(1):101 (2016) Taylor DL Taylor N of analysis association Genetic (2016) JL Kennedy Mar;31(2):121 Psychopharmacol Hum clozapine. to response and clinical TY, Shun A, Gough 96:12 1; Mar Methods screens. and profiles phenotypic in heterogeneity of analysis influences the relationship between repetitive negative thinking and psychopathology symptoms. symptoms. psychopathology and thinking negative repetitive between relationship the influences 30;238:277 Apr Res. Psychiatry EJ, KE, White Frosio DM, Grant MR, Judah Anxiety. in Social Monitoring Performance Enhanced Scott LJ, Erdos MR, Huyghe JR, Welch RP, Beck AT, Wolford BN, Chines PS, Didion JP, Narisu N, Stringham Stringham N, Narisu JP, Didion PS, BN, Chines Wolford AT, Beck RP, Welch JR, MR, Huyghe LJ, Erdos Scott HM, KL, Mohlke TA, Lakka RM, Watanabe X, Wen H, Jiang GE, Crawford J, A, Hensley Kiseleva RD, Albanus regulatory genetic The (2016) SC. Parker FS, Collins M, Boehnke HA, Koistinen J, Tuomilehto M, Laakso muscle skeletal human in diabetes 2 type of signature EJ, White MR, Judah DM, Grant AC, Mills D. Lansing Taylor, Ph.D. Taylor, Lansing D. YF, Wang BC, Litzenburger AJ, Casa AM, Nagle C, Erdem insulin and insulin in signaling differential reveal regression lasso and screening pathways. 104 Section V : Bibliography

Res. top pelagic predatorsfrom offshore New England watersof the northwest Atlantic Ocean Teffer AK, Staudinger MD, toxicity. Golberg I, DebiasioR, Usta BO, Bale VernettiSS, L,Senutovitch N, Jindal R, Hegde GoughM, A, McCarty WJ, Bakan BhushanA, A, Shun TY, Jul;82(7):2980 nodulation Taylor DL the SUMO protease Ulp2 PLoS One 9(5): e94182. [JIF=3.057] SJ, Watts FZ (2014) The S. pombe translation initiation factor eIF4G isSumoylated and associateswith JongjitwimolFeng J, M, ZhouL, Wilkinson O,Small L, Baldock R, (2014) AORN J.Jul: 100 (1): 8 Taylor DL analysis: application of heterogeneity indices drugto discovery. Stern AM, Schurdak ME, Gough AH, Chen N,Shun TY, Lezon TR, Boltz RC,Reese CE,Wagner J,Vernetti LA,Grandis JR, Lee AV, [JIF=4.476] dependent protein kinase PfCDPK1. Biochemical and antiparasitic properties of inhibitors of the Plasmodium falciparum calcium Birchall K,Large J, Bouloc N,Smiljanic Ansell KH,Jones HM, Whalley D, Hearn A, health perspective. from Southern EnglandNew coastal waters: contamination from a trophic ecology and human Taylor DL target interactions Cobanoglu MC, Oltvai ZN, capillary electrophoresis. MD, GaborieauM, Castignolles (2015)P Real Taylor DL cholerae vexABRND efflux systemrequires vexR. Taylor DL

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[JIF=3.447] , Rieben R, Candinas D, Beldi G. (2016) , , Epidemiology of PelvicFractures Germany:in Considerably HighIncidence Rates

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Vollmer LL Vollmer , Mascarello A Mascarello

, Vogt A Vogt Sum of the Times Cited: 1138 Cited: Times the of Sum Published Items in Each Year Each in Items Published , Mochizuki K. (2014). The Taming of the Shrew: Regulation of a catalytically active , Day BW, , Giesert F, Pan L, Antonucci F, Kiel C, ZhangM, Weinkauf S, Sattler M,Sala C, Matteoli M, [JIF=3.088]

Joy ME, Vollmer LL, Hulkower K, Stern AM, Peterson CK, Boltz RC, Roy P, P, Roy RC, Boltz CK, Peterson AM, Stern K, Hulkower LL, Vollmer ME, Joy Vogt A CS, Agliori KA, KT, Greene Debiec K, Sheetz A, Bakan LL, M, Vollmer Saydmohammed VN, Korotchenko Salum LB Salum Department of Computational and Systems Biology Systems and Computational of Department FY16 Report, Annual Bimolecular Fluorescence Complementation Assay for Small Molecule Inhibitors of HIV of Inhibitors Molecule Small for Assay Complementation Fluorescence Bimolecular Screen. Biomol J Dimerization. [JIF=5.036] Profilin Identifies Cells Cancer Breast in Human Screen Multiplexed One. PLoS Activities L, Vollmer JA, Poe Denks K, Denks eukaryotes. and in prokaryotes transport F, Zweydorf von A, Marte F, Pischedda A, P, Kastenmüller Jagtap CJ, MD, Kaiser Cirnaru F, Onofri G, Piccoli A Vogt Leucine (2014) CJ. Gloeckner M, Ueffing C by Its Mediated Interactions allosteric targeting of a dual specificity phosphatase. specificity a dual of targeting allosteric 45. S, Bierschock S, Boehme M, Bodenstein ventilation. mechanical of model animal an in tomography impedance electrical by flow gas respiratory 73. 14: Med. Pulm BMC e29383 27;4: May 2014 Elements. Genet Mob Transposase. domesticated I Bahar LD Delatorre LF Nascimento Inhibition and Activation Autophagy for Chemical Function Is a Dual [JIF=3.234]

Annual Report, FY16 118 Department of Computational and Systems Biology

Section VI Executive Summary………………………………....119 Budget………………………………………..……...……124 Financial Plan for Fiscal Year 2017

EXECUTIVE SUMMARY

The Department of Computational & Systems Biology (CSB) financial plan for Fiscal Year 2017 reflects the continued growth in the department in 2016. Our current goals fall into three categories: 1. Recruiting additional faculty members, 2. Increasing the amount of funding for research and educational programs, and 3. Continuing our strong tradition of training postdoctoral fellows as well as graduate, undergraduate, and high school students in a tiered mentoring framework. These goals are driven by the department’s educational mission to train the next generation of computational and systems biology researchers, as well as our two-fold research mission to (i) advance the scientific understanding of biological systems through computational tools and theoretical approaches based on the fundamental principles of physical sciences and (ii) design new computational/mathematical models and methods for simulating complex biological processes and for extracting useful information from accumulating data.

The CSB research mission is supported by recruitment of faculty with balanced interests and experience to broaden the department’s expertise in four key areas of specialization/growth: 1. Bioinformatics and Computational Genomics and Proteomics, 2. Cellular and Systems Biology, 3. Molecular Structural Biology, and 4. Pharmacology and Drug Discovery. We also strive to continue our current research collaborations while reaching out to establish new collaborations within the University of Pittsburgh and beyond through aggressively pursuing extramural grant support, external funding, additional interdisciplinary projects as well as by providing administrative support as necessary to ensure smooth operation of the department and attainment of departmental goals.

Our educational efforts are being achieved by our faculty’s involvement in the Joint Carnegie Mellon- University of Pittsburgh Computational Biology (CPCB) Graduate Program, other graduate programs at the University, the Training and Experimentation in Computational Biology (TECBio) Research Experiences for Undergraduates (REU) summer program, and the Drug Discovery, Systems and Computational Biology (DiSCoBio) Summer Academy for High School Students, which is a partnership with the Drug Discovery Institute under the umbrella of the University of Pittsburgh Cancer Institute Academy. These endeavors have been and are supported by funds from the NIH, NSF, Department of Defense, Howard Hughes Medical Institute, and the Doris Duke Foundation, as well as our infrastructure which includes a state-of-the-art computational biology classroom, a department compute cluster with over 2,800 CPU cores and 88 GPUs for intensive computations and simulations, and the additional involvement of CSB graduate students and postdoctoral fellows.

Introduction Under the direction of Dr. Ivet Bahar, Distinguished Professor and John K. Vries Chair, the Department of Computational Biology (DCB) was created in October 2004 and was renamed in February 2010 to The Department of Computational and Systems Biology (CSB) to reflect our present and future research and education efforts and signal our increasing focus on multi-scale interactions in biological systems, enabling us to tackle more research at an integrated, Systems level. Moreover, our close association with the University of Pittsburgh Drug Discovery Institute facilitates an important effort combining computational, systems, and experimental approaches to improve the design, screening, and testing of new therapeutics for a host of diseases. Lastly, our extensive collaborations have built the infrastructure that support nationwide, large-scale, center grants for the investigation of new therapies for liver fibrosis and hyperproliferation and for multiscale (molecular, cellular, and systems level) modeling of biological systems to study the structural basis of glutamate transporters, the mechanisms of synaptic and T-cell signaling, and the architecture of neuronal pathways.

The FY17 plan of the CSB largely reflects three main goals: 1. Recruiting new faculty members 2. Increasing the amount of funding for research and educational programs 3. Training graduate, undergraduate, and high school students and Postdoctoral fellows

As stated in the proposal: Our intent is for the Department of Computational Biology to become a fully functioning and nationally acclaimed department. We continue to recruit the most promising faculty candidates, to obtain substantial extramural funding from a variety of public and private agencies, and to train top graduate students and postdoctoral fellows. In early FY17, Dr. Anne-Ruxandra Carvunis will be joining CSB. She

Annual Report, FY16 Department of Computational and Systems Biology 119 120 Section VI : Financial Plan for FY17

function and dynamics;RNAsand microbial network modeling.small repair; and recognition damage DNA of mechanisms molecular immunity; acquired vaccine of modeling systemspolypharmacology; for modeling systems responses; acute and diseases chronic of the and postdepartment mechanismsmolecularthe as such areasseveralresearchprogress in our makingare Medicine.We of School of advancement and growth students,further the for critical be will potentialwho the computations, and of in Crete, Institute, Discovery group of Drug the University with partnership California the our University, and addition University,toadding effortsSystems Yale Biologyto departmental our focus,willallow us toa reach broader larger and These from Stanford Carolina. collaborators University, North by of RutgersUniversity joined are Technology, They of sets. dataInstitute diverse and large using causality representtheir and discover to seeking scientists biomedical by needed architecture system University Mellon and software, Carnegie algorithms, the develop will (Pitt), who (PSC) Center PittsburghSupercomputingPittsburgh the and (CMU), of University the from scientists data among partnership representsGlymour,CenterClarkatheGregoryand Drs.Bahar,Jeremy Berg, Ivet Cooper, by Led 2015: in generatenovelandgroundbreaking research. exampleAnCenterthis is the of Causal for Discoveryaward undoubtedlywill newthat nation spearheadingthe across and universityin the at role here collaborators with leadinginitiatives research botha taken has from Biology Systems and Computational scientists of Department The together on bring expertise and will methods, that ideas, of environment exchange an the helping creatingbiologically motivatedcomplex problems of today by for interactions enhance need to a communities interest. is biomedical or biological there of problem a Thus, by, driven impetus or to, focus necessary the lack they datasets. existing The mereapplication these of technologiesonalone addsthetoknowledge little underlying theof biologythem if test and engineering, and sciences physical methods science, andcomputer from theories derived using algorithms sophisticated develop researchers These research. science life emerged. has expertise Computationalmodelingmodeling expertise necessary is in ordertocomputational address the new dimensionsandchallenges with in researchers of generation new a years, recent In Pharmacology and Drug4. Discovery. and Biology, Structural Molecular 3. Biology, Systems and Cellular 2. Proteomics, and GenomicsComputational and Bioinformatics specialization/growth: 1. of areas four pursuesdepartment the topics, research of terms In community. scientific the to data generated and closetools the disseminating in and publicizing data,efficiently (iii) and accumulating fields, these from in research doing information groups experimental with useful cooperation extracting for and processes simulating for biological methods complex and biological models computational/mathematical of new design understanding to scientific (ii) sciences; the physical advance to (i) is: department systems through computational tools and theoretical approaches the based on the fundamental principles of of mission research The Research education. and research components: two has Biology Systems & Computational of Department the of mission The Financial Plan Initiatives and Implementation Strategy and Engineering, Technology, Laboratory, Amazon,Pinterest, and Apple. (Science, Goldman establishments premier at STEM positions hold currently who graduates the in appointed facultyto positions, atStatePenn UniversityTheand University Georgia;of wellas Academiaprogramas been have who graduates two and include accomplishments of universities examples Present disciplines. Mathematics) top Industry at selecting members faculty equally be will Department institutions. research CSB the from cutting on working scientists trainees or nationwide, that was aim Our scientists. first a offering by and environment,development and research unique a creating by levels, senior and junior the both at faculty recruiting by Pittsburgh to contributes CSB top States. United the in visit and a work live, as CSB pre a of as Pittsburgh Department future to the importance equal of is establish it believe we funding, grant of terms in quantifiable be will success our While yeast and bioinformatics using networks molecular of evolution the genetics. modeling on works she where Diego SanCalifornia ofUniversity the at Ideker Trey Dr. oflaboratory the fromdepartment the to comes

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Section VI : Financial Plan for FY017 . 121 -

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biology expertise, and for coordinating experimental and computational studies, and studies, computational and experimental coordinating for and expertise, biology scientific community at Pitt, if there were a larger number of Faculty at the CSB years. ten last the over clear became that grant opportunities in biological/biomedical computing currently being announced by agencies other and NIH

(i) (i) there is a huge demand in the life and physical sciences community at Pitt for computational proposals. grant future or pending for foundations the provide interactions such (ii) the of requests and needs the to effectively more much responded have would CSB the (ii) (iv) the higher number of Faculty will permit the Department to respond to a larger number of focused focused approaches will be applied in several of these areas (e.g., the modeling of vaccine -

Department of Computational and Systems Biology Systems and Computational of Department for for funding pending and continue to diversify funding applications to National Science Foundation, the Breast Cancer Foundation, The Edward Samuel Mallinckrodt, Jr. & Foundation, The Emma Kaufman Winters Biomedical Foundation, The Foundation, Research Five3Genomics LLC, Inc, to new amount 8 grants These or pending will be have awarded. been 38 Of the applications Corporation. The Department Burroughs Wellcome of Fund, Defense, Leidos Department FY16 Report, Annual of Energy and General Electric investigator investigator on 38 active awarded. funded grants new 15 with grow to continued funding of portfolio The 2016. in expensed research projects in FY16 totaling Our $ junior faculty members $39 understand the importance mil; of obtaining extramural $6.9mil grant support, and of are encouraged which to was make every effort in securing external Given funding the and pursuing current interdisciplinary funding projects. climate projects and (R01s), our projected goals for the salary support for all projected tenure stream faculty, although attainable, decline in will the likely require more number ambitious efforts on of our part to NIH obtain adequate research lines, funding. Along 65 Applications for these funding were submitted in FY16 and entering FY 2017, we have 47 applications Institute, Yale University, Oak Ridge National Laboratory, Massachusetts Hospital, Institute, Yale General Massachusetts Oak University, Laboratory, the Ridge National University of Groningen, Harvard University, Mt. Sinai, University Center. of Supercomputing Pittsburgh the and Scientifique Recherche la de Tennessee, National Institut Georgia, University of Miami, University of Funding Grant Co or Investigators Principal as served Faculty CSB 2016 30, June As of or Co CSB Investigator 2001. as Principal since offaculty served projects million inresearch $61.6 funded Collaborations In addition to faculty recruitment, the research CSB programs that is exist continuing at to the tap University of as into of external building, consortium between ismembers evidenced research by collaborations successful Pittsburgh. the We extensive are basic also strengthening and our Children Institute, Cancer clinical Pittsburgh of University the from efforts faculty and the CSB at UPMC, Carnegie Mellon University, Duquesne University, University of Connecticut, UC Davis, The Salk full 26 has currently The CSB one in new starting faculty FY 17 we may make to faculty and effort search a recruit additional concerted promotion of current Research Associates is not adjunct out of faculty the from question. The other CSB University. Duquesne departments and of Groningen currently University Yale, University, at has 39 the joint/ University of Pittsburgh, as well as Carnegie Mellon

Faculty Recruitment Recruitment Faculty To broaden the department experience. and interests balanced with individuals recruiting to given be would The main reason Department: the of creation the for proposal the in stated As projects. collaborative for for opportunities seeking to that evident is it projects, active of diversity and increase number large the From the number of faculty was to increase the number of determining determining the onset and progression of chronic diseases and acute responses in humans. Our systems biology acquired immunity and drug discovery). Moreover, the CSB will be imaging, pursuing of sets in genomic, how large efforts, especially Big the Data analysis to opportunities expertise computational to apply our and other types of anticipate data that a large number of researchers from various Schools can and Departments in the University of be effort. incorporated this in participate will University Mellon Carnegie the and Pittsburgh into our emerging Personalized Medicine efforts. We The The members of CSB will work together to generate new ideas, methods, and that will therapeutics potential tools, jointly address tomorrow biological information, framework. biology a systems To expand on these efforts, the CSB will Pathology, Critical Care also Medicine, the Center build for Vaccine Research, on the Drug Discovery Institute, existing and the collaborations with those Institute in Medicine, for Personalized Medicine. These research activities focus on the molecular mechanisms 122 Section VI : Financial Plan for FY17

exclusive use by one research group. for reserved is second the and department the by shared is cluster first The total. clusters two are There were pipes The sizedand routedweeks. suchthat one more unitcould be added90ºF+ in the future if necessary.several haswith summerroom Thea through temperature redundancy. cool plus consistent capacity maintained cooling previous the double have now We project. upgrade In twoThe building The system. suppression fire FM200 a and (BST3) provides Supply, backup power generator as well aschilled water plant. Power Uninterruptible 225kVA a has also across parallel in run can nodes. or complete to time long a take that computations and models, simulations, rack of number manner. effective and efficient an in research its conduct to computers of number large a uses Biology Computational of Department The Resources with space, lab this utilizing are Ayoob, Anne Joseph and Lee, Robin Xing, Jianhua Clark, Nathan currently, labs:wet the house to BST3 of floor 10th the on space of ft sq. approximatelyacquired4,200 we 2012, In lounge area is located outside of the main office suite. kitchenette/ A offices. staff additional and area, reception a room, conference enclosed an students, for workstations 28 houses area Thecubicle a offices.area, conference/meetinginformal staff an of additional up made and is core equipment, central printing and research copying 2005. postdoctoral room, September and server classroom, in staff, the BST3, faculty,houses the for in offices located has suite suite Twenty office theassociates. ft rectangle, sq a 10,000 as a roughly facility,Configured its into moved CSB The Administration been submitted to NSF. also is and co 2013 beingin years additional 4 for renewed was 5 areREU for TECBio renewedthe TECBio isand support andgrant additional training of T32 CPCB years CPCB Additionally, the respectively; students. NSF, the school and high NIH the of from grants cohort by funded new the of addition the other with each and from greatly benefited have programs The program. (REU) Undergraduates for Experiences undergraduatestudents CarnegieMellon joint students graduate for programs in training existing our training of success the didactic on built andbeen has and experience mirrors research mentored a endeavor Thisdiscovery. them drug and biology cancer study provide toapproaches computational and experimental and level, school high the at studentsto out reach to initiativeongoing an have we Institute,Discovery Drug instance,the collaboration with in For programs. our and Academy, Summer International support Institute Cancer Pittsburgh of to University the of opportunitiesumbrella the under funding pursuing and students, of group larger a to out reaching programs, existing our improve to striving constantly also are We forscientists. emerging experience mentoring important providing while levels, experience similar at tiered trainees and students a into efforts postdoctoral these and incorporated students train to haveWe experiments somethemselves.perform manycollaborators, biologicalexperimentaland with work often is simulating computer, aim a on the problems biological cases,multipleatmodelingphenomenascales, signaltransduction pathways, andmanyother efforts.Students complex all tackle and In identify to sciences. researchers the in underrepresented thatare backgrounds from students recruit and to out reach to (iv) and research, biology computational in to biology and biomedicalsciences sciencesstudents, (iii) to preparephysical and encourage students atall careerfor levels a and quantitative the of concepts fundamental teach to (ii) computationalsciences, computer introduceand to (i) purposes:mathematics, engineering, physics, chemistry, biology, multiple from students to serve methods and problems biology which of all biology, computational in levels first providing to committed is CSB The Education $5.2 million in Total Cost.

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Cluster 1 2 Totals Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual

several network accessible databases of biological research data. research biological of databases accessible network several Storage: 200+TB full VMware 3TB servers. of FC data. cluster for server backup Network 160TB network. the over workstations configured for N+1 High N+1 for configured of dual 6 channel attached Promise vTrak enterprise storage array configured with both SATA and SAS drives for reliability and increased performance when failure. of point single no with availability needed. The virtualized environment provides for high The department also uses a number of Windows and Linux servers for sharing functions. domain files, printers, and other workstations workstations with cards varying between GTX 480 and Titan X. Each workstation runs Linux and all use nVidia CUDA software development kit running (SDK). Softwaresuch cluster. the by 680s GTX two be to considered is 690 card GTX * Each NAMD as Amber and already support Servers: Application The department has a VMware vSphere 5 cluster for running multiple application servers on 3 ESXi hosts on GPU hardware. Computing: GPU In addition to the CPU Processing Units cluster nodes, we are are 102 various GPUs. available Currently GPUs of consist adding six nVidia GeForce GTX 690 cards, six GTX Titan rackmount servers to Black house cards, 28 several nVidia Graphics Titan X cards, 2 cards, two nVidia nVidia Tesla M2090 cards, 7 GTX Tesla K40 cards and 780s, three Tesla K20 cards. There 32 are also 14 GPU nVidia GTX 780Ti cards, 16 nVidia GTX 980 *Nodes include compute nodes only. The (3) Login and (15) Storage servers are extra. extra. are servers Storage (15) and Login (3) The only. nodes compute include *Nodes (QDR) Gbps 40 and (DDR) Gbps 20 are speeds Infiniband Ethernet. Gigabit have clusters **All 124 Section VI : Financial Plan for FY17

UniversityEndowment as of 6/30/16 Restricted Balance as of 6/30/15 Transfers (Pitt sub8260 only) Overhead (incl.stepdown) Other Expenses Financial Aid Rent (if Professional Services Travel and Business Capital Equip. & Renovations Suppliesand Minor Equipment Fringe Benefits Fellows, Students(non Fellows, Students(taxable) GSA/GSR/TA/TF Research Associates Staff Medical School FacultySalaries Expenses: Other University Support EndowmentIncome Interest Income Grant Revenues SVC Support School of Medicine Other Support School of Medicine ECU Allocation Revenue: Computational & Systems Biology University of Pittsburgh School of Medicine Surplus/(Deficit) Total Expenses Subtotal Other Expenses Subtotal Compensation Total Revenue

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FY 2017 Budget

$ $ 10,818,177 $ $ 95,280 $ $ 6,259,369 $ 2,440,878 $ 1,728,000 $ 294,650 School of Medicine Accounts $ $ 2,212,764 $ 165,766 $ (86,105) $ 10,904,282 $ 5,069,249 $ 309,294 $ 2,071,812 $ 125,000 $ $ 80,000 $ 1,268,000 $ 202,292 $ 495,000 $ 517,851 $ 5,835,033 $ 1,454,636 $ $ (2,712) $ 651,500 $ 804,000 $ 678,225 $ 2,249,384 Department of Computational and Systems Biology - - - -

Annual Report, FY16

Section VI : Financial Plan for FY017 . 125

Department of Computational and Systems Biology Systems and Computational of Department Annual Report, FY16 Report, Annual