Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli

Mahadevan, Radhakrishnan; Edwards, Jeremy S.; Doyle, Francis J.

doi:10.1016/s0006-3495(02)73903-9

Cited by 835 publications

(907 citation statements)

References 35 publications

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“…Glucose uptake is carried out by the PTS system (Reaction 1). (3). In this reduced network, NADPH is not considered separately but lumped into NADH under the assumption that there exists free interchange of reducing equivalents between these cofactor pools.…”

Section: Reduced Networkmentioning

confidence: 99%

“…In the lumped biomass synthesis reaction, the stoichiometric coefficient of ATP is fixed at 41.25 mmol ATP/g-biomass obtained from the internet-based database of Palsson. 26 (3). An irreversible reaction which converts ATP for maintenance purposes is added.…”

Section: More Detailed Networkmentioning

confidence: 99%

“…Once such biological information has been amassed, a mathematical approach can be used to predict metabolic behavior to the extent determined by the nature of the modeling framework. Thus flux balance analysis (FBA), [1][2][3] metabolic flux analysis (MFA), [4][5][6] and other flux-based approaches such as elementary flux modes, 7,8 which are based on steady-state assumptions, concentrate on predicting all metabolic fluxes of interest (for example, excretion rates of end-products, growth rate of biomass, exchange rate of cofactors, and so on). Dynamic approaches, on the other hand, will require dynamic measurements of various extra and intracellular variables for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

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Section: Reduced Networkmentioning

confidence: 99%

Section: More Detailed Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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“…In this study, we utilize a dynamic extension of FBA (Hjersted et al, 2007;Mahadevan et al, 2002) to simulate and optimize batch cultures of specialized glucose and xylose consuming microbes. The first system consists of the E. coli mutants ALS1008 and ZSC113, two strains engineered to selectively uptake glucose and xylose, respectively (Curtis and Epstein, 1975;Eiteman et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

129

131

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Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

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“…11,12 Various experimental and associated computational methods have been developed to estimate dynamic fluxes through metabolic networks including kinetic models, 13 13 C-metabolic flux analysis (MFA), [14][15][16] and dynamic flux balance analysis (DFBA). 17,18 The development of kinetic models is hampered by limited availability and accuracy of information about condition-specific (in vivo) kinetic parameters, 19 13 C MFA is expensive and tracer availability is limited. 20 Flux balance analysis (FBA) is a constraint-based modelling approach to determine fluxes in steady state.…”

Section: Introductionmentioning

confidence: 99%

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

et al. 2015

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. (2015). MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Molecular BioSystems, 11(1), 137-145. DOI: 10.1039/c4mb00510d General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters.Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13 C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

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Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli

Cited by 835 publications

References 35 publications

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

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