Using optimal control to understand complex metabolic pathways
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ETHODOLOGY ARTICLE
Open Access
Using optimal control to understand complex metabolic pathways Nikolaos Tsiantis1,2 and Julio R. Banga1*
*Correspondence: [email protected] 1 Bioprocess Engineering Group, Spanish National Research Council, IIMCSIC, C/Eduardo Cabello 6, 36208 Vigo, Spain Full list of author information is available at the end of the article
Abstract Background: Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. Results: Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. Conclusions: We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks. Keywords: Optimal control, Dynamic modeling, Multi-criteria optimization, Pareto optimality
Background This paper is based on two main concepts: (i) genetic and biochemical dynamics are key to understand biological function, and (ii) optimality hypotheses enable predictions in biology. We start by briefly reviewing the relevant previous literature, with emphasis on © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indi
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