Using optimal control to understand complex metabolic pathways
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...
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| Format: | Article |
| Language: | English |
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BMC
2020-10-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-020-03808-8 |
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| author | Nikolaos Tsiantis Julio R. Banga |
| author_facet | Nikolaos Tsiantis Julio R. Banga |
| author_sort | Nikolaos Tsiantis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T01:36:28Z |
| format | Article |
| id | doaj.art-f63cff300a0447d8b6eac6b51f7d2dec |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T01:36:28Z |
| publishDate | 2020-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f63cff300a0447d8b6eac6b51f7d2dec2022-12-21T19:20:16ZengBMCBMC Bioinformatics1471-21052020-10-0121113310.1186/s12859-020-03808-8Using optimal control to understand complex metabolic pathwaysNikolaos Tsiantis0Julio R. Banga1Bioprocess Engineering Group, Spanish National Research Council, IIM-CSICBioprocess Engineering Group, Spanish National Research Council, IIM-CSICAbstract 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.http://link.springer.com/article/10.1186/s12859-020-03808-8Optimal controlDynamic modelingMulti-criteria optimizationPareto optimality |
| spellingShingle | Nikolaos Tsiantis Julio R. Banga Using optimal control to understand complex metabolic pathways BMC Bioinformatics Optimal control Dynamic modeling Multi-criteria optimization Pareto optimality |
| title | Using optimal control to understand complex metabolic pathways |
| title_full | Using optimal control to understand complex metabolic pathways |
| title_fullStr | Using optimal control to understand complex metabolic pathways |
| title_full_unstemmed | Using optimal control to understand complex metabolic pathways |
| title_short | Using optimal control to understand complex metabolic pathways |
| title_sort | using optimal control to understand complex metabolic pathways |
| topic | Optimal control Dynamic modeling Multi-criteria optimization Pareto optimality |
| url | http://link.springer.com/article/10.1186/s12859-020-03808-8 |
| work_keys_str_mv | AT nikolaostsiantis usingoptimalcontroltounderstandcomplexmetabolicpathways AT juliorbanga usingoptimalcontroltounderstandcomplexmetabolicpathways |