Systems biology

As an academic discipline, systems biology aims to understand and predict the functions, properties and behaviors of biological systems from knowledge of its interacting components combined with sophisticated mathematical tools, differing modeling approaches, network analyses, computer simulations, and analysis-inspired further experiementation. By studying the relationships and interactions among various parts of a biological system (e.g., gene and protein interactions involved in cell signaling, metabolic pathways, organelles, cells, physiological systems, organisms, etc.), and by organizing and analyzing those parts in terms of abstractions such as modules and networks, feedback and feedforward loops, robustness and complexity, and emergence, systems biologists aim to develop realistic models of biological systems that can allow property and behavior predictions to given stimuli and conditions. Ultimately, progress in systems biology will yield understanding biological systemsvasv a whole sufficient to yield applications in ecology, medicine, agriculture and commerce. Some systems biologists consider the discipline critical to further progress in biology.

On the Nature of Biological "Systems"

A system in biology consists of an assemblage of components or elements. For example, the vertebrate body system consists of an assemblage of organs, among other components. Each component or element in a system interacts in some way(s) with one or more co-components or co-elements in the system. For example, in the system constituting a cell, proteins interact with genes, metabolites, and other elements. Systems exhibit a characteristic set of behaviors or operate to perform one or more functions.

Subsystems consist of smaller systems embedded in a larger system, and constitute at least part of the components or elements of the larger system. Whether a systems biologist treats a given assemblage of components or elements as a subsystem or as a system depends on the level at which she focuses her attention. If she focuses her research at the level of a whole vertebrate organism, for example, she treats its organs as subsystems. If she focuses her research at the level of the lung, she treats it as a system, recognizing that it remains a component or element of a larger system.

Even the larger system, e.g., the vertebrate body system, functions as a component or element of a even larger system, a species, say, where individual species components interact with each to generate a characteristic set of species behaviors.

For purposes of trying to understand biological systems, in systems biology the components or elements of a system (or subsystem) need not take the form of discrete objects or entities (e.g., molecules, organelles, cells, etc.), but may take the form of abstracted concepts of organizational units of those objects or entities. Those include such concepts as networks and modules, more about will follow below.

Examples of biological systems (subsystems) include:

• ecosystems (e.g., a forest)
• species (e.g., Homo sapiens)
• organisms (e.g., E. coli)
• organs (e.g., brain)
• cells (e.g., epithelial cell)
• metabolic pathways (e.g., glycolysis)
• genes (e.g., protein blueprints)
• gene complexes (e.g., co-expressing genes)
• genomes (e.g., the entire complement of genes in an organism, as the ’mouse geneome)

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.

Approaches

There are two major and complementary 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 perturbations while mathematical models are used to predict how different perturbations 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.

International conferences

• International Conference on Complex Systems
• International Conference on Systems Biology, 2006

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
• Genevestigator
• 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

Bibliography

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
• Z Szallasi, J Stelling and V Periwal (eds). System Modelling in Cellular Biology: From Concept to Nuts and Bolt. A Bradford Book, The MIT Press: 2006. ISBN 0-262-19548-8 [4 SECTIONS; 17 CHAPTERS; 36 CONTRIBUTORS]

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)

External links

• BioChemWeb.org - The Virtual Library of Biochemistry and Cell Biology: A Guide to Biochemistry, Molecular Biology & Cell Biology on the Web
• Systems Biology Portal - administered by the Systems Biology Institute
• Community for Interactomics - Systems Biology Wiki
• Systems Biology Medicine Symposium 2006 - View webcasts (in Real Player or Windows Media Player) of a symposium on research and applications of systems approaches in Medicine

See also

• Gene regulatory network
• Metabolic network modelling
• Model
• Computer simulation
• Important publications in systems biology
• Systems ecology
• Regulome
• Biomedical cybernetics
• Artificial life