Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation. (2nd November 2018)

• Record Type: Journal Article
• Authors: Scott, Felipe; Wilson, Pamela; Conejeros, Raúl; Vassiliadis, Vassilios S.
• Is Part Of: Computers & chemical engineering. Volume 119(2018)
• Page Start: 152
• Page End: 170
• Publication Date: 2018-11-02
• Subjects: Dynamic flux balance analysis -- Ordinary differential equations with embedded optimization -- Linear programming -- Genome-scale metabolic network; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.08.041
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new approach for solving Dynamic Flux Balance Analysis (dFBA) problems. Interior-point method reformulation of embedded LP problem in bulk kinetic models. Tunable differentiable approximation to dFBA problems suitable for optimal control. Approach demonstrated on genome-scale simulation and optimal control case studies. Solutions obtained at highly competitive computational times to other approaches. Abstract: 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.