Probing the performance limits of the <i>Escherichia coli</i> metabolic network subject to gene additions or deletions

Burgard, Anthony P.; Maranas, Costas D.

doi:10.1002/bit.1127

Cited by 95 publications

(53 citation statements)

References 55 publications

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“…These observations were also supported by other studies (Larson et al 1993;Rerenci 1999;Burgard & Maranas 2001;Canonaco et al 2001;Zhao et al 2004a,b). The pgi mutant grew significantly more slowly and consumed less glucose (Fig.…”

Section: Discussionsupporting

confidence: 88%

“…When cells were cultivated in continuous mode at a dilution rate of 0.10 h −1 , the steady-state growth rate was attained after six to eight volume changes (Burgard & Maranas 2001;Hoque et al 2004;Hua et al 2004). To observe carbon balance during cellular growth, several yields and the intracellular metabolite concentrations were calculated.…”

Section: Chemostat Cultures and Growth Characteristicsmentioning

confidence: 99%

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Comparison of dynamic responses of cellular metabolites in Escherichia coli to pulse addition of substrates

et al. 2011

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Abstract:We conducted an integrated study of cell growth parameters, product formation, and the dynamics of intracellular metabolite concentrations using Escherichia coli with genes knocked out in the glycolytic and oxidative pentose phosphate pathway (PPP) for glucose catabolism. We investigated the same characteristics in the wild-type strain, using acetate or pyruvate as the sole carbon source. Dramatic effects on growth parameters and extracellular and intracellular metabolite concentrations were observed after blocking either glycolytic breakdown of glucose by inactivation of phosphoglucose isomerase (disruption of pgi gene) or pentose phosphate breakdown of glucose by inactivation of glucose-6-phosphate dehydrogenase (disruption of zwf gene). Reducing power (NADPH) was mainly produced through PPP when the pgi gene was knocked out, while NADPH was produced through the tricarboxylic acid (TCA) cycle by isocitrate dehydrogenase or NADP-linked malic enzyme when the zwf gene was knocked out. As expected, when the pgi gene was knocked out, intracellular concentrations of PPP metabolites were high and glycolytic and concentrations of TCA cycle pathway metabolites were low. In the zwf gene knockout, concentrations of PPP metabolites were low and concentrations of intracellular glycolytic and TCA cycle metabolites were high.

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Section: Discussionsupporting

confidence: 88%

Section: Chemostat Cultures and Growth Characteristicsmentioning

confidence: 99%

Comparison of dynamic responses of cellular metabolites in Escherichia coli to pulse addition of substrates

et al. 2011

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“…For example, if a previously identified pathway uses reactions 1, 2, and 3, then the following constraint prevents the same reactions from being simultaneously considered in subsequent solutions: y 1 + y 2 + y 3 Յ 2. More details can be found in an earlier paper by Burgard and Maranas (2001).…”

Section: Discussionmentioning

confidence: 99%

“…to enable a desired biotransformation. Our group has already contributed toward this objective on a much smaller scale (Burgard and Maranas 2001). Using this work as a starting point here, we aim to pinpoint gene additions identified from a Universal database composed of ∼4000 elementally balanced reactions as well as to investigate multiple hosts and substrate choices (see Supplemental material at www.genome.org and http://fenske.che.psu.edu/Faculty/CMaranas/pubs.html).…”

Section: The Optstrain Proceduresmentioning

confidence: 99%

OptStrain: A computational framework for redesign of microbial production systems

Pharkya¹,

Burgard²,

Maranas³

2004

Genome Res.

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442

311

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This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.[Supplemental material is available online at www.genome.org. The Universal database can be found at http://fenske.che.psu.edu/Faculty/CMaranas/pubs.html.]A fundamental goal in systems biology is to elucidate the complete "palette" of biotransformations accessible to nature in living systems. This goal parallels the continuing quest in biotechnology to construct microbial strains capable of accomplishing an ever-expanding array of desired biotransformations. These biotransformations are aimed at products that range from simple precursor chemicals (Nakamura and Whited 2003;Causey et al. 2004) or complex molecules such as carotenoids (Misawa et al. 1991), to electrons in biofuel cells (Liu et al. 2004) or batteries (Bond et al. 2002;Bond and Lovley 2003), to even microbes capable of precipitating heavy metal complexes in bioremediation applications (Finneran et al. 2002;Lovley 2003;Methe et al. 2003). Recent developments in molecular biology and recombinant DNA technology have ushered in a new era in the ability to shape the gene content and expression levels for microbial production strains in a direct and targeted fashion (Stephanopoulos 2002). The astounding range and diversity of these newly acquired capabili...

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“…Imielinski and Belta have recently introduced N etKO, a relaxed implementation of Klamt and Gilles' method that scales to arbitrarily large metabolic networks; however, this method does not guarantee the discovery of a knockout combination with a given desired property [16]. Finally, a related work employs mixed integer linear programming (MILP) to uncover minimal knockout combinations that TuB05.1 978-1-4244-3124-3/08/$25.00 ©2008 IEEEachieve a certain network objective [17], [18], [19]. This approach is limited primarily by the computational difficulty associated with solving high-dimensional MILPs.…”

Section: Introductionmentioning

confidence: 99%

Metabolic networks analysis using convex optimization

Julius

Imieliński

Pappas

2008

2008 47th IEEE Conference on Decision and Control

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Metabolic networks map the biochemical reactions in a living cell to the flow of various chemical substances in the cell, which are called metabolites. A standard model of a metabolic network is given as a linear map from the reaction rates to the change in metabolites concentrations. We study two problems related to the analysis of metabolic networks, the minimal network problem and the minimal knockout problem. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/grasp_papers/34Metabolic Networks Analysis using Convex Optimization A. Agung Julius, Marcin Imielinski and George J. Pappas Abstract-Metabolic networks map the biochemical reactions in a living cell to the flow of various chemical substances in the cell, which are called metabolites. A standard model of a metabolic network is given as a linear map from the reaction rates to the change in metabolites concentrations. We study two problems related to the analysis of metabolic networks, the minimal network problem and the minimal knockout problem.The minimal network problem amounts to finding the smallest set of reactions that can sustain the production of a metabolite. The minimal knockout problem deals with the question of finding the smallest set of knockouts (reactions with zero rates) that renders the production of a metabolite infeasible.In this paper we present a convex relaxation technique that results in a very fast computation for the solution to both problems. We also demonstrate that the minimal knockout problem is related to the dual of the minimal network problem.

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Probing the performance limits of the Escherichia coli metabolic network subject to gene additions or deletions

Cited by 95 publications

References 55 publications

Comparison of dynamic responses of cellular metabolites in Escherichia coli to pulse addition of substrates

Comparison of dynamic responses of cellular metabolites in Escherichia coli to pulse addition of substrates

OptStrain: A computational framework for redesign of microbial production systems

Metabolic networks analysis using convex optimization

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