Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation

Meadows, Adam L.; Karnik, Rahi; Lam, Harry; Forestell, Sean; Snedecor, Brad

doi:10.1016/j.ymben.2009.07.006

Cited by 106 publications

(80 citation statements)

References 28 publications

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“…The biomass mass transfer coefficients k X (0) were tuned such that steady-state, mature biofilms would have biomass concentrations within published ranges [13]. Due to the lack of experimentally determined values for B. thetaiotaomicron and F. prausnitzii, glucose and amino acid maximum uptake rates v max reported for E. coli [70] were used for all three species. All byproducts were assumed to have the same v max values as glucose.…”

Section: Model Parameterization and Solutionmentioning

confidence: 99%

“…All byproducts were assumed to have the same v max values as glucose. Furthermore, all nutrients and byproducts were assumed to have the same Michaelis-Menten constants K m as reported for glucose uptake in E. coli [70]. Planktonic experiments could be conducted to estimate these uptake kinetic parameters, although different parameter values may be appropriate under biofilm conditions.…”

Section: Model Parameterization and Solutionmentioning

confidence: 99%

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Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Henson

Phalak

2017

Processes

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Abstract:The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability.

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Section: Model Parameterization and Solutionmentioning

confidence: 99%

Section: Model Parameterization and Solutionmentioning

confidence: 99%

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Henson

Phalak

2017

Processes

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“…The core of the models was a genome-scale constraint-based metabolic model of S. cerevisiae (Herrgård et al 2008;Jouhten et al 2012;Aung et al 2013). dFBA has been shown to successfully approximate culture dynamics over a longer time scale when the extracellular culture dynamics are magnitudes slower than the intracellular metabolic dynamics (Varma and Palsson 1994;Mahadevan et al 2002;Sainz et al 2003;Hjersted and Henson 2009;Meadows et al 2010;Jouhten et al 2012). This is often the case since microbes tend to strive for steady-state metabolism to maintain intracellular homeostasis.…”

Section: Dynamic Flux Balance Analysismentioning

confidence: 99%

Xylose-induced dynamic effects on metabolism and gene expression in engineered Saccharomyces cerevisiae in anaerobic glucose-xylose cultures

Alff-Tuomala

Salusjärvi

Barth

et al. 2015

Appl Microbiol Biotechnol

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Xylose is present with glucose in lignocellulosic streams available for valorisation to biochemicals. Saccharomyces cerevisiae has excellent characteristics as a host for the bioconversion, except that it strongly prefers glucose to xylose, and the co-consumption remains a challenge. Further, since xylose is not a natural substrate of S. cerevisiae, the regulatory response it induces in an engineered strain cannot be expected to have evolved for its utilisation. Xylose-induced effects on metabolism and gene expression during anaerobic growth of an engineered strain of S. cerevisiae on medium containing both glucose and xylose medium were quantified. The gene expression of S. cerevisiae with an XR-XDH pathway for xylose utilisation was analysed throughout the cultivation: at early cultivation times when mainly glucose was metabolised, at times when xylose was co-consumed in the presence of low glucose concentrations, and when glucose had been depleted and only xylose was being consumed. Cultivations on glucose as a sole carbon source were used as a control. Genome-scale dynamic flux balance analysis models were simulated to analyse the metabolic dynamics of S. cerevisiae. The simulations quantitatively estimated xylose-dependent flux dynamics and challenged the utilisation of the metabolic network. A relative increase in xylose utilisation was predicted to induce the bi-directionality of glycolytic flux and a redox challenge even at low glucose concentrations. Remarkably, xylose was observed to specifically delay the glucose-dependent repression of particular genes in mixed glucose-xylose cultures compared to glucose cultures. The delay occurred at a cultivation time when the metabolic flux activities were similar in the both cultures.

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“…However, we also note that the coefficient of KNO C2 is slightly negative (À0.018) which means that removal of competitive pathways (i.e., genetic knockout) may not successfully improve the biosynthesis yield. While this conclusion seems highly uncertain ( P-value ¼ 0.88; standard error ¼ 0.11), it has been shown via in silico analysis that knockout strategies cannot ensure the improvement for product yield even though it is expected to channel more carbon to the final product (Blazeck and Alper, 2010;Boghigian et al, 2010;Feist et al, 2010;Meadows et al, 2010). This inconsistency can be attributed to unfavorable metabolite accumulation and low capacity of biosynthesis pathways for flux amplification after removal of competitive pathways.…”

Section: à0018mentioning

confidence: 99%

“…To this end, systems biology-based models have been developed to provide useful information for rationally engineering microbial hosts with the desired phenotype as well as to design optimal fermentation conditions (Blazeck and Alper, 2010;Boghigian et al, 2010;Feist et al, 2010;Meadows et al, 2010). Cell-wide metabolic analysis via fluxomics and metabolic control theories are often used to estimate metabolic potential, product yield, nutrient limitations, and gene targets for metabolic engineering (Feist et al, 2010;Wildermuth, 2000).…”

Section: Introductionmentioning

confidence: 99%

Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

Colletti

Goyal

Varman

et al. 2010

Biotech & Bioengineering

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In the production of chemicals via microbial fermentation, achieving a high yield is one of the most important objectives. We developed a statistical model to analyze influential factors that determine product yield by compiling data obtained from engineered Escherichia coli developed within last 10 years. Using both numerical and ordinal variables (e.g., enzymatic steps, cultivation conditions, and genetic modifications) as input parameters, our model revealed that cultivation modes, nutrient supplementation, and oxygen conditions were the three significant factors for improving product yield. Generally, the model showed that product yield decreases as the number of enzymatic steps in the biosynthesis pathway increases (7-9% loss of yield per enzymatic step). Moreover, overexpression of enzymes or removal of competitive pathways (e.g., knockout) does not necessarily result in an amplification of product yield (P-value>0.1), possibly because of limited capacity in the biosynthesis pathway to accommodate an increase in flux. The model not only provides general guidelines for metabolic engineering and fermentation processes, but also allows a priori estimation and comparison of product yields under designed cultivation conditions.

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Application of dynamic flux balance analysis to an industrial Escherichia coli fermentation

Cited by 106 publications

References 28 publications

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Byproduct Cross Feeding and Community Stability in an In Silico Biofilm Model of the Gut Microbiome

Xylose-induced dynamic effects on metabolism and gene expression in engineered Saccharomyces cerevisiae in anaerobic glucose-xylose cultures

Evaluating Factors That Influence Microbial Synthesis Yields by Linear Regression with Numerical and Ordinal Variables

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