Genome‐Scale Metabolic Modeling and<i>In silico</i>Strain Design of<i>Escherichia coli</i>

Lee, Na-Rae; Lee, Dong-Yup

doi:10.1002/9783527696130.ch4

Cited by 3 publications

(1 citation statement)

References 145 publications

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“…Constraint-based metabolic modeling (CBM) is a simple and widely used approach that requires only metabolic network stoichiometry and environmental constraints to describe the cellular phenotype from genotype, and thus can be readily exploited to characterize and predict cellular behaviours under perturbed conditions [ 7 , 8 ]. In this regard, several algorithms based on CBM framework have been developed for finding relevant metabolic engineering targets towards the enhanced production [ 9 – 11 ]. While most of these algorithms can suggest various strain design strategies via gene knockout, upregulation and downregulation [ 9 , 12 ], metabolite intensification/attenuation [ 13 ] and also cofactor balancing [ 14 , 15 ], only a handful of them are related to TR manipulation targeting.…”

Section: Introductionmentioning

confidence: 99%

In silico model-guided identification of transcriptional regulator targets for efficient strain design

Koduru

Lee

2018

Microb Cell Fact

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BackgroundCellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application.ResultsWe developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification.ConclusionsIn silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-1015-7) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

In silico model-guided identification of transcriptional regulator targets for efficient strain design

Koduru

Lee

2018

Microb Cell Fact

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A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Liu

Zhang

et al. 2021

Bioprocess Biosyst Eng

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Genome-Scale In Silico Analysis for Enhanced Production of Succinic Acid in Zymomonas mobilis

et al. 2018

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Abstract:Presented herein is a model-driven strategy for characterizing the production capability of expression host and subsequently identifying targets for strain improvement by resorting to network structural comparison with reference strain and in silico analysis of genome-scale metabolic model. The applicability of the strategy was demonstrated by exploring the capability of Zymomonas mobilis, as a succinic acid producer. Initially, the central metabolism of Z. mobilis was compared with reference producer, Mannheimia succiniciproducens, in order to identify gene deletion targets. It was followed by combinatorial gene deletion analysis. Remarkably, resultant in silico strains suggested that knocking out pdc, ldh, and pfl genes encoding pyruvate-consuming reactions as well as the cl gene leads to fifteen-fold increase in succinic acid molar yield. The current exploratory work could be a promising support to wet experiments by providing guidance for metabolic engineering strategies and lowering the number of trials and errors.

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Genome‐Scale Metabolic Modeling andIn silicoStrain Design ofEscherichia coli

Cited by 3 publications

References 145 publications

In silico model-guided identification of transcriptional regulator targets for efficient strain design

In silico model-guided identification of transcriptional regulator targets for efficient strain design

A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models

Genome-Scale In Silico Analysis for Enhanced Production of Succinic Acid in Zymomonas mobilis

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