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- W2731805355 endingPage "1486" @default.
- W2731805355 startingPage "1449" @default.
- W2731805355 abstract "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." @default.
- W2731805355 created "2017-07-14" @default.
- W2731805355 creator A5006005215 @default.
- W2731805355 creator A5051202360 @default.
- W2731805355 creator A5077172862 @default.
- W2731805355 date "2017-06-27" @default.
- W2731805355 modified "2023-10-05" @default.
- W2731805355 title "Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends" @default.
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