Abstract

BackgroundOptimality 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.ResultsHere 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.ConclusionsWe 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.

Highlights

  • 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

  • In this study we have considered metabolic pathways, but this computational framework can be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks

  • The problem is subject to constraints, including the dynamics of the system described by Eq (2), i.e. the set of ordinary differential equations and their corresponding initial values ( x(t0) ), forming the so-called initial value problem (IVP); inequality ( g ) path constraints are encoded in equations (3), representing inequalities relationships that must be enforced during the time horizon considered

Read more

Summary

Introduction

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. Wolkenhauer and Mesarovic [4] argue that the central dogma of systems biology is that system dynamics give rise to the functioning and function of cells In this view, the language of dynamical systems is used to represent mechanisms at different levels (metabolic, signalling and gene expression) in order to describe the observed biochemical and biological phenomena. Dynamical systems theory has a long history in physiology [2, 7, 8], and is receiving increasing attention in molecular systems biology [9,10,11,12,13,14,15,16,17]

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.