ICSB-2006

The 7th International Conference on October 8 - 13, 2006 Pacifico Yokohama, Yokohama, Japan

CONTENTS Welcome to ICSB-2006 ...... 2 Committees ...... 3 Program Day 1 (October 9) ...... 4 Day 2 (October 10) ...... 5 Day 3 (October 11) ...... 6 Program At-A-Glance ...... 9 Student Session (October 10) ...... 10 Session Abstracts Plenary Talk Abstracts ...... 11 I Systems Biology for Medicine ...... 13 II Systems Biology of Basic Biological Systems ...... 19 III Fronts in Systems Biology ...... 27 Poster Session ...... 34 Tutorial Program (October 8) ...... 48 Workshops (October 7,12,13) ...... 49 Access ...... 50 Floor Plan ...... 51

1 Welcome to ICSB-2006

Welcome to the Seventh International Conference on Systems Biology!

The International Conference on Systems Biology (ICSB) started in 2000 in Tokyo, and traveled around the world—Pasadena, Stockholm, St. Louis, Heidelberg, and Boston. Owing to the efforts of organizers, sponsors, and participants, the conference has grown into the main conference in the field. This year, ICSB employed parallel sessions for the first time, and invited over 65 distinguished speakers in the field reflecting the overall landscape of systems biology today. With about 400 posters, 12 tutorial sessions, and 5 workshop programs, ICSB-2006 presents the most comprehensive program to date. The program was enabled by generous support from several government agencies and sponsor companies for which we, as the systems biology community, are very grateful.

When the first ICSB was held in 2000 at Tokyo with the support of Japan Science and Technology Corporation (JST), systems biology was yet to be recognized as a serious area of study. Nevertheless, it attracted over 300 attendees, over a half of them from outside of Japan. That year featured Prof. Sydney Brenner, a Nobel Laureate in or medicine in 2002, as a banquet speaker. Systems biology is now gaining serious attention world-wide and a number of major research programs and centers have been created. It is my great pleasure that ICSB has grown into the main forum of scientific discussions in systems biology, and has contributed to the field’s progress. However, we should remind ourselves that the field is still in its infancy and much more needs to be done in all areas of research to bring still more maturity to the field. Given the long history of pioneering research leading to the modern form of systems biology, the field may have finally arrived to the point of experiencing a steep learning curve. A scientific problem, experimental techniques, computational analysis, and theoretical frameworks all have to be aligned to address significant questions in biology and decipher their system-level properties and principles. You will witness the growth and future of systems biology at ICSB-2006.

Please enjoy the conference and beautiful Japan!

Sincerely yours,

Hiroaki Kitano Conference Chair, The Seventh International Conference on Systems Biology

2 Committees *alphabetical order by region Program Committee North America: Asia: Adam Arkin (Lawrence Berkeley National Lab., USA) Uri Alon (Weitzman Institute of Science, Israel) Frederick Cross (Rockefeller Univ., USA) Upi Bhalla (National Centre for Biological Science, India) Marie Csete (Emory Univ. USA) Kwang-Hyun Cho (Seoul National Univ., Korea) Francis Doyle (UCSB, USA) Hsuan-Cheng Huang (National Yang-Ming University, John Doyle (Caltech, USA) Taiwan) Joe Gray (Lawrence Berkeley National Lab. USA) Akira Funahashi (SBI & JST ERATO-SORST Kitano Mike Hucka (Caltech, USA) Project, Japan) (UCSD, USA) Mariko Hatakeyama (RIKEN GSC, Japan) Boris Kholodenko (Thomas Jefferson Univ., USA ) Yoshihide Hayashizaki (RIKEN GSC, Japan) Douglas Lauffenburger (MIT, USA) Do Han Kim (Gwangju Institute of Science and Technology, Pedro Mendes (Virginia Tech., USA) Korea) Bernhard Palsson (UCSD, USA) Hiroaki Kitano (SBI & Sony CSL, Japan) Conference John Tyson (Virginia Tech., USA) Chair Marc Vidal (Dana-Farber Cancer Institute, USA) Shinya Kuroda (Univ. of Tokyo, Japan) Tau-Mu Yi (UCI, USA) Sang Yup Lee (Korea Advanced Institute of Science and Technology, Korea) Student Session Organizer: Edison Liu (Genome Institute of Singapore, Singapore) Satya Arjunan (Keio Univ., Japan) Douglas Murray (SBI & JST ERATO-SORST Kitano John Cumbers (Brown Univ., USA) Project, Japan) Shuichi Onami (Keio Univ., Japan) Advisory Committee Reiko Tanaka (RIKEN BioMimetic Center, Japan) Sydney Brenner (Founding President, Okinawa Institute of Masaru Tomita (Keio Univ., Japan) Science and Technology & The Salk Institute) Hiroki Ueda (RIKEN CDB, Japan) Kiyoshi Kurokawa (President, The Science Council of Japan) Europe: Koichi Kitazawa (Senior Executive-Director, Japan Lilla Alberghina (Univ. of Milano-Bicocca, Italy) Science and Technology Agency) Marta Cascante (Univ. of Barcelona, Spain) Yoshiyuki Sakaki (Director, RIKEN GSC) Roland Eils (DFKI (=German Cancer Research Center), Sin-ichi Nishikawa (Deputy Director, Laboratory for Germany) Stem , Center for , Peter Ghazal (GTI (=Scottish Centre for Genomic RIKEN) Technology and Informatics), UK) Yoshiki Hotta (President, Research Organization of Igor Goryanin (Univ. of Edinburgh, UK) Information and Systems) Stefan Hohmann (Goteborg Univ., Sweden) Shigetada Nakanishi (Director, Osaka Bioscience Institute) (Univ. of Manchester, UK) Edda Klipp (Max-Planck Institute for Molecular , Executive Committee Germany) Kazuyuki Aihara (Univ. of Tokyo and JST ERATO Aihara Pierre De Meyts (Hagedorn Research Institute, Denmark Project) and Novo Nordisk A/S) Akiyasu Fujii (SBI) Jens Nielsen (DTU (=Technical Univ. of Denmark), Denmark) Ken Fukuda (AIST) Staffan Normark (Karolinska Institute, Sweden) Akira Funahashi (SBI & JST ERATO-SORST Kitano Nicolas Le Novere (EBI (=European Project) Institute), UK) Mariko Hatakeyama (RIKEN GSC) Klaus Prank (GlaxoSmithKline) Hiroaki Kitano (SBI & Sony CSL) Hans Westerhoff (Univ. of Amsterdam, the Netherlands) Tatsuhiko Kodama (Univ. of Tokyo) Shinya Kuroda (Univ. of Tokyo) Yukiko Matsuoka (SBI & JST ERATO-SORST Kitano Project) Satoru Miyano (Univ. of Tokyo) Masahiko Noda (Japan Science and Technology Agency) Makoto Suematsu (Keio Univ. School of Medicine) Hiroki Ueda (RIKEN CDB) Toru Yao (RIKEN GSC)

3 Program Day 1: Monday, October 9, 2006 9:30 Opening 1F Main Hall Speaker Hiroaki Kitano, The Systems Biology Institute, Sony Computer Science Laboratories, Inc. 10:00 P1 Plenary Session 1 1F Main Hall Electricity meets Chemistry: Fast and Slow Signaling in Memory Upinder S. Bhalla, National Centre of Biological Sciences

10:30 P2 Plenary Session 2 1F Main Hall Spatio-temporal Patterns of Intracellular Signaling Atsushi Miyawaki, RIKEN Brain Science Institute

11:00 Break

11:30 P3 Plenary Session 3 1F Main Hall Biological Large Scale Integration Stephen R. Quake, Stanford University / HHMI

12:00 P4 Plenary Session 4 1F Main Hall Evolvability and hierarchy in rewired bacterial gene networks Luis Serrano, European Laboratory

12:30 Lunch Session Talks 14:00 S1a Systems Biology for Drug Discovery 1F Main Hall Chairs John Morser, Nihon Schering Research Center Osamu Sato, Dai-ichi Pharmaceuticals Speakers From Molecular Events to Clinical Outcome: Computational Systems Biology in the Pharmaceutical Industry Klaus Prank, GlaxoSmithKline Harnessing Systems Biology Using Chemical Synergies Joseph Lehár, CombinatoRX, Inc. Application of Systems Biology for Pharmaceutical Drug Development Jeff Trimmer, Entelos Inc. Systems Biology in Drug Discovery and Development: Impact and Challenges Didier Scherrer, AstraZeneca Technological breakthrough for cell-based target discovery Masato Miyake, CytoPathfinder Inc. 14:00 S1b Cyclic and Dynamic Behaviours 5F Sub Hall 1 Chairs Hiroki Ueda, RIKEN Center for Developmental Biology Lilia Alberghina, University of Milano-Bicocca Speakers A Generic Model of Cell Cycle Regulation in Eukaryotes John J. Tyson, Virginia Tech Probing structure and dynamics of cell cycle in budding yeast Lilia Alberghina, University of Milano-Bicocca Circadian Systems of Cyanobacheria Takao Kondo, Nagoya University Analysis and Synthesis of Mammalian Circadian Clocks Hiroki Ueda, RIKEN Center for Developmental Biology Multi-loop Architecture in Clock Circuits Andrew J. Millar, University of Edinburgh

16:30 - Poster Session I 4F 18:00

18:00 Welcome Reception 5F

4 Day 2: Tuesday, October 10, 2006 9:30 P5 Plenary Session 5 1F Main Hall Dealing with the complexity of a 'simple' eukaryotic cell Stephen G. Oliver, The University of Manchester

10:00 Break Session Talks 10:30 S2a Cardiovascular Systems Biology 1F Main Hall Chair Do Han Kim, Gwangju Institute of Science and Technology Speakers Cardiac Systems Biology Giovanni Paternostro, Burnham Institute for Medical Research, La Jolla, CA Kyoto Model, a comprehensive cardiac cell model Akinori Noma, Kyoto University, Graduate School of Medicine Cardiomyopathy in Mice and Men Jonathan Seidman, Harvard Medical School Direct observation of transcription in the human cell using tiling array Tatsuhiko Kodama, The University of Tokyo 10:30 S2b Yeast Systems Biology 5F Sub Hall 1 Chairs Stefan Hohmann, Goteborg University Stephen G. Oliver, The University of Manchester Speakers Dynamic Modeling of Stress Response of Yeast Cells Edda Klipp, Max Planck Institute for Molecular Genetics Quantitative physiology of a cellular information sensing and relaying system Roger Brent, The Molecular Sciences Institute Interrogation of cellular networks Mike Tyers, Samuel Lunenfeld Research Institute Sources and control of cell to cell variation in the response of yeast to mating pheromone Alejandro Colman-Lerner, The Molecular Sciences Institute 10:30 S2e Network Biology 5F Sub Hall 2 Chairs Marc Vidal, Dana-Farber Cancer Institute / Harvard Medical School Yoshihide Hayashizaki, RIKEN Yokohama Institute Speakers Interactome Networks Marc Vidal, Dana-Farber Cancer Institute / Harvard Medical School Protein Network Comparative Trey Ideker, University of California, San Diego Genome Network Project in Japan Yoshihide Hayashizaki, RIKEN Yokohama Institute Single molecule imaging of motor proteins in living cells - deciphering physical networks of molecular motions Hideo Higuchi, Tohoku University

12:30 Lunch Session Talks 14:00 S2d Systems 1F Main Hall Chair Marie Csete, Emory University Speakers Evolution and divergence of herpesviral protein interaction networks Juergen Haas, University of Munich Pathway Biology Approach to the Interferon System Peter Ghazal, University of Edinburgh Medical School The Center for Inflammation and Regenerative Medicine: a service model Gilles Clermont, University of Pittsburgh To Kill or not to Kill - Decision making in Natural Killer Cells Roland Eils, German Cancer Research Center (DKFZ), Heidelberg Stem Cells and Pain: Linking Immunity to Regeneration Marie Csete, Emory University

5 14:00 S2c Metabolomics and Bioprocess 5F Sub Hall 1 Chairs Sang Yup Lee, KAIST Marta Cascante, University of Barcelona Speakers Bottom-up Reconstruction of the Human Metabolic Network based on Build-35 and Bibliomic Data Bernhard Palsson, University of California, San Diego Systems Level Analysis and Engineering of Industrial Bacteria Sang Yup Lee, KAIST Metabolome Analysis and Masaru Tomita, A Systems Biology Approach to Identify and Therapeutically Exploit the Weakness of the Robust Tumour Metabolism Marta Cascante, University of Barcelona 14:00 S2f Student Session 5F Sub Hall 2 Chair Marc Vidal, Dana-Farber Cancer Institute / Harvard Medical School Speakers Silvia Jantos, EMBL Mikael Andersen, Technical University of Denmark Yusuke Maeda, The University of Tokyo Dale Muzzey, MIT- Harvard Alexander Fletcher, University of Oxford Ariosto Silva, Centro Infantil Boldrini

16:30- Poster Session II 4F 18:30

19:00 Banquet Pan Pacific Hotel

Day 3: Wednesday, October 11, 2006 Session Talks 9:15 S3a Systems Biology of Diabetes (Novo Nordisk-sponsored) 1F Main Hall Chair Pierre De Meyts, Hagedorn Research Institute Speakers Understanding diabetes pathogenesis: the need for systems biology Pierre De Meyts, Hagedorn Research Institute A Systems Biology Approach to Type 1 Diabetes Allan E. Karlsen, Novo Nordisk A/S The transcriptome as a window into pathogenesis of type 1 diabetes Nathan Goodman, Institute for Systems Biology 9:15 S3b Developmental Systems Biology 5F Sub Hall 1 Chairs Shuichi Onami, RIKEN Genomic Sciences Center Fabio Piano, New York University Speakers C. elegans early embryogenesis: global, local and evolutionary views Fabio Piano, New York University Interactions among the Pigment Cells of Zebrafish Give Rise to Turing Pattern Shigeru Kondo, Nagoya University Quantitative analysis of C. elegans embryogenesis Shuichi Onami, RIKEN Genomic Sciences Center 9:15 S3c Complex Systems Biology 5F Sub Hall 2 Chairs Kwang-Hyun Cho, Seoul National University Sree N. Sreenath, Case Western Reserve University Speakers Interaction Balance Coordination as Organizing Principle in Complex Systems Biology Mihajlo D. Mesarovic, Case Western Reserve University Coordination of Gene Expression by RNA Operons Jack Donald Keene, Duke University Medical Center Applications of Complex Systems Biology to the Study of Neural Systems Kenneth Alan Loparo, Case Western Reserve University

6 10:30 Break Session Talks 11:00 S3d Cancer Systems Biology 1F Main Hall Chairs Roland Eils, German Cancer Research Center (DKFZ), Heidelberg Charles Auffray, CNRS and Pierre & Marie Curie University Speakers Design Principles of the JAK-STAT Signaling Pathway Ursula Klingmüller, German Cancer Research Center (DKFZ), Heidelberg Predicting the Outcome of Chemotherapy through Pathway Modelling Charles Auffray, CNRS and Pierre & Marie Curie University From Simulation to Therapy: A Systems Biology Approach to Oncogene Detection Avijit Ghosh, Drexel University 11:00 S3e Systems Neurobiology 5F Sub Hall 1 Chairs Upinder S. Bhalla, National Centre of Biological Sciences Boris N. Kholodenko, Thomas Jefferson University Nicolas Le Novere, EMBL-EBI Speakers Understanding Molecular Complexity at the Neuronal Synapse August B. Smit, Vrije Universiteit Amsterdam Modelling Structure and Function of the Post-Synaptic Proteome James Douglas Armstrong, University of Edinburgh Systems Analysis of Spike-Timing Dependent Synaptic Plasticity Shinya Kuroda, The University of Tokyo 11:00 S3f Control and System Theory for Systems Biology 5F Sub Hall 2 Chairs Francis J. Doyle, University of California, Santa Barbara Rudi Gunawan, National University of Singapore Speakers Robustness Analysis of Biological Networks Using Sensitivity Measures Francis J. Doyle, University of California, Santa Barbara Feedback Control Regulation of Cell Division Pablo A. Iglesias, The Johns Hopkins University The Architecture of Cellular Regulation John Doyle, California Institute of Technology

12:30 Lunch Session Talks 14:00 S3g Synthetic Biology 1F Main Hall Chair Drew Endy, Massachusetts Institute of Technology Speakers Languages and Grammars for Programming in DNA Drew Endy, Massachusetts Institute of Technology Applications in Systems and Synthetic Biology Adam Arkin, University of California, Berkeley Impact of a Whole Genome Cloning on Systems Biology Mitsuhiro Itaya, Keio University Adaptive Response of a Gene Network to Environmental Changes by Fittness-induced Attractor Selection Tetsuya Yomo, Graduate School of Information Science and Technology, Osaka University Programmable Bacterial Catalysts Vitor Martins dos Santos, Helmholtz Centre for Infection Research

7 14:00 S3h Signal Transduction 5F Sub Hall 1 Chairs Philippe Bastiaens, EMBL Heidelberg Boris N. Kholodenko, Thomas Jefferson University Shinya Kuroda, The University of Tokyo Speakers Cell-signaling Dynamics in Time and Space Boris N. Kholodenko, Thomas Jefferson University Emerging Principles of Living Systems Hans V. Westerhoff, Manchester Centre for Integrative Systems Biology and Biocentre Amsterdam Ligand-dependent Cell Fate Control of ErbB Signaling Network in Breast Cancer Cells Mariko Hatakeyama, RIKEN Genomic Sciences Center Rules for Modeling Signal-transduction Systems William S. Hlavacek, Los Alamos National Laboratory Reaction Cycles in the Spatial and Temporal Organization of Cell Signaling Philippe Bastiaens, EMBL Heidelberg 14:00 S3i Novel Computational Environments for Systems Biology 5F Sub Hall 2 Chairs Douglas Kell, The University of Manchester Pedro Mendes, Virginia Tech. Speakers Linking Text with Knowledge - Challenges in Text Mining for Biology Junichi Tsujii, The University of Tokyo Going with the Flow: Distributed Computing for Systems Biology using Taverna Carole Anne Goble, The University of Manchester The DREAM project: Establishing a Community-based Gold Standard for Systems Biology Andrea Califano, Columbia University Medical Center The Systems Biology Markup Language (SBML): Where It's Been and Where It's Going Michael Hucka, California Institute of Technology MIRIAM and BioModels DB: Curation and Exchange of Quantitative Models Nicolas Le Novere, EMBL-EBI

16:30 Break

17:00 Panel Discussion 1F Main Hall Perspective on Systems Biology in National Research Agenda Chairs Hiroaki Kitano Stefan Hohmann

17:30 Closing 1F Main Hall

8 Tutorial Sunday Pacifico Yokohama October 8 411 + 412 413 414 + 415 416 417 418 419 9:30 T2: Signaling T1: T3: T12: T8: T7: Network Design Principle New Math Method SBML Copasi Basic Virtual Cell (CellNetAnalyzer) T11: 12:30 PottersWheel 14:00 T4: T5: T 6: T9: T10: MatLab Teranode E-Cell Copasi Advanced CellDesigner

17:00

Conference Monday Pacifico Yokohama October 9 1F Main Hall 4F 5F Sub Hall 1 5F 9:00 9:30 Opening 10:00 P1 Upinder S. Bhalla 10:30 P2 Atsushi Miyawaki 11:00 Break 11:30 P3 Poster Display Stephen Quake 12:00 P4 Luis Serrano 12:30 Lunch 14:00 S1a S1b Drug Discovery Cyclic and Dynamic Behaviours

16:30 Poster Session 18:00 Welcome Reception 19:30 21:00

Tuesday Pacifico Yokohama Pan Pacific October 10 1F Main Hall 4F 5F Sub Hall 1 5F Sub Hall 2 Hotel 9:00 9:30 P5 Stephen Oliver 10:00 Break 10:30 S2a S2b S2e Poster Display Cardiovascular Systems Biology Yeast Systems Biology Network Biology 12:30 Lunch Lunch Lunch 14:00 S2d S2c S2f Systems Immunology Metabolomics and Bioprocess Student Session

16:30 Poster Session 18:30 19:00 Banquet 21:00

Wednesday Pacifico Yokohama October 11 1F Main Hall 4F 5F Sub Hall 1 5F Sub Hall 2 9:15 S3a S3b S3c Systems Biology of Diabetes Developmental Systems Biology Complex Systems Biology 10:30 Break 11:00 S3f S3d S3e Control and System Theory for Cancer Systems Biology Systems Neurobiology Systems Biology 12:30 Lunch 14:00 S3i S3g S3h Novel computational environments Synthetic Biology Signal Transduction for systems biology 16:30 Break 17:00 Panel Discussion Closing 18:00

Workshop Thursday Miraikan AIST October 12 Miraikan Hall Meeting Room 1 Meeting Room 2 Meeting Room Systems Biology and the Human Synthetic Biology Health Risks of Environmental SBML Forum RTK Workshop Chemicals

Friday Miraikan AIST October 13 Miraikan Hall Meeting Room 1 Meeting Room 2 Meeting Room

SBML Forum RTK Workshop

9 Student Session Venue: Pacifico Yokohama, 5F Sub Hall 2 Date: Tuesday, October 10, 2006, 14:00-16:30

Chair: Marc Vidal, Dana-Farber Cancer Institute

Speakers The following Speakers will give an oral presentation on their Posters. These Speakers' posters were selected by the Student Session Review Committee as the best poster abstracts submitted in the Student Category.

MAPK signaling network properties giving rise to specific cellular fate decisions (BS24) Silvia Santos, Philippe Bastiaens EMBL

Applied genome-scale modelling of Aspergillus niger (FI58) Mikael Andersen, Michael L. Nielsen, Jens Nielsen Technical University of Denmark

Regulatory Dynamics of Synthetic Gene Networks with Positive Feedback (FS06) Yusuke Maeda, Masaki Sano The University of Tokyo

Monitoring MAPK osmo-signaling in individual yeast cells (BS09) Dale Muzzey, Carlos Gomez-Uribe, Jerome Mettetal, Alexander van Oudenaarden MIT-Harvard

Mathematical modelling of the role of HIF-1 in tumour growth (FN27) Alexander Fletcher, Jonathan Chapman, Christopher Breward University of Oxford

Study of Dependency of Synchronization of Beta-cells Insulin Secretion on Size of Langerhans Islets (MD04) Ariosto Silva, Jose Yunes Centro Infantil Boldrini

The following scientists reviewed the abstracts for the Student Session: - Hsuan-Cheng Huang, National Yang-Ming University, Taiwan - Bruce Shapiro, California Institute of Technology - Tau Mu Yi, UCI - Upinder Bhalla, National Centre for Biological Sciences, Bangalore, India - Marie Csete, Emory University School of Medicine - Martin Robert, Keio University

Organizers: Satya Arjunan, Keio University John Cumbers, Brown University

10 Session Abstracts Plenary Talk Abstracts P1 10:00-10:30, October 9 Main Hall Electricity meets chemistry: fast and slow signaling in memory Upinder S. Bhalla National Centre for Biological Sciences, TIFR, Bangalore, India Contacts: [email protected]

Deliberations on memory mechanisms often seem to proceed on at least three independent tracks. One of these involves biochemical mechanisms for plasticity, including feedback loops and cellular activation. Space is another dimension, and is the arena for interactions between synapses, and propagation of signals between synapses, dendrites, and the cell body. Finally, electrical activity is a function of cell as well as network dynamics, and here too feedback may play a role through reverberating activity in network loops. It is an interesting process to develop models that impinge on all of these levels, because of the wide range of timescales, numerical techniques, and sheer computational load. It is especially tricky to get parameters for such models. I will describe a study where we have used coupled electrical and biochemical compartmental modeling, and weeded out several candidate models by comparing their predictions to our experiments. The surviving models incorporate chemical, spatial and electrical ingredients. They exhibit network- activity controlled single-cell reverberating activation, with interesting spatial consequences. We suggest that this is a form of short- term and spatially defined memory. It sits at the interface between individual synapses and dendrites, and also between network and cellular attributes of memory. http://www.ncbs.res.in/~bhalla/index.html

P2 10:30-11:00, October 9 Main Hall Spatio-temporal patterns of intracellular signaling Atsushi Miyawaki RIKEN Brain Science Institute Contacts: [email protected]

"Why bio-imaging, i.e. real time fluorescence imaging?" Currently, this is a topic of great interest in the bioscience community. Many molecules involved in signal transduction have been identified, and the hierarchy among those molecules has also been elucidated. It is not uncommon to see a signal transduction diagram in which arrows are used to link molecules to show enzyme reactions and intermolecular interactions. To obtain a further understanding of a signal transduction system, however, the diagram must contain the three axes in space as well as a fourth dimension, time, because all events are controlled ingeniously in space and time. Since the isolation of green fluorescent protein (GFP) from the bioluminescent jellyfish in 1992 and later with its relatives, researchers have been awaiting the development of a tool, which enables the direct visualization of biological functions. This has been increasingly enhanced by the marriage of GFP with fluorescence resonance energy transfer (FRET) or fluorescence cross-correlation spectroscopy (FCCS), and is further expanded upon by the need for "post-genomic analyses." It is not my intent to discourage the trend seeking the visualization of biological function. I would like to propose that it is time to evaluate the true asset of "bio-imaging" for its potential and limitations in order to utilize and truly benefit from this novel technique. http://www.brain.riken.go.jp/english/b_rear/b5_lob/a_miyawaki.html

P3 11:30-12:00, October 9 Main Hall Biological large scale integration Stephen Quake Dept of Bioengineering and (by courtesy) Applied Physics, Stanford University and Howard Hughes Medical Institute Contacts: [email protected]

The integrated circuit revolution changed our lives by automating computational tasks on a grand scale. My group has been asking whether a similar revolution could be enabled by automating biological tasks. To that end, we have developed a method of fabricating very small plumbing devices – chips with small channels and valves that manipulate fluids containing biological molecules and cells, instead of the more familiar chips with wires and transistors that manipulate electrons. Using this technology, we have fabricated chips that have thousands of valves in an area of one square inch. We are using these chips in applications ranging from bioreactors to structural genomics to systems biology. However, there is also a substantial amount of basic physics to explore with these systems – the properties of fluids change dramatically as the working volume is scaled from milliliters to nanoliters! http://med.stanford.edu/profiles/Stephen_Quake/

11 P4 12:00-12:30, October 9 Main Hall Evolvability and hierarchy in rewired bacterial gene networks Luis Serrano1,2, Mark Isalan*1, Caroline Lemerle2, Konstantinos Michalodimitrakis2, Barbara Di Ventura2, Pedro Beltrao2, Carsten Horn2 and Emanuele Raineri2 1. EMBL-CRG Systems Biology Programme, Centre for Genomic Regulation, Spain 2. EMBL, Germany *Contacts: [email protected]

Bacterial gene networks are highly plastic, allowing radical reconnections at the summit of the gene network hierarchy, fuelling evolvability. Sequencing of genetic material from several organisms has revealed that duplication and drift of existing genes has primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network plasticity, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or s-factors in Escherichia coli, over the genetic background of the wild-type. We found that ~95% of reconnected networks are tolerated by the bacterial cell and very few give different growth profiles. Expression levels correlate with the position of the factor in the wild-type network hierarchy. Most importantly, we find that certain combinations consistently survive over the wild-type under various selection pressures. This suggests that new links in the network could readily confer a fitness advantage to individuals in a population and hence may fuel evolution. http://www-db.embl.de/jss/EmblGroupsHD/per_397.html

P5 9:30-10:00, October 10 Main Hall Dealing with the complexity of a ‘simple’ eukaryotic cell. Stephen G. Oliver Faculty of Life Sciences, The University of Manchester, U.K. Contacts: [email protected]

Systems biology aims at taking a more synthetic or holistic approach to deciphering the workings of living organisms. Although the ultimate aim is to construct mathematical models of complete cells or organisms that have both explanatory and predictive power, we are some way from achieving such global syntheses and we need a principled way of reducing the complexity of the problem. Accordingly, we require a top-down strategy to provide an initial coarse-grained model of the cell, and a bottom-up strategy in which individual sub-systems are modeled. Metabolic Control Analysis (MCA) is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it. To exploit MCA in an initial top-down systems analysis of the eukaryotic cell, two categories of experiments are required. In category 1 experiments, flux is changed and the impact on the levels of the direct and indirect products of gene action is measured. We have measured the impact of changing the flux on the transcriptome, proteome, and metabolome of Saccharomyces cerevisiae. In this whole- cell analysis, flux equates to growth rate. In category 2 experiments, the levels of individual gene products are altered, and the impact on the flux is measured. We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high flux control coefficients. For the bottom-up approach, the initial problem is one of systems identification. While a lot of time is currently spent debating the question “What is Systems Biology?”, why (in an organism where we know so much about its , physiology, and cell biology as S. cerevisiae) should it be a problem to identify the biological sub-systems that must be fully characterised and built into a comprehensive model of the eukaryotic cell? This problem arises because we have previously studied these biological systems in isolation and in a rigorously reductionist fashion. Now, we must study them as parts of an integrated whole. The problem is that our current view of, say, a metabolic or signal transduction pathway is often two-dimensional (rather than four-dimensional) and is frequently poorly integrated, if at all, with other cellular pathways. Thus our view of the network of metabolic pathways may not be the same as the yeast’s. In order to gain a “yeast’s eye view”, we have coupled flux balance analysis with both metabolomics and genetics. Although the initial aim of these approaches is the identification of the ‘natural’ metabolic systems of yeast, the principles involved should be more widely applicable to the problem of biological systems identification. http://www.ls.manchester.ac.uk/people/profile/index.asp?tb=0

12 I S1a SYSTEMS BIOLOGY FOR MEDICINE 14:00-16:00, October 9 Systems Biology for Drug Discovery Main Hall S1a-1 From molecular events to clinical outcome: computational systems biology in the pharmaceutical industry Klaus Prank GlaxoSmithKline

S1a-2 Harnessing systems biology using chemical synergies Joseph Lehár , CombinatoRx Inc, Cambridge MA, Bioinformatics, Boston University Contacts: [email protected]

Combination drugs can overcome compensatory mechanisms or evolved resistance by attacking disease on several fronts, and also provide a new window on biological systems. We present simulations and experiments that show the relationship between chemical synergies and target connectivity, as well as preliminary results from combination screens of targeted agents in yeast and tumor cell lines. Despite the success of discovery efforts focused on specific targets, many drugs are less effective than expected [Sams-Dodd 2005], due to mechanistic complexity [Hartwell 2001] or evolved resistance. Network simulations [Csermely 2005] and clinical oncology [Kaelin 2005] suggest that disease is more controllable through multi-target approaches, and indeed combinations are the norm for cancer and many infectious diseases. CombinatoRx is systematically screening combinations of approved drugs in cellular disease models [Borisy 2003], to find synergies that can be optimized towards novel combination drugs. We show that chemical combinations can also yield connectivity information through their response surface shapes. Pathway simulations with pairs of inhibitors produce distinct responses depending on how the targets are connected. The predicted shapes are reproduced in a yeast experiment, with further support from screens using human cells. While analogous to genetic interactions [Tong 2003], chemical synergies provide complementary and more detailed information for network models about connections between their protein targets. Chemicals can also be efficiently screened in disease models that are not amenable to genetic studies. We also present preliminary results from screens testing combinations of ~100 chemical probes with known targets using yeast and tumor cell lines. The experiments are designed to improve our understanding of yeast networks, and to identify new multi-target mechanisms with therapeutic potential for cancer. http://users.rcn.com/lehar

S1a-3 Application of systems biology for pharmaceutical drug development Jeff Trimmer Entelos Inc.

S1a-4 Systems biology in drug discovery and development: impact and challenges Didier Scherrer AstraZeneca

S1a-5 Technological breakthrough for cell-based target discovery Masato Miyake CytoPathfinder, Inc. Contacts: masato.miyake@cytopathfinder.com

Strategy to identify new and right targets at early stage of drug discovery has long been discussed to improve the R&D productivity. Genome-wide target screening and target combination finding are growing as new waves of cell-based target discovery on the background of emerged antibodies, aptamers, and nucleic acid medicines. The experimental scale of the cell-based screening is getting larger year by year for successful target discovery. Therefore, high-throughput screening and data management & integration technologies are rapidly growing. On the other sides, technologies to select right targets are studied in cell biology. Various technologies such as stem cell differentiation, cell separation, immortalization, xenografts, etc. are used to establish in vitro models (cells and markers) which correlate to clinical data. However the culture scale of reliable in vitro model is still small in general. The experimental scale gap between new target discovery and right target discovery may be pointed out as technological innovation needs. Here, we introduce Transfection microarray (TMA), which is a microarray for highly paralleled cell transfection. By using TMA, large-scale functional screening of cDNAs / siRNAs is operated in the small culture. We may show the TMA-based strategy to identify new and right targets at early stage of drug R&D. http://www.cytopathfinder.com

13 I S2a SYSTEMS BIOLOGY FOR MEDICINE 10:30-12:30, October 10 Cardiovascular Systems Biology Main Hall S2a-1 Cardiac systems biology Andrew McCulloch1, Jacob Feala1, Sarah Flaim1, Roy Kerckhoffs1, Giovanni Paternostro2, Jeffrey Saucerman3 1Department of Bioengineering, University of California, San Diego, La Jolla, CA, 2The Burnham Institute, La Jolla, CA, 3Department of , University of Virginia, Charlottesville, VA

Computational models of the heart can be integrative in several important ways. First, they permit information integration of genome scale data sets that would otherwise be difficult to interpret and understand. We illustrate this in the analysis of high-throughput phenotypic and metabonomic data on cardiac function in the fruitfly drosophila melanogaster. We use drosophila as a model organism for studying cardiac aging and hypoxia tolerance, and integrating these data with constrain-based metabolic models built in large part with data from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Systems models of cardiac myocytes achieve functional integration, by predicting how the functions of individual network components combine to give rise to physiological functions. We illustrate this with our recent models of β-adrenergic regulation of myocyte excitation-contraction coupling. Multi- scale computational models aim to achieve structural integration across physical scales of biomedical organization from molecule to organize. We illustrate this with examples from our recent research on arrhythmia mechanisms in genetic disease and the effects of external interventions such as pacing and pericardiectomy on ventricular-vascular coupling in vivo. http://cmrg.ucsd.edu

S2a-2 Kyoto model, a comprehensive cardiac cell model Akinori Noma, Satoshi Matsuoka and Nobuaki Sarai Cell/Biodynamics Simulation Project Kyoto University, Department of Physiology, Faculty of Medicine Kyoto University, Japan Contacts: [email protected]

2+ The magnitude and time course of developed tension is regulated by multiple functional units, such as [Ca ]i, molecular machinery of contraction, membrane excitation, ion homeostasis within the cell, excitation-contraction coupling, neural regulation, cell volume regulation and energy metabolism in cardiac myocytes. So far, a huge amount of experimental findings on each issue has been accumulated, and it is possible to develop a comprehensive cardiac cell model to analyze the interactions of many molecular functional units underlying the regulation of the cardiac contraction. Starting from the models of cardiac membrane excitation (1), we developed a comprehensive cell model, Kyoto Model (2). Kyoto model can respond to various experimental interventions in a 2+ reversible manner. Increasing [Ca ]o results in a positive inotropy accompanied with the slight shortening of the action potential + 2+ duration. Decreasing [Na ]o increases the force of contraction through the accumulation of Ca within the sarcoplasmic reticulum + by modulating the Na/Ca exchange. Varying [K ]o affects both the action potential shape and the resting membrane potential. The + 2+ force of contraction increases with decreasing [K ]o. The Ca stored within the sarcoplasmic reticulum (2~4 mM) is released by 2+ 2+ 2+ 2+ the activation of the RyR channel through the influx of Ca via L-type Ca channels. [Ca ]i is determined by the intracellular Ca buffer as well as the binding to troponin C. The contraction model is based on the Negroni and Lascano (2001), but is improved for the positive cooperativity for Ca2+-mediated activation by combining with the model of Robinson et al (2002). The ATP homeostasis is established between the consumption by the myofilament, Na/K pump and SERCA and the production by mitochondria. Removing oxygen results in shortening of the action potential and loss of contraction. Quantitative dynamic computer models, which integrate a variety of molecular functions into a cell model, provide a powerful tool to create and test working hypotheses. Our new modeling tool, the simBio package (freely available from http://www.sim-bio.org/) is used for constructing cell models such as cardiac cells, epithelial cells and pancreatic β cells. The simBio package is written in Java, uses XML and solves ordinary differential equations. 1) DiFrancesco, D., Noble, D., 1985. Philos. Trans. R. Soc. Lond. B Biol. Sci. 307:353-398. 2) Matsuoka S., Sarai N., Jo H., and Noma A. 2004 Prog. Biophys. Mol. Biol. 85:279-299. 3) Negroni, J. A., Lascano, E.C. 1999. J.Mol.Cell.Cardiol. 31: 1509-1526. 4) Robinson JM., Wang Y., Kerrick GL., Kawai R., and Cheung HC. 2002 J. Mol. Biol. 322:1065-1088. http://www.card.med.kyoto-u.ac.jp

S2a-3 Cardiomyopathy in mice and men Jonathan Seidman Harvard Medical School

Hypertrophic cardiomyopathy (HCM), caused by missense mutations in sarcomere protein genes, increases left ventricular wall thickness, susceptibility to cardiac arrhythmias and sudden cardiac death. Cardiac tissue from affected individuals demonstrate fibrosis, myocyte disarray and myocyte hypertrophy. Disorganized cell-cell contact (myocyte disarray) and cardiac fibrosis, prototypic but protean features of HCM histopathology, are suggested as potential triggers for ventricular arrhythmias that lead to sudden death. We have studied the of left ventricular hypertrophy and have demonstrated that rare sarcomere protein gene mutations account for more than 50% of unexplained in young and middle aged, while other causes may account for most hypertrophy in the elderly. The mechanisms by which sarcomere protein gene mutations lead to unexplained hypertrophy are being investigated in mouse models of this disease. Rigorous assessment of whether these factors increase arrhythmia vulnerability in humans is difficult due to limited

14 myocardial tissue access and confounding variables including genetically heterogeneous HCM mutations, background genotypes and lifestyles. We report electrophysiological studies and comprehensive myocardial histopathologic analyses of mice engineered to carry an HCM mutation. Genetically inbred HCM mice had remarkably inhomogeneous histopathology and susceptibility to arrhythmias. Among inbred HCM mice, neither the extent nor distribution of myocyte disarray or cardiac fibrosis correlated with abnormalities in ex vivo signal conduction properties or in vivo electrophysiological properties. In contrast, increased ventricular hypertrophy significantly increased arrhythmia susceptibility. These data indicate that somatic responses to a sarcomere gene mutation account for variable HCM histopathology and demonstrate that myocyte hypertrophy more than fibrosis or disarray correlates with arrhythmic risk. Independent somatic triggers appear to stimulate fibrosis, and myocyte disarray. A shared factor activates cardiac hypertrophy and arrhythmias. Presumably each of these separate factors activates a distinct pathway that stimulates different responses to sarcomere protein gene mutations. http://genetics.med.harvard.edu/~seidman/

S2a-4 Direct observation of transcription in the human cell using tiling array Tatsuhiko Kodama The University of Tokyo

Sequential gene expression upon stimulation is a key cellular mechanism for adaptation, and temporal analysis of transcriptional activation by TNFa-nuclear factor kappa B, nuclear receptors and p53 indicate that oscillating activation may be an important mechanism to generate a temporal cascade. Transcription of pre-mRNA in the human cell is coupled with its processing and selective degradation. Observation of this complex process is hampered by an interruption of coding sequence by a large non- coding intorns. Oligonucleotide tiling array covering whole genome sequence has been developed to obtain higher resolution of gene expression,. As compared with exon RNA, intron RNA is degraded rapidly during splicing, and may be useful tool to monitor the rate of transcriptional activation. Here we report that first wave of oscillating expression of intron RNA and its processing can be monitored using tiling array with high temporal resolution (7.5min). We selected 100 genes induced by TNFa and designed tiling array monitoring pre-mRNAs of these gene loci. First wave of oscillating expression was monitored in 4 large gene loci (>100kb) and average speed of transcriptional elongation was 3.2kb/min, which is faster than previously reported value without measureing the oscillation (1.2-24kb/min). Temporal analysis of transcription using tiling arrays will be a useful tool analyzing the transcription and processing. http://www.lsbm.org

I S2d SYSTEMS BIOLOGY FOR MEDICINE 14:00-16:30, October 10 Systems Immunology Main Hall S2d-1 Evolution and divergence of herpesviral protein interaction networks Even Fossum*1, Armin Baiker*1, Caroline C. Friedel2, Silpa Suthram3, Seesandra V. Rajagopala4, Björn Titz4, Tina Schmidt5, Theo Kraus1, Sourav Bandyopadhyay3, Dietlinde Rose1, Mareike Uhlmann1, Christine Zeretzke1, Yu-An Dong2, Hélène Boulet1, Susanne M. Bailer5,Ulrich Koszinowski1, Trey Ideker6, Peter Uetz4, Ralf Zimmer2 and Jürgen Haas1 1Max-von-Pettenkofer Institut, LMU München, 2Institut für Informatik, LMU München, 3Program in Bioinformatics, University of California San Diego, 4Institut für Genetik, Forschungszentrum Karlsruhe , 5Institut für Medizinische Biochemie und Molekularbiologie, Universität des Saarlandes, Homburg, 6Department of Bioengineering, University of California San Diego Contacts: [email protected]

Herpesviruses are a family of large DNA viruses widely spread in vertebrates which cause a variety of different diseases in animals and man. We performed a comprehensive yeast-two-hybrid analysis of intraviral protein interactions in five different species of the herpesvirus family: Herpes simplex virus 1 (HSV-1), Varicella zoster virus (VZV), murine Cytomegalovirus (mCMV), Epstein-Barr virus (EBV) and Kaposi’s sarcoma associated herpesvirus (KSHV) revealing 1,252 interactions including 231 interactions between 41 core ortholog proteins. Whereas previous studies compared species that had been analysed in different laboratories by various methods, we explored 5 different species using exactly the same protocols. Nevertheless, at first glance there was little overlap between the networks of the five herpesvirus species, similar to previous Y2H datasets of different cellular networks. The general network topology was similar in all herpesviral networks: the degree distribution differed from cellular networks (if a power law distribution is approximated the power coefficient is considerably lower in comparison to cellular networks) and showed no high local clustering as in small-world networks1. Moreover, herpesviral networks could not be subdivided into distinct functional submodules or complexes, which appears to be an inherent characteristic of large cellular networks such as that of yeast and human. Co- immunoprecipitation experiments indicated that the majority of interactions is conserved between core orthologs and demonstrated that protein function can be conserved without sequence homology. In contrast, the network outside the common core is either species- or subfamily-specific, reflecting distinct functional properties of these proteins. Whereas the five species were equidistant based on their core interaction networks, the analysis of the complete viral networks yielded a phylogeny that was consistent with the known evolutionary relationships between the alpha, beta, and gamma subfamilies. Our study provides evidence that protein interactions and network structures constitute adaptive evolutionary traits and can thus be used to assign phylogenetic relationships. http://www.baygene.de/pro-engl-3_1a.htm

15 S2d-2 Pathway biology approach to the interferon system Peter Ghazal University of Edinburgh Medical School

Interferons (IFNs) play a pivotal role in innate and adaptive immunity against infection. Here, we present a systematized interpretation and analysis of the IFN pathway based on a research synthesis review of 257 nodes in the network. The relationships of the components were graphically notated as a consensus logic interaction diagram. Topological characterisation of the constructed pathway showed network features consistent with a scale-free connectivity and systems robustness with predicted flexibility for a number of different states. A genetic network of 56 genes and 100 edges from the consensus diagram was used for analysis of gene expression data obtained by profiling primary mouse macrophages treated with IFNγ and/or infected with murine cytomegalovirus. Statistical and simulation analyses showed the expression changes mapped to discrete and specific sub-systems of the IFN pathway, indicative of pro-inflammatory and anti-viral functions. Notably, IFNγ co-stimulation of infected cells results in the dominance of IFNγ over the viral specific sub-network. These results reveal for the first time discrete states of sub-system activity of the IFN pathway and represent a systematic methodology for constructing and exploiting biological pathways in general.

S2d-3 The center for inflammation and regenerative medicine: a service model Gilles Clermont The University of Pittsburgh Contacts: [email protected]

The University of Pittsburgh Medical Center is a large tertiary care center hosting more than 130 intensive care unit beds, is the largest solid organ transplant, and hosts the McGowan Institute dedicated to regenerative medicine. Systems modeling of inflammation, the crucial pathophysiologic process determining outcome in the critically ill patient, is a prime focus of interest of a team including clinicians, biologists, mathematicians, engineers and computer scientists. This interdisciplinary team has created the Center for Inflammation and Regenerative Medicine, which seeks to (1) offer transdisplinary expertise to academic and corporate researchers interested in the analysis and modeling of complex datasets, (2) train a new generation of scientists in translational systems biology and (3) promote scientific diffusion of the relevance of an integrative approach to patient care and the development of therapies. www.mirm.pitt.edu/cirm www.scai-med.org www.pittsburghcomplexity.net www.iccai.org

S2d-4 To kill or not to kill - decision making in natural killer cells Roland Eils1,3, Sven Mesecke1, Doris Urlaub2, Hauke Busch1, Carsten Watzl2 1Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany; 2Institute for Immunology, University of Heidelberg, Germany; 3Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular , University of Heidelberg, Germany Contacts: [email protected]

The immune system is essential in the protection of the host against a variety of infectious agents and the growth of transformed cells. Natural Killer (NK) cells are at the junction of the innate and the adaptive immune response and are important in the fight against viral infections and cancer. The effector functions of NK cells are controlled by a balance of positive and negative signals that are transmitted via various kinds of surface receptors. Inhibitory receptors guarantee the tolerance of NK cells towards target cells with normal MHC class I expression. Loss of this expression, e.g. during viral infection or transformation, reduces the inhibitory signal, and enables stimulatory surface receptors to shift the balance towards activation and subsequent killing of a locally attached target cell. To date our understanding about the integration of positive and negative signals and the decision making process inside NK cells remains poor. Here, we propose a mechanism by which NK cells first integrate antagonising signals and then compute a reliable killing decision. Predictions derived from this mechanistic model are tested experimentally and will be compared with established knowledge. This proposed mechanism of NK cell regulation enables a novel insight into the decision making process during lymphocyte activation. It can serve as basis for any future manipulation of NK cell function to enhance the anti-tumour activity of these important immune cells. http://www.dkfz.de/tbi

S2d-5 Stem cells and pain: linking immunity to regeneration Marie Csete Emory/GaTech Human Embryonic Stem Cell Core Contacts: [email protected]

Pain is experienced by all people, yet is poorly understood and often poorly treated. In particular many traumatic or degenerative disease processes (for which stem cell therapies are being studied) are complicated by pain. In this way, pain is part of normal regeneration, yet the role of stem cells in treating pain has not been explored. Pain can be broadly divided into nociceptive

16 (appropriate to the stimulus) and neuropathic (inappropriate severe and long-lasting) types. We used an animal model of neuropathic pain, chronic constriction injury of the rat sciatic nerve, to study the potential of stem cells for pain therapy. Our data show that infusion of bone marrow mononuclear cells containing a mixed stem cell population after the injury, results in reversal of pain behaviors 10 days later, whereas untreated control rats remain in pain. Several possible mechanisms for pain reversal by bone marrow transplant will be discussed including modulation of immune cell infiltrates, neurotrophic factor signaling (particularly erythropoietin and GDNF), vasodilatation, elaboration of heat shock proteins, and regeneration of Schwann and vascular cells. This work suggests that stem cells modulate pain signals as part of their regenerative roles, and serve as a link to the nervous system after injury. http://userwww.service.emory.edu/~mcsete

I S3a SYSTEMS BIOLOGY FOR MEDICINE 9:15-10:30, October 11 Systems Biology of Diabetes (Novo Nordisk-sponsored) Main Hall S3a-1 Understanding diabetes pathogenesis: the need for systems biology Pierre De Meyts Receptor Systems Biology Laboratory, Hagedorn Research Institute, Gentofte, Denmark Contacts: [email protected]

The two major forms of diabetes mellitus, type 1 and type 2 diabetes, are both complex diseases of poorly understood pathogenesis. The poor understanding of molecular mechanisms underlying pathogenesis results in a penury of effective drug therapies. Type 1 diabetes results from progressive beta cell destruction by incompletely defined autoimmune processes. Insulin treatment is an absolute requirement to prevent death from ketoacidosis. Type 2 diabetes is a complex disease which is reaching epidemic proportions due a combination of lifestyle factors that favor obesity, and genetic and environmental factors. The pathogenesis of type 2 diabetes involves alterations in insulin production and secretion by the pancreatic beta cells, as well as disturbances in the target cells sensitivity to insulin action. Reductionist approaches have failed to unravel the complex interactions between transcriptional networks that regulate beta cell development and differentiated function, and the signal transduction mechanisms in both beta cells and insulin target tissues, the combined disturbance of which results in impaired metabolic homeostasis. A systems biology approach is clearly warranted.

S3a-2 A systems biology approach to type 1 diabetes Allan E. Karlsen1,2, Jørn Nerup2 & Flemming Pociot2 1Insulin, Incretin and Islet Biology, Novo Nordisk A/S, 2Steno Diabetes Center, Denmark Contacts: [email protected]

Type 1 diabetes mellitus (T1D) is the result of an autoimmune associated destruction of the beta-cells in the islets of Langerhans in the pancreas. The pathogenesis of T1D is accompanied by infiltration of the islets with auto-reactive lymphocytes and macrophages (insulitis), releasing a cocktail of cytokines of which especially interleukin-1β is specifically toxic to the beta-cells in the islets. We have combined transcriptome and proteome analysis of different in vitro and in vivo models of T1D to elucidate the mechanisms behind cytokine and immune mediated beta-cell destruction. The results have identified a number of proteins, that when over-expressed in beta-cells influence their response to cytokines. Combining this information with data from our genetic studies in a systems biology approach supports the hypothesis that spontaneous and cytokine induced beta-cell destruction is caused by complex, intracellular protein – protein or protein/DNA interactions, ”controlled” at the level of expression by common alleles (polymorphisms) of normal genes in unfavourable combinations. Thus, a battle between protective and deleterious effects in the beta-cells determines the outcome. Knowledge about the underlying mechanisms may provide novel targets for future pharmacological and/or genetic intervention in the beta-cell destructive processes in diabetes. http://www.novonordisk.com

S3a-3 The transcriptome as a window into pathogenesis of type 1 diabetes Nathan Goodman, Burak Kutlu Institute for Systems Biology [email protected]

We have generated a comprehensive atlas of genes expressed in beta cells, islets, and related cell types and lines by combining data from MPSS analysis and array studies. We find around 9800 genes expressed consistently in human islets regardless of individual variation. Slightly more genes are expressed in exocrine tissue, 11200, duct cells have around 4000, and rat beta cells and insulin- producing cell lines have around 7500. Due to technical difficulties, it is impossible to isolate human beta cells, but based on computational analysis, we have determined a core list of 2025 and 860 genes that are enriched in human islets and beta cells, respectively. We are also studying the dynamics of cytokine-induced beta cell destruction by microarray and qPCR analysis. Results from arrays show 1057 genes are differentially expressed over a period of 96h IL1+IFN exposure. Chemokines, NF-kB and JAK-STAT signaling pathways are dramatically activated. Our approach mainly relies on integrating and comparing observations from different species

17 and publicly available pathway and interaction data. Comparison with similar experiments in rodents points reveals many similarities, however with delayed behavior in human cells. The time series will hopefully help us draw casual relationships between the activated pathways and beta cell death. http://T1DBase.org

I S3d SYSTEMS BIOLOGY FOR MEDICINE 11:00-12:30, October 11 Cancer Systems Biology Main Hall S3d-1 Design Principles of the JAK-STAT Signaling Pathway Ursula Klingmueller Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ)

Growth and differentiation processes are regulated by complex intracellular signaling networks. In many cancer cells signaling through the JAK-STAT or the SMAD signaling pathways are altered. To identify general principles how these pathways influence cellular decisions and to predict targets for intervention we are combining quantitative data generation with mathematical modeling. We applied quantitative immunoblotting to monitor the interleukin (IL)-6 induced activation of gp130, JAK1 and STAT3 in primary hepatocytes. The time-resolved experimental data could only be fitted at sufficient quality by a mathematical model capturing the possibility that STAT3 cycles between the nucleus and the cytoplasm. Although it has been demonstrated that SMAD2 cycles between the cytoplasm and the nucleus it was not possible to fit time resolved data for transforming growth factor (TGFbeta) induced SMAD2 phosphorylation in in primary hepatocytes by a simple cycling model. We had to include a negative feedback loop and experimentally verified a critical role for the oncoprotein SnoN. Thus, by dynamic pathway modeling rapid nuclear-cytoplasmic cycling could be identified as a general building principle of signaling cascades that make use of latent transcription factors and rapidly mediate signal transmission from the cell surface to the nucleus. www.dkfz.de/de/systembiologie/index.html

S3d-2 Predicting the outcome of chemotherapy through pathway modelling Charles Auffray CNRS and Pierre & Marie Curie University – Villejuif, France Contacts: [email protected]

The molecular mechanisms underlying innate tumor drug resistance to cancer therapy remain poorly understood, as molecular studies have focused on drug-selected tumor cell lines or individual candidate genes using samples derived from patients already treated with drugs. Transcriptional profiles of clinical samples collected from colorectal patients prior to their exposure to a combined chemotherapy were established using microarrays. Vigilant experimental design, power simulations and robust statistics allowed successful discrimination, for the first time prior to drug exposure, of subsequently diagnosed chemo-sensitive and resistant patients, based on the expression profile of 679 genes. Functional annotation, ontology enrichment analysis and pathway modelling using CellDesigner provided a unique representation of the cellular states underlying drug responses. It forms a solid foundation for a system-level analysis of the molecular interaction networks involved, and for the design of by-pass chemotherapy schemes allowing critical therapeutic intervention. In addition, through multivariate analysis and cross validation, powerful predictors of the response to chemotherapy were developed. Graudens et al. Deciphering cellular states of innate tumor drug responses. (2006) Genome Biology 7:R19 http://genomebiology.com/2006/7/3/R19

S3d-3 From simulation to therapy: a systems biology approach to oncogene detection Avijit Ghosh Drexel University Contacts: [email protected]

Activation of the Extracellular signal Regulated Kinases (ERK1/2; p42/p44 MAPK) is one of the most extensively studied signaling pathways, not least because it occurs downstream of oncogenic RAS. We take advantage of the wealth of experimental data available on the canonical RAS/ RAF/ MEK/ ERK pathway of Bhalla et. al. to test the utility of a newly developed nonlinear analysis algorithm designed to predict likelihood of cellular transformation. By using ERK phosphorylation as an "output signal", the method analyzes experimentally determined kinetic data and predicts putative oncogenes and tumor suppressor gene products impacting the RAS/ MAPK module using a purely theoretical approach. This analysis identifies several modifiers of ERK/ MAPK activation described previously. In addition, several novel enzymes are identified which are not previously described to affect ERK/ MAPK phosphorylation. We have extended this method to study disparate pair mutations in enzyme/protein interactions and in expression levels in signal transduction pathways and have applied it to the MAPK signaling pathway to study how synergistic or cooperative mutations in signaling networks act together to cause malignant transformation. The set of in silico transformed cells designed in this manner have been subject to a series of virtual drugs designated by a binding

18 affinity kd and concentration. A map of best targets within the pathway can be quantitated and furthermore ranked. Among the highly ranked targets are several well known in the literature such as the Ras inhibitors. Several novel targets are also highly ranked. In addition, the analysis points to the use of Calcium blockers, not currently used in cancer therapy, as a possible avenue for therapeutic intervention.

II S1b SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 14:00-16:00, October 9 Cyclic and Dynamic Behaviours 5F S1b-1 A generic model of cell cycle regulation in eukaryotes John J. Tyson, Attila Csikasz-Nagy, Katherine C. Chen, and Bela Novak Department of Biological Sciences, Virginia Polytechnic Institute & State University, USA, and Department of Agricultural Chemical Technology, Budapest University of Technology & Economics, Hungary Contacts: [email protected]

The protein interaction network regulating DNA synthesis and mitosis seems to be universal among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms are attributable to specific settings of the rate constants in the dynamical network, and these settings are determined ultimately by the genetic makeup of an organism. Alternative parameter settings are known for fission yeast, budding yeast, early embryos of frog and fruit fly, and for mammalian cells in culture. Generic properties of the model are revealed by one-parameter bifurcation diagrams, which show (1) how cell growth drives progression through the cell cycle, (2) how cell size homeostasis may be achieved (3) how cell cycle checkpoints operate, and (4) how mutations alter the way cells grow and divide. Two-parameter bifurcation diagrams compactly summarize the dependence of cell phenotype on gene dosage. This theoretical framework reveals both the universality and particularity of cell cycle regulation and facilitates construction, in modular fashion, of increasingly complex models of the networks controlling cell growth and division. Ref. Csikasz-Nagy, Battogtokh, Chen, Novak & Tyson (2006) Biophys J 90:4361-79 This research is supported, in part, by the Defense Advanced Research Project Agency (USA), the James S. McDonnell Foundation (USA), and COMBIO (EU). http://mpf.biol.vt.edu/

S1b-2 Probing structure and dynamics of cell cycle in budding yeast Lilia Alberghina#, Matteo Barberis# *, Marco Vanoni #, Edda Klipp* #Dept. of Biotechnology and Biosciences - University of Milano-Bicocca - Milano, Italy, *Max-Planck Institute for Molecular Genetics - Berlin Center for Genome based Bioinformatics (BCB) - Berlin, Germany Contacts: [email protected]

The construction of a novel mathematical model of the G1 to S transition is reported. It accounts both for literature findings and for our recent results (1,2). The model was implemented by a set of ordinary differential equations and analyzed by simulation. We investigate the dynamics of the G1 to S transition by simulation in several genetic and nutritional set ups and in response to different signalling pathways. The model was found highly consistent with experimental data. The main control of the G1 to S transition is given by the requirement for a critical cell size (Ps) to enter S phase. The model has been shown to be able to correctly estimate Ps in various growth conditions. Sensitivity analysis of Ps indicate that it is an emergent property of the network, dependent also from growth rate. This fact supports a new interpretation on the reported role of ribosome biogenesis in controlling Ps. 1. L. Alberghina, R.L. Rossi, L. Querin, V. Wanke, and M. Vanoni “A cell sizer network involving Cln3 and Far1 controls entrance into S phase in the mitotic cycle of budding yeast” – J. Cell Biol. 167, 433-443 (2004) 2. R.L. Rossi, V. Zinzalla, A. Mastriani, M. Vanoni and L. Alberghina “Subcellular localization of the cyclin dependent kinase inhibitor Sic1 is modulated by the carbon source in budding yeast” – Report - Cell Cycle 4, 1798-1807 (2005) http://www.btbs.unimib.it/indexuk.htm (Keywords: Research-groups-containing: cell cycle) http://www.unimib.it

S1b-3 Circadian systems of cyanobacheria Takao Kondo Division of Biological Science, Graduate School of Science, Nagoya University and CREST/SORST, JST, Japan Contacts: [email protected]

Cyanobacteria are the simplest organisms that exhibit circadian rhythms. In the cyanobacterium, Synechococcus elongatus PCC 7942, three genes (kaiA, kaiB and kaiC) were identified to code essential components of the circadian clock. As we found robust circadian cycling of KaiC phosphorylation even without kaiBC mRNA accumulation, we attempted to reconstitute the oscillation of KaiC phosphorylation in vitro. By incubating KaiC with KaiA, KaiB and ATP, we found the self-sustainable circadian oscillation of KaiC phosphorylation. The in vitro oscillation of KaiC phosphorylation persisted for at least three cycles and the period was compensated against temperature change. Furthermore, changes in circadian period observed in vivo in various KaiC mutant strains were consistent with those measured in vitro when the incubations were carried out with the respective mutant KaiC proteins. These results demonstrate that the oscillation of KaiC phosphorylation is the primary pacemaker of the cyanobacterial circadian clock. We analyzed the in vitro KaiC phosphorylation cycle from four aspects as follows; 1) interaction of KaiA and KaiB to KaiC,

19 2) interactions of two phosphorylation site, 3) energetics of the phosphorylation cycle of KaiC and 4) interactions among KaiC hexamers. From these analyses, we will propose a circadian programs of three proteins system for robust and tunable oscillations that tick biological time in the living cell. http://www.bio.nagoya-u.ac.jp/seminar/b1.html (in Japanese) http://www.bio.nagoya-u.ac.jp/english/index.html (in English)

S1b-4 Analysis and synthesis of mammalian circadian clocks Hiroki R. Ueda Laboratory for Systems Biology, Center for Developmental Biology, RIKEN Contacts: [email protected]

The logic of complex and dynamic biological networks is difficult to elucidate without (1) comprehensive identification of network structure, (2) prediction and validation based on quantitative measurement and perturbation of network behavior, and (3) design and implementation of biological networks driven by the same logic as the original network. Mammalian circadian clock system is such a system consisting of complexly integrated regulatory loops and displaying the various dynamic behaviors including 1) endogenous oscillation with about 24-hour period, 2) entrainment to the external environmental changes (temperature and light cycle), 3) temperature compensation over the wide range of temperature, and 4) synchronization of multiple cellular clocks against the inevitable molecular noise. To elucidate complex structure and dynamic behavior of mammalian circadian clock, we comprehensively identify the transcriptional regulatory circuits composed of 20 transcription factors, and three type of DNA elements including “morning” element (Bmal1/Clock Binding element, E-box/E’-box), “day” element (DBP/E4BP4 binding element, D-box) and “night” element (RevErbA/ROR binding element, RREs). The following quantitative measurement and perturbation of clock circuits revealed that E-box/E'-box regulation represents a topological and functional vulnerability in mammalian circadian clocks, and also found the interesting property of peripheral circadian clocks. In this conference, we will also report a current progress in the synthesis of transcriptional circuits underlying mammalian clock and discuss the logic governing this complex and dynamic biological networks. References 1. Ueda, H.R. et al. A transcription factor response element for gene expression during circadian night. Nature 418, 534-539 (2002). 2. Ueda, H.R. et al. System-level identification of transcription circuit underlying mammalian circadian clocks. Nat. Genet. 37, 187-192 (2005). 3. Sato T K, et al. Feedback repression is required for mammalian circadian clock function. Nat Genet. 38, 312-9 (2006). http://www.cdb.riken.jp/jp/02_research/0202_creative20.html

S1b-5 Multi-loop architecture in clock circuits Andrew J. Millar University of Edinburgh

II S2b SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 10:30-12:30, October 10 Yeast Systems Biology Sub Hall 1 S2b-1 Dynamic modeling of stress response of yeast cells Edda Klipp Max Planck Institute for Molecular Genetics, Berlin, Germany Contacts: [email protected]

Investigation of cellular systems is more and more supported by computational methods like bioinformatics and mathematical modeling, which is an important aspect of Systems Biology. A frequently used method is the description of reaction systems by sets of ordinary differential equations. The structure of the equations is established based on the knowledge about the network structure, i.e. about the relevant pathways and protein-protein interactions, while the parameters are determined from experimental observations, preferentially time course measurements. Using the power of such models, we investigate stress response processes in the yeast Saccharomyces cerevisiae. The adaptation of the cells to environmental changes like nutrient supply, pheromone stimulation or osmotic stress is mediated by signaling pathways that eventually regulate the expression of many genes. The products of such genes, in turn, regulate the metabolism or the cell cycle progression in order to compensate for or adapt to the external stimuli. The predictions of the models agree well with experimental results obtained under different stress conditions or using certain mutants. Simulations reveal properties of the signaling process and enlighten the roles of different components in the adaptation process. The cross talk between different networks and the regulation of cycle progression by signal transduction pathways will be discussed. The presented examples show that mathematical models are helpful to formulate experimental knowledge in a testable form, to explain hitherto unsolved phenomena, and to even predict the outcome of new experiments. http://www.molgen.mpg.de/~ag_klipp

20 S2b-2 Quantitative physiology of a cellular information sensing and relaying system Roger Brent The Molecular Sciences Institute, Berkeley, California, USA

I will review some of recent findings about the yeast pheromone response system from the Alpha project. Many of these findings are at least somewhat surprising. Some seem important, may be fundamental, in the sense that they may apply across eukaryotic biology for systems of this basic type. For signal dynamics (Yu et al., 2006), these include the fact that signal propagation is slow, taking minutes to reach the nucleus, that a each measurement point the signal shows a consistent pattern of rise, overshoot, correction, and decline toward a stable value, that overshoot correction depends on negative feedback, that the negative feedback(s) do not bring about densensitization (adaptation), and that we can experimentally decouple signal propagation time from signal amplitude. For equilibrium system output (in prep), findings include that this system does not undergo desensitization (adaptation) over longer times, that is not an amplifier, and that it is not a switch. Rather, the system goes to great lengths, across many steps, to transmit accurate information about extracellular signal concentration, as measured by the percent receptor occupied. Put another way, one of the functions of the system is to keep information in the signal, in this case information about measured pheromone concentration, from being degraded during the many stages of its transmission. This behavior is not a trivial consequence of the known reactions in the system but in one case we have established that proper behavior again involves a negative feedback. Using protein mass spectrometry, we have also undertaken a comprehensive search for feedbacks within the system. We have so far identified circa 100 candidate sites, and have shown that some are functional. The picture in 2006 is thus of progress gained from of patient multidisciplinary development and experimentation on a tightly defined model system in a model organism. By pursuing this course, and by maintaining a resolutely agnostic stance as to how the system might operate, we have discovered a number of basic attributes of this function. Our point of understanding seems roughly equivalent to the degree of understanding of nerve impulse transmission in the 1930s, with two major differences. First, simple concepts from information theory seem applicable to this system, and we are particularly excited that work to date has given us unexpected insight into the signal’s semantic content. Second, the pheromone response system operates within a genetically tractable organism for which genetic analysis is already yielding insight. This combination of disciplinary approaches to the study of a simple prototype system should continue to yield insights and may help define one thing that 21st century systems biology can be. http://www.molsci.org/

S2b-3 Interrogation of cellular networks Mike Tyers Samuel Lunenfeld Research Institute, University of Toronto, Toronto, Canada Contacts: [email protected]

Systematic approaches to chart biological networks include global analyses of gene expression, deletion phenotypes, genetic interactions and protein interactions. We have used these approaches to investigate the long-standing problem of cell size homeostasis in budding yeast. Before commitment to division in late G1 phase, cells must achieve a minimum critical cell size, as dynamically regulated by nutrient conditions. To uncover new pathways that coordinate growth and division, we have determined the cell size phenome using population-level size selection and barcode microarray analysis. Hundreds of small (whiskey or whi) and large (lge) mutants have been recovered and are currently being assembled into networks by systematic size epistasis and proteomic analysis. A crucial property of biological networks is compensation in gene function (buffering), as often revealed by genetic interactions. Systematic interrogation of the genetic landscape will therefore require simultaneous and precisely controlled perturbation of multiple network nodes, a task most readily accomplished with small molecule probes. To this end, we have begun to assemble a collection of small molecules that exhibit genotype-specific toxicity, as scored against many dozens of deletion mutants that affect various aspects of cell biology. An initial chemical-genetic interaction matrix, a corresponding chemical-chemical interaction matrix and possible implications for drug discovery will be presented. http://www.mshri.on.ca/tyers/

S2b-4 Sources and control of cell to cell variation in the response of yeast to mating pheromone Alejandro Colman-Lerner, Rich Yu, Andrew Gordon, Tina Chin, C. Gustavo Pesce and Roger Brent The Molecular Sciences Institute, Berkeley, California, USA

We studied the quantitative behavior of a prototypical eukaryotic signal transduction system, the pheromone response pathway in yeast. We used fluorescent proteins, in transcriptional reporters and in fusions to pathway components expressed from native chromosomal promoters, to measure signal trasmission through the pathway in single cells. In this talk, I will focus on how faithfully this system relays information. We studied 1) how precisely pheromone dose information is “seen” at different steps down signal propagation in population of cells and 2) on the sources and control of variability system output in single cells. We found that at most points we measured, system output maps precisely onto the proportion of receptors that are bound by pheromone and that this behavior requires the operation of negative feedbacks. We also found large cell to cell variation in system output. Only a small proportion of total cell-to-cell variation is caused by ‘gene expression noise’. Instead, variation is dominated by differences in the capacity of individual cells to transmit signals through the pathway (‘pathway capacity’) and to express genes into proteins (‘expression capacity’). Our results indicate that various aspects of system’s quantitative behavior, including the degree of cell to cell variation, are under genetic regulation. Taken together, our results highlight the importance of a quantitative and dynamic measurments for the understanding of information carrying systems.

21 II S2c SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 14:00-16:30, October 10 Metabolomics and Bioprocess Sub Hall 1 S2c-1 Bottom-up reconstruction of the human metabolic network based on Build-35 and bibliomic data B. Palsson, N. Hurlen, S. Becker, N. Jamshidi, M. Mo, I. Thiele Department of Bioengineering, UCSD, La Jolla, CA

A genome-scale metabolic network for H. sapiens has been constructed based on the annotated build-35 of the human genome sequence and a comprehensive evaluation of the published literature on human metabolism over the past 50 years. The reconstruction accounts for 1496 ORFs, and the metabolic network has 2766 metabolites and 3311 reactions. The network can be used to; 1) assess where our knowledge is weak or missing, 2) analyze HT data sets, and 3) perform in silico analysis of network properties. Examples of all three applications will be given. http://gcrg.ucsd.edu/personnel/palsson.htm

S2c-2 Systems level analysis and engineering of industrial bacteria Sang Yup Lee Dept. Chemical and Biomolecular Eng., Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea Contacts: [email protected]

Recent advances in omics research and modeling and simulation tools are enabling systems-level understanding of cellular behaviors, and consequently systematic engineering of the organisms to achieve desired goals. We have been developing various mathematical and analytical tools. One of the important technologies we are employing for strain development is metabolic flux analysis, which allows quantitative analysis of all intracellular fluxes based on certain constraints cells must obey under the given conditions. Metabolic flux analyses have been carried out under various genetically and environmentally perturbed conditions using genome scale in silico models. In this talk, the approaches taken in my group using genome-scale metabolic flux analysis in developing various biotechnological processes. In particular, I will focus on metabolic pathway engineering of Escherichia coli and other microorganisms towards enhanced production of desired products. Combination of in silico analysis and actual metabolic engineering allowed efficient creation of strains having improved performance. Detailed procedure and results will be presented. Also, general strategies for the improvement of strain based on the systems level analysis, especially how to find gene deletion and amplification targets for strain engineering, will be presented. [This work was supported by the Korean Systems Biology Research Grant from the MOST.] http://mbel.kaist.ac.kr

S2c-3 Metabolome analysis and synthetic biology Masaru Tomita Institute for Advanced Biosciences, Keio University, Human Metabolome Technologies, Inc. Contacts: [email protected]

We have developed a metabolome analysis method based on capillary electrophoresis time-of-flight mass spectrometry (CE/TOFMS). This technology has been applied to numerous fields such as medical diagnosis (blood, urine, tissue), food production (farm products, fermentation), environmental biology (soil microbes), and systems biology of model organisms (E.coli and other bacteria) at our institute. We have recently discovered a biomarker of acetaminophen-induced hepatotoxicity, ophthalmate being a sensitive biomarker of glutathione depletion (Soga 2006 J. Biol. Chem.). The metabolome technology has made “multi-omics” analysis possible. We systematically obtained multi-omics data sets for Escherichia coli BW25113 and its single gene deletion mutants. Our data covers the metabolome (CE-TOFMS), proteome (western blot, shotgun proteomics, and 2D-DIGE), fluxome (GC-MS and NMR) and transcriptome (real time RT-PCR and DNA microarray). Using the collected multi-omics data, a simulation model of central carbon metabolism was constructed and the dynamic responses of the model after pulse addition of glucose were compared with experimental results. Finally, I will describe our ongoing research on genome engineering with the ultimate goal of designing and synthesize useful microorganisms based on computer models. http://www.iab.keio.ac.jp

S2c-4 A systems biology approach to identify and therapeutically exploit the weakness of the robust tumour metabolism Marta Cascante1*, S. Marin1, G. Alcarraz1, P. Vizan1, A. Ramos1, S. Diaz1, P. de Atauri1, J.J. Centelles1, P. W-N. Lee2, V. Selivanov1 1Dept. Bioquímica i Biol. Molec., CeRQT-Parc Científic Barcelona (PCB), Associated Unit to CSIC, University of Barcelona, Barcelona,C./ Martí i Franqués 1, 08028 Barcelona, Spain, 2Harbor-UCLA Research and Education Institute, University of California, Los Angeles School of Medicine, USA. Contacts: *Corresponding author E-mail: [email protected]

22 Metabolic profile is the end point of the signaling events, where changes caused by diseases may be reflected and so it could be of key importance in correlating genotype with altered cellular phenotypes. A package in C++ has been developed for the dynamic tracer-based Mass Spectrometry (MS) or NMR isotopomer data analysis, which is connected with underlying enzyme kinetics. A core module of isotopomer distribution could be linked with various tissue- specific kinetic models, constructed for glucose metabolic network. This tool is applied to analyze metabolic adaptations in cancer cells and hepatocytes based in both enzyme kinetic information and tracer-based metabolomic data. From this “in silico” model, simultaneous estimation of metabolic fluxes, involved in the different glycolytic/gluconeogenic hepatic futile cycles, have been performed under different glycolytic and gluconeogenic substrates present in the medium. This analysis showed the importance of metabolic flux redistribution through futile cycles in maintaining cellular homeostasis. These examples demonstrate advantages of our new method which is not restricted by steady state and makes use of the enzyme kinetic information. This work is a first step in the construction of “in silico” metabolic network flux simulators in different cell types that can be very useful to design new therapies in multifactorial chronic diseases as cancer or diabetes as well as to predict drug side effects. http://www.bq.ub.es/bioqint/arecerca.html

II S3b SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 9:15-10:30, October 11 Developmental Systems Biology Sub Hall 1 S3b-1 C. elegans early embryogenesis: global, local and evolutionary views Fabio Piano Center for Comparative Functional Genomics and Department of Biology, New York University Contacts: [email protected].

Once fertilized, animal embryos must follow a series of events that give rise to different cell identities raising important fundamental questions in development. We are using C. elegans early embryogenesis (EE) as a model for approaching metazoan development at a systems level. In recent years C. elegans has become a prime model for powerful high-throughput functional genomics approaches, including global RNAi-based analyses followed by time-lapse microscopy. These approaches are particularly useful for studying developmental processes since development can be regarded as driven by the complex dynamic interactions of thousands of genes and their products. Hence, functional genomics techniques that give insight into gene function on a global level have opened the door for decoding gene interaction networks that control development. Since many components of developmental networks are conserved in evolution, specific insights into the genome-wide architecture of developmental networks in C. elegans are likely to be important for understanding molecular networks driving developmental decisions in other metazoans, including humans. Yet key evolutionary network changes are likely to underpin evolution of development. Our approaches to tackle these problems will be discussed.

S3b-2 Interactions among the pigment cells of zebrafish give rise to turing pattern Shigeru Kondo Nagoya University, Japan Contacts: [email protected]

The question of how complex animal body patterns arise from seemingly disorganized or formless initial structures represents an intriguing challenge not only to biologists but also to mathematicians, physicists and chemists. In 1952, the British mathematician Alan Turing proposed a simple mathematical equation capable of generating a wide range of patterns commonly found in the natural world, such as stripes, spots and reticulations. This model, known as the reaction-diffusion model, mathematically demonstrates that the interaction between a local activator and a long-range inhibitor can give rise to various periodic structures in response to differences in their individual diffusion rates. Although it was clear to have the potential capability to solve the fundamental problem of , the difficulty of proving in an experiment had obstructed the proof of this epoch-making theory for a long time. Animal skin patterns are ideal subjects to study the molecular basis of the Turing mechanism. They are visible from outside, and it is clear that they form without any prepattern because most skin patterns usually are not similar to the inside structures. We discovered in 1995 that the skin pattern of a certain tropical fish changed continuously so that a Turing model might predict, and it became proof that the principle of Turing was actually working in a living thing. By using zebrafish as a new experimental system, we are trying to clarify the molecular network that constructs the putative Turing system. This research is funded by Riken(CDB), Grant in Aid “Gakujutu-Sousei”, and “Genome” from MEXT. http://www.bio.nagoya-u.ac.jp/~z3/index.html

S3b-3 Quantitative analysis of C. elegans embryogenesis Shuichi Onami RIKEN Genomic Sciences Center, Japan Contacts: [email protected]

An embryo is a temporally and spatially dynamic system made up of many components. Quantitative analysis is essential for understanding such a large-scale dynamic system, in which actual behavior of the systems under various conditions is measured, mathematical models of the systems are created based on the measured behavior, the created models are refined by comparing

23 actual behavior of the systems and simulated behavior of the models, and then the refinement of the model is repeated. To find basic principles of animal development, I am studying C. elegans embryogenesis using quantitative analysis. In this talk, I will present our quantitative cell division pattern analysis of RNAi embryos. In this analysis, position of nuclei in RNAi embryos are four- dimensionally measured using an image processing-based automated system and functions of genes are predicted from statistical comparisons of cell division patterns of these embryos. I will then present our quantitative analysis on molecular mechanisms that determine arrangement of cells in the embryo. I will discuss how computer simulations and image-processing-based in vivo measurements revealed the mechanism for male pronuclear migration and that for the symmetric/asymmetric positioning of nucleus in one-cell embryos. Finally, I will present recent progress of our research. http://www.so.bio.keio.ac.jp, http://www.gsc.riken.jp

II S3e SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 11:00-12:30, October 11 Systems Neurobiology Sub Hall 1 S3e-1 Understanding molecular complexity at the neuronal synapse August B Smit and Ka Wan Li Dept. Molecular & Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, The Netherlands Contacts: [email protected]

Synapses are the central elements of communication between brain cells. The unsurpassed capacity of the brain to store and retrieve information, to make associations and to adapt to a changing environment are all considered to arise primarily from molecular adjustments in synapses. Synaptic mal-adaptations are known to cause or contribute to disease. Many drugs currently prescribed for brain disorders have known synaptic targets, however, the indirect effects on the synaptic protein network are poorly understood. Understanding the effects of drugs, or perturbations in general, on the complete synaptic protein/gene network is highly relevant both for a better understanding of synapse function and the identification of candidate proteins for future drug development. Therefore, it is crucial to understand the functional organization of the synaptic proteins network. Based on novel quantitative proteomics data, we work towards the construction of computational models, which explain how a complex protein network drives synaptic functions, and which predict its adaptive capacities in response to environmental cues, such as behavioral challenges and pharmacological interventions. Specific objectives are to provide targets for synaptic modulation and to dissect disease phenotypes by identifying crucial nodes and connectivity of the network. http://www.cncr.nl/mcn/index.html

S3e-2 Modelling structure and function of the post-synaptic proteome J Douglas Armstrong1, Andrew J Pocklington1 & Seth GN Grant2 1School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh, 2Wellcome Trust Sanger Institute, Cambridge Contacts: [email protected]

Proteomic study of the mammalian synapse has generated an extensive list of molecular components, revealing it as one of the most complex biological systems. While fundamental to information processing, behaviour and cognitive disorders, the molecular architecture of signalling in the synapse and its relation to higher-level function is now beginning to emerge. We present a model of the synaptic proteome that captures its structural organisation. Each component is annotated with information describing its molecular features/domains, evolutionary orthologues across 19 species, measurements of gene/protein expression in brain regions and functional annotation from yeast, fly, mouse and human. The model reveals a highly integrated and modular structure. Modules, defined by molecular interactions, not only share common network properties but also functional annotation, regional expression patterns and evolutionary origins. The picture that emerges is of a set of input modules (e.g. receptor complexes) that are closely linked to higher order cognition (and disorders), of more recent evolutionary origin and with high regional variation in the brain. These then link through more central processing modules to a series of output modules (e.g. gene regulatory complexes) that are more closely related to vital functions, ancient evolutionary origin and little variability across the brain. www.inf.ed.ac.uk/~jda

S3e-3 Systems analysis of spike-timing dependent synaptic plasticity Shinya Kuroda1, Hidetoshi Urakubo1, and Robert C. Froemke2 1CREST, Japan Science and Technology Agency, Department of and Biochemistry, Graduate School of Science, The University of Tokyo, Japan, 2Department of Otolaryngology, University of California, San Francisco, USA Contacts: [email protected]

Spike timing-dependent synaptic plasticity (STDP), which depends on the relative timing of pre- and postsynaptic spiking, is thought to play an important role in neural development and information storage. However, mechanism of spike-timing detection in STDP

24 remains unclear. To understand the mechanism of spike-timing detection in STDP, we developed a computational model of STDP, and found that long-term potentiation (LTP), but not long-term depression (LTD), could be reproduced, indicating an existence of an unknown mechanism for spike-timing detection in STDP. We searched a requirement for spike-timing detection in LTD, and predicted an allosteric kinetics of NMDARs. We experimentally validated the prediction of allosteric kinetics of NMDARs based on the NMDARs-mediated EPSPs. Furthermore, the allosteric kinetics of NMDARs was valid over more complex spike-timing dependent synaptic plasticity in experiments. Thus, our results indicate that a simple allosteric kinetics of NMDARs can code complex spike-timing information into synaptic plasticity. In this talk, I will discuss the possible role of the allosteric kinetics of NMDARs in coding of complex spike-timing information into STDP, which may restructure neural circuits and embed experiences into the brain. http://www.kurodalab.org

II S3h SYSTEMS BIOLOGY OF BASIC BIOLOGICAL SYSTEMS 14:00-16:30, October 11 Signal Transduction Sub Hall 1 S3h-1 Cell-signaling dynamics in time and space Boris N. Kholodenko Thomas Jefferson University

We employ computational and experimental approaches to reveal kinetic and molecular factors that control the temporal dynamics of the EGFR signaling network, including transient versus sustained activation patterns, discontinuous bistable dynamics and oscillations. Quantitative analysis of signal transduction is confronted by a combinatorial explosion in the number of feasible molecular species presenting different states of signaling networks that include receptors and scaffolds with multiple binding domains. We show that a mechanistic description of a highly combinatorial network may be drastically reduced using a “domain- oriented”, macro-modeling framework. Using this approach, we explored the role of the scaffold protein GAB1 in the control of mitogenic (Ras/MAPK) and survival (PI3K/Akt) signaling. The results demonstrate that the essential function of GAB1 is to enhance PI3K/Akt activation and extend the duration of Ras/MAPK signaling. The spatial separation of kinases and phosphatases in MAPK cascades may cause precipitous spatial gradients of phosphorylated kinases with high concentration near the cell surface and low in the perinuclear area. The results suggest that there are additional (besides diffusion) mechanisms that facilitate signaling to distant targets. They may involve endocytosis, scaffolding and active transport of signaling complexes by molecular motors. We show how traveling waves of phosphorylated kinases spread the signals over long distances. In addition to mechanistic modeling, an integrative, modular approach to inferring the structure of cellular signaling and gene networks is proposed. We demonstrate how dynamic connections leading to a particular module (e.g., an individual gene/protein or a cluster) can be retrieved from experimentally measured network responses to perturbations influencing other modules. References. Kholodenko, B.N. (2006) Cell-signalling dynamics in time and space. Nat Rev Mol Cell Biol, 7, 165-176. Sontag, E., Kiyatkin, A. and Kholodenko, B.N. (2004) Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics, 20, 1877-1886. http://www.cellnetworks.org

S3h-2 Emerging principles of living systems Hans V. Westerhoff 1,2, Frank Bruggeman1,2, Barbara Bakker2 and Jacky L. Snoep1,2,3 1Manchester Center for Integrative Systems Biology, The University of Manchester, UK, EU, 2BioCenter Amsterdam, Vrije Universiteit Amsterdam, NL, EU and 3Department of Biochemistry, University of Stellenbosch, South Africa Contacts: [email protected]

Much of the primary function of living cells is in chemical and physical processes, such as the generation of amino acids for protein synthesis or the contraction of actomyosin. These primary processes are carried out by metabolic and other pathways of substantial length, which are activated by signal transduction and gene-expression pathways. One issue is how the dynamics of any of these pathways and the flux through any of these processes is controlled by the various molecular processes. This control turns out to be a system property that cannot be assessed from the study of individual steps of the pathway alone. It is an important characteristic of living systems that the individual reaction rates in these pathways need to be regulated so as to stay in tune with each other whilst they change strongly with changing functional requirements. This regulation is effected through gene expression but also through metabolic regulation and is again a systems property rather than a property of the individual processes that are being regulated. This presentation will show that both control and regulation are indeed system properties and that both are subject to a number of general laws or principles. These laws relate to robustness (lack of control), fragility, homeostasis, adaptation ad differentiation. We shall discuss how recognition of some of these principles may help understand important aspects of multifactorial disease and lead to new strategies for drug design. http://www.mcisb.org/ http://www.bio.vu.nl/hwconf/

25 S3h-3 Ligand-dependent cell fate control of ErbB signaling network in breast cancer cells Mariko Hatakeyama Cellular Systems Biology Team, RIKEN Genomic Sciences Center Contacts: [email protected]

Over-expression and mutation of ErbB receptors are implicated with various kinds of human cancers. On the other hand, ErbB receptor ligands, epidermal growth factor (EGF) and heregulin (HRG) induce transient and sustained extracellular signal-regulated kinase (ERK) and protein kinase B/Akt activities in human MCF-7 breast cancer cells, of which distinct kinetics result in cellular proliferation and differentiation, respectively. Comprehensive analysis of ErbB signaling network is necessary to understand the mechanism of cell fate control and cancer development. In our study, we quantitatively analyzed time-course events of the intracellular signaling and transcription that are initiated by the EGF and HRG. Our results indicated that ligand-induced intracellular signaling and early transcription was first regulated in ligand dose-dependent manner. However, those early transcripts crosstalk with intracellular signaling and then convert dose-dependent signals to binary response that may determine cell fate. We will discuss about mechanistic properties of ErbB receptors and its contribution for controlling cellular network based on the result given by mathematical modeling. 1. Naka, et al. Compensation effect of MAPK cascade on formation of Phospho-protein gradient. BioSystems 83, 2-3, 167-177, 2006. 2. Suenaga, et al. Novel mechanism of interaction of p85 subunit of PI3K and ErbB3 receptor-derived phosphotyrosyl peptides. J. Biol. Chem. 280, 1321-1326, 2005. 3. Kimura, et al. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 21, 1154-1163, 2005. 4. Hatakeyama, et al. Transformation potency of ErbB heterodimer signaling is determined by B-Raf kinase. Oncogene 23, 5023-5031, 2004. 5. Suenaga, et al. Tyr317 phosphorylation increases Shc structural rigidity and reduces coupling of domain motions remote from the phosphorylation site as revealed by molecular dynamics simulations. J. Biol. Chem. 279, 4657-4662, 2004. 6. Hatakeyama, et al. A computational model on the modulation of MAPK and Akt pathways in heregulin induced ErbB signaling. Biochem J., 373, 451-463, 2003. 7. Suenaga, et al. Molecular dynamics, fee Energy and SPR analyses of the interactions between SH2 domain of growth factor receptor binding protein 2 and ErbB phosphotyrosyl peptides. Biochemistry (U.S), 42, 5195-5200, 2003. http://csb.gsc.riken.jp/

S3h-4 Rules for modeling signal-transduction systems William S. Hlavacek Center for Nonlinear Studies, Los Alamos National Laboratory, U.S.A. Contacts: [email protected]

The behavior of a signal-transduction system depends on the dynamics of protein interactions. The site-specific details of these interactions can be represented using formal rules, which can be visualized using graphs. A set of rules can be processed to automatically generate a dynamical model that accounts comprehensively for the protein complexes implied by the interactions encoded in the rules. The number of possible complexes is often large, which limits conventional approaches to model specification. After summarizing the motivation for rule-based modeling, I will introduce the method, including recent improvements that should facilitate high-throughput modeling and simulations of large-scale networks, and then illustrate how a rule-based approach is being used to model aspects of epidermal growth factor and antigen-recognition receptor signaling. Finally, I will point to future challenges and the promise of an extensible standardized modeling language suited for the complexities of cellular signaling. William S. Hlavacek, James R. Faeder, Michael L. Blinov, R. G. Posner, M. Hucka, W. Fontana (2006) Rules for modeling signal- transduction systems. Sci. STKE 2006, re6. http://www.t10.lanl.gov/wish/

S3h-5 Reaction cycles in the spatial and temporal organization of cell signaling Philippe Bastiaens EMBL Heidelberg, Germany

26 III S2e FRONTS IN SYSTEMS BIOLOGY 10:30-12:30, October 10 Network Biology Sub Hall 2 S2e-1 Interactome networks Marc Vidal Center for Cancer Systems Biology (CCSB), and Department of Cancer Biology, Dana-Farber Cancer Institute, Department of Genetics, Harvard Medical School Contacts: [email protected]

For over half a century it has been conjectured that macromolecules form complex networks of functionally interacting components, and that the molecular mechanisms underlying most biological processes correspond to particular steady states adopted by such cellular networks. However, until recently, systems-level theoretical conjectures remained largely unappreciated, mainly because of lack of supporting experimental data. To generate the information necessary to eventually address how complex cellular networks relate to biology, we initiated, at the scale of the whole proteome, an integrated approach for modeling protein-protein interaction or “interactome” networks. Our main questions are: How are interactome networks organized at the scale of the whole cell? How can we uncover local and global features underlying this organization, and how are interactome networks modified in human disease, such as cancer?

DFCI Center for Cancer Systems Biology (CCSB) http://ccsb.dfci.harvard.edu/home.html Vidal Lab http://vidal.dfci.harvard.edu/

S2e-2 Protein network comparative genomics Trey Ideker University of California San Diego Contacts: [email protected]

With the appearance of large networks of protein-protein and protein-DNA interactions as a new type of biological measurement, methods are needed for constructing cellular pathway models using interaction data as the central framework. The key idea is that, by comparing the molecular interaction network with other biological data sets, it will be possible to organize the network into modules representing the repertoire of distinct functional processes in the cell. Three distinct types of network comparisons will be discussed, including those to identify: (1) Protein interaction networks that are conserved across species (2) Networks in control of gene expression changes (3) Networks correlating with systematic phenotypes and synthetic lethals Using these computational modeling and query tools, we are constructing network models to explain the physiological response of yeast to DNA damaging agents. Relevant articles and links: Yeang, C.H., Mak, H.C., McCuine, S., Workman, C., Jaakkola, T., and Ideker, T. Validation and refinement of gene regulatory pathways on a network of physical interactions. Genome Biology 6(7): R62 (2005). Kelley, R. and Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nature Biotechnology 23(5):561-566 (2005). Sharan, R., Suthram, S., Kelley, R. M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R. M., and Ideker, T. Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci U S A. 8:102(6): 1974-79 (2005). Suthram, S., Sittler, T., and Ideker, T. The Plasmodium network diverges from those of other species. Nature 437: (November 3, 2005). http://www.pathblast.org http://www.cytoscape.org Acknowledgements: We gratefully acknowledge funding through NIH/NIGMS grant GM070743-01; NSF grant CCF-0425926; Unilever, PLC, and the Packard Foundation.

S2e-3 Genome network project in Japan Yoshihide Hayashizaki Genome Exploration Research Group, Riken Genomic Sciences Center, Riken Yokohama Institute, Japan Contacts: [email protected]

Analysis of the Fantom-3/GenomeNetwork transcriptome dataset, including 158,807 cDNAs, 11.5 million CAGE tags and 2.4 million GSC tags, suggests that mammalian genomes express an unprecedented number of independent transcripts, of which about half are grouped in Transcriptional Unit (TU) containing non-coding RNAs, while a considerable fraction of these was shown to be non- coding RNA variant of TU containing protein coding mRNAs. In this very large transcript collection, we found extensive transcription overlap identifying a very large number of novel RNAs sense-antisense (S/AS) potential transcript pairs, to which a large number of transcribed repeats and repeat containing RNA has to be added. These S/AS show significant coexpression and are regulated in

27 various tissues, organs and cells in our analysis. Experimental evidence shows that RNAs constituting S/AS pairs regulate each other concentration, through RNA-based gene regulation mechanisms such as RNAi intermediates. The FANTOM full-length cDNAs are the collection of the elements and the integrated database provides us the tool to understand the molecular system connecting gene to phenotype. Our next goal is focused on constructing the system to accelerate the analysis of the network of transcription. For this purpose, the deep CAGE technology, the technology to analyze physical binding of transcription factors and DNA, such as Chip on CHIP, and matrix RNAi were integrated into a pipeline of analysis. In this talk the future system to analyze genome network will be discussed. http://www.gsc.riken.go.jp/eng/output/topics/genome.html

S2e-4 Single molecule imaging of motor proteins in living cells - deciphering physical networks of molecular motions Hideo Higuchi and Tomonobu M Watanabe Biomedical Engineering Research Organization, Tohoku University, Japan

Motor proteins, e.g., myosin, kinesin and dynein, are involved in membrane transport in a cell. Single-molecule measurements in an in vitro assay provide us great deal of information of motor mechanism. However, physiological conditions in the cell are much different from those in vitro. Here, we developed novel techniques to observe the elementary steps of the movement of motor proteins in living cells. To record the movement of motor proteins in cells with high spatio-temporal resolution, membrane Receptor (HER2) was labeled with stable and intense Quantum dots via antiHER2. They were endocytosed into a breast cancer cell in which HER2 was overexpressed. We observed the movements of the transported vesicle of antiHER2-QD in three dimensions using a new microscopic system. AntiHER2-QD was transported along an actin filament by myosin VI, and toward the nucleus by dynein on microtubules. The position of movement was analyzed with 2 nm and 0.3 ms resolutions. The movement along the membrane consisted of 29 and 16 nm steps. The movements toward and away from the nucleus consisted of successive 8 nm steps. This movement will be generated by kinesin, also composed of sequential 8 nm steps. Thus, the techniques we developed were new and useful tools for investigation of molecular functions of proteins. http://www.cir.tohoku.ac.jp/higuchi-p/NanoSystem/index.htm http://web.tubero.tohoku.ac.jp/~nim/

III S3c FRONTS IN SYSTEMS BIOLOGY 9:15-10:30, October 11 Complex Systems Biology Sub Hall 2 S3c-1 Interaction balance coordination as organizing principle in complex systems biology Mihajlo Mesarovic, Sree N.Sreenath and Girish Balakrishnan Complex Systems Biology Center, EECS, Case Western Reserve University Contacts: [email protected]

Complexity and sophistication of behavior of eukaryotes depend directly on their genome regulatory functions. Evidence indicates that evolution of eukaryotes is accompanied with increase in genetic resources allocated to regulatory functions. In human more than 90% of the genetic materials are protein non-coding, including introns -considered as junk genes-but is identified as performing vital regulatory functions leaving only 1.5% for exon-active genes. Progress in understanding complexity in systems biology will depend on understanding eukaryote’s regulatory functions. Obstacles to progress are conceptual and representational. Conceptually engineering concepts like feedbacks - negative and positive - are dominant regulatory motifs in systems biology. They are insufficient to understand organized complexity of eukaryotes. Organizational sciences and in particular mathematical multilevel hierarchical systems theory provide a wealth of new motifs for understanding in systems biology. Two case studies will be presented: • Explanation of angiogenesis as an exemplification of Interaction Balance Coordination Principle (IBCP) not only in cancers growth but also in metastasis (non-local distant action) as well in neural growth. • Modular coordination in JAK-STAT in myeloproliferative diseases. Representationaly, the focus in systems biology should not be restricted to quantification. Non-numerical branches of mathematics are more appropriate since biological phenomena cannot be represented with the precision of classical physics. Result of, mathematical general systems theory will be presented proving an exceedingly broad applicability of IBCP. New direction in systems biology research is needed for a search for coordination motifs as well as experimental validation of IBCP and other newly discovered motifs. http://eecs.case.edu/people/faculty/mdm5

S3c-2 Coordination of gene expression by RNA operons Jack D. Keene Molecular Genetics & , Duke University Medical Center, Durham, N.C. USA

The organization of genes along the genomes of higher eukaryotes does not correlate well with the functions of their encoded proteins as these genomes do not contain DNA operons like bacteria. For example, eukaryotic genes that are clustered in “expression

28 neighborhoods” may be co-expressed but they do not appear to be functionally related to one another. Studies have suggested that eukaryotic transcription is leaky, sometimes stochastic, and probably not as precisely coordinated as previously thought. However, since the question of how eukaryotic gene expression is coordinated is essentially unknown, it has been assumed that dispersed genes are coordinated by temporal activation of their promoters. The Posttranscriptional RNA Operon model offers a mechanism of coordinated expression for higher eukaryotes that is based on the organization of functionally-related eukaryotic mRNAs within messenger ribonucleoprotein complexes (mRNPs). RNA-binding proteins (RBPs) and microRNAs both interact with discrete groups (classes or modules) of mRNAs that can be regulated together to provide collective functional outcomes and coordinated gene expression networks. These RNA operons or regulons can coordinate and couple mRNA decay, mRNA translation, splicing, export or localization of functionally related genes. I will discuss combinatorial interactions of RBPs and microRNAs that regulate posttranscriptional RNA operons. http://mgm.duke.edu/faculty/keene/index.htm

S3c-3 Applications of complex systems biology to the study of neural systems Kenneth A. Loparo EECS Department, Case Western Reserve University Contacts: [email protected]

In this talk we will discuss the role of “complexity” in the study of the dynamics of neural systems and how the analysis of complex physiological time series is related to health and disease. Of particular interest is the dynamical process of neural plasticity in developing organisms. In this context, the “triple helix” of nature-nurture-niche plays a critical role in the neurodevelopmental process. Developmental sleep physiology recognizes this interplay and can quantify this interaction through a computational description of the process of “ontogenetic adaptation”. Understanding how remodeling and restructuring of the neural circuits is affected by the interplay between genetics and environment can have a significant impact on a variety of diverse fields including genetics, behavior, developmental psychology, neurobiology, and medicine. Our approach is to develop a computational phenotype that can quantify the process of neurodevelopment and provide a deeper understanding of the role of sleep in the maturational process in developing organisms. Our computational work fills an important gap in integrative by identifying biomarkers for neurodevelopment between the molecular and behavioral/cognitive level that can provide additional insight into the neurobiology and dynamical processes of maturation of brain networks in developing organisms. Using computational neurophysiological techniques, including dynamic analyses of temporal features and patterns that are associated with the sleep process, we are able to capture the dynamic interactions among multiple behaviors that comprise the sleep process. Applying these analyses of sleep as phenotypic markers of brain organization and maturation provides a more complete picture of how adaptation and remodeling relates to physiologic function and development. http://www.case.edu

III S3f FRONTS IN SYSTEMS BIOLOGY 11:00-12:30, October 11 Control and System Theory for Systems Biology Sub Hall 2 S3f-1 Robustness analysis of biological networks using sensitivity measures Francis J. Doyle III Dept. of Chemical Engineering, and Biomolecular Science & Engineering, University of California, Santa Barbara, USA Contacts: [email protected]

A property of particular interest in systems biology is the robustness of a biophysical network: the ability to maintain some target level of behavior or performance in the presence of uncertainty and/or perturbations. In biological systems, these disturbances can be environmental (heat, pH, etc.) or intrinsic to the organism (changes in kinetic parameters). While preliminary results are available for simple (low-dimensional, deterministic) biological systems, general tools for analyzing these tradeoffs are the subject of active research. In this talk we introduce tools from systems theory that elucidate design principles in these complex architectures through the analysis of robust and fragile regions of the network. The problems used to illustrate the issue are drawn from circadian rhythm gene networks, hence the tools are extended to deal with oscillatory systems. Examples are presented for Drosophila, Mouse, and Arabidopsis networks. Recent to address discrete stochastic models are also covered. Finally, we highlight some recent results that analyze robustness properties at the tissue level, where intercellular coupling appears to be responsible for the generation of robust rhythms. http://doyle.chemengr.ucsb.edu

S3f-2 Feedback control regulation of cell division Pablo A. Iglesias The Johns Hopkins University Contacts: [email protected]

During cell division, cells must accurately duplicate and segregate their genetic material before rearranging their morphology so as to produce two daughter cells of equal size. To achieve this, mitosis proceeds as a well-ordered sequence of biochemical events

29 involving the temporal and spatial distribution of a wide variety of proteins, regulated by with numerous checkpoints that rely on active feedback loops. In contrast, cytokinesis, the last step in the mechanical separation of the two daughter cells, is typically viewed as an open-loop process, despite the fact that aberrant cytokinesis can lead to tumorigenesis. In this talk we demonstrate that feedback loops are also employed during cytokinesis. Specifically, we will show the presence of mitotic-specific feedback pathways that regulate the location of actin motor and crosslinking proteins vital to cell division. This feedback loop is activated by mechanical perturbations that affect cellular shape. Our results will demonstrate how the study of cellular division can benefit from a systems- level approach. http://www.ece.jhu.edu/~pi/

S3f-3 The architecture of cellular regulation John Doyle California Institute of Technology Contacts: [email protected]

This talk focuses on architectural and organizational principles of cellular regulation, building on insights about the fundamental nature of complex biological and technological networks drawn from three converging research themes. 1) With molecular biology’s detailed description of components and growing attention to systems biology the organizational principles of biological networks are becoming increasingly apparent (www..org). 2) Advanced technology’s complexity is now approaching biology’s. While the components differ, there is striking convergence at the network level of architecture and the role of layering, protocols, and feedback control in structuring complex multiscale modularity. New theories of the Internet and related networking technologies have led to test and deployment of new protocols for high performance networking (www.hot.caltech.edu, netlab.caltech.edu). 3) A new mathematical framework for the study of complex networks suggests that this apparent network-level evolutionary convergence within/between biology/technology is not accidental, but follows necessarily from the universal system requirements to be efficient, adaptive, evolvable, and robust to perturbations in their environment and component parts. [1] H. El-Samad, H. Kurata , J.C. Doyle , C.A. Gross, and M. Khammash, 2005, Surviving Heat Shock: Control Strategies for Robustness and Performance, PNAS 102(8): FEB 22, 2005 [2] Jin C, Wei D, Low SH, Bunn J, Choe HD, Doyle JC,et al, FAST TCP: From theory to experiments IEEE NETWORK 19 (1): 4-11 JAN-FEB 2005 [3] Doyle et al, (2005), The “Robust Yet Fragile” Nature of the Internet, PNAS 102 (41), October 11, 2005 [4] MA Moritz, ME Morais, LA Summerell, JM Carlson, J Doyle (2005) Wildfires, complexity, and highly optimized tolerance, PNAS, 102 (50) December 13, 2005; , [5] H El-Samad , A Papachristodoulou, S Prajna, J Doyle, and M Khammash (2006), Advanced Methods and Algorithms for Biological Networks Analysis, PROCEEDINGS OF THE IEEE, 94 (4): 832-853 APR 2006 [6] Kurata, H El-Samad, R Iwasaki, H Ohtake, JC Doyle, et al. (2006) Module-based analysis of robustness tradeoffs in the heat shock response system. PLoS Comput Biol 2(7): July 2006 [7] M Chiang, SH Low, AR Calderbank, JC. Doyle (2006) Layering As Optimization Decomposition, PROCEEDINGS OF THE IEEE, to appear http://www.cds.caltech.edu/sostools/ http://www.cds.caltech.edu/~doyle/

III S3g FRONTS IN SYSTEMS BIOLOGY 14:00-16:30, October 11 Synthetic Biology Main Hall S3g-1 Languages and grammars for programming in DNA Drew Endy MIT , Massachusetts Institute of Technology Contacts: [email protected]

Biology is going through a fundamental transition, from preexisting, natural, and evolving systems to synthetic, engineered, and disposable systems. Here, we’ll review how the adoption of past engineering lessons such as standardization and abstraction are beginning to make the process of engineering biology simpler, cheaper, and more reliable. We’ll also explore how and why engineers are beginning to redesign simple genomes from scratch. From these technical foundations we’ll discuss recent and imaginable future progress on the engineering of living organisms to process information, construct materials, produce chemicals, provide energy and food, and help maintain or enhance human health and our environment. The talk will end with a brief discussion for what needs to happen next. http://mit.edu/endy/

S3g-2 Applications in systems and synthetic biology Adam Arkin University of California, Berkeley

30 S3g-3 Impact of a whole genome cloning on systems biology Mitsuhiro Itaya Institute for Advanced Biosciences, Keio University Contacts: [email protected]

Most DNA cloning vectors cannot handle a large number of genes at one time. We have developed a method to transfer whole genomes from one bacterial species into another (1). The BGM cloning vector derived from the 4.2-Mb genome of Bacillus subtilis bacterium was suitable for target DNA, the 3.5-Mb genome of the nonpathogenic, unicellular photosynthetic bacterium Synechocystis. The resultant bacterium putatively named as CyanoBacillus, with a composite genome 7.7 Mb in size, grew only in the B. subtilis culture medium in which the cloning procedures were carried out. Technical breakthroughs unveiled for the first time biological significance such as exclusiveness of the second RNA operon genes (rrn), and structural requirement of symmetry on bacterial chromosome in vivo. (1) Itaya, M., Tsuge, K. Koizumi, M., and Fujita, K. Combining two genomes in one Cell: Stable cloning of the Synechosystis PCC6803 genome in the Bacillus subtilis 168 genome. Proc. Natl. Acad. Sci., U. S. A., 102, 15971-15976 (2005). http://www.iab.keio.ac.jp/ja/jprofile_mitsuhiro_itaya.html

S3g-4 Adaptive response of a gene network to environmental changes by fittness-induced attractor selection Tetsuya Yomo Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Graduate School of Frontier Biosciences and Complex Systems Biology Project, ERATO, JST, Osaka University Contacts: [email protected]

Cells switch between various stable genetic programs (attractors) to accommodate environmental conditions. Signal transduction machineries efficiently convey environmental changes to gene expression. However, since the number of environmental conditions is larger than that of commensurate cellular programs, not every condition, notably those that are rarely encountered, have led to the evolution of a cognate signal transduction pathway. Here we show that in the absence of signal transduction, switching to the appropriate attractor state expressing the genes that afford adaptation to the external condition can occur. In a synthetic bistable gene switch in Escherichia coli in which mutually inhibitory operons govern the expression of two genes required in two alternative nutritional environments, cells reliably selected the “adaptive attractor” driven by gene expression noise. A mathematical model suggests that the non-adaptive attractor state is avoided because the lower cellular activity suppresses mRNA metabolism, leading to larger fluctuations in gene expression which renders the non-adaptive state less stable. Although attractor selection is not as efficient as signal transduction via a dedicated cascade, it is simple and robust and may represent the general primordial mechanism for adaptive responses that preceded the evolution of signaling cascades for the frequently encountered environmental changes. http://www-symbio.ist.osaka-u.ac.jp/sbe.html

S3g-5 Programmable bacterial catalysts Vítor Martins dos Santos, Miguel Godinho de Almeida, Jacek Puchalka, Amit Khachane, Kenneth Timmis German Research Centre for Biotechnology (GBF), Division of Microbiology, Braunschweig, Germany Contacts: [email protected]

We will report on the preliminary results of a transnational, recently begun project that aims at constructing a functioning, streamlined bacterial cell devoid of most of its genome and endowed with a series of highly coordinated, newly assembled genetic circuits for the biotransformation of a range of chloroaromatics into high added value compounds and that include circuits for synchronized behaviour, noise minimisation and/or low-temperature biocatalysis and, in addition, amenable to directed, accelerated evolution so that the function of each or some of the individual circuits can be optimised. This will be tested for the production of high added value compounds from chloroaromatics in bioreactors. By achieving such constructs as a proof-of-principle, it is aimed at establishing a solid, rational framework for the engineering of cells performing effectively and efficiently specific functions of biotechnological and medical interest. This encompasses the production of series of different, versatile circuits and the corresponding components that can be used as building blocks in circuit engineering. The proposed workflow intertwines mathematical modelling with wet-lab experimental work as an integral module at every stage. www.helmholtz-hzi.de

31 III S3i FRONTS IN SYSTEMS BIOLOGY 14:00-16:30, October 11 Novel computational environments for systems biology Sub Hall 2 S3i-1 Linking text with knowledge - challenges in text mining for biology Junichi Tsujii Department of Computer Science, Faculty of Information Science and Technology, The University of Tokyo, The University of Manchester and National Centre for Text Mining (NaCTeM), Manchester Interdisciplinary Biocentre Contacts: [email protected], [email protected]

While there are a few Text Mining tools on market, they hardly satisfy actual requirements of biologists. Simple application of data mining techniques to text does not work. Since language and text have their own inherent structures, it is essential for TM tools to be able to recognize and exploit their structures to reveal information encoded in them. However, the major difficulties in treating information encoded in language are caused by the nature of the mapping between surface linguistic forms and information conveyed by them. It is hugely ambiguous. Furthermore, the same information can be conveyed by using many different surface forms. Before more ambitious goals such as discovering new, hidden knowledge, we have to resolve these essential properties of the mapping between language and information. Although techniques have been developed in natural language processing (NLP) research to resolve the difficulties, they were considered, until very recently, non-deployable for large scale text mining. However, due to recent technological development in corpus-based NLP techniques, many of NLP techniques have become robust and efficient enough for large scale text mining applications. The progresses in the filed have been enormous, which will open up many possible applications of NLP-based Text Mining in the near future. http://www-tsujii.is.s.u-tokyo.ac.jp/

S3i-2 Going with the flow: distributed computing for systems biology using Taverna Carole Goble The School of Computer Science, The University of Manchester, UK Contacts: [email protected]

Linking together the many hundreds of publicly available or privately generated data sets and analytical tools into discovery pipelines is a key necessity for Systems Biology, but also a costly and frustrating business. Workflows offer a flexible and systematic approach for representing and executing these in silico experimental protocols. The Taverna workbench, a product of the myGrid project (http:// www.mygrid.org.uk), is an open source workflow system that enables the scientists themselves to design, build and run workflows that control the flow of data between web-enabled and local Java resources. It has been designed primarily for the Life Sciences, and developed with a great deal of support and practical feedback from bioinformaticians. Taverna is very popular. It is used for gene alerting, sequence annotation, proteomics, functional genomics, chemoinformatics, systems biology and protein structure prediction applications, amongst others. The background and philosophy behind Taverna will be presented, highlighting how it complements a data warehouse approach. A number of real examples of its use in Systems Biology will be presented. The need for further tools and better data publishing practices will be highlighted, as will reflections on the changes to scientific practice observed when biologists adopt a Taverna approach to scientific discovery. http://www.cs.man.ac.uk/~carole

S3i-3 The DREAM project: establishing a community-based gold standard for systems biology Andrea Califano Columbia University Medical Center

S3i-4 The systems biology markup language (SBML): where it's been and where it's going Michael Hucka California Institute of Technology Contacts: [email protected]

A cornerstone of systems biology is the use of computational modeling, by which hypotheses can be cast into a quantitative form that can be tested systematically. The use of computational modeling by biologists promises to pave the way for more rigorous analyses of biological functions, and ultimately will lead to new and better treatments for disease. A crucial enabler for more widespread use of computational modeling in biology is reaching agreement on how to represent, store, and communicate models between software tools. The Systems Biology Markup Language (SBML) project is an effort to create a machine-readable format for representing computational models in biology. By supporting SBML as an input and output format, different software tools can operate on the same representation of a model, removing chances for errors in translation and assuring a common starting point for analyses and simulations. SBML has become the most successful effort in this direction so far, with over 100 software systems supporting it today. In this presentation, I will discuss the current state of SBML, including recent developments such as this year's finalization of Version

32 2 of SBML Level 2. I will also survey some of the software tools that support SBML, and related projects that have arisen to support more effective use of computational models. Lastly, I will discuss expected future developments in SBML. http://sbml.org http://bnmc.caltech.edu

S3i-5 MIRIAM and BioModels DB: curation and exchange of quantitative models Nicolas Le Novere EMBL-EBI, Hinxton, United Kingdom Contacts: [email protected]

With Computational Systems Biology now being "mainstream" life science, the generation and use of quantitative models is no longer a lonely exercise reserved to the unchallenged specialist. On the contrary, those models have to be verified and, if possible, reused. Beside the effort of developing common syntaxes, the community of modellers and tool developers recently focused on the semantics of models. MIRIAM is a standard that describes the conditions which a biological model should meet to be correctly understood and reused. The guidelines address the problem of the correspondance of the model with its reference description, the details of the creation process and the annotation of all model components. An effective way of increasing the exchange of models compliant with MIRIAM is to store them in a relational database. BioModels Database is a resource that stores curated versions of peer-reviewed models. After careful verification of the syntax and semantics of a model, including its simulation, a thorough annotation procedure ensures that the model will be retrieved quickly and its components easily identified. Models can be retrieved in various formats. It is expected that those efforts will increase the average quality of published quantitative models, and make those models a standard piece of each biologist's toolbox. http://www.ebi.ac.uk/~lenov/

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German A. Enciso German Hogenesch, B. John Yamada, G. Rikuhiro Kobayashi, J. Tetsuya Kasukawa, Takeya R. Ueda Hiroki Shuji Ishihara, Mikiya Otsuji, Atsushi Mochizuki Koh-hei Nishio, Junko Uno, D. Kenichiro Maki Ukai-Tadenuma, Kumaki, Yuichi Hogenesch, B. John Shigeyoshi, Yasufumi Komori, Takashi Nagano, Mamoru Masumoto, R. Ueda Hiroki models circadian rhythm James Lu Zeba Wunderlich, Grigory Kolesov, Leonid Mirny Neda Bagheri, Joerg Stelling, Francis Doyle J. III Tetsuya Kobayashi, Shigeki Kazuyuki Tsuji, Aihara Takao Maki Ukai-Tadenuma, Yamada, G. Hideki Ukai, Rikuhiro Kobayashi, J. Tetsuya R. Ueda Hiroki Kondo, A. Beed, S. Legewie, Westernark, P. H. Herzel Seung Kee Han, Cuong Nguyen Yukinobu Arata, Hitoshi Sawa Anna Chernova screening Hiroshi Fujishima, Takeya Kasukawa, Hideki Ukai, Ryotaku Kito, Hiroki R. Ueda mathematical models Hisako Takigawa-Imamura, Atsushi Mochizuki (the repressilator) Stefan Mueller, James Lu biochemical in cyclic of transcriptional delays The effect high-throughput cell-based Genome-wide identification of morning by element regulators gene expression identification of robust and sensitive System-level localization in mass-conserved systems Competitive circadian clock amplitude in the mammalian Affinity drives and engineering of cell cycle applied to the design and reverse bifurcation Inverse regulation on speed and reliability of transcription Spatial effects for robustness in the mammalian circadian oscillator Phase-based performance analysis noisy? oscillators biological What makes of mammalian circadian clock perturbation analysis System-level of the phosphorylation of KaiC clock protein using Predicting regulation cycle of a synthetic gene regulatory network bifurcation analysis and inverse Forward regeneration during liver Modeling the G1/S phase transition regulation of cell cycle analysis Dynamic modular embryo in C. elegans distribution cell volume of law a power duration exhibits cycle Cell chemotactic bacterial colony of growth modelling of Multiscale BC26 BC27 BC28 BC16 BC17 BC18 BC19 BC20 BC21 BC22 BC23 BC24 BC25 BC13 BC14 BC15 4F 4F Poster Session Poster membrane Douglas Murray, David Lloyd, Manfred Beckmann, Hiroaki Kitano gradients Brian Fett, Marc Riedel Eduardo Mendoza, Maria Celeste del Rosario, David Ramirez, Riza Batista, Maia Malonzo, Roselyn Santos Koh-hei Masumoto, Hideki Ukai, Rikuhiro Yamada, Mamoru Nagano, Takeya Kasukawa, Minami, Yoichi Kenichiro Uno, Maki Ukai-Tadenuma, Yasufumi Shigeyoshi, Hiroki R. Ueda Koichi Masaki, Hiroyuki Kurata Keene D. Jack Jan M Skotheim, Stefano DiTalia, Frederick R Cross, Eric D Siggia R Cross, Frederick M Skotheim, Stefano DiTalia, Jan Alexander van Oudenaarden Kaufmann, B. Benjamin Yang, Qiong Yu-ichi Ozaki, Satoru Sasagawa, Kazuhiro Fujita, Shinya Kuroda A. Kolpakov Fedor Sharipov, Ruslan N. Alexander E. Kel, Kondrakhin, V. Yuriy cycle Adrien Faure, Aurelien Naldi, Cyrille Lepoivre, Claudine Chaouiya, Andrea Ciliberto, Denis Thieffry coli Satya Nanda Vel Arjunan, Masaru Tomita Arjunan, Masaru Vel Satya Nanda systems energetics The regulatory landscape of yeast Characterizing the stochastic decisions of biomolecular systems with probability An information for cicadian systems biology system EUCLIS - in mammalian central clocks Light response programs assimilation system for the E.coli nitrogen Dynamic simulation and system analysis Escherichia of MinD spiral formation A 3D pole-to-pole oscillation model on growing operons RNA by Coordination of gene expression Stochastic start: the G1/S transition in budding modeling yeast single cell genealogy by dynamics revealed Epigenetic inheritance of gene-expression ERK activation temporal dynamics of transient and sustained The distinct genes of binding site patterns Identification of human cell cycle in regulatory regions cell the yeast controlling of the regulatory modelling network logical Comprehensive BC12 BC11 BC10 BC09 BC08 BC06 BC07 BC05 BC04 BC03 BC01 BC02 Cyclic and Dynamic Behaviours Behaviours Cyclic and Dynamic Schedule: Session I Poster 9, 16:30 - 18:00 October Monday, Poster Session II Poster 16:30 - 18:30 October 10, Tuesday, Systems Biology of Basic Biological Systems of Basic Biological Systems Biology

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response to response Lafras Uys, Johann M. Rohwer Uys, Johann Lafras Michael S. Koeris, Michael A. Kohanski, R. Allison, Kyle Gabor Balazsi, Boris Hayete, James Collins J. in transgenic rice plants lipoxygenase Oksoo Han, Kyoungwon Cho, Sungkuk Jang metel induction in Datura (HPLC-MS) following Elsadig A. Eltayeb, Fatma Barwani, Al Basma Salhi, Khan Al T. and data mining spectral analysis multi-wavelength Arsen Batagov, Frank Baganz oxidative stress induced by cumene hydroperoxide cumene by stress induced oxidative Leepika Ana Martins, Tuli, Sha, Wei Pedro Mendes,Vladimir Shulaev In silico analysis of the causes of heterogeneous gene expressions in liver ammonia in liver gene expressions of the causes of heterogeneous In silico analysis metabolism Yasuhiro Naito, Hiroshi Ono, Hiromu Nakajima, Masaru Tomita Escherichia coli system in Escherichia Yousuke Nishio, Yoshihiro Usuda, Kazuhiko Matsui, Hiroyuki Kurata stress using GC/MS based on metabolomics Bong Chul Chung, Sang Hee Lee, Sun Kim, Yeou Eunjoo H Lee simulation Ayako Kinoshita, Makoto Suematsu, Nakayama, Yoichi Soga, Tomoyoshi Masaru Tomita analysis of systematic response to the change of enzyme concentration based on of enzyme concentration to the change of systematic response In silico analysis cerevisiae of Saccharomyces biochemical network glycolysis Pingkai Ouyang Changqing Liu, Kong, Lin Xu, Dechong Yan, Ming Yang, Xuelian glycolysis pathway model pathway glycolysis S. cerevisiae Dechong Kong, Xuelian Ming Changqing Yan, Yang, Liu, Lin Xu in maturing sugarcane Modelling sucrose accumulation response to enzyme amount changes in of ethanol concentration and analysis Simulation induction of persistence Contact-dependent phosphotransferase by rate uptake glucose of validation experimental and analysis System and membrane association of the nontraditional maize Calcium-dependent activation immobilization to of ginsenoside Rb1 in brain tissue of mice exposed evaluation Efficacy spectrometry high performance chromatography-mass liquid by of phytoalexins Analysis metabolomics and assessed by cell metabolism response of human red blood Hypoxic techniques of capillary using novel electrophoresis by Metabolome profiling of E. coli

cerevisiae of Saccharomyces study of the kinetics systems biology A BM11 BM12 BM02 BM03 BM04 BM05 BM06 BM08 BM09 BM10 BY15 BM01 Metabolomics and Bioprocess and Metabolomics S. in cerevisiae QDR3 . operon genetic regulation in regulation ace operon genetic by using nMDS without sinusoidal using nMDS S. pombe by cells Saccharomyces cerevisiae in Saccharomyces protein influence networks - Roberta Mustacchi, Jens Nielsen Eugenio Marco, Dorn, Jonas F. Khuloud Jaqaman, Gregory S. Jelson, Gaudenz Danuser, Peter K. Sorger Yi,Tau-Mu Hiromasa Tanaka, Moore Travis Justin B. Kinney, Gasper Tkacik, Curtis G. Callan, Jr. Callan, Tkacik, Curtis G. Gasper Kinney, B. Justin Goutham Vemuri, Jens Nielsen, Lisbeth Olsson Yuya Fukano,Yuya Shunsuke Yamamichi, Hiroyuki Kurata Ryotaku Kito, Hiroshi Fujishima, Hideki Ukai, Yoichi Minami, Hiroki R. Ueda Minami, Hiroki Yoichi Hideki Ukai, Fujishima, Hiroshi Ryotaku Kito, Wee Kheng UttamWee Yio, Surana, Baltazar Aguda Kirill Peskov, Oleg Demin Computational analysis of relevant aspects of the G1 to S transition model in budding aspects of the G1 to S transition of relevant Computational analysis yeast Alberghina Lilia Vanoni, Matteo Barberis, Edda Klipp, Marco Differential evolutionary conservation of motif modes in the yeast protein interaction in the yeast of motif modes conservation evolutionary Differential network Wen-Shyong Lee Tzou, Wei-Po Karthik Raman, Nagasuma Chandra Karthik Raman, Nagasuma fitting Taguchi Y-H. Yuki Shimizu-Yoshida, Hisao Moriya, Hiroaki Kitano Escherichia coli Escherichia cerevisiae Duygu Dikicioglu, Pinar Z. Pir, Ilsen Onsan, Kutlu Ulgen, O. Betul Kirdar, Steve G. Oliver Study of expression. modeling of gene Kinetic proteins of mammalian clock regulators for stability Cell-based screening of fermentation characteristics of the drug resistance gene Investigation network systems biology Yeast metaphase spindle of the yeast Organization of the heterotrimeric G-protein cycle and regulation Dynamics system in Saccharomyces mitotic exit of the of regulatory networks Analysis of upper limit Cdc proteins dosage in S. cerevisiae Quantification data binding from high-throughput of protein-DNA Inferring models physical Protein metabolism to reduce overflow cerevisiae balance in Saccharomyces Engineering redox genes of regulated Detecting cell cycle model in silico for constructing cell cycle strategy a large-scale Integrative

BY13 BY14 BY12 BY11 BY09 BY07 BY08 BY06 BY04 BY05 BY03 BY02 BY01 BC30 BC29 Yeast Systems Biology Systems Yeast

35 using updated using updated sp. and its value added Oscillatoria sp. and its value Henrik Jonsson, Marcus Heisler, Bruce E. Shapiro, Elliot M. Meyerowitz, Eric Mjolsness Elliot M. Meyerowitz, Bruce E. Shapiro, Heisler, Marcus Henrik Jonsson, Aitor Gonzalez, Claudine Chaouiya, Denis Thieffry Yunlong Liu, Hiroki Yokota Eric Mjolsness, Sergey Nikolaev, Przemek Prusinkiewicz, Alex Sadovsky, S. Fadeev, Nikolay Kolchanov Chao-Ping (Cherri) Hsu processing analyses Akatsuki Kimura, Shuichi Onami Kaneko Kunihiko Shuji Ishihara, Fujimoto, Koichi an in silico cell model by Takuya Maeda, Itsuki Ajioka, Kazunori Nakajima CPU implementation faster than conventional processing unit is significantly Tomita Masaru Anton Kratz, Arjunan, Vel Satya Nanda deficient mutant mice suggests a dynamic regulatory mice suggests a deficient mutant system in cardiac development Kathryn B. Tang, Alan Y. S.Paul Tang, L. E. F. Cheah, You-qiang Song enhancement and lateral-inhibition system Atsushi Mochizuki, Etsushi Nakaguchi, Naoki Mine, Nakamura, Tetsuya Hamada Hiroshi Meno, Chikara Yashiro, Kenta Yamamoto, Masamichi products Varalakshmih, Palanivel Viswajith, Viswadevan Anandhraj, Balaiah Prabha, Sethuraman Malliga Perumal Christopher, Abraham Pichaimuthu system Soo-Jin Kim, Je-Keun Rhee, Nam, Jin-Wu Je-Gun Joung, Zhang Byoung-Tak genome annotation Hyohak Song, Myeong Park, Uk Kim, Jong Kim, Jin Sik Kim, Hyun Yong Tae Lee Yup Sang initiation An auxin transport organ of plant model for regulation the cyanobacterium Degradation of coir pith by boundary defining the dorsal-ventral of model of the regulatory network Logical learning of dynamical evolutionary by in Drosophila embryonic development Modeling elements Artificial for regulatory ants deposit pheromone to search DNA for cells and tissues models of growth Simplified cell fates in a proneural cluster differentiating Factors embryo based on image A model for dynamic positioning of centrosomes in C. elegans plan in animal body and development Simulating evolution formation of cerebral cortex during cell-sorting and layer Prediction of cellular behavior site placement on a graphics 3D Monte Carlo stochastic simulation of bacterial division Generation of robust left-right asymmetry in the mouse embryo requires a self- IIA procollagen Type of congenital heart of a genetic modifier Identification in defect succiniciproducens of Mannheimia network Genome based-metabolic BD19 BD20 BD08 BD09 disc the drosophila wing imaginal BD10 BD11 BD12 BD13 BD14 BD15 BD16 BD18 BD06 BD07 KT2440 Bacillus subtilis central carbon metabolism based on multi-omics based on multi-omics carbon metabolism central Pseudomonas putida genome scale modeling of genome scale modeling Kaoru Sugimura, Uemura, Tadashi Atsushi Mochizuki Hitomi Itoh, Yasuhiro Naito, Masaru Tomita of gender differences Louisa Cheung, Hans Stenlund, Petra Tollet-Egnell, Thomas Moritz, Amilcar Flores-Morales Yoshihiro Morishita, Iwasa Yoh Hironori Fujita, Atsushi Mochizuki Yoshinobu Igarashi, Alexey Eroshkin, Svetlana Gramatikova, Kosi Gramatikoff, Gramatikoff, Kosi Svetlana Gramatikova, Eroshkin, Alexey Igarashi, Yoshinobu Osterman Andrei Godzik, Adam Smith, W. Jeffrey H. Lee Kwang Kiryong Ha, Doheon Lee, Lee, KiYoung Inho Park, Saito Kazuki Hirai, Y. Masami Yano, Kanaya, Mitsuru Ryoko Morioka, Shigehiko Jun Ohta regulation and substrate dependency of mitochondrial energy metabolism of mitochondrial energy and substrate dependency regulation Ryuta Saito, Nobuaki Sarai, Satoshi Matsuoka, Akinori Noma bacteria Shir-Ly Huang, Zhang-yan Huang, Yu-Jing Zhuang, Yu-Ling Su High-throughput proteomic identification of thermal stable proteins from thermophilicHigh-throughput proteomic identification of thermal stable In silico data Nakahigashi, Akio Kanai, Kenji Soga, Tomoyoshi Baba, Tomoya Ishii, Nobuyoshi Hirai, Kenta Miki Naba, Hirasawa, Takashi Togashi, Takashi Martin Robert, Takeshi Satoshi Harada, Saori Igarashi, Kaori Sugawara, Kakazu, Yuji Aminul Hoque, Iwata, Nayuta Arakawa, Kazuharu Yugi, Katsuyuki Sugiyama, Masuda, Naoyuki Yoshino, Masataka Akiko Hagiya, Nakayama, Yoichi Tsuyoshi Iwasaki, Toya, Yoshihiro Tomita Mori, Masaru Shimizu, Hirotada Nishioka, Kazuyuki Takaaki Dagmar Iber, Joanna Clarkson, Michael D. Yudkin, Iain D. Campbell Seung Bum Sohn, Tae Yong Kim, Sang Yup Lee Yup Kim, Sang Yong Tae Seung Bum Sohn, Serum to study hormone metabolomic profiling actions: the case of hormonal regulation events a database of proteolytic CutDB − data GC-MS tool for analyzing a software MetaAnalyzer: thaliana in Arabidopsis Prediction of sulfur stress responses on Matlab/Octave matrix-based isotopomer analysis connectivity networks: Metabolic studies of Ca2+-dependent model of cardiac mitochondria: simulation A computational coli of Escherichia Dynamic modeling

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36 overexpression γ B pathway, its role in apoptosis, carcinogenesis, inflammation its role in apoptosis, carcinogenesis, B pathway, κ Xiao Peng, Kam-Len Daniel Lee, Chi-Hung Qi Tzang, Zhang, Meng-Su Yang of gene expression regulation Anderssen, Endre Eitrem, Bruland, Berit Doseth Torunn S. Steigedal, Tonje Laegreid Astrid Thommesen, Liv overexpression Klingmueller Ursula Timmer, Jens Christian Fleck, C. Pfeifer, Andrea Carl-Fredrik Marcus Tiger, Krantz, Stefan Hohmann, Hiroaki Kitano Oudenaarden Alexander van Mettetal, T. Jerome A. Gomez-Uribe, Carlos Dale Muzzey, pathways receptor signaling Chiung-wen Chang, Ravi Iyengar, Harel Weinstein Kristine Misund, Sunniva Hoel, Torunn Bruland, Endre Anderssen, Astrid Laegreid, Liv Thommesen dependent responses A. Ogunnaike, Babatunde Yumoto, Noriko Mariko Hatakeyama, R. Birtwistle, Marc Kholodenko Hoek, Boris N. B. Jan transduction in single cells transduction in single Martin Monnigmann, Dirk Engel, Andreas Herrmann, Michael Vogt, Peter C. Heinrich, Gerhard Mueller-Newen genes in macrophages Janne Oestvang, Hans-Richardt Brattbakk, Sjur Huseby, Mette Langaas, Astrid Laegreid, Berit Johansen T-cell 1 Type and theory:At the interface of experiment of central mechanisms differentiation Edda Schulz, Andreas Radbruch, Thomas Hoefer signal transduction the JAK2/STAT5 feedback loops regulating Modelling of negative pathway Gonzalez, Vera Julio Thomas Maiwald, Fleck, Christian Bachmann, Julie Klingmueller Ursula Timmer, Jens Wolkenhauer, Olaf and ways of its inactivation for prediction of new targets for anti-cancer and anti- targets for prediction of new of its inactivation and ways inflammatory therapy A. Kolpakov Fedor Kolpakova, Alla F. Sharipov, Ruslan N. of ligand- and analysis modeling of the MCF-7 ErbB signaling network Computational description of NF- Formal network HMGB1 associated intracellular signal of Jak/STAT and in silico parameter identification Model-based real-time analysis of transcript of proinflammatory stimulates increased level Lysophosphatidylcholine mediated - studies of gastrin tumour biology of gastrointestinal biology Systems ICER I or II in HEK 293 cells with controllable gene expression Global STAT5 by signaling pathway of the JAK2/STAT5 dynamic behavior Altered MAP-Kinase network the yeast Modelling cells yeast in individual osmo-signaling Monitoring MAPK serotonin of interactions between modeling quantitative and Experimental analysis

BS19 BS20 BS11 BS12 BS13 BS14 BS15 BS16 BS17 BS18 BS08 BS09 BS10 L.) for seed L.) for Matthew Onsum, Christopher V. Rao V. Onsum, Christopher Matthew Marcus Krantz, Stefan Hohmann, Hiroaki Kitano Sohyoung Kim, Mirit I. Aladjem, Geoffery B. McFadden, Kurt W. Kohn W. Kurt McFadden, B. Aladjem, Geoffery Kim, Mirit I. Sohyoung Yuri Matsuzaki, Shinichi Kikuchi, Masaru Tomita Klingmueller Ursula Timmer, Jens Thomas Frahm, Thomas Maiwald, Sebastian Bohl, EricFernandez, Renaud Schiappa, Jean-Antoine Girault, Nicolas Le Novere Joerg Schaber, Bodil Nordlander, Dagmara Medrala, Stefan Hohmann, Edda Klipp Andrew J. Pocklington, Seth G. N. Grant, J. Douglas Armstrong Douglas J. Grant, N. Seth G. Pocklington, J. Andrew to ionizing radiation? exposure by networks Michiyo Suzuki, Tetsuya Sakashita, Kana Tsuji, Fukamoto, Toshio Nobuyuki Hamada, Yasuhiko Kobayashi Melanie Stefan, Nicolas Le Novere Takashi Nakano, Tomokazu Doi, Junichiro Yoshimoto, Kenji Doya Koji Kyoda,Koji Shuichi Onami Cristina - Maria Dabu intracellular enzymes Isao Goto, Kiyohisa Natsume plasticity Kuroda Shinya Robert C. Froemke, Hidetoshi Urakubo, coating technology Nuchnapa Kotabin, Nonglak Saithep, Araya Jatisatienr, Chaiwat Jatisatienr concentration of by be regulated calcium responses may astrocytic of The heterogeneity integrator signalling of dopamine and glutamate a complex DARPP-32, timing-dependent mechanism for spike of a timing-detection and validation Prediction receptor complex Realistic models of the NMDA A kinetic model of cortico-striatal synaptic plasticity synaptic function and disease underlying molecular pathways Identifying change on : is there any in nematode C. elegans of chemotactic networks Simulation embryos of C. elegans analysis phenotypic Quantitative cell model for the Ca2+ signalling in the neuronal Systemic chinensis as seed-borne Use of eugenol in pakchoi(brassica fungi controller Local model for neutrophil gradient sensing and polarization map The yeast a reservoir complex dynamics by Mdm2-MdmX-p53 regulatory tuning of network Fine adaptation error of CheA-CheYp affinity and Analysis chemotaxis in bacterial in primary hepatocytes signaling pathway modeling of the gp130-JAK1-STAT3 Dynamic osmosensing testing and model discrimination in yeast Hypothesis BS06 BS07 BS05 Signal Transduction Signal BS03 BS04 BS01 BN08 BN07 BN05 BN06 BN04 BN03 BN02 Systems Neurobiology BN01 BD22 BD21

37 CMCP6 metabolism for the facilitated drug facilitated the for targeting metabolism CMCP6 vibrio vulnificus vibrio analysis of of analysis Hyun Uk Kim, Tae Yong Kim, Kwangjoon Jeong, Soo Young Kim, Joon Haeng Rhee, Haeng Rhee, Kim, Joon Young Soo Jeong, Kim, Kwangjoon Yong Tae Hyun Uk Kim, Lee Yup Sang Sheikh Md. Enayetul Babar, Eun Joo Song, Young Sook Yoo Sook Young Song, Eun Joo Sheikh Md. Enayetul Babar, Masanori Kuzumoto, Satoshi Matsuoka, Akinori Noma in fatal arrhythmias difference of sex-related Junko Kurokawa, Chang-Xi Bai, Tetsushi Furukawa Xinghua Shi, Fangfang Xia, Rick Stevens Corey Adams, Mike Keiser, Patricia Babbitt, Brian Shoichet T47D cells in human breast cancer comparison to Doxorubicin Ebrahim Azizi, Mohammadhossein Abdolmohammadi, Shamileh Fouladdel, Gholamreza Amin, Abbas Shafiee Einav, ReuvenYulia Agami, Dan Canaani KenjiYosuke Miyamoto, Terao, Hiromichi Ohta in the pathway oxygenase-1 a potential role of heme medicine formula ISF-1 reveals therapy antioxidant Jianhui Rong, Cynthie Yim-Hing Cheung, Jiangang Shen, Paul Kwong-Hang Tam, LauAllan Sik-Yin Inju Park, Hoyong Lee, Kim, Taewan Do Han Kim, Chunghee Cho Sung-Young Shin, Won-Sung Bae, Tae-Hwan Kim, Sang-Mok Choo, Kwang-Hyun Cho Katja Rateitschak, Olaf Wolkenhauer Ivan Martinez-Forero, Jaime Iranzo, Jorge Elorza, Pablo Villoslada In silico In drug A computational and drug target screening pipeline molecule metabolic space in small Drug discovery in Astrodaucus Persicus of of extracts assay and cytotoxicity analysis cytometric Flow interference tool RNA shRNA-mediated lethality screening by Genetic synthetic both enantiomers One enzyme can give response fingerprinting Genome-wide biological (BioReF) of traditional Chinese

capillary electrophoresis in HL-1 cell line by Measurement of calcineurin activity cascade in cardiac myocyte signaling A computer model of beta1-adrenergic Mechanism testosterone: by shortening intervals QT on pathway non-genomic a of Impact in mouse cardiac expression genes with of novel analysis Identification and integrative in cardiac myocytes signaling pathways of beta-adrenergic Dynamical analysis pathways of signal transduction in transient dynamics Thresholds pathway receptor signaling I interferon Type model of A computational MV03 MV04 MV05 Systems Biology for Drug Discovery for Systems Biology MR01 MR02 MR03 MR04 MR05 MR07 MR08 Biology Systems Cardiovascular MV01 MV02 BS39 BS40 Medicine for Systems Biology mediated signal transduction pathways Takeshi Nagashima, Hidetoshi Shimodaira, Kaori Ide, Takashi Nakakuki, Yukitaka Mariko Tani, Hatakeyama Pontus Melke, Henrik Jonsson, Evangelia Pardali, Peter ten Dijke, Carsten Peterson of subcellular compartments Perla Del Conte-Zerial, Lutz Brusch, Claudio Collinet, Jochen Rink, Yannis Kalaidzidis, Andreas Deutsch, Marino Zerial gene expression Stefan Lim, Yu-Kai The, Sihui Gunaretnam Wang, Rajagopal, Philippa Melamed James N. McDougal N. James Tatsunori Nishimura, Masaru Tateno Dongsan Kim, Kwang-Hyun Cho Silvia Santos, Philippe Bastiaens Robert Endres, Ned Wingreen Ulgen O. Kutlu Arga, Yalcin Saliha Durmus, K. Jason Locasale, Andrey Shaw, Arup Chakraborty Wiggins Chris H Ziv, Etay Manuel J Middendorf, Nemenman, Ilya Dhiraj Kumar, Shilpi Jayaswal, Kanury VS Rao systems and processes Fedor Kolpakov, Ruslan Sharipov, Ekaterina Kalashnikova, Elena Cheremushkina application to MAPK signal transduction Takashi Nakakuki, Noriko Yumoto, Takashi Naka, Mariko Hatakeyama MAP kinase cascade Cellina Cohen-Saidon, Ariel Cohen, Alexander Sigal, Natalie Danon, Perzov, Tamar Uri Alon approach to formal biological description and simulation of complex Biopath - a new systems and its technique based on transfer function for biological A modeling RTK- transcription controlled by of dose- and time-dependent early analysis Quantitative TGF-beta pathway and kinetics of the Stability isoforms the principles of the computational process performed Understanding in by catastrophe and signaling: competition of Rab proteins for maintenance and Endocytosis gonadotropic subunit mechanism for differential the frequency-decoding Elucidating irritation for cutaneous chemical data to determine Gene expression pathways signaling cellular compartments model including multiple FGF signaling network of the hidden dynamic features in the ERK pathway Elucidation rise to specific cellular fate properties decisions giving MAPK signaling network chemotaxis through "assistance neighborhoods" Precise adaptation in bacterial of epidermal factor signaling pathway receptor growth Holistic analysis regulatory properties cascades to protein kinase confer diverse proteins Scaffold Optimal information biochemical networks in small stochastic processing perturbations targeted against robust Response of a signaling network BS38 BS37 BS36 BS35 BS32 BS31 BS30 BS28 BS29 BS27 BS25 BS26 BS24 BS22 BS23 BS21

38 Maria JieZhi Werner, Zou, Jenny Almqvist, Ingemar Ernberg, Erik Aurell Julio Saez-Rodriguez, Luca Simeoni, Jonathan Lindquist, Rebecca Hemenway, Ursula Bommhardt, Borge Arndt, Utz-Uwe Haus, Robert Weismantel, Ernst Dieter Gilles, Steffen Klamt, Burkhart Schraven signal peptide peptidase core protein by Hsin-Chieh Ku, Yi-Ching Ma, Yi-Yung Hsieh, Lo Shih-Yen Haifeng Philip Diaz, Wu, Ming Jin Ursula Kummer, Jens Christian Brasen construction to network cells: from gene expression monocytic Hsueh-Fen Juan, Kun-Chieh Cheng, Hsuan-Cheng Huang, Chern-Han Ou, Jenn-Han Chen, Chen, Shui-Tein Wen-Bin Yang, Chi-HueyWong superfamily. Abu-Amer Yousef Kunnavakam, Rangesh Kiesel, Jennifer Aurora, Rajeev in cardiac myocytes Oka Hotta, Kotaro Kohji Yashima, Kenta Takahashi, Masayuki Shaposhnikova,Yuliya Shkolnik Vera D6 MartinezYeny de la Chiara Torre, Buracchi, Elena M. Borroni, Raffaella Bonecchi, Manuela Nebuloni, Francesco Sergio Tedesco, A. Lira, Annunciata Vecchi, Massimo Locati, Alberto Mantovani dichloropropionaniline Thomas M. Harty Kathleen Brundage, David Klinke, Ustyugova, V. Barnett, Irina B. John DNA following in human astrocytes regulation characterizes gene analysis Microarray damage Jun-ichi Satoh, Hiroko Tabunoki antibodies by of fetal loss due to systemic inflammation and anti-phospholipid Prevention phosphorylation channel by of L-type calcium regulation approaches for Computational ischemic heart in the patients with of atorvastatin diseases efficiency Antiischemic " "systems in human cells elucidated by switch A genetic signaling network T-cell-receptor-induced of the Structural analysis treated with in macrophages NF-kB activation Modeling changes in IkB and peptide at the C-terminus of signal of hepatitis C virus of the cleavage Characterization insights biological reveals monocytes proteomic profiling of blood Inter-individual the influence of diabatic glucose concentration on neutrophil activation Modeling in human lucidum polysaccharides of ganoderma the molecular regulation Elucidating TNF ligand the members of two for macrophages initiated by networks Coexpression

MI10 MI01 MI02 MI03 MI04 MI05 MI06 MI07 MI08 MI09 MV20 MV21 Systems Immunology Natalie S. Schneider, Takao Shimayoshi, Akira Amano, Tetsuya Matsuda Tetsuya Amano, Akira Shimayoshi, Takao Natalie S. Schneider, Yuhua Li, Min-Jeong Son, Sunwoo Lee, Le Thi Thu, Do Han Kim, Joung Real Ahn, Sun-Hee Woo combination in patients with essential hypertension ShaposhnikovaYuliya Kenta Yashima, Kotaro Oka genetical aspects of arterial hypertensioin Trakhinin, L. Yuriy A. Biberdorf, Elina Sharipov, Ruslan N. A. Kolpakov, Fedor L. Markel, Arkadiy Alexander M. Blokhin, Koshukov, V. Alexander Puzanov, V. Mikhail Ivanova Ludmila N. through the induction of PPAR-delta and its downstream effector of iNOS effector its downstream and of PPAR-delta through the induction Shu-Hui Juan, Ja-Ling Lee, Heng Lin Yoo Sook Young Bong Chul Chung, Jung, Byung Hwa Song, Eun Joo using mass spectrometry by muscle system and its validation in myocardial pathway techniques based high throughput proteome analysis Hyejin Song, Hyekyung Park Zee-Yong Kwon, Gil Bu Kang, Jun Hyuck Lee, Mun-Kyoung Kim, Sung Hyun Kim, Soo Hyun Eom to drug-inducedcomponents in response perturbations of calcium homeostasis In Sun Chu Park, Seong-Min Yang, SungWoo So Chon Han, Joshua Yeom, Il Young Brendan O'Malley, Panovska, J. M Tindall, L. L Pickersgill, Ware, J King in heart failure involved In-Sun Chu Youm, Il Yong Yang, Woo Sung Joshua prevention, diagnosis and therapy. prevention, Ingerid Arbo, Hans-Richard Brattbakk, Siv Aagaard, Ann Kristin De Soysa, Lindseth,Inge Fedon Lindberg, Mette Langaas, Endre Anderssen, Bard Kulseng, Berit Johansen HCNet: an integrated database of heart and calcium functional networks for systems biology HCNet: an integrated database of heart and calcium functional networks Seong-Hwan Rho, Seong-Eui Hong, Do HanKim Elucidation of a potential frank-starling mechanism through modeling and simulation cell line HL-1 Characteristics of local and focal Ca2+ signaling in cardiomyocyte and nebivolol of atorvastatin efficiency and endothelinprotective Antihypertensive microvasculature inside transport and NO diffusion of oxygen Theoretical analysis and molecular- biomechanical, Integrated approach for simulation of physiological, beraprost, a PGI2 analogue, of rat aortic by smooth muscle cells effects Anti-proliferative of HL-1 cardiomyocytes Proteomic and metabolomic analysis of calcium signaling A construction protein-protein interaction network of large-scale coupling in E-C network Protein interaction of calcium signalling of genetic network Compensatory changes in the activity models of lipoprotein metabolism Mathematical heart of the calcium signalsome in hubs and identification of molecular Genetic analysis disease to be used in chronic a “lifestyle algorithm” approach to define A nutrigenomics

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39 Chen-hsiung Chan, Sheng-An Lee, Kao Cheng-Yan Yurie Okabe, Yagi, Masaki Yuu Sasai library reporters of fluorescent transcriptional Alon Zaslaver, Anat Bren, Michal Ronen, Shalev Itzkovitz, Ilya Kikoin, Seagull Shavit, Liebermeister,Wolfram Mike Surette, Uri Alon Pilar Hernandez, Jaime Huerta-Cepas, David Montaner, Fatima Al-Shahrour, Joan Valls, Valls, Joan Al-Shahrour, Fatima Huerta-Cepas, David Montaner, Pilar Hernandez, Jaime Pujana Angel Miquel Dopazo, Capella, Joaquin Laia Gomez, Gabriel genes regulated Alexander E. Kel, Sharipov, Ruslan N. Poroikov, V. Vladimir Kondrakhin, V. Yuriy A. Kolpakov Fedor Alexander Kel Luciano Milanesi, Ruslan Sharipov, Vladimir Poroikov, Kolpakov, Fedor Gavaghan, Gillies, David J. Robert J. A. Gatenby, Robert Smallbone, Kieran Renaud Seigneuric, Maud Starmans, Michael Magagnin, Riel, Natal Brad Van Wouters, Philippe Lambin Hisakazu Iwama, Tsutomu Masaki, Shigeki Kuriyama receptor network Brian Harms, Allen Lee, Ricardo Paxson, Ulrik Nielsen, Birgit Schoeberl tumor cells in human analysis microarray Tresch, Achim Ruschhaupt, Buness, Markus Andreas Fellmann, Mark Sultmann, Holger Poustka Annemarie Kuner, Ruprecht Beissbarth, Tim Systematic discovery of candidate genetic interactions leading to breast cancer susceptibility of candidate genetic interactions Systematic discovery Douglas F Easton, AJ Ponder, Bruce Tyrer, Jonathan Mikkel Zahle Oestergaard, DP Pharoah Roth, Paul Frederick Philip K. Maini model pathway dynamic a by elucidated cells erythroid in progenitor signaling MAP-kinase Klingmueller Ursula Timmer, Jens Thomas Maiwald, Schilling, Marcel mechanisms of tumorigenesis Systems-level identification of up- and down- data: reliable of breast cancer microarray Meta-analysis and carcinogenesis regulation Cyclonet - an integrated database on cell cycle potential impact on invasiveness Metabolic changes during carcinogenesis: response profiling applied to the hypoxic gene expression Temporal profiling of hepatocellular carcinoma using microarray expression MicroRNA ErbB in the development and for drug discovery analysis Applications of sensitivity and global interference RNA systems by expression breast cancer gene Delineating

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40 Yukino Ogawa, Kazuharu Arakawa, Fumihiko Miyoshi, Kazunari Kaizu, Masaru Tomita genome evolution Amit Khachane, Jacek Puchalka, Kenneth Timmis, Vitor Martins dos Santos Aykan Nuri Faruk Anna Georgieva, Satish Tadepalli, Brian Stoll Breward W. Chapman, Christopher J. S. Jonathan Fletcher, Alexander G. Oliver Mason, Mark Verwoerd Jean-Francois Rual, Kavitha Venkatesan, Hao, Hirozane-Kishikawa, Tomoko Tong Amelie Dricot, Ning Li, Gabriel Berriz, Francis Gibbons, Matija Dreze, Nono Ayivi-Guedehoussou, Niels Klitgord, Christophe Simon, Mike Boxem, Stuart Milstein, Jennifer Rosenberg, Debra S. Goldberg, Lan Zhang, Sharyl Giovanni Wong, Franklin,Siming Li, Joanna Albala, Janghoo Lim, Carlene Fraughton, Estelle Llamosas, Sebiha Cevik, Camille Bex, Philippe Lamesch, Robert S. Sikorski, Jean Vandenhaute, Huda Zoghbi, Alex Smolyar, Stephanie Bosak, Reynaldo Sequerra, Doucette-Stamm, Lynn Michael E. Cusick, David E. Hill, Frederick Roth, Marc P. Vidal profiles from temporal expression Kwang-Hyun Yoon, Yeoin Cho motifs profiles and DNA-binding Hyoung-Seok Choi, Sang-Mok Choo, Jeong-Rae Kim, Kwang-Hyun Cho Qinghua Cui, Zhenbao Enrico Yu, Purisima,Edwin Wang Toepel Thoralf Sridhar Hariharaputran, HeisnerUte interactome Hanspeter Herzel Wanker, Erich Gautam Chaurasia, Matthias E. Futschik, sclerosis in health and multiple Pablo Villoslada, Ricardo Palacios, Joaquin Goni, Nieves Jorge Velez, Sepulcre of drosophila of circadian oscillatory network analysis and sensitivity Modeling dynamics of reductive of the approach for the analysis systems biology An evolutionary (GEP) endocrine system (MAN) in gastro-entero-pancreatic Message-adjusted networks the integration of human protein scale system biology: for large Setting the framework functional differences reveals approach to the immune system network A system biology Wnt signaling impacting and cell-surface environment Modeling the extracellular of the role of HIF-1 in tumour growth Mathematical modelling in biology Graph theory and networks scale map of the human interactome network a proteome Towards genes for inferring method PCA: a new Lag-appended between the causal relationship expression based on initial mRNA of transcriptional regulatory Inference networks signaling network of a human cellular regulation of microRNA Principles signaling pathways for analysing SignAlign: a tool in Germany biology for systems network Set-up of the national FN32 FN33 FN34 FN24 FN25 FN26 FN27 FN28 FN29 FN30 FN31 FN21 FN22 FN23

Steffen Klamt, Julio Saez-Rodriguez, Ernst Dieter Gilles networks biological Anatoly Sorokin, AlexSelkov, Shakir Ali, Stuart Moodie, Goryanin Igor Yoshiya Matsubara, Shinichi Kikuchi, Masahiro Sugimoto, Kotaro Oka, Masaru Tomita Gasper Tkacik, Elad Schneidman, William Bialek metabolic pathway Ho Jung Nam, Hyojin Kang, SejunLee, Doheon Lee Daniel MacLean, Noah Whitman, Chris Wilks, Seung Rhee Yon networks imterpretation with protein interaction of pleiotropy Lihua Zou, Brian Ross, Jun Liu, Hui Ge Hitesh Mistry Maya Mincheva Grace Shwu-Rong Shieh, Chung-Ming Yu, Ching-Yun Chen, Juiling Huang, Wang Woei-Fuh Chung-Ming Cheng Cheng-Long Chuang, Shwu-Rong Shieh, Grace coli Escherichia Samik Ghosh, Preetam Ghosh, Kalyan Basu, Sajal K. Das Hongwu Ma, Anatoly Sorokin, Alex Selkov, Evgeni Selkov, Shakir Ali, Alexander Mazein, Saowalak Kalapanulak, Oleg Demin, Goryanin Igor Luciano Milanesi, Ferdinando Chiaradonna, Roberta Alfieri, Daniela Gaglio, Daniela Gaglio, Alfieri, Roberta Chiaradonna, Ferdinando Luciano Milanesi, Alberghina Lilia Vanoni, Marco R. Ueda Hiroki Maki Ukai-Tadenuma, detected by graph clustering (DPClus) detected by Kanaya, Shigehiko Kurokawa, Altaf-Ul-Amin, Ken Atsushi Fukushima, Md. Kazuki Saito Hirai, Yokota Masami of signaling crosstalk Algebraic method for the analysis reconstruction to network approach Maximum entropy of signaling networks for the structural Methods and functional analysis approach to creating and annotating complex editor: a novel pathway Edinburgh The Arabidopsis of the abiotic stress response in regulation Transcriptional systematic and embryogenesis early during C. elegans complexity Phenotypic clock model of a biological Stochastic integrating genetic regulatory and by perturbation-specific pathways regulation Finding thaliana correlated Highly groups in Arabidopsis of multiple genes and/or metabolites of molecular dynamics in for genome scale study simulation framework A hybrid disease and its relation with human Reconstruction of the human metabolic network models conditions for instabilities in biochemical Network A stepwise structural genetic networks equation modeling algorithm to predict method for inferring Pattern recognition of genetic networks in mammalian cells for the G1 to S transition identification Network clocks mammalian circadian underlying of transcriptional circuits Logic FN19 FN20 FN18 FN17 FN15 FN16 FN14 FN13 FN12 FN11 FN10 FN09 FN06 FN08 FN05 FN04

41 respiration: gene network reconstruction and mathematical modeling E.coli respiration: gene network genomic screenings: coordinated activity of early endosome motors of early genomic screenings: coordinated activity Yannis Kalaidzidis, Jochen Rink, Marino Zerial A. Loparo, Kenneth Bunting, D. Qu, Kevin Cheng-Kui Avva, Jayant Soebiyanto, Radina P. Sreenath N. Sree Mihajlo Mesarovic, Yong Wang, Xiang-Sun Wang, Yong Zhang, Luonan Chen Masashi Tachikawa R. Ueda Hiroki Hirai, Tamami Uno, Kenichiro Kasukawa, Takeya Minami, Yoichi Andrei Korobeinikov, Sergei Petrovskii Gunnar Cedersund, Henning Schmidt, Mats Jirstrand Soga, Tomoyoshi Baba, Tomoya Ishii, Nobuyoshi Nakahigashi, Martin Robert, Kenji Aminul Hoque, Hirai, Kenta Miki Naba, Hirasawa, Takashi Togashi, Takashi Akio Kanai, Masuda, Takeshi Satoshi Harada, Saori Igarashi, Kakazu, Kaori Sugawara, Yuji Toya, Yoshihiro Iwata, Nayuta Arakawa, Kazuharu Yugi, Sugiyama, Katsuyuki Naoyuki Mori, Nishioka, Kazuyuki Shimizu, Hirotada Takaaki Hagiya, Akiko Nakayama, Yoichi Tomita Masaru Mendes Pedro Kell, Douglas B. Adaoha E. C. Ihekwaba, selected mutations negatively Or-Guil Michal Valai, Atijeh Weiser, A. Armin Lei Zhang, Ruiqi Wang, LuonanRuiqi Wang, Chen, Kazuyuki Aihara Alexander Ratushny, Khlebodarova, Tamara Vitaly Likhoshvai embryo during anaphase in the C.elegans Cleopatra Kozlowski, Martin Srayko, Francois Nedelec In silico modelling of embryonic morphogenesis Philippe-Alexandre Pouille, Emmanuel Beaurepaire, Emmanuel Farge interactions multiple domain by Inferring protein network system in a multicellular behaviors Noise induced robust collective functional reconstruction the in model learning Non-supervisedphenotype phenotype and approach systems biology signaling: a complex IL3-Induced JAK-STAT community and study for the stability of a microbial modeling Phenomenological expression dependent gene of cell cycle Genome-wide analysis Plankton blooming development of model levels Model reduction for various of Regulation of Escherichia coli metabolic network A multi-omics analysis spindle pole oscillations and posterior displacement Microtubule dynamics regulate modelling approach for plant systems biology A top-down and positively in antibody affinity maturation: a hint at of mutation frequency Analysis

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Soichi Ogishima, Yasuhiro Suzuki, Takeshi Hase, So Nakagawa, Hiroshi Tanaka Etsuko Miyamoto-Sato, Masamichi Ishizaka, Shigeo Fujimori, Rintaro Saito, Takanori Naoya Washio, Hirai, Kazuyo Masuoka, Tatsuhiro Yamashita, Tomohiro Oshikubo, Hiroshi Yanagawa Ka-Lok Ng, Hsiang-Chuan Liu, Chien-Hung Huang Rajeev Aurora, Rangesh Kunnavakkam, Abhay K. Singh, Maitrayee Bhattacharya-Pakrasi, Himadri B. Pakrasi of virulence aureus determinants in Staphylococcus Erik Gustafsson, Stefan Karlsson, Jan Oscarsson, Peter Sogard, Patric Nilsson, Staffan Arvidson Sudip Kundu, Md. Aftabuddin search algorithm using a new profiles compendium Seong-Eui Hong, Seong-Hwan Rho, Do Han Kim Akutsu Tatsuya C. Nacher, Jose Takemoto, Kazuhiro Fangfang Xia, Rick Stevens Adolfo Torres, Jose Gonzalez, Haret Rosu Diogo Camacho, Pedro Mendes Hitomi Umeki, Shigeo Fujimori, Naohiro Yanagisawa, Takanori Washio, Etsuko Miyamoto-Sato, Masaru Hiroshi Tomita, Yanagawa genome scale Mukesh Bansal, Giusy Della Gatta, Alberto Ambesi methods Ishii,Kyota Seira Nakamura, Mineo Morohashi, Yoshiaki Ohashi, Shinichi Kikuchi, Masahiro Sugimoto, Masaru Tomita interactions Fan-Kai Lin, Lin, Chung-Yen Chao A. Hsiung using IVV of human transcription factor networks analysis a comprehensives Toward protein interaction network of yeast modular evolution biology: evolutionary Systems SNPs using in vitro functional for causative Search status in a photosynthetic organism redox and functions that regulate Genes transcription governing the regulatory approach to understand network systems biology A reconstruction from domain-domain interactions interaction pathways Protein-protein of P63 on to identiy the targets approach Integrated computational and experimental of protein families network automatic construction of an evolutionary Toward simulation using a Monte-carlo Clustering of gene networks analysis network indices generated by Comparison of metabolite production capability within protein networks amino acids' and charged Hydrophobic, hydrophilic structures inferred Identification of mouse heart-specific subnetwork from the expression temperature with growth Structural in prokaryotic metabolic networks transition regression nonlinear by networks engineering biological Reverse hidden family-family from conservatively Inferringprotein interaction network human FN49 FN48 FN47 FN46 FN45 FN44 FN43 FN41 FN42 FN40 FN39 FN37 FN38 FN36 FN35

42 . Torbjoern E. M. Nordling, Elling W. Jacobsen W. Elling E. M. Nordling, Torbjoern Markus Kollmann, Linda Lovdok, Kilian Bartholome, Jens Timmer, Victor Sourjik Lars Kuepfer, Matthias Uwe Peter, Sauer, Joerg Stelling Kalyan Mynampati, Ling, Peter Wan Lee Wen Noriko Hiroi, Akira Funahashi, Hiroaki Kitano Arun Krishnan, Masaru Tomita Tae-Hwan Kim, Sung Hoon Jung, Kwang-Hyun Cho in yeast uptake control of nitrogen metabolic pathways: Riel Van W. A. Natal Akihiko Nakajima, Kunihiko Kaneko biological systems biological Magnus Fagerlind, Erik Gustafsson, Elie Jarnmark, Maria Svensson, Peter Sogard Stefan Bauersachs, Claudia Klein, Frank Berendt, Thomas Frohlich, Helmut Blum, Georg Arnold, J. Ulrich Mansmann, Eckhard Wolf Chikara Furusawa, Kunihiko Kaneko Noriko Hiroi, Akira Funahashi, Hiroaki Suzuki, Takatoki Yamamoto, Douglas Murray crassum Lobophytum M. Azizur Rahman, Yeishin Isa, Tsuyoshi Uehara medicine Lin, Katherine Chen Song-shiang Pao, Chu-shiang Chen, Sheng-Ying coli A. Agung Julius, Adam Halasz, Vijay Kumar, George Pappas J. traditional Chinese to explore in systems biology An attempt at using the approaches for understanding complex and experiments biology The benefit of combining systems of embryo-maternal communication and implantation biology Quantitative stochastic noise state by of optimal growth Selection for cellular systems dynamics approach -a new “Systems energetics” alcyonarian, of sclerites from the proteins from the matrix Characterization of of gene regulatory networks Experiment design for optimal excitation Evolutionary design principles of bacterial chemotaxis signalling in yeast TOR of logic novel a modelling reveals Ensemble of bone remodeling in osteoporosis Systems biology of Escherichia system in the lactose regulation A finite model for the random behavior DRRK reaction modeling by vivo In of p53-mdm2 oscillations simulation & stability analysis Modeling, of bacterial chemotaxis perturbation Parameter analysis Experiment design for parameter estimation in models combining signal transduction and tissue Proportion in a growing preservation FT07 FT08 FT09 FT10 FX32 FX33 FX34 Systems Biology Theory for and System Control FT01 FT02 FT03 FT04 FT05 FT06 FX29 FX30 FX31 affects glycolytic network kinetics network glycolytic in silico affects Takanori Ito, Hiroshi Osada, Kikukatsu Ito Bin Hu, Matthias P. Mayer, Masaru Tomita Masaru Mayer, Bin Hu, Matthias P. classification. Franck Rapaport, Andrei Zinovyev, Emmanuel Barillot, Jean-Philippe Vert and applications software, S. Hlavacek William Goldstein, Byron Yang, Jin L. Blinov, Michael R. Faeder, James control lifecycle Leor Weinberger, Thomas Shenk Gabriel Ken Weinreb, Jacobson, Timothy Elston systems biological Yusuke Imai dynamics in yeast Tegner Jesper Bjorkegren, Johan Hornquist, Mika Gustafsson, Michael Jayant Avva, Michael Weis, Radina P. Soebiyanto, Kenneth A. Loparo, Sanjay Gupta, Sanjay Gupta, Loparo, A. Kenneth Soebiyanto, Radina P. Weis, Michael Avva, Jayant Sreenath N. Sree Juergen Pahle, Sven Sahle, Ursula Kummer Satya Nanda Vel Arjunan, Kumar Selvarajoo Arjunan, Kumar Vel Satya Nanda planar cell polarity Axelrod D. Jeffrey Amonlirdviman, Keith Tomlin, J. Claire Robin L. Raffard, approach Tatyana Karelina, Oleg Demin method (NICM) Kumar Selvarajoo, Masa Tsuchiya coding sequences Shalev Itzkovitz, Uri Alon with application to based parameter identification algorithm for protein networks Adjoint Chaotic thermoregulation in the spadix of skunk cabbage, Symplocarpus foetidus into microarray knowledge A spectral approach for a priori integration of gene network theory, based on protein interactions: Rule-based modeling of biochemical networks glucokinase sequestration Factoring that HIV & Herpes use feedback circuits to and modeling reveal Single-cell analysis Modeling Hsp70-mediated protein folding non-integral connectivity topology: network Systematic determination of biological equations in thermodynamics phenomenological can express Bond graphs of network network flexible yet stable reveals Genome-wide system identification and analysis CMAP tool for systems biology: Graphical causal reasoning modeling phosphotransferase system: mathematical Enzyme I of the PEP-dependent cancer in prostate and PI3K/Akt pathway NFkB stochastic and deterministic calcium ions in simulations of buffered Dynamic behavior arbitrary within protein- additional codes optimal for allowing The genetic code is nearly FX28 FX27 FX26 FX24 FX25 FX23 FX22 FX21 FX20 FX19 FX17 FX18 FX16 FX14 FX13

43 Melanie Courtot, Michael Hucka, Nicolas Le Novere Camille Laibe, Melanie Courtot, Marco Donizelli, Chen Li,Nicolas Le Novere Hucka, Michael Arnaud Henry, Donizelli, Lu Li, Harish Dharury, Chen Li, Marco Nicolas Le Novere Miguel Godinho de Almeida, Jacek Puchalka, Amit Khachane, Kenneth Timmis, Timmis, Kenneth Amit Khachane, Puchalka, Almeida, Jacek Miguel Godinho de Martins dos Santos Vitor Masaki Sano Maeda, T. Yusuke Daisuke Kiga, Masahiro Takinoue, Koh-ichiroh Shohda, Akira Suyama Austin Che Takeshi Sunami, Kanetomo Sato, Keitaro Ishikawa, Tetsuya Yomo Andrew Finney, Michael Hucka, Nicolas Le Novere Ralph Gauges, Sven Sahle, Ursula Rost, Katja Wegner from population-level-measurement Aihara, Kazuyuki Yamamoto, Ryo Okano, Hiroyuki Kobayashi, J. Tetsuya Tozaki, Hirokazu Hidenori Kimura reaction Nancy Kelley-Loughnane, Mauricio Rodriguez, Marlin Latha Linger, Narayanan, John Frazier of the hyperthermophilic bacterium, Thermotoga In silico genome-scale analysis neapolitana Jin Sik Kim, Jae Jong Kim, Ki Jung Park, Sang Lee Yup or desynchronization of oscillators? or desynchronization R. Ueda Hideki Ukai, Hiroki Kobayashi, J. Tetsuya Construction and performance in vitro metabolic characteristics of a self-generating bacterial catalysts Programmable feedback with positive Regulatory of synthetic gene networks dynamics in small vesicle construction network of gene regulation De novo trans-splicing ribozyme systems Engineering synthetic network of liposomes with protein synthesis and a cascading genetic analysis Population and reconstruction of single-cell-behavior switch of toggle ergodicity biological Testing oscillators individual of death pulse: light critical by clock circadian mammalian Stopping

ontology biology The systems and webservices MIRIAM database models BioModels database, a curated resource of annotated published 2 2 version SBML level and beyond extension The SBML layout Novel Computational Environments for Systems Biology for Computational Environments Novel FI03 FI04 FI05 FS03 FS05 FS06 FS07 FS08 FS09 FS10 FI01 FI02 Synthetic Biology Synthetic FS01 FS02 and Biomphalaria alexandrina ) Henning Schmidt, Gunnar Cedersund, Mats Jirstrand Hespanha Pedro Singh, Joao Abhyudai Ariosto Siqueira Silva, Jose Andres Yunes Andres Silva, Jose Ariosto Siqueira Frisco Pierluigi Corne, W. David Henning Schmidt, Mats Jirstrand, Gunnar Cedersund Reiko J. Tanaka, Hidenori Kimura, Hiroyuki Okano Hiroyuki Hidenori Kimura, Tanaka, Reiko J. snails ( natalensis and the schistosomiasis vector Lymnaea to BioAmbients and analysis Jongrae Kim, Declan G. Bates, Ian Postlethwaite, Pat Heslop-Harrison, Heslop-Harrison, Pat Bates, Ian Postlethwaite, G. Kim, Declan Jongrae Cho Kwang-Hyun Tsuchiya Masa Tomita, Masaru Yeo, Arjunan, Zhen Xuan Vel Satya Nanda III Doyle J. Francis E. Shoemaker, Jason Abhik Mukherjee, Durjoy Majumder, Suryasarathi Barat mammalian circadian clock Jacobsen W. Elling Trane, Camilla Debora Schuch da Rosa, Magali Roux-Rouquie, Marie-Noelle Terrasse, Corrado Priami Corrado Terrasse, Marie-Noelle Roux-Rouquie, da Rosa, Magali Schuch Debora Bulinus truncatus Thomas Maiwald, Clemens Kreutz, Sebastian Bohl, Marcel Schilling, Ursula Klingmueller, Klingmueller, Ursula Schilling, Marcel Bohl, Sebastian Kreutz, Clemens Maiwald, Thomas Timmer Jens From bursts memory: molecular to From biochemical units of fundamental finding the fluctuations Paulsson Johan Pedraza, M. Juan Fayez A. Bakry, Sharaf Ahmed El-Din T. cost functions estimation using alternative Parameter systems for studying stochasticity in biological closure approximations Moment with application to the feedback structures in gene regulatory networks Unraveling TSim, a platform for simulation of multi-cellular systems Dynamics of HIV infection studied with Conformon-P systems systems biological for biochemical and A systematic modeling framework process as finite markov regulations Biological molluscs, the for three important Genome size estimates and karyotypes freshwater pathways model selection for signal transduction Experimental design and transformations using concept of proof biology: systems of coding on perspective MDE A from noisy measurements Robust identification of biochemical networks of cis-regulatory and control the dynamics systems tool to analyze a Cellogica: application to apoptosis networks: robustness in biophysical Analyzing the uncertainty of tumour dynamics Tackling

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Saccharomyces cerevisiae’s from Saccharomyces Candida albicans NaoakiOno, Shingo Suzuki, Chikara Furusawa, Akiko Kashiwagi, Tetsuya Yomo A. Goldstein Richard Blackburne, Benjamin P. A. Chernova, Anna Bin Zheng, Xinghua Lu Kutlu O. Ulgen, K. Yalcin Arga, Z. Ilsen Onsan, Betul Kirdar, Jens Nielsen Jens Betul Kirdar, Z. Ilsen Onsan, Arga, Yalcin K. Ulgen, O. Kutlu Del Rio Gabriel Hector M Montiel Molina, Stevens Xinghua Shi, Rick genes in transcriptional regulatory networks expressed Duangdao Wichadakul, Ram Samudrala Sebastian Mirschel, Martin Ginkel, Julio Saez-Rodriguez, Ernst Dieter Gilles Axel Kowald, Sebastian Schmeier, Marvin Schulz, Liebermeister, Wolfram Simon Borger, Edda Klipp Matlab framework Thomas Maiwald, Clemens Kreutz, Jens Timmer Marino Zerial, Jochen Rink, Claudio Collinet, Charles Bradshaw, Bianca Habermann, Yannis Kalaidzidis, Perla Del Conte-Zerial, Lutz Brusch, Andreas Deutsch Renate Kania, Ulrike Wittig, Martin Golebiewski, Andreas Weidemann, Olga Krebs, Saqib Isabel Mir, Rojas reporter features algorithm Nielsen Jens Patil, Raosaheb Kiran Oliveira, Ana Paula data microarray Kim F. Jihyun Hur, Dougu Nam, Cheol-Gu protein-protein interactions Chieh-Hua Lin, Lin, Chung-Yen Fan-Kai Lin, Chi-Shiang Cho, Chia-Ling Chen, Chen,Pao-Yang Chen-Zen Lo, Chao A. Hsiung short on oligonucleotide microarray hybridization Thermodynamical model of DNA models SNPs with site-specific evolutionary Predicting deleterious the functional coherence of protein groups network via protein-semantic Evaluating of yeast a signal transduction network Towards screening of functional conformers of proteins Computer-based An infrastructure to construct models automatically genome-scale metabolic dynamics of differentially the topological TRNDy: a bioinformatics tool for investigating construction of network Protein networks and structured of complex visualization biological Interactive of kinetic parameters, created using high throughput techniques. a database KinetikonHT, high-performance selection parameter estimation & model user-friendly PottersWheel: cells and signalling in mammalian of endocytosis analysis Systems biology reaction kinetics: SABIO-RK for biochemical database An integrative using by networks bio-molecular interaction data with Integration gene expression of time series from to infer gene regulatory selection method networks Using variable FI35 FI36 FI37 FI26 FI27 FI28 FI29 FI30 FI31 FI32 FI33 FI34 FI23 FI24 FI25 Sarala Dissanayake, Matt Halstead, Poul Nielsen Ghazal Goryanin, Peter Igor Sorokin, Anatoly Stuart L. Moodie, Kinya Okada, Masanori Arita, Kiyoshi Asai biology Lin,Chung-Yen Chi-Shiang Cho, Chia-Ling Chen, Fan-Kai Lin, Chieh-Hua Lin, Chen,Pao-Yang Shu-Hwa Chen, Chen-Zen Lo, Chao A. Hsiung models pathway Kazuharu Arakawa, Yukino Ogawa, Masaru Tomita Kentarou Inoue, Hiroyuki Kurata Jittisak Senachak, Tomoyuki Yamamoto, Kokichi Futatsugi Bruce E. Shapiro, Alexey Vorobyov, Joanna G. Murakami, Eric D. Mjolsness Eric D. Murakami, G. Joanna Vorobyov, Alexey Bruce E. Shapiro, Atsushi Shinkai, Junichiro Yoshimoto, Kenji Doya protocols simulation of experimental Shimayoshi,Takao Akira Amano, Tetsuya Matsuda Brett G. Olivier, Johann M. Rohwer, Jacky L. Snoep, Jan-Hendrik S. Hofmeyr L. Snoep, Jan-Hendrik Jacky M. Rohwer, Johann Olivier, G. Brett Peter Li, Andy Brass, Oinn, John Pinney, Tom Douglas Kell, Carole Goble Kummer Ursula Mendes, Pedro Pahle, Juergen Ralph Gauges, Stefan Hoops, Sven Sahle, Nicolas Rodriguez, Nicolas Le Novere, Marco Donizelli Akira Funahashi, Akiya Jouraku, Yukiko Matsuoka, Norihiro Kikuchi, Hiroaki Kitano validator Michael Murakami, Joanna Bruce Shapiro, M. Keating, Sarah Benjamin Bornstein, Hucka modeling ChangMay Wang, F Quo of CellML models Visualization pathways interactions of biological A graphical notation to describe the logical interactions from protein-protein interaction networks credible Googling in the approach of systems melanogaster for D. database of protein interactomes Fly-DPI: for automatic construction approach of dynamic cell-wide metabolic driven Database systems design of living CADLIVE: computer-aided oscillatory in complex systems biochemical solvers study of numerical Comprehensive motifs" over in "dynamical change behavior A tool for analysis in cellerator support recent developments in mathematica: software Systems biology of biochemical reaction networks SIBioNet: SBML application for system identification models to facilitate physiological general simulator for cell a new DynaBioS.Cell: PySCeS and JWS online Integrating tools for computational systems biology: for systems biology workflows Taverna simulator pathway - a complex COPASI markup language systems biology models in the SBMLeditor: an editor for CellDesigner3.1: a process diagram editor for gene-regulatory and biochemical networks online SBML.org and the SBMLToolbox, updates to libSBML, MathSBML, Recent FI21 FI22 FI20 FI19 FI18 FI17 FI15 FI16 FI14 FI12 FI13 FI11 FI09 FI10 FI08 FI07 FI06

45 genomic sequences and the structural information Koyama,Yohei Tetsuya Kobayashi, J. Shuji Tomoda, Hiroki R. Ueda Tyson J. John A. Shaffer, Clifford Ranjit Randhawa, James Schaff, Anuradha Lakshminarayana, Ion Moraru, Leslie Loew Mikael R. Andersen,Michael L. Nielsen, Jens Nielsen Thomas Maiwald, Marcel Schilling, Sebastian Bohl, Clemens Kreutz, Ursula Klingmueller, Jens Timmer Deng, Cheng-Min Yeing-Wen Wang, Ueng-Cheng Wei, Yang Yu-Tai Junichiro Yoshimoto, Atsushi Shinkai, Kenji Doya regulatory during carcinogenesis programs Atsushi Niida, Shuich Tsutsumi, Andrew Smith, Michael Zhang, Q. Hiroyuki Aburatani, AkiyamaTetsu and stat experimental Laurent Gautier, Christopher Harold Brooks, Taylor, Philip Iversen, Charles Spencer, Xiang Greg Yang, Tucker-Kellogg Torunn Bruland, Endre Anderssen, Berit Doseth Eitrem, Hallgeir Bergum, Vidar Beisvag, Astrid Laegreid Wanwipa Vongsangnak, Peter Bjarke Olsen, Steen Krogsgaard, Jens Nielsen models variable Je-Gun Joung, Dongho Shin,Rho Hyun Seong, Zhang Byoung-Tak labeled proteins Fabian Kamper, Olaf Selchow, Dimitrios Kalamatianos, Harald Wajant, Klaus Pfizenmaier, Eric Bullinger optimization with data collocation optimization Wen-Hung Hunag, Chiou-Hwa Feng-Sheng Yuh, Wang the using by and intra-molecular atomic network and design of inter- Identification modeling pathway Composition and aggregation for biological protocols and instrumentation - modular modeling of experimental microscopy Virtual of subcellular localization patterns of fluorescently High-throughput image analysis niger aspergillus Applied genome-scale modelling of with the gelinspector blotting western quantitative Quality control and improved pathways Integration of biological identification of biochemical signaling pathways system Bayesian changes of transcriptional the that deconvolutes bioinformatics analysis Integrative for drug-induced Genomic signatures apoptosis in cancer cell lines: a combined on external standards using criteria that do not rely microarrays of cDNA Optimization zebrafish via evolutionary in embryonic gene regulatory engineering for network Reverse oryzae construction of Aspergillus metabolic network Genome annotation and latent by Characterization of regulatory with stem cell differentiation modules associated FI65 FI66 FI56 FI57 FI58 FI59 FI60 FI61 FI62 FI63 FI64 FI53 FI54 FI55 mapping to identify to identify mapping in silico in stationary coli phase operon of Escherichia bgl QTLs to system biology: an example of using example an biology: to system QTLs in silico Vitaly Selivanov, Silvia Marin, Antonio Ramos-Montoya, Josep Centelles, Paul Lee, Lee, Centelles, Paul Josep Antonio Ramos-Montoya, Silvia Marin, Selivanov, Vitaly Wang Merrill, May D. 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Rohwer, Johann model metabolic network Hongseok Yun, Choamun Yun, Hyun Uk Kim, Sang Lee Yup Koichi Takahashi networks Yoshiya Matsubara, Shinichi Kikuchi, Masahiro Sugimoto, Masaru Tomita genetic contribution to variation of bono mass density in inbred strains density in inbred of bono mass genetic contribution to variation MatthewPaul Tang, L. Chan, S. F. Y. C. Pak Sham,You-qiang Song Marta Cascante Richard Baran, Takamasa Ishikawa, Natsumi Saito, Yuji Kakazu, Tomoyoshi Soga, Soga, Tomoyoshi Kakazu, Yuji Natsumi Saito, Ishikawa, Takamasa Baran, Richard Tomita Nishioka, Martin Robert, Masaru Takaaki A generic mass spectrometry-based platform for enzyme kinetic data acquisition and analysis Ranjna Madan, S. Mahadevan state isotope tracer data of non-steady approach to the analysis A kinetic data using visualization Interpret lipid metabolomic pathway Role of the systems biology centre for integrative The Manchester screening data of metabolite methods for analysis of computational Development from approach for deciphering characteristics of metabolic reactions Top-down drosophila circadian identification of transcriptional circuits underlying System-level time-course in toxicological with significant interaction effects Identifying genes systems biology for workbench BioUML − open source extensible biology rate equation for computational systems A universal of genome-scale for modeling and simulation MetaFluxNet2: an integrated environment biology molecular systems Numerical simulation and using radial basis function equations of biosystems for stiff estimation Parameter From

FI52 FI51 FI49 FI50 FI48 FI47 FI46 FI45 FI44 FI43 FI41 FI42 FI40 FI39 FI38

46 Finding human miRNA genes located within promoter regions and associated with CpG with CpG associated and regions within promoter genes located human miRNA Finding islands Ming-Cheng Jan-Gowth Tsai, Chang, Ka-Lok Ng

OT03 E. coli B and K-12 Jeong-Rae Kim, Hyoung-Seok Choi, Junil Kim, Dongsan Kim, Yoon, Yeoin Sang-Woo Lee, Kwang-Hyun Cho of mass spectrometry standard protein with a peptide-concatenated Keiji Kito, Fujita, Tomoko Kazuhisa Ota, Takashi Ito dynamic biological networks which represent the characteristics of gene expression represent the characteristics of gene expression which networks dynamic biological pattern Peter Pan, Kuan-Yeu Chen-Hsiung Chan, Sheng-An Lee, Kau, Cheng-Yan I-Ming Chu Huaiyu Mi, Nan Guo, Anish Kejariwal, Paul Thomas Tun-Wen Pai, Wei-Jun Zhung,Tun-Wen Chih-Hong Liu, Wen-ShyongTzou Julien Lorec, Gerard Ramstein, Yannick Jacques Sangwoo Kim, KiryongHa, Doheon Lee Hiroaki Kitano, Michael Hucka, Nicolas Le Novere, Akira Funahashi Yu-Chieh Min-Shi Ko, Liao, Chin-Yu Lee, Chao A. Hsiung models network Choamun Lee, Yun, Dong-Yup Ayoun Cho, Sunwon Park, Sang Lee Yup Markus Heinonen, Ari Rantanen, Mielikainen, Taneli Esa Pitkanen, Juho Rousu Yow Iwaoka,Yow Yasunori Osana,Masato Yoshimi, Tosinori Kojima, Yuri Nishikawa, Akira Funahashi, Noriko Hiroi, Yuichiro Shibata, Naoki Iwanaga, Hiroaki Kitano, Hideharu Amano Frank T. Bergmann, Herbert M. Sauro Bergmann, T. Frank Aihara Luonan Chen, Kazuyuki Xian-Ming Zhao, Sung Ho Yoon, Haeyoung Jeong, Cheol-Goo Oh, Kwang Jihyun Kim Tae Hur, F. means quantification of component stoichiometry Accurate by of a multiprotein complex systems in into the two-component investigations Systems microbiological protein-protein interaction database to construct and data analysis Integrating microarray using PANTHER. pathways of biological to the evolution A view Linear and conformational from member of protein families epitopes prediction and resolution of anaphora texts in biology named entities of heterogeneous Identification service based integrated system for inferring BioCAD: web genetic regulatory circuits graphical notation (SBGN) project Systems biology A virus H3 hemagglutinin influenza Predicting antigenic drift in human cellular for managing and analyzing integrated environment a web-based WebCell: from tandem mass spectrometry fragments tool for prediction of molecular - a data FiD coli Escherichia of analysis ‘Omics’ Comparative hardware on programmable of a biochemical simulator An acceleration for Systems Biology SBW − a modular framework technique Gene function prediction using one-class 9 8 OT02 OT01 Others Others FI7 FI7 FI77 FI76 FI75 FI73 FI74 FI72 FI70 FI71 FI69 FI68 FI67

47 Tutorials

Sunday, October 8 Venue: Pacifico Yokohama

Room 9:30-12:30 14:00-17:00 T2: Structural and functional analysis of signaling T4: Tutorial on the Systems Biology Toolbox for 411 networks MATLAB + Steffen Klamt, Julio Saez-Rodriguez (Max Planck Henning Schmidt (Fraunhofer Chalmers Research Centre) 412 Institute Magdeburg, Germany)

T1: Engineering design principles for biologists T5: Pathway Modeling with Teranode XDA 413 Kyaw Tun, (Univ.Singapore), Arun Krishnan (Keio Univ.), Mike Kellen (Teranode) Pawan K Dhar (RIKEN GSC) (*2 hours)

414 T3: New Mathematical Methods for Systems Biology + Eric Mjolsness (UC Irvine) 415

T12: The Systems Biology Markup Language (SBML) T6: Analyzing Biochemical Systems using the E-Cell 416 Level 2 Version 2 System Michael Hucka (California Institute of Technology) Satya Arjunan (Keio University)

T8: Modeling, simulating, and analyzing biochemical T9: Advanced model analysis with Copasi 417 systems with Copasi Pedro Mendes (Virginia Bioinformatics Institute) Pedro Mendes (Virginia Bioinformatics Institute)

T11: Application of Experimental Design and Model Selection to Signal Transduction Pathway Modeling 418 Thomas Maiwald, Marcel Schilling, Sebastian Bohl (University of Freiburg, German Cancer Research Center ) (*1 day)

T7: Computational Cell Biology with the Virtual Cell T10: CellDesigner 419 Ion I. Moraru and James C. Schaff (University of Akira Funahashi (The Systems Biology Institute/JST) Connecticut Health Center)

48 Workshops

A. Systems Biology Graphical Notation (SBGN Workshop) Date: Saturday, October 7, 2006 09:30 – 18:00 Venue: Yokohama World Porters Building (5 min. walk from Pacifico Yokohama) Organizers: Hiroaki Kitano (SBI & Sony CSL), Yukiko Matsuoka (SBI & JST), Akira Funahashi (SBI & JST), Michael Hucka (California Institute of Technology), Nicolas Le Novère (EBI)

B. RTK workshop: Receptor tyrosine kinases (RTK) training course Date: Thursday-Friday, October 12-13 2006 09:15 – 17:30 Venue: AIST Tokyo Waterfront Bio-IT Research Building, Odaiba Organizers: RTK Consortium & RIKEN Genomic Sciences Center (GSC)

C. Systems Biology Markup Language (SBML) Forum Meeting; The 11th Workshop on Software Platforms for Systems Biology Date: Thursday-Friday, October 12-13 2006 09:30 – 17:30 Venue: National Museum of Emerging Science and Innovation “Miraikan” Organizers: Michael Hucka (California Institute of Technology), The Systems Biology Markup Language (SBML) Team

D. International Workshop on Synthetic Biology “Synthetic Approaches to Cellular Functions” Date: Thursday, October 12, 2006 09:30 – 18:00 Venue: National Museum of Emerging Science and Innovation “Miraikan” Organizers: Daisuke Kiga (Tokyo Institute of Technology), Drew Endy (MIT), Vitor Martins dos Santos (German Research Centre for Biotechnology), Hiroki Ueda (RIKEN)

E. Systems Biology Workshop: “Systems Biology and the Human Health Risks of Environmental Chemicals” Date: Thursday, October 12, 2006 09:00 – 15:00 Venue: National Museum of Emerging Science and Innovation “Miraikan” Organizers: Rory Conolly (U.S. Environmental Protection Agency)

Venue Map (October 12-13 Workshops)

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49 Access

Narita Airport

Airport Narita Akihabara Terminal 2 Stn.

Narita Airport Stn. ( ) Shinbashi Terminal 1 Tokyo

Shinagawa Fune-no Kagakukan (Odaiba Area) National Museum of Emerging Science and Innovation (Miraikan) Narita Express AIST/CBRC JR Line

Yokohama Yurikamome Minato Mirai Tokyo Bay Minato Mirai Line PACIFICO World YOKOHAMA Porters

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Sea Bass to Yokohama Kokusai Odori Boulevard Shinko Area

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50 Floor Plan

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51

© ICSB-2006 Conference Committee