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RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed -
Applied Category Theory for Genomics – an Initiative
Applied Category Theory for Genomics { An Initiative Yanying Wu1,2 1Centre for Neural Circuits and Behaviour, University of Oxford, UK 2Department of Physiology, Anatomy and Genetics, University of Oxford, UK 06 Sept, 2020 Abstract The ultimate secret of all lives on earth is hidden in their genomes { a totality of DNA sequences. We currently know the whole genome sequence of many organisms, while our understanding of the genome architecture on a systematic level remains rudimentary. Applied category theory opens a promising way to integrate the humongous amount of heterogeneous informations in genomics, to advance our knowledge regarding genome organization, and to provide us with a deep and holistic view of our own genomes. In this work we explain why applied category theory carries such a hope, and we move on to show how it could actually do so, albeit in baby steps. The manuscript intends to be readable to both mathematicians and biologists, therefore no prior knowledge is required from either side. arXiv:2009.02822v1 [q-bio.GN] 6 Sep 2020 1 Introduction DNA, the genetic material of all living beings on this planet, holds the secret of life. The complete set of DNA sequences in an organism constitutes its genome { the blueprint and instruction manual of that organism, be it a human or fly [1]. Therefore, genomics, which studies the contents and meaning of genomes, has been standing in the central stage of scientific research since its birth. The twentieth century witnessed three milestones of genomics research [1]. It began with the discovery of Mendel's laws of inheritance [2], sparked a climax in the middle with the reveal of DNA double helix structure [3], and ended with the accomplishment of a first draft of complete human genome sequences [4]. -
Microblogging the ISMB: a New Approach to Conference Reporting
Message from ISCB Microblogging the ISMB: A New Approach to Conference Reporting Neil Saunders1*, Pedro Beltra˜o2, Lars Jensen3, Daniel Jurczak4, Roland Krause5, Michael Kuhn6, Shirley Wu7 1 School of Molecular and Microbial Sciences, University of Queensland, St. Lucia, Brisbane, Queensland, Australia, 2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California, United States of America, 3 Novo Nordisk Foundation Center for Protein Research, Panum Institute, Copenhagen, Denmark, 4 Department of Bioinformatics, University of Applied Sciences, Hagenberg, Freistadt, Austria, 5 Max-Planck-Institute for Molecular Genetics, Berlin, Germany, 6 European Molecular Biology Laboratory, Heidelberg, Germany, 7 Stanford Medical Informatics, Stanford University, Stanford, California, United States of America Cameron Neylon entitled FriendFeed for Claire Fraser-Liggett opened the meeting Scientists: What, Why, and How? (http:// with a review of metagenomics and an blog.openwetware.org/scienceintheopen/ introduction to the human microbiome 2008/06/12/friendfeed-for-scientists-what- project (http://friendfeed.com/search?q = why-and-how/) for an introduction. room%3Aismb-2008+microbiome+OR+ We—a group of science bloggers, most fraser). The subsequent Q&A session of whom met in person for the first time at covered many of the exciting challenges The International Conference on Intel- ISMB 2008—found FriendFeed a remark- for those working in this field. Clearly, ligent Systems for Molecular Biology -
The Basic Units of Life How Cell Atlases Can Shed Light on Disease Mechanisms with Remarkable Accuracy
Press Information, July 28, 2021 Cells: The Basic Units of Life How cell atlases can shed light on disease mechanisms with remarkable accuracy The Schering Stiftung awards the Ernst Schering Prize 2021 to Aviv Regev. A pioneer in the field of single-cell analysis, she successfully combines approaches from biology and computer science and thus revolutionizes the field of precision medicine. Aviv Regev is considered a pioneer in the field of single-cell biology and has broken new ground by combining the disciplines of biology, computation, and genetic engineering. She has uniquely succeeded in combining and refining some of the most important experimental and analytical tools in such a way that she can analyze the genome of hundreds of thousands of single cells simultaneously. This single-cell genome analysis makes it possible to map and characterize a large number of individual tissue cells. Aviv Regev was the first to apply these single-cell technologies to solid tumors and to successfully identify those cells and genes that influence tumor growth and resistance to treatment. In addition, she discovered rare cell types that are involved in cystic fibrosis and ulcerative colitis. Last but not least, together with international research groups, and her colleague Sarah Teichmann she built the Human Cell Atlas and inspired Prof. Aviv Regev, PhD scientists all over the world to use these tools to create a Photo: Casey Atkins comprehensive atlas of all cell types in the human body. These cell atlases of parts of the human body illuminate disease mechanisms with remarkable accuracy and have recently also been used successfully to study disease progression in COVID-19. -
CV Aviv Regev
AVIV REGEV Curriculum Vitae Education and training Ph.D., Computational Biology, Tel Aviv University, Tel Aviv, Israel, 1998-2002 Advisor: Prof. Eva Jablonka (Tel Aviv University) Advisor: Prof. Ehud Shapiro (Computer Science, Weizmann Institute) M.Sc. (direct, Summa cum laude) Tel Aviv University, Tel Aviv, Israel, 1992-1997 Advisor: Prof. Sara Lavi A student in the Adi Lautman Interdisciplinary Program for the Fostering of Excellence (studies mostly in Biology, Computer Science and Mathematics) Post Training Positions Executive Vice President and Global Head, Genentech Research and Early Development, Genentech/Roche, 2020 - Current HHMI Investigator, 2014-2020 Chair of the Faculty (Executive Leadership Team), Broad Institute, 2015 – 2020 Professor, Department of Biology, MIT, 2015-Current (on leave) Founding Director, Klarman Cell Observatory, Broad Institute, 2012-2020 Director, Cell Circuits Program, Broad Institute, 2013 - 2020 Associate Professor with Tenure, Department of Biology, MIT, 2011-2015 Early Career Scientist, Howard Hughes Medical Institute, 2009-2014 Core Member, Broad Institute of MIT and Harvard, 2006-Current (on leave) Assistant Professor, Department of Biology, MIT, 2006-2011 Bauer Fellow, Center for Genomics Research, Harvard University, 2003-2006 International Service Founding Co-Chair, Human Cell Atlas, 2016-Current Honors Vanderbilt Prize, 2021 AACR Academy, Elected Fellow, 2021 AACR-Irving Weinstein Foundation Distinguished Lecturer, 2021 James Prize in Science and Technology Integration, National Academy of -
Modeling and Analysis of RNA-Seq Data: a Review from a Statistical Perspective
Modeling and analysis of RNA-seq data: a review from a statistical perspective Wei Vivian Li 1 and Jingyi Jessica Li 1;2;∗ Abstract Background: Since the invention of next-generation RNA sequencing (RNA-seq) technolo- gies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date. Results: We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations. Conclusion: The development of statistical and computational methods for analyzing RNA- seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statical models and exhibit dif- ferent performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development. 1 Introduction RNA sequencing (RNA-seq) uses the next generation sequencing (NGS) technologies to reveal arXiv:1804.06050v3 [q-bio.GN] 1 May 2018 the presence and quantity of RNA molecules in biological samples. Since its invention, RNA- seq has revolutionized transcriptome analysis in biological research. RNA-seq does not require any prior knowledge on RNA sequences, and its high-throughput manner allows for genome-wide profiling of transcriptome landscapes [1,2]. -
Seunghyun Sim Bas Van Genabeek Takuzo Aida Bert Meijer
In association with NASA Takuzo Aida December 10, 2019 Seunghyun Sim Bas van Genabeek Bert Meijer Tokyo University Caltech SyMO-Chem Eindhoven University . cmeacs.org In association with NASA ACS GLOBAL OUTSTANDING GRADUATE STUDENT & MENTOR AWARDS IN POLYMER SCIENCE AND ENGINEERING SPONSORED BY CME Seunghyun Sim Bas van Genabeek Postdoc Researcher Caltech SyMO-Chem Takuzo Aida Bert Meijer Prof. Tokyo University Distinguished Professor Riken Group Director Eindhoven University . December 10, 2019 cmeacs.org In association with NASA 2:00 pm – Awards Presentation and Talk on Engineering self-assembly of protein polymers for functional materials. Seunghyun Sim – Proteins are monodisperse polymers that fold into a specific nanoscale structure. These state-of-art nanoscale machineries exert highly precise mechanical motions and process environmental inputs by the combination of allosteric effects. My research focuses on finding interdisciplinary solutions for designing a library of functional protein materials, mainly from the principles in supramolecular chemistry, molecular biology, and polymer science. In this symposium, I will discuss the design of protein-based macromolecular architectures in multiple dimensions, understanding their property for the therapeutic application, and in situ synthesis of extracellular protein network by living organisms for generating engineered living materials. Profile – Seunghyun Sim graduated from Seoul National University with B.S. degrees in Chemistry and Biological Sciences in 2012. She conducted her doctoral research with professor Takuzo Aida at the University of Tokyo and received her M.Eng. and Ph.D. in 2017. Her thesis work focused on engineering protein-based supramolecular nanostructures and functions. She is currently a postdoctoral fellow at California Institute of Technology in the lab of professor David Tirrell. -
Using Bayesian Networks to Analyze Expression Data
JOURNAL OFCOMPUTATIONAL BIOLOGY Volume7, Numbers 3/4,2000 MaryAnn Liebert,Inc. Pp. 601–620 Using Bayesian Networks to Analyze Expression Data NIR FRIEDMAN, 1 MICHAL LINIAL, 2 IFTACH NACHMAN, 3 andDANA PE’ER 1 ABSTRACT DNAhybridization arrayssimultaneously measurethe expression level forthousands of genes.These measurementsprovide a “snapshot”of transcription levels within the cell. Ama- jorchallenge in computationalbiology is touncover ,fromsuch measurements,gene/ protein interactions andkey biological features ofcellular systems. In this paper,wepropose a new frameworkfor discovering interactions betweengenes based onmultiple expression mea- surements. This frameworkbuilds onthe use of Bayesian networks forrepresenting statistical dependencies. ABayesiannetwork is agraph-basedmodel of joint multivariateprobability distributions thatcaptures properties ofconditional independence betweenvariables. Such models areattractive for their ability todescribe complexstochastic processes andbecause theyprovide a clear methodologyfor learning from(noisy) observations.We start byshowing howBayesian networks can describe interactions betweengenes. W ethen describe amethod forrecovering gene interactions frommicroarray data using tools forlearning Bayesiannet- works.Finally, we demonstratethis methodon the S.cerevisiae cell-cycle measurementsof Spellman et al. (1998). Key words: geneexpression, microarrays, Bayesian methods. 1.INTRODUCTION centralgoal of molecularbiology isto understand the regulation of protein synthesis and its Areactionsto external -
Biological Pathways Exchange Language Level 3, Release Version 1 Documentation
BioPAX – Biological Pathways Exchange Language Level 3, Release Version 1 Documentation BioPAX Release, July 2010. The BioPAX data exchange format is the joint work of the BioPAX workgroup and Level 3 builds on the work of Level 2 and Level 1. BioPAX Level 3 input from: Mirit Aladjem, Ozgun Babur, Gary D. Bader, Michael Blinov, Burk Braun, Michelle Carrillo, Michael P. Cary, Kei-Hoi Cheung, Julio Collado-Vides, Dan Corwin, Emek Demir, Peter D'Eustachio, Ken Fukuda, Marc Gillespie, Li Gong, Gopal Gopinathrao, Nan Guo, Peter Hornbeck, Michael Hucka, Olivier Hubaut, Geeta Joshi- Tope, Peter Karp, Shiva Krupa, Christian Lemer, Joanne Luciano, Irma Martinez-Flores, Zheng Li, David Merberg, Huaiyu Mi, Ion Moraru, Nicolas Le Novere, Elgar Pichler, Suzanne Paley, Monica Penaloza- Spinola, Victoria Petri, Elgar Pichler, Alex Pico, Harsha Rajasimha, Ranjani Ramakrishnan, Dean Ravenscroft, Jonathan Rees, Liya Ren, Oliver Ruebenacker, Alan Ruttenberg, Matthias Samwald, Chris Sander, Frank Schacherer, Carl Schaefer, James Schaff, Nigam Shah, Andrea Splendiani, Paul Thomas, Imre Vastrik, Ryan Whaley, Edgar Wingender, Guanming Wu, Jeremy Zucker BioPAX Level 2 input from: Mirit Aladjem, Gary D. Bader, Ewan Birney, Michael P. Cary, Dan Corwin, Kam Dahlquist, Emek Demir, Peter D'Eustachio, Ken Fukuda, Frank Gibbons, Marc Gillespie, Michael Hucka, Geeta Joshi-Tope, David Kane, Peter Karp, Christian Lemer, Joanne Luciano, Elgar Pichler, Eric Neumann, Suzanne Paley, Harsha Rajasimha, Jonathan Rees, Alan Ruttenberg, Andrey Rzhetsky, Chris Sander, Frank Schacherer, -
Dynamic Regulatory Module Networks for Inference of Cell Type
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Dynamic regulatory module networks for inference of cell type specific transcriptional networks Alireza Fotuhi Siahpirani1,2,+, Deborah Chasman1,8,+, Morten Seirup3,4, Sara Knaack1, Rupa Sridharan1,5, Ron Stewart3, James Thomson3,5,6, and Sushmita Roy1,2,7* 1Wisconsin Institute for Discovery, University of Wisconsin-Madison 2Department of Computer Sciences, University of Wisconsin-Madison 3Morgridge Institute for Research 4Molecular and Environmental Toxicology Program, University of Wisconsin-Madison 5Department of Cell and Regenerative Biology, University of Wisconsin-Madison 6Department of Molecular, Cellular, & Developmental Biology, University of California Santa Barbara 7Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison 8Present address: Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Wisconsin-Madison +These authors contributed equally. *To whom correspondence should be addressed. 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.18.210328; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled transcriptomes and epigenomes at different stages of a developmental process. -
Of 16 in the DISTRICT COURT of the UNITED STATES for THE
IN THE DISTRICT COURT OF THE UNITED STATES FOR THE MIDDLE DISTRICT OF ALABAMA EASTERN DIVISION MRINAL THAKUR, ) ) Plaintiff, ) ) v. ) CASE NO. 3:16-cv-811-TFM ) [wo] ERIC BETZIG, et. al., ) ) Defendants, ) MEMORANDUM OPINION AND ORDER This action is assigned to the undersigned magistrate judge to conduct all proceedings and order entry of judgment by consent of all the parties pursuant to 28 U.S.C. § 636(c). See Docs. 24, 25. Now pending before the Court is Defendants’ Motion to Dismiss and brief in support (Docs. 12-13, filed December 5, 2016). After a careful review of all the written pleadings, motions, responses, and replies, the Court GRANTS Plaintiff’s alternative requests (Docs. 20 and 29) that the case be severed and transferred to the more appropriate venues in the Northern District of California and District of Maryland pursuant to 28 U.S.C. § 1404(a). The motion to dismiss pursuant to Fed. R. Civ. P. 12(b)(2) (Doc. 12) is DENIED as moot. Any remaining motions including the remaining portion of the motion to dismiss for 12(b)(6) (Doc. 12) remain pending for the determination of the transferee courts. I. JURISDICTION Plaintiff asserts claims pursuant to 28 U.S.C. § 1332 (diversity jurisdiction). Specifically, that the citizenship of all parties is diverse and the amount in controversy exceeds $75,000.00. He asserts state law claims for (1) fraud and suppression, (2) Negligence, (3) Wantonness, (4) Negligent and/or Wanton Training, Supervision, and Monitoring, (5) Unjust Enrichment, and (6) Page 1 of 16 Conversion. See Doc. -
Fueled by a Desire to Help Biologists, Eric Betzig Has Spent the Last Decade Devising Ways to Break Free from the Constraints of Defyinglight Microscopy—
Fueled by a desire to help biologists, Eric Betzig has spent the last decade devising ways to break free from the constraints of defyinglight microscopy— work recognized with the limits 2014 Nobel Prize in Chemistry. by jennifer michalowski illustration by markos kay 14 Winter 2015 / HHMI Bulletin for their development of super-resolved fuorescence microscopy—methods of visualizing objects so small that, until recently, distinguishing them with a light microscope was considered a feat that would defy the fundamental laws of physics. Living Color Betzig helped launch a revolution in super-resolution microscopy in 2006, when he developed a method that he and his collaborator Harald Hess called photoactivated localization microscopy, or PALM. The technique creates stunningly detailed images of cells by taking advantage of fluorescent labeling molecules that can be switched on and off with a pulse of light. In PALM, a sample labeled with these fuorescent tags is imaged many times, with a small subset of the fuorescent tags switched on each time. Because just a smattering of molecules is glowing in the image, each one can be pinpointed with precision. Compiling thousands of images yields a picture in which nearly all fuorescently labeled molecules show up as individual and distinct. eric betzig is a physicist and an engineer: he thinks The Janelia campus—where both Betzig and Hess are now in terms of light waves and energy, and when he tinkers group leaders—was still under construction when Betzig first in the lab, it is with lasers and mirrors and beam splitters. learned of the fuorescent probes that would make PALM He’s the first to admit that he is no biologist.