Dynamic optimization of metabolic networks coupled with gene expression

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in...

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Bibliographic Details
Published in:Journal of theoretical biology 2015-01, Vol.365, p.469-485
Main Authors: Waldherr, Steffen, Oyarzún, Diego A., Bockmayr, Alexander
Format: Article
Language:English
Subjects:
Bacterial growth
Biocatalysis
Biomass
Carbon - metabolism
Computer Simulation
Constraint-based methods
Flux optimization
Gene Expression Regulation
Gene Regulatory Networks
Kinetics
Metabolic Networks and Pathways - genetics
Metabolic–genetic networks
Models, Biological
Oxygen - metabolism
Time Factors
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Summary:The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. [Display omitted] •A dynamic optimization framework integrating a metabolic network with the dynamics of biomass production and composition.•Predicting the temporal regulation of gene expression from an optimization principle.•No knowledge of regulatory interactions required
ISSN:0022-5193
1095-8541
DOI:10.1016/j.jtbi.2014.10.035