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Methods of model reduction for large-scale biological systems: a survey of current methods and trends

Snowden, T. J., van der Graaf, P. H. and Tindall, M. J. (2017) Methods of model reduction for large-scale biological systems: a survey of current methods and trends. Bulletin of Mathematical Biology, 79 (7). pp. 1449-1486. ISSN 1522-9602

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To link to this item DOI: 10.1007/s11538-017-0277-2


Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.

Item Type:Article
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:71598


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