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 Systems Biology

Systems biology is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway).

The systems biology approach is characterised by a cycle of theory, computational modelling and experiment to quantitatively describe cells or cell processes. Since the objective is a model of all 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.

Techniques used in systems biology
The defining feature of System Biology is 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 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
- interactomics for interactomes

The investigations are frequently combined with large scale perturbation methods, including gene-based (RNAi, miss-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.