Literature DB >> 23055276

A reliable simulator for dynamic flux balance analysis.

K Höffner1, S M Harwood, P I Barton.   

Abstract

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 are that the integration scheme has a variable step size, that the linear program only has to be solved when qualitative changes in the optimal flux distribution of the metabolic network occur, and 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.
Copyright © 2012 Wiley Periodicals, Inc.

Mesh:

Year:  2012        PMID: 23055276     DOI: 10.1002/bit.24748

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  24 in total

1.  Combining metabolomics and network analysis to improve tacrolimus production in Streptomyces tsukubaensis using different exogenous feedings.

Authors:  Cheng Wang; Jiao Liu; Huanhuan Liu; Shaoxiong Liang; Jianping Wen
Journal:  J Ind Microbiol Biotechnol       Date:  2017-08-03       Impact factor: 3.346

2.  Dynamic flux balance analysis with nonlinear objective function.

Authors:  Xiao Zhao; Stephan Noack; Wolfgang Wiechert; Eric von Lieres
Journal:  J Math Biol       Date:  2017-04-11       Impact factor: 2.259

Review 3.  A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).

Authors:  Ilija Dukovski; Djordje Bajić; Jeremy M Chacón; Michael Quintin; Jean C C Vila; Snorre Sulheim; Alan R Pacheco; David B Bernstein; William J Riehl; Kirill S Korolev; Alvaro Sanchez; William R Harcombe; Daniel Segrè
Journal:  Nat Protoc       Date:  2021-10-11       Impact factor: 13.491

4.  Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.

Authors:  João R C Ramos; Gil P Oliveira; Patrick Dumas; Rui Oliveira
Journal:  Bioprocess Biosyst Eng       Date:  2022-10-16       Impact factor: 3.434

Review 5.  Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

Authors:  Michael A Henson
Journal:  Biochem Soc Trans       Date:  2015-12       Impact factor: 5.407

Review 6.  Towards predictive models of the human gut microbiome.

Authors:  Vanni Bucci; Joao B Xavier
Journal:  J Mol Biol       Date:  2014-04-12       Impact factor: 5.469

Review 7.  Dynamic flux balance analysis for synthetic microbial communities.

Authors:  Michael A Henson; Timothy J Hanly
Journal:  IET Syst Biol       Date:  2014-10       Impact factor: 1.615

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

Authors:  Jose A Gomez; Kai Höffner; Paul I Barton
Journal:  BMC Bioinformatics       Date:  2014-12-18       Impact factor: 3.169

9.  Dynamic flux balance modeling to increase the production of high-value compounds in green microalgae.

Authors:  Robert J Flassig; Melanie Fachet; Kai Höffner; Paul I Barton; Kai Sundmacher
Journal:  Biotechnol Biofuels       Date:  2016-08-04       Impact factor: 6.040

10.  Spatiotemporal modeling of microbial metabolism.

Authors:  Jin Chen; Jose A Gomez; Kai Höffner; Poonam Phalak; Paul I Barton; Michael A Henson
Journal:  BMC Syst Biol       Date:  2016-03-01
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.