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The Best Models of Metabolism

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Abstract

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

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References
1.
Chou I, Voit E . Estimation of dynamic flux profiles from metabolic time series data. BMC Syst Biol. 2012; 6:84. PMC: 3495652. DOI: 10.1186/1752-0509-6-84. View

2.
del Rosario R, Mendoza E, Voit E . Challenges in lin-log modelling of glycolysis in Lactococcus lactis. IET Syst Biol. 2008; 2(3):136-49. DOI: 10.1049/iet-syb:20070030. View

3.
Savageau M . Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol. 1969; 25(3):365-9. DOI: 10.1016/s0022-5193(69)80026-3. View

4.
Voit E . Characterizability of metabolic pathway systems from time series data. Math Biosci. 2013; 246(2):315-25. PMC: 3709000. DOI: 10.1016/j.mbs.2013.01.008. View

5.
Clermont G, Zenker S . The inverse problem in mathematical biology. Math Biosci. 2014; 260:11-5. PMC: 6657349. DOI: 10.1016/j.mbs.2014.09.001. View