Systems biology

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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 experimentation. 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 systems as a whole sufficient to yield applications in ecology, medicine, agriculture and commerce. Some systems biologists consider the discipline critical to further progress in biology [1]

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')

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 [2]; 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 [3]. Such emergent processes have been recognised as, for example, contributing to subcellular morphology [4], developmental biology [5], metabolic networks [6], proteomics [7] and indeed in purely physical systems as well as biological systems [8]. At a higher level, emergent processes are a widespread concept in cellular neuroscience [9] and in cognitive science [10]. At a still higher level, emergent properties are recognised for example in the behaviour of ant colonies and the concept of swarm intelligence,[11]; they have been simulated in artificial systems [12], and parallels have been drawn with human societies [13].

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 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:

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

Tools for systems biology

References

  1. ↑ Westerhoff HV, Palsson BO (2004) The evolution of molecular biology into systems biology Nature Biotechnology 22:1249-52
  2. ↑ Berg EL et al (2005) Biological complexity and drug discovery: a practical systems biology approach Syst Biol 152:201-6 PMID 16986261
  3. ↑ Gilbert SF, Sarkar S (2000) Embracing complexity: organicism for the 21st century Dev Dyn 219:1-9 PMID 10974666
  4. ↑ Tabony J (2006) Microtubules viewed as molecular ant colonies Biol Cell 98:603-17 PMID 16968217
  5. ↑ e.g. Theise ND, d'Inverno M (2004) Understanding cell lineages as complex adaptive systems Blood Cells Mol Dis 32:17-20 PMID 14757407 and Ruiz i Altaba A, et al (2003) The emergent design of the neural tube: prepattern, SHH morphogen and GLI code Curr Opin Genet Dev 13:513-21 PMID 14550418
  6. ↑ Jeong H et al(2000) The large scale organisation of metabolic networks Nature 407:651-4 [1]
  7. ↑ e.g. Grindrod P, Kibble M (2004) Review of uses of network and graph theory concepts within proteomics Expert Rev Proteomics 1:229-38 PMID 15966817 and Ye X, Chu J, Zhuang Y, Zhang S (2005) Multi-scale methodology: a key to deciphering systems biology Front Biosci 10:961-5 PMID 15569634
  8. ↑ Cho YS et al (2005) Self-organization of bidisperse colloids in water droplets J Am Chem Soc 127:15968-75 PMID 16277541
  9. ↑ see e.g. Burak Y, Fiete I (2006) Do we understand the emergent dynamics of grid cell activity? J Neurosci 26:9352-4 PMID 16977716
  10. ↑ e.g. Courtney SM (2004) Attention and cognitive control as emergent properties of information representation in working memory Cogn Affect Behav Neurosci 4:501-16 PMID 15849893
  11. ↑ Theraulaz G et al (2002) Spatial patterns in ant colonies Proc Natl Acad Sci USA 99:9645-9 PMID 12114538
  12. ↑ Theraulaz G, Bonabeau E (1999)A brief history of stigmergy Artif Life 5:97-116 PMID 10633572
  13. ↑ Bonabeau E, Meyer C (2001) Swarm intelligence. A whole new way to think about business Harv Bus Rev 79:106-14 PMID 11345907

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

  • 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)
  • Trewavas, Anthony. A Brief History of Systems Biology: "Every object that biology studies is a system of systems." Francois Jacob (1974). Plant Cell 18:2420-30, 2006, Fulltext or PDF need access rights

External links

See also

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