|Title||A reliable simulator for dynamic flux balance analysis|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Höffner K, Harwood SM, Barton PI|
|Journal||Biotechnology and Bioengineering|
|Keywords||Computational Methods, Dynamic Flux Balance Analysis, Flux Balance Analysis, Numerical Tools, System Biology|
Dynamic flux balance analysis (DFBA) provides a platform for detailed design, control and optimization of biochemical process technologies. It is a promising modeling framework that combines genome-scale metabolic network analysis with dynamic simulation of the extracellular environment. Dynamic flux balance analysis assumes that the intracellular species concentrations are in equilibrium with the extracellular environment. The resulting underdetermined stoichiometric model is solved under the assumption of a biochemical objective such as growth rate maximization. The model of the metabolism is coupled with the dynamic mass balance equations of the extracellular environment via expressions for the rates of substrate uptake and product excretion, which imposes additional constraints on the linear program (LP) defined by growth rate maximization of the metabolism. The linear program is embedded into the dynamic model of the bioreactor, and together with the additional constraints this provides an accurate model of the substrate consumption, product secretion, and biomass production during operation. A DFBA model consists of a system of ordinary differential equations for which the evaluation of the right-hand side requires not only function evaluations, but also the solution of one or more linear programs. The numerical tool presented here accurately and efficiently simulates large-scale dynamic flux balance models. The main advantages that this approach has over existing implementation is first that the integration scheme has a variable step size and the linear program only has to be solved when qualitative changes in the optimal flux distribution of the metabolic network occur, and second that it can reliably simulate behavior near the boundary of the domain where the model is defined. This is illustrated through large-scale examples taken from the literature.