DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis.

Jose A Gomez,Kai Höffner,Paul I Barton

doi:10.1186/s12859-014-0409-8

Jose A Gomez, Kai Höffner + Show 1 more

Open Access

https://doi.org/10.1186/s12859-014-0409-8

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Journal: BMC bioinformatics	Publication Date: Dec 1, 2014
Citations: 131	License type: CC BY 4.0

Affiliation: Massachusetts Institute of Technology

Abstract

BackgroundDynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.ResultsDFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab’s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.ConclusionsLexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0409-8) contains supplementary material, which is available to authorized users.

Highlights

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes
The following examples demonstrate the reliability and speed of Dynamic flux balance analysis laboratory (DFBAlab) compared to existing implementations of the static optimization (SOA) and direct approaches (DA)
SOA is represented by the constraint-based reconstruction and analysis (COBRA) dFBA implementation and DA by the dynamic multispecies metabolic modeling (DyMMM) implementation

Summary

Introduction

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). The acceleration in the process of genome sequencing in recent years has increased the availability of genomescale metabolic network reconstructions for a variety of species These genome-based networks can be used within the framework of flux balance analysis (FBA) to predict steady-state growth and uptake rates accurately [1]. There exist three approaches to simulate DFBA models: the static optimization approach (SOA) [2], the dynamic optimization approach [2] (DOA), and the direct approach (DA). The DOA approach discretizes the time horizon and optimizes simultaneously over the entire time period of interest by solving a nonlinear programming problem (NLP) The dimension of this NLP increases with time discretization, it is limited to small-scale metabolic models [3]. The DA has been implemented by Hanly and Henson [5], Mao and Verwoerd in the ORCA toolbox [6], Zhuang

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