IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction

Gu, Deqing; Zhang, Cheng; Zhou, Shengguo; Wei, Liujing; Hua, Qiang

doi:10.1016/j.compbiolchem.2016.02.014

Cited by 24 publications

(28 citation statements)

References 35 publications

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“…In recent decades, genome-scale metabolic models (GEMs) have been utilized to better understand the genotype–phenotype relationship in microbial metabolism (Famili et al 2003; Lewis et al 2012; Matsuoka and Shimizu 2015) and suggest manipulation strategies for strain design (Burgard et al 2003; Gu et al 2016; Jian et al 2016). Meanwhile, a steady-state flux space is defined, which contains all possible functional states due to the constraint-based reconstructed network (Orth et al 2010; Schellenberger et al 2011).…”

Section: Introductionmentioning

confidence: 99%

In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Jian

Zhang

et al. 2016

Bioresour. Bioprocess.

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BackgroundSuccinic acid is a valuable product due to its wide-ranging utilities. To improve succinate production and reduce by-products formation, Escherichia coli NZN111 was constructed by insertional inactivation of lactate dehydrogenase (LDH) and pyruvate formate lyase (PFL) encoded by the genes ldhA and pflB, respectively. However, this double-deletion mutant is incapable of anaerobically growing on glucose in rich or minimal medium even with acetate supplementation. A widespread hold view is that the inactivation of NADH-dependent LDH limits the regeneration of NAD+ and consequently disables proper growth under anaerobic conditions.ResultsIn this study, genome-scale metabolic core model of E. coli was reconstructed and employed to perform all simulations in silico according to the reconstruction of engineered strain E. coli NZN111. Non-optimized artificial centering hit-and-run (ACHR) method and metabolite flux-sum analysis were utilized to evaluate metabolic characteristics of strains. Thus, metabolic characteristics of the strains wild-type E. coli, ldhA mutant, pflB mutant, and NZN111 under anaerobic conditions were successfully unraveled.Conclusions We found a viewpoint contrary to the widespread realization that an NADH/NAD+ in NZN111 mainly resulted from the inactivation of PFL rather than the inactivation of LDH. In addition, the two alternative anaerobic fermentation pathways, lactate and ethanol production pathways, were blocked owing to the disruption of ldhA and pflB, resulting in insufficient NAD+ regeneration to oxidize or metabolize glucose for cell growth. Furthermore, we speculated reaction NADH16, the conversion of ubiquinone-8 (q8) to ubiquinol-8 (q8h2), as a potential amplification target for anaerobically improving cell growth and succinate production in NZN111.Electronic supplementary materialThe online version of this article (doi:10.1186/s40643-016-0125-5) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Jian

Zhang

et al. 2016

Bioresour. Bioprocess.

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“…Fast algorithm of knockout screening for target production based on shadow price analysis (FastPros) is an iterative screening approach to discover reaction knockout strategies [11]. IdealKnock utilizes the ideal-type flux distribution and ideal point=(GR, PR) [4]. Parsimonious enzyme usage FBA (pFBA) [8] finds a subset of genes and proteins that contribute to the most efficient metabolic network topology under the given growth conditions.…”

Section: Introductionmentioning

confidence: 99%

Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

Tamura

2019

Preprint

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Metabolic network analysis through flux balance has been established as a method for the computational design of production strains in metabolic engineering. A key principle often used in this method is the production of target metabolites as by-products of cell growth. Recently, the strong coupling-based method was used to demonstrate that the coupling of growth and production is possible for nearly all metabolites through knocking out reactions in genome-scale metabolic models of five major metabolite producing organisms under aerobic conditions. However it remains unknown whether this coupling is always possible using reaction knockouts under anaerobic conditions. Particularly, when growing Saccharomyces cerevisiae under anaerobic conditions, knockout strategies using the strong coupling-based method were possible for only 3.9% of all target metabolites. Here, we found that the coupling of growth and production is theoretically possible for 91.6% metabolites in genome-scale models of S. cerevisiae even under anaerobic conditions. This analysis was conducted for the worst cases using flux variability analysis. To demonstrate the feasibility of the coupling, we derived appropriate reaction knockouts using a new algorithm for target production in which the search space was divided into small cubes (CubeProd). Our results are of fundamental importance for computational metabolic engineering under anaerobic conditions. The developed software CubeProd implemented in MATLAB and obtained reaction knockouts are available on "

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“…In addition to TAG production, acetyl-CoA and malonyl-CoA are also used for fatty acid synthesis, amino acid synthesis, and glycerophospholipid synthesis. To gain additional insight into TAG biosynthesis in Y. lipolytica, our previously published simulation methods OptGeneKnock (Zhang et al 2015a), IdealKnock (Gu et al 2016), and APGC (Jian et al 2016b), were employed to identify metabolic engineering strategies to enhance TAG production in cells grown MG medium. The OptGeneKnock algorithm was modified from OptKnock algorithm through LTM , where OptKnock is able to screen the best potential reaction knockout strategies based on a bilevel programming framework.…”

Section: Prediction Of Metabolic Engineering Strategies For Improvingmentioning

confidence: 99%

“…For analysis of TAG production, two novel knockout algorithms OptGeneKnock (Zhang et al 2015a) and IdealKnock (Gu et al 2016) and one overexpression algorithm, analysis of production and growth coupling (APGC) (Jian et al 2016b) were carried out for cells grown in MG medium. The APGC program was applied to identify amplification targets for improving production, and OptGeneKnock and IdealKnock were used to identify knockout targets.…”

Section: Identification Of Potential Targets Through Gene-level Algormentioning

confidence: 99%

“…Normally, gene-level targets are more feasible and efficient than reaction-level ones according to the complex associations between genes and reactions in metabolic model. For instance, our previously published algorithms, OptGeneKnock (Zhang et al 2015a), IdealKnock (Gu et al 2016), and APGC (Jian et al 2016b), which could provide potential gene-level strategies based on logic transformation of model (LTM) (Zhang et al 2015b), are more feasible for in vivo implementation.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol

Wei

Jian

Chen

et al. 2017

Bioresour. Bioprocess.

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Background: Yarrowia lipolytica is widely studied as a non-conventional model yeast owing to the high level of lipid accumulation. Therein, triacylglycerol (TAG) is a major component of liposome. In order to investigate the TAG biosynthesis mechanism at a systematic level, a novel genome-scale metabolic model of Y. lipolytica was reconstructed based on a previous model iYL619_PCP published by our lab and another model iYali4 published by Kerkhoven et al. Results:The novel model iYL_2.0 contains 645 genes, 1083 metabolites, and 1471 reactions, which was validated more effective on simulations of specific growth rate. The precision of 29 carbon sources utilities reached up to 96.6% when simulated by iYL_2.0. In minimal growth medium, 111 genes were identified as essential for cell growth, whereas 66 essential genes were identified in yeast extract medium, which were verified by database of essential genes, suggesting a better prediction ability of iYL_2.0 in comparison with other existing models. In addition, potential metabolic engineering targets of improving TAG production were predicted by three in silico methods developed in-house, and the effects of amino acids supplementation were investigated based on model iYL_2.0. Conclusions:The reconstructed model iYL_2.0 is a powerful platform for efficiently optimizing the metabolism of TAG and systematically understanding the physiological mechanism of Y. lipolytica. Keywords:

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IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction

Cited by 24 publications

References 35 publications

In silico profiling of cell growth and succinate production in Escherichia coli NZN111

In silico profiling of cell growth and succinate production in Escherichia coli NZN111

Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

Reconstruction of genome-scale metabolic model of Yarrowia lipolytica and its application in overproduction of triacylglycerol

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