Genome-scale modeling for metabolic engineering

Abstract

We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.

Fig. 1

Conceptual illustration of flux balance analysis formulation and solution. a Reconstruction of a genome-scale metabolic network is performed by mathematically representing the flux through the reactions of the network. b The stoichiometric matrix for the system is constructed to represent the stoichiometry of all reactions, and the mathematical formulation for FBA is based on the steady-state condition. These stoichiometric constraints coupled with minimum and maximum bounds on reaction rates define the steady-state solution space. c FBA provides a method for calculating achievable fluxes through the system (c2), based only on the knowledge of the stoichiometry of a metabolic network (c1). Through simulations, alternative solutions can also be identified and/or the effects of alterations to the network, such as gene deletions or additions, can be predicted (c3). The “1” in the graph signifies that a reaction is “on”, i.e., there is flux through it