Dynamic flux balance analysis for synthetic microbial communities. Issue 5 (1st October 2014)

• Record Type: Journal Article
• Authors: Henson, Michael A.; Hanly, Timothy J.
• Is Part Of: IET systems biology. Volume 8:Issue 5(2014)
• Page Start: 214
• Page End: 229
• Publication Date: 2014-10-01
• Subjects: cellular biophysics -- genomics -- sugar -- differential equations -- mechanoception; dynamic flux balance analysis -- synthetic microbial community -- classical flux balance analysis -- extracellular environment -- microbial metabolism -- genome‐scale metabolic reconstructions -- DFBA approach -- mixed hexose‐pentose sugar mixtures -- synthetic microbial coculture systems -- species metabolic reconstructions -- extracellular mass balances -- linear program‐differential equations -- species interactions; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2013.0021
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Dynamic flux balance analysis (DFBA) is an extension of classical flux balance analysis that allows the dynamic effects of the extracellular environment on microbial metabolism to be predicted and optimised. Recently this computational framework has been extended to microbial communities for which the individual species are known and genome‐scale metabolic reconstructions are available. In this review, the authors provide an overview of the emerging DFBA approach with a focus on two case studies involving the conversion of mixed hexose/pentose sugar mixtures by synthetic microbial co‐culture systems. These case studies illustrate the key requirements of the DFBA approach, including the incorporation of individual species metabolic reconstructions, formulation of extracellular mass balances, identification of substrate uptake kinetics, numerical solution of the coupled linear program/differential equations and model adaptation for common, suboptimal growth conditions and identified species interactions. The review concludes with a summary of progress to date and possible directions for future research.