Systems biology

As an academic discipline, systems biology aims to understand and predict the functions, properties and behaviors of biological systems—assemblages of interrelated and dynamically interacting elements. Systems biologists try to achieve those aims in part through quantitative study of experimentally derived data about the interrelated and interacting elements in conjunction with theoretical models. They work to develop realistic representations of biological systems that can allow property and behavior predictions of the systems to given stimuli and given sets of conditions. They use sophisticated mathematical, statistical and computational tools in differing modeling approaches, in network analyses, in computer simulations, in design and building synthetic networks and models; and, iteratively, with data derived from systems-analysis-inspired further experimentation. They try to accomplish their aims in part (a) by studying the interrelations (structural, organizational) and interactions (dynamical, kinetic) among various elements of a biological system (e.g., gene and protein interactions involved in metabolic pathways or cell signaling), (b) by organizing and analyzing those elements in terms of abstractions such as modules and networks, feedback and feedforward loops, robustness and complexity, and emergence, and, (c) by design, construction and testing of synthetic networks,. Progress in systems biology will expectantly lead to understanding biological systems as a whole appropriate to yield applications in ecology, ethnology, medicine, agriculture, business, and technology. Some systems biologists consider the discipline critical to further progress in biology.

No definition or succinct description of systems biology can capture the breadth and depth of the interdisciplinary enterprise. This article elaborates on the above description of the discipline.

On the Nature of Biological "Systems"

A 'system' in biology is any interconnected, interacting assemblage of components or elements. For example, the vertebrate body system consists of an assemblage of interacting organs, among other components. Each component or element in a biological system interacts in some way(s) with one or more co-components or co-elements in the system--a dynamical assemblage of components. For example, in the system constituting a cell, proteins interact with genes, metabolites and other elements. Systems exhibit behavior or behaviors characteristic of the system-as-a-whole, or subsystem-as-a-whole (see below), and not shared to any degree, or to any major degree, with any of its components (so-called "emergent properties"). A tree fruits, for example, because its dynamically interacting components enable it to, but no component of a tree can.

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 the lung's interacting assemblage of components as a system, recognizing that the lung system remains a component or element of a larger system.

Even the larger systems, e.g., the vertebrate body system, function as components or elements of even larger systems, a species of vertebrates, say, where individual members of the species interact with each other, as components, to generate a set of behaviors or properties characteristic of the species but not of the individual members of the species.

For purposes of trying to understand biological systems, in systems biology the components or elements of a system (or subsystem) need not be discrete or concrete objects or entities (e.g., molecules, organelles, cells, etc.), but may be abstracted concepts of organizational collections of those objects or entities, admitting of study by advanced mathematical and statistical tools. Those include such concepts as circuits, networks and modules, more about which will follow below. Such concepts have a way of appearing less abstract or hypothetical as biologists more fully define them in terms of structure and dynamical interactions, predict systems behavior from them using computational models, and relate them functionally in the the larger systems embedding them.

Biological system behaviors typically perform one or more evolution-informed functions, so unravelling the evolutionary history of a biological system contributes importantly in fully understanding it.

Examples of biological systems (subsystems) include:

• ecosystems (e.g., a forest)
• species (e.g., Homo sapiens)
• organisms (e.g., Homo sapiens; E. coli)
• organs (e.g., brain; the vascular endothelium)
• 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 DNA in an organism, as the ’mouse genome')

History of Systems Biology

Knowledge of the historical path(s) leading to a modern scientific program offers one perspective that contributes to an understanding of the nature of the program, in particular, its goals and its methodologies. So for systems biology.

Historians writing on the same topic have different focuses and approaches. Westerhoff and Palsson [insert ref] introduce the school of thought “…that systems biology of the living cell has its origin in the expansion of molecular biology to genome-wide analyses.” They point out, however, that molecular biology has an earlier history of systems thinking, in particular in the elucidation of numerous molecular regulatory circuits and of their contribution to the logic of the cell. They see technologic advances that permit rapid collection of large data sets (so-called high-throughput technologies) as upping the scale of that research “…enabling us [molecular biologists] to view the genome as the ‘system’ to study.”

They go on to discuss two separate pathways of enquiry that they see as having merged into modern systems biology of the cell.

They discuss the first root of modern molecular/cellular systems biology as the advances in molecular biology that “…led to efforts toward genome-scale model building to analyze the systems properties of cellular function”: recognition of DNA as the genetic material, identifying the structure of DNA, recombinant technology, automated determination of DNA base sequences, and high-throughput technologies yielding the sequences of entire genomes.

They discuss as the second, parallel root of modern molecular/cellular systems biology the development of non-equilibrium thermodynamics, a predictive mathematical theory for describing the behavior of systems that transfer energy from one place to another or convert energy from one form to another in processes the move towards an irreversible state of stability (i.e., equilibrium) characterized by randomness or disorder. Since living matter produces order and maintains a state of non-randomness it must carry out processes that keeps its condition far from the equilibrium of randomness, which it achieves by transforming energy taken in from the environment. The non-closed biological system produces order at the expense of the environment it is open to, which becomes more disordered.

Westerhoff and Palsson describe advances in the development of non-equilibrium thermodynamics as presaging molecular and cellular systems biology through quantitative integration of system components and through discovery of principles connecting molecular mechanisms and system behaviors. Westerhoff and Alberghina [insert ref] ask of systems biology: “Did we know it all along?”

NB: Next, a few words on other views of the history of systems biology….

Systems biology, emergent behaviour and the 'new vitalism'

In terms of cell biology , a type of 'vitalism' can be recognized in contemporary molecular biology, for example in the proposal that some high level features of organisms, perhaps including even life itself, are examples of emergent processes which cannot be accurately described simply by understanding each of the chemical processes which occur in the cell in isolation from all the others ; When individual chemical processes form interconnected feedback cycles which produce products perpetuating these cycles rather than unconnected products, they can form systems with properties that the reactions, taken individually, lack . Such emergent processes have been recognised as, for example, contributing to subcellular morphology , developmental biology , metabolic networks , proteomics and indeed in purely physical systems as well as biological systems . At a higher level, emergent processes are a widespread concept in cellular neuroscience and in cognitive science . At a still higher level, emergent properties are recognised for example in the behaviour of ant colonies and the concept of swarm intelligence,; they have been simulated in artificial systems , and parallels have been drawn with human societies .

History

In 1952, the British neurophysiologists and nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley constructed a mathematical model of the action potential - the fundamental mechanism underlying communication between nerve cells, expressing it as the consequence of a dynamic interaction between interdependent ionic conductances of the cell membrane. 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 perspective.

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 a mathematically model of 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 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

• Kitano H (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
• Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H (2005) Systems Biology in Practice. Wiley-VCH: ISBN 3-527-31078-9
• Palsson B (2006) Systems Biology - Properties of Reconstructed Networks. Cambridge University Press: 2006. ISBN 9780521859035
• Szallasi Z, Stelling J, Periwal V (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

• Vidal M, Furlong EEM (2004) Nature Reviews Genetics From OMICS to systems biology

• Werner E (2005) The future and limits of systems biology, Science STKE 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)
• Trewavas AJ (2006) A brief history of systems biology: "Every object that biology studies is a system of systems." Francois Jacob (1974). Plant Cell 18:2420-30 Fulltext or PDF need access rights

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