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According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are:
According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are:
* Gene expression measurement through [[DNA microarray]]s and [[Serial analysis of gene expression|SAGE]]
* [[Transcriptomics]]: whole cell or tissue gene expression measurements by [[DNA microarray]]s or [[Serial analysis of gene expression|SAGE]]
* Protein levels through [[two-dimensional gel electrophoresis]] and [[mass spectrometry]], including [[phosphoproteomics]] and other methods to detect chemically modified proteins.
* [[Proteomics]]: complete identification of proteins and protein expression patterns of a cell or tissue through [[two-dimensional gel electrophoresis]] or other multi-dimensional protein separation techniques and [[mass spectrometry]]. Sub disciplines include [[phosphoproteomics]], [[glycoproteomics]] and other methods to detect chemically modified proteins.
* [[metabolomics]] for small-molecule [[metabolites]]
* [[Metabolomics]]: identification and measurement of all small-molecules [[metabolites]] within a cell or tissue
* [[Glycomics]]: identification of the entirety of all carbohydrates in a cell or tissue.
* [[glycomics]] for sugars
In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:
* [[interactomics]] for [[interactomes]]
* [[Interactomics]] which is used mostly in the context of protein-protein interaction but in theory encompasses interactions between all molecules within a cell
*[[Fluxomics]], which deals with the dynamic changes of molecules within a cell over time


The investigations are frequently combined with large scale perturbation methods, including gene-based ([[RNAi]], mis-expression 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 to accurately reflect observations.
The investigations are frequently combined with large scale perturbation methods, including gene-based ([[RNAi]], mis-expression 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 to accurately reflect observations.

Revision as of 09:30, 16 May 2007

Systems biology is a term used very widely in the biosciences, particularly from the year 2000 onwards, and in a variety of contexts. Systems biology can be considered from a number of different aspects:

  • Some sources discuss systems biology as a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway)[1][2].
  • Other sources consider systems biology as a paradigm, usually defined in antithesis to the so-called reductionist paradigm, although fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations:

"The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitiative measures, multiple components simultaneously and by rigorous data integration with mathematical models" Science[3]

"Systems biology...is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but differrent....It means changing our philosophy, in the full sense of the term" Denis Noble[4]

  • Still other sources view systems biology in terms of the operational protocols used for performing research, namely a cycle composed of theory, computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory.[5][6]. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models.
  • Finally, some sources see it as a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel.

This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide.

History

Systems Biology finds its roots in quantitative modelling of enzyme kinetics, a discipline that flourished between 1900 and 1970, but also in the simulations developed to study neurophysiology, and the control theory, or cybernetics. One of the theorists who can be seen as a precursor of systems biology is Ludwig von Bertalanffy with his general systems theory. In 1952, the British neurophysiologists and nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley constructed a mathematical model explaining the action potential propagating along the axon of a neuronal cell. In 1960, Denis Noble developed the first computer model of a beating heart. The years 60s and 70s view the development of several approaches to study complex molecular systems, such as the Metabolic Control Analysis and the Biochemical Systems Theory. The successes of molecular biology throughout the 80s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modelling of biological processes to become a somehow minor field. However the birth of functional genomics in the 90s meant that large quantity of good quality data became available, while the computing power exploded, making possible more realistic models. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell. Around the year 2000, when Institute of Systems Biology were established in Seattle and Tokyo, Systems Biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g. genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics. Since then, various research institutes dedicated to systems biology have been developed. As of summer 2006, due to a shortage of people in systems biology[7] several doctoral training centres in systems biology have been established in many parts of the world.

Techniques associated with systems biology

According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are:

In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:

  • Interactomics which is used mostly in the context of protein-protein interaction but in theory encompasses interactions between all molecules within a cell
  • Fluxomics, which deals with the dynamic changes of molecules within a cell over time

The investigations are frequently combined with large scale perturbation methods, including gene-based (RNAi, mis-expression 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 to accurately reflect observations.

The investigations of a single level of biological organization (such as those listed above) are usually referred to as Systematic Systems Biology. Other areas of Systems Biology includes Integrative Systems Biology, which seeks to integrate different types of information to advance the understanding the biological whole, and Dynamic Systems Biology, which aims to uncover how the biological whole changes over time (during evolution, for example, the onset of disease or in response to a perturbation). Functional Genomics may also be considered a sub-field of Systems Biology.

The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used. Other aspects of computer science and informatics are also used in systems biology. These include new forms of computational model, such as the use of process calculi to model biological processes, the integration of information from the literature, using techniques of information extraction and text mining, the development of online databases and repositories for sharing data and models (such as BioModels Database), and the development of syntactically and semantically sound ways of representing biological models, such as the Systems Biology Markup Language.

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
  • A Kriete, R Eils. Computational Systems Biology., Elsevier - Academic Press: 2005. ISBN 0-12-088786-X
  • B Palsson. Systems Biology - Properties of Reconstructed Networks. Cambridge University Press: 2006. ISBN 978-0-521-85903-5
  • U Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. CRC Press: 2006. ISBN 1-58488-642-0 - emphasis on Network Biology
  • K. Sneppen and G. Zocchi, (2005) Physics in Molecular Biology, Cambridge University Press, ISBN 0-521-84419-3
  • Z. Szallasi, J. Stelling, and V.Periwal (eds.) System Modeling in Cellular Biology: From Concepts to Nuts and Bolts (Hardcover), MIT Press: 2006, ISBN 0-262-19548-8
  • CP Fall, E Marland, J Wagner and JJ Tyson (Editors). "Computational Cell Biology." Springer Verlag: 2002 ISBN 0-387-95369-8

Articles

See also

References

  1. ^ Snoep J.L. and Westerhoff H.V. (2005.). "From isolation to integration, a systems biology approach for building the Silicon Cell". Systems Biology: Definitions and Perspectives. Springer-Verlag. pp. p7. {{cite conference}}: |pages= has extra text (help); Check date values in: |date= (help); Unknown parameter |booktitle= ignored (|book-title= suggested) (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)
  2. ^ "Systems Biology - the 21st Century Science".
  3. ^ Sauer, U. et al. "Getting Closer to the Whole Picture" Science (journal) 316 550 17 April 2007
  4. ^ Denis Noble The Music of Life Oxford University Press (2006) p21
  5. ^ "Systems Biology: Modelling, Simulation and Experimental Validation".
  6. ^ Kholodenko B.N., Bruggeman F.J., Sauro H.M. (2005.). "Mechanistic and modular approaches to modeling and inference of cellular regulatory networks". Systems Biology: Definitions and Perspectives. Springer-Verlag. pp. p143. {{cite conference}}: |pages= has extra text (help); Check date values in: |date= (help); Unknown parameter |booktitle= ignored (|book-title= suggested) (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: multiple names: authors list (link)
  7. ^ "Working the Systems".


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