This work presents a novel, differentiable, way of solving dynamic Flux Balance Analysis (dFBA) problems by embedding flux balance analysis of metabolic network models within lumped bulk kinetics for biochemical processes. The proposed methodology utilizes transformation of the bounds of the embedded linear programming problem of flux balance analysis via a logarithmic barrier (interior point) approach. By exploiting the first-order optimality conditions of the interior-point problem, and with further transformations, the approach results in a system of implicit ordinary differential equations. Results from four case studies, show that the CPU and wall-times obtained using the proposed method are competitive with existing state-of-the art approaches for solving dFBA simulations, for problem sizes up to genome-scale. The differentiability of the proposed approach allows, using existing commercial packages, its application to the optimal control of dFBA problems at a genome-scale size, thus outperforming existing formulations as shown by two dynamic optimization case studies.
Bibliographical noteFunding Information:
• F. Scott gratefully acknowledges financial support from CONICYT (Proyectos REDES ETAPA INICIAL, Convocatoria 2017, REDI170254).
• R. Conejeros would like to thank CONICYT ’s research grant FONDECYT 1151295 for funding this research.
• R. Conejeros would like to thank CONICYT's research grant FONDECYT 1151295 for funding this research.
? R. Conejeros would like to thank CONICYT's research grant FONDECYT 1151295 for funding this research.
- Dynamic flux balance analysis
- Genome-scale metabolic network
- Linear programming
- Ordinary differential equations with embedded optimization