Systems biology

Systems biology is an academic field that seeks to integrate different levels of information to understand how biological systems function. By studying the relationships and interactions between various parts of a biological system (e.g., gene and protein networks involved in cell signaling, metabolic pathways, organelles, cells, physiological systems, organisms, etc.) it is hoped that eventually an understandable model of the whole system can be developed. Since the mathematical and analytical foundation of systems biology is far from being perfect, computer simulation and heuristics are often used as research methods.

History
In 1952, the British neurophysiologists and nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley constructed a mathematical model of the nerve cell. In 1960, Denis Noble developed the first computer model of a beating heart. Systems biologists invoke these pioneering pieces of work as illustrative of the systems biology project. The possibility of performing systems biology increased around the year 2000 with the completion of various genome projects and the proliferation of genomic and proteomic data, and the accompanying advances in experimental methodology.

The experimental procedures available during the 20th century necessitated 'one protein at a time' projects which have been the mainstay of molecular biology since its inception. Some biologists and biochemists believe that this approach of individual biomolecules has fostered a reductionist perspective, and that it is just the first step toward an understanding of the overall (integrated) life process, which can only be properly addressed from a systems biology persepective. However, the current advances in biology (coming from bioinformatics in the post genomic era) are a direct result of the successes of 20th century molecular biology, and it is clear to most biologists that individual biomolecules or complexes will always be the central focus in drug development and will continue indefinitely to play a role in developing the higher-level understanding which systems biology claims to pursue.

Approaches
There are two major and complimentary focuses in systems biology:
 * Quantitative Systems Biology - otherwise known as "systems biology measurement", it focuses on measuring and monitoring biological systems on the system level.
 * Systems Biology Modeling - focuses on mapping, explaining and predicting systemic biological processes and events through the building of computational and visualization models.

Quantitative systems biology
This subfield is concerned with quantifying molecular reponses in a biological system to a given perturbation.

Some typical technology platforms are:
 * Gene expression measurement through DNA microarrays and SAGE
 * Protein levels through two-dimensional gel electrophoresis and mass spectrometry, including phosphoproteomics and other methods to detect chemically modified proteins.
 * metabolomics for small-molecule metabolites
 * glycomics for sugars

These are frequently combined with large scale perturbation methods, including gene-based (RNAi, misexpression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition.

These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models until the predicted behavior accurately reflects the phenotype seen.

Systems biology modeling
Using knowledge from molecular biology, the systems biologist can causally model the biological system of interest and propose hypotheses that explain a system's behavior. These hypotheses can then be confirmed and be used as a basis for mathematically model the system. The difference between the two modeling approaches is that causal models are used to explain the effects of a biological perterbations while mathematical models are used to predict how different perterbations in the system's environment affect the system.

Applications
Many predictions concerning the impact of genomics on health care have been proposed. For example, the development of novel therapeutics and the introduction of personalised treatments are conjectured and may become reality as a small number of biotechnology companies are using this cell-biology driven approach to the development of therapeutics. However, these predictions rely upon our ability to understand and quantify the roles that specific genes possess in the context of human and pathogen physiologies. The ultimate goal of systems biology is to derive the prerequisite knowledge and tools. Even with today's resources and expertise, this goal is immeasurably distant.

Systems biology people and places
A large number of organizations have been created to further the study of systems biology. Of note in the United States include the Institute for Systems Biology (ISB), the BioX program at Stanford University, the Department of Systems Biology at Harvard Medical School, the Systems Biology Research Group at the Pacific Northwest National Laboratory, and the Center for the Study of Biological Complexity. The ISB is headed by Leroy Hood and is a non-profit research institute with a goal to identify strategies for predicting and preventing diseases such as cancer, diabetes and AIDS. Work at PNNL is focused on a variety of research areas, including oxidative stress and radiation, cell signaling networks, and microbial communities. Internationally, some notable systems biology organizations include Japan's Systems Biology Institute headed by Hiroaki Kitano; UK's Biosystems Informatics Institute; Canada's Ottawa Institute of Systems Biology; Swizerland's Institute for Molecular Systems Biology and SystemsX, Ireland's Systems Biology Ireland, and Russia's Institute for Systems Biology.

Systems biology societies and projects

 * International Society for Systems Biology - the international systems biology society
 * Munich Systems Biology Forum - systems biology collaboration portal
 * Yeast Systems Biology Network - systems biology researchers working in yeast
 * E-Cell Project - international effort to model the cell in silico
 * National Simulation Research Physiome Project - international effort to model biology administered by the University of Washington
 * Proteome Interaction Project

Independent systems biology research centers

 * Biosystems Informatics Institute (United Kingdom)
 * Institute for Systems Biology (United States)
 * Ottawa Institute for Systems Biology (Canada)
 * Systems Biology Institute (Japan)
 * SystemsX (Switzerland)
 * Virtual Cell Group (United States)

Systems biology research groups

 * Kaern Dynamical Systems Biology Lab at University of Ottawa (Canada)
 * Systems Biology at Hamilton Institute in the National University of Ireland, Maynooth (Ireland)
 * Systems Biology Ireland at Trinity College, Dublin (Ireland)
 * Institute for Molecular Systems Biology at ETH Zürich (Switzerland)
 * Center for Systems and Synthetic Biology at University of Texas at Austin (United States)
 * Computational Neurobiology Group at European Bioinformatics Institute (United Kingdom)
 * Computational Systems Biology at University of Edinburgh (United Kingdom)
 * Manchester Centre for Integrative Systems Biology at University of Manchester (United Kingdom)
 * Computational Systems Biology at University of Sheffield (United Kingdom)
 * Multicellular Systems Research at Cellnomica (United States)
 * Cancer Modeling Project at Cellnomica (United States)
 * Computational Systems Biology Group at Carnegie Mellon University
 * Department of Systems Biology at Harvard Medical School (United States)
 * Virtual Cell Program at Harvard University (United States)
 * Computational Systems Biology at the Keck Institute (United States)
 * Cell Signaling Team at Los Alamos National Laboratory (United States)
 * Computational and Systems Biology Initiative at MIT (United States)
 * Systems Biology at Pacific Northwest National Laboratory (United States)
 * Systems Biology Research Group at UCSD (United States)
 * Computational Systems Biology Lab at University of Virginia (United States)
 * Center for the Study of Biological Complexity at Virginia Commonwealth University (United States)
 * Center for Genome Dynamics at The Jackson Laboratory (United States)

Systems biology researchers

 * Alon, Uri at Weizmann Institute of Science
 * Arkin, Adam at Lawrence Berkeley National Laboratory
 * Balaban, Nathalie Questembert at Hebrew University of Jerusalem
 * Baral, Chatta at Arizona State University
 * Boone, Charlie at University of Toronto
 * Cluzel, Phillippe at University of Chicago
 * Collins, Jim at Boston University
 * Cox, Edward at Princeton University
 * Davidson, Eric at CalTech
 * Elowitz, Michael at CalTech
 * Endy, Drew at MIT
 * Ferrell, Jim at Stanford University
 * Franceschi, Claudio at University of Bologna
 * Hughes, Tim at University of Toronto
 * Ideker, Trey at UCSD
 * Iyengar, Ravi at Mount Sinai School of Medicine
 * Iyer, Vishwanath at University of Texas
 * Jacobsen, Elling W. at KTH
 * Klipp, Edda at Max Planck Institute
 * Leibler, Stanislas at Rockefeller University
 * Marcotte, Edward at University of Texas
 * McAdams, Harley at Stanford University
 * Mendes, Pedro at Virginia Tech
 * O'Shea, Erin at Harvard
 * Pachter, Lior at UC Berkeley
 * Palsson, Bernhard Ø. at UCSD
 * Quake, Stephen at Stanford University
 * Schomburg, Dietmar at Cologne University
 * Sidow, Arend at Stanford University
 * Simpson, Michael at Oak Ridge National Laboratory
 * Snyder, Michael at Yale University
 * Stathopoulos, Angelike at CalTech
 * Thattai, Mukund at Tata Institute of Fundamental Research
 * Tidor, Bruce at MIT
 * Tyson, John at Virginia Tech
 * van Oudenaarden, Alexander at MIT
 * Vidal, Marc at Dana Farber Cancer Institute
 * Weissman, Jonathan at UCSF
 * Werner, Eric at Cellnomica
 * Young, Richard at MIT

Systems biology companies

 * BG Medicine
 * Cellnomica
 * Entelos
 * GeneGo
 * Genstruct (wiki entry)
 * Ingenuity Systems

International conferences

 * ICCS 2006 - 6th International Conference on Complex Systems
 * ICCS 2004 - 5th International Conference on Complex Systems
 * ICCS 2002 - 4th International Conference on Complex Systems
 * ICCS 2000 - 3rd International Conference on Complex Systems
 * ICCS 1998 - 2nd International Conference on Complex Systems
 * ICCS 1997 - 1st International Conference on Complex Systems


 * ICSB 2006 - 7th International Conference on Systems Biology
 * ICSB 2005 - 6th International Conference on Systems Biology
 * ICSB 2004 - 5th International Conference on Systems Biology
 * ICSB 2003 - 4th International Conference on Systems Biology
 * ICSB 2001 - 2nd International Conference on Systems Biology
 * ICSB 2000 - 1st International Conference on Systems Biology

Tools for systems biology

 * Systems Biology Markup Language - developed by the Computational Neurobiology group at the European Bioinformatics Institute
 * PSIbase Database structural interactome map of all proteins
 * SimTK
 * Gaggle
 * Systems Biology Workbench
 * Systems Biology Markup Language
 * The CellML language
 * The little b Modeling Language
 * Copasi (Version 4 of Gepasi)
 * E-Cell System
 * StochSim
 * Virtual Cell
 * JigCell (John Tyson Lab)
 * Python Simulator for Cellular Systems
 * Ingenuity Pathways Analysis
 * SAVI Signaling Analysis and Visualization
 * JSim
 * BioNetGen
 * SBML-PET Systems Biology Markup Language based Parameter Estimation Tool
 * BIOREL web-based resource for quantitative estimation of the gene network bias in relation to available database information about gene activity/function/properties/associations/interactions

Books

 * H Kitano (editor). Foundations of Systems Biology. MIT Press: 2001. ISBN 0-262-11266-3
 * G Bock and JA Goode (eds).In Silico" Simulation of Biological Processes, Novartis Foundation Symposium 247. John Wiley & Sons: 2002. ISBN 0-470-84480-9
 * E Klipp, R Herwig, A Kowald, C Wierling, and H Lehrach. Systems Biology in Practice. Wiley-VCH: 2005. ISBN 3-527-31078-9
 * B Palsson. Systems Biology - Properties of Reconstructed Networks. Cambridge University Press: 2006. ISBN 9780521859035

Articles

 * Marc Vidal and Eileen E. M. Furlong. Nature Reviews Genetics 2004 From OMICS to systems biology


 * Werner, E., "The Future and Limits of Systems Biology", Science STKE 2005, pe16 (2005).


 * ScienceMag.org - Special Issue: Systems Biology, Science, Vol 295, No 5560, March 1, 2002
 * Nature - Molecular Systems Biology
 * Systems Biology: An Overview - a review from the Science Creative Quarterly
 * Guardian.co.uk - 'The unselfish gene: The new biology is reasserting the primacy of the whole organism - the individual - over the behaviour of isolated genes', Johnjoe McFadden, The Guardian (May 6, 2005)