Metabolic Flux Analysis for Escherichia coli by Flux Balance Analysis

Matsuoka, Yu; Shimizu, Kazuyuki

doi:10.1007/978-1-4939-1170-7_15

Cited by 4 publications

(3 citation statements)

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“…The deletion of genes encoding for enzymes involved in the by-product generation is a common practice in biotechnology (Förster and Gescher 2014; Ruiz et al 2012). However, it has been observed a decrease in the biomass yield after the deletion of pflB (Wang et al 2010), pflA (Matsuoka and Shimizu 2014), pta (Matsuoka and Shimizu 2014), adhE (Matsuoka and Shimizu 2014), and ldhA (Yang et al 1999). On the other hand, De Mey and co-workers did not find differences in the maintenance ATP (around 0.088 mol of ATP/C-mol biomass /h) between E. coli MG1655 and its mutant lacking the genes ackA , pta , and poxB while growing anaerobically on glucose as the sole carbon source (De Mey et al 2007).…”

Section: Discussionmentioning

confidence: 99%

“…It is possible that those gene deletions provoked metabolic bottlenecks, triggering the expression of genes involved in futile cycles. As a matter of fact, in the cases of the deletion of pflA , pta , and adhE , increments in the concentration of several central pathway metabolites were observed, including fructose-1,6-bisphosphate (Matsuoka and Shimizu 2014), which is widely acknowledged as a signaling metabolite (Kochanowski et al 2013).…”

Section: Discussionmentioning

confidence: 99%

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Metabolism of sucrose in a non-fermentative Escherichia coli under oxygen limitation

Olavarría

Fina

Velasco

et al. 2019

Appl Microbiol Biotechnol

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Biotechnological industry strives to develop anaerobic bioprocesses fueled by abundant and cheap carbon sources, like sucrose. However, oxygen-limiting conditions often lead to by-product formation and reduced ATP yields. While by-product formation is typically decreased by gene deletion, the breakdown of oligosaccharides with inorganic phosphate instead of water could increment the ATP yield. To observe the effect of oxygen limitation during sucrose consumption, a non-fermentative Escherichia coli K-12 strain was transformed with genes enabling sucrose assimilation. It was observed that the combined deletion of the genes adhE , adhP , mhpF , ldhA , and pta abolished the anaerobic growth using sucrose. Therefore, the biomass-specific conversion rates were obtained using oxygen-limited continuous cultures. Strains performing the breakdown of the sucrose by hydrolysis (SUC-HYD) or phosphorolysis (SUC-PHOSP) were studied in such conditions. An experimentally validated in silico model, modified to account for plasmid and protein burdens, was employed to calculate carbon and electron consistent conversion rates. In both strains, the biomass yields were lower than expected and, strikingly, SUC-PHOSP showed a yield lower than SUC-HYD. Flux balance analyses indicated a significant increase in the non-growth-associated ATP expenses by comparison with the growth on glucose. The observed fructose-1,6-biphosphatase and phosphoglucomutase activities, as well as the concentrations of glycogen, suggest the operation of ATP futile cycles triggered by a combination of the oxygen limitation and the metabolites released during the sucrose breakdown. Electronic supplementary material The online version of this article (10.1007/s00253-019-09909-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Metabolism of sucrose in a non-fermentative Escherichia coli under oxygen limitation

Olavarría

Fina

Velasco

et al. 2019

Appl Microbiol Biotechnol

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“…For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17].…”

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Of (Biological) Models and Simulations

Ling¹

2016

MOJPB

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All models are wrong, but some are useful. Box and Draper [1].All models are right… Most are useless. Tarpey [2]The role of models has been recognized as an important aspect to the scientific method [3] for more than 70 years [4]. In recent years, there are increasing interests in metabolic modeling in biology [5,6]. The process of building a model is known as modeling. Generally speaking, models can be classified as either material models or formal models. Rosenblueth & Wiener [7] consider material models to be a physical representation of the object under study whereas formal models are symbolic or logical representations of the object under study. For example, a scale down model of an airplane for testing its aerodynamic properties in a wind tunnel is a material model. The main purpose of a scaleddown material model is the ease of study, which had been clearly demonstrated by a study on fireplace more than 200 years ago [8].A formal model, on the other hand, is an abstract but unambiguous description of a physical object or phenomenon, usually in logical or mathematical constructs. Commonly used mathematical constructs for building formal models are rate equations, such as differential equations [9], or state transitions, such as petri nets [10]. Other modeling formalisms had been reviewed by Machado [11]. Besides differentiating models by their underlying formalism, models can also be classified by their informational complexity. At the lowest level, the presence or absence of associations between 2 entities (such as cells or molecules) can be represented as a Boolean term. This results in a binary state petri net or Boolean network, which can be used to study cycles and reachability. For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17]. The main advantage of kinetic models over steady state model is the ability to simulate each state (or molecules in the context of metabolic modeling) across time, in addition to the ability to model homeostasis [12]. This will allow for identification of bottlenecks in the metabolism [18].With advancements in computing power, formal models, being constructed in mathematical or logical format, render it possible for computation. Such computation or execution of formal models is commonly known as simulation. Hence, by definition, one can "build a model", then "run or execute a model" or "run or execute a simulation" but in no means, one can "build a simulation". The ability to render execution of a model; thus, an executable or simulatable model; defines the fundamental difference between a material model an...

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