Abstract
Gene circuits that control metabolism should restore metabolic functions upon environmental changes. Whether gene networks are capable of steering metabolism to optimal states is an open question. Here we present a method to identify such optimal gene networks. We show that metabolic network optimisation over a range of environments results in an input-output relationship for the gene network that guarantees optimal metabolic states. Optimal control is possible if the gene network can achieve this input-output relationship. We illustrate our approach with the best-studied regulatory network in yeast, the galactose network. We find that over the entire range of external galactose concentrations, the regulatory network is able to optimally steer galactose metabolism. Only a few gene network parameters affect this optimal regulation. The other parameters can be tuned independently for optimisation of other functions, such as fast and low-noise gene expression. This study highlights gene network plasticity, evolvability, and modular functionality.
Highlights
Gene circuits that control metabolism should restore metabolic functions upon environmental changes
The studies by Dekel et al.[11] and Ibarra et al.[13] indicate that gene networks can readily evolve this capability at a single environmental condition, but they do not address whether gene networks can steer metabolism to optimal states over a range of environmental states
We will present the approach for the identification of the properties of a gene network that is capable to steer metabolic gene expression to a desired steady state at different nutrient levels
Summary
Gene circuits that control metabolism should restore metabolic functions upon environmental changes. Sophisticated adaptation mechanisms restore basic cellular functions upon environmental changes[1,2,3,4,5,6] These mechanisms invariably involve the sensing and integration of the dynamics of the extra- and intracellular state, and induce adjustments in protein levels through gene expression regulation. We deduce from metabolic information alone the requirement, i.e. the input-output relationship, for the gene network to regulate its target metabolic network in an optimal fashion over a range of environmental conditions. Our method can be used in three ways: (i) to parameterise a gene network for which the topology is known but not all the kinetic parameters have been identified, (ii) to identify a (minimal) gene network that is capable of controlling a metabolic system; for instance, by using software to evolve gene network models in the computer[17,18], or (iii) to identify a gene network and metabolic network that both agree with an experimentally determined input-output relationship. We focus in this work on the first application to study the control capabilities of a well-studied gene network
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