Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

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Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch014 ◽

2013 ◽

pp. 240-258 ◽

Cited By ~ 1

Author(s):

Takeyuki Tamura ◽

Kazuhiro Takemoto ◽

Tatsuya Akutsu

Keyword(s):

Integer Programming ◽

Metabolic Network ◽

Drug Targets ◽

Metabolic Networks ◽

Boolean Model ◽

Pentose Phosphate ◽

Minimum Size ◽

E Coli ◽

Potential Applications ◽

Vertex Set

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

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Integer Programming-Based Method for Designing Synthetic Metabolic Networks by Minimum Reaction Insertion in a Boolean Model

PLoS ONE ◽

10.1371/journal.pone.0092637 ◽

2014 ◽

Vol 9 (3) ◽

pp. e92637 ◽

Cited By ~ 6

Author(s):

Wei Lu ◽

Takeyuki Tamura ◽

Jiangning Song ◽

Tatsuya Akutsu

Keyword(s):

Integer Programming ◽

Metabolic Networks ◽

Boolean Model

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An Unexpected Route to an Essential Cofactor:Escherichia coliRelies on Threonine for Thiamine Biosynthesis

mBio ◽

10.1128/mbio.01840-15 ◽

2016 ◽

Vol 7 (1) ◽

Cited By ~ 14

Author(s):

Jannell V. Bazurto ◽

Kristen R. Farley ◽

Diana M. Downs

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Metabolic Networks ◽

Thiamine Pyrophosphate ◽

Metabolic Potential ◽

E Coli ◽

Regulatory Strategies ◽

Phosphoribosylpyrophosphate Amidotransferase ◽

Pyrimidine Moiety ◽

Thiamine Biosynthesis

ABSTRACTMetabolism consists of biochemical reactions that are combined to generate a robust metabolic network that can respond to perturbations and also adapt to changing environmental conditions.Escherichia coliandSalmonella entericaare closely related enterobacteria that share metabolic components, pathway structures, and regulatory strategies. The synthesis of thiamine inS. entericahas been used to define a node of the metabolic network by analyzing alternative inputs to thiamine synthesis from diverse metabolic pathways. To assess the conservation of metabolic networks in organisms with highly conserved components, metabolic contributions to thiamine synthesis inE. coliwere investigated. Unexpectedly, we found that, unlikeS. enterica,E. colidoes not use the phosphoribosylpyrophosphate (PRPP) amidotransferase (PurF) as the primary enzyme for synthesis of phosphoribosylamine (PRA).In fact, our data showed that up to 50% of the PRA used byE. colito make thiamine requires the activities of threonine dehydratase (IlvA) and anthranilate synthase component II (TrpD). Significantly, the IlvA- and TrpD-dependent pathway to PRA functions inS. entericaonly in the absence of a functionalreactiveintermediatedeaminase (RidA) enzyme, bringing into focus how these closely related bacteria have distinct metabolic networks.IMPORTANCEIn most bacteria, includingSalmonellastrains andEscherichia coli, synthesis of the pyrimidine moiety of the essential coenzyme, thiamine pyrophosphate (TPP), shares enzymes with the purine biosynthetic pathway. Phosphoribosylpyrophosphate amidotransferase, encoded by thepurFgene, generates phosphoribosylamine (PRA) and is considered the first enzyme in the biosynthesis of purines and the pyrimidine moiety of TPP. We show here that, unlikeSalmonella,E. colisynthesizes significant thiamine from PRA derived from threonine using enzymes from the isoleucine and tryptophan biosynthetic pathways. These data show that two closely related organisms can have distinct metabolic network structures despite having similar enzyme components, thus emphasizing caveats associated with predicting metabolic potential from genome content.

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Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

10.1101/2021.08.13.456222 ◽

2021 ◽

Author(s):

Thomas James Moutinho ◽

Benjamin C Neubert ◽

Matthew L Jenior ◽

Jason A. Papin

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Biological Data ◽

Supporting Evidence ◽

Phenotypic Data ◽

E Coli ◽

Simultaneous Integration ◽

Improve Model ◽

K 12 ◽

Genome Scale

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

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Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli

Journal of Integrative Bioinformatics ◽

10.1515/jib-2019-0073 ◽

2020 ◽

Vol 17 (1) ◽

Author(s):

Mee K. Lee ◽

Mohd Saberi Mohamad ◽

Yee Wen Choon ◽

Kauthar Mohd Daud ◽

Nurul Athirah Nasarudin ◽

...

Keyword(s):

Escherichia Coli ◽

Metabolic Engineering ◽

Metabolic Network ◽

Metabolic Networks ◽

E Coli ◽

A Cell ◽

Wet Lab ◽

Genome Level ◽

Metabolites Production ◽

Modelling Methods

AbstractThe metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

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Probabilistic Thermodynamic Analysis of Metabolic Networks

Bioinformatics ◽

10.1093/bioinformatics/btab194 ◽

2021 ◽

Author(s):

Mattia G Gollub ◽

Hans-Michael Kaltenbach ◽

Jörg Stelling

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Supplementary Information ◽

Thermodynamic Quantities ◽

Substrate Channeling ◽

Comprehensive Description ◽

E Coli ◽

Metabolic Fluxes ◽

Steady State Flux ◽

Current Sampling

Abstract Motivation Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. Results We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli’s metabolic capabilities. Availability Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta. Supplementary information Supplementary data are available at Bioinformatics online.

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Solving the Dynamic Model of Metabolic Network of Escherichia coli by Adams Methods

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.424-425.900 ◽

2012 ◽

Vol 424-425 ◽

pp. 900-903 ◽

Cited By ~ 1

Author(s):

Qing Hua Zhou ◽

Jing Cui ◽

Juan Xie

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Dynamic Model ◽

Metabolic Networks ◽

Flux Distribution ◽

Penalty Function Methods ◽

E Coli ◽

Adams Methods ◽

Metabolite Concentrations ◽

Simulation Results

We previously analyzed the dynamic flux distribution and predicted glucose, biomass concentrations of metabolic networks ofE. coliby using the penalty function methods. But the consequences were not as well as we expected. In order to improve the predicated accuracy of the metabolite concentrations, instead of using Runge-Kutta algorithm, we apply Adams methods which belong to the multi-step ones in the process that solves the dynamic model of metabolic network ofE. coliand obtain better simulation results on the metabolic concentrations.

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Effective Dynamic Models of Metabolic Networks

10.1101/047316 ◽

2016 ◽

Author(s):

Michael Vilkhovoy ◽

Mason Minot ◽

Jeffrey D. Varner

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Dynamic Models ◽

Biochemical Networks ◽

Promising Alternative ◽

E Coli ◽

Flux Balance ◽

Comparable Performance ◽

Elementary Modes ◽

Balance Analysis

AbstractMathematical models of biochemical networks are useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models in combination with elementary modes (HCM) is a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating elementary modes. In this study, we developed the hybrid cybernetic modeling with flux balance analysis or HCM-FBA technique which uses flux balance solutions instead of elementary modes to dynamically model metabolism. We show HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobicE. colinetwork. Next, HCM-FBA was applied to a larger metabolic network of aerobicE. colimetabolism which was infeasible for HCM (29 FBA modes versus more than 153,000 elementary modes). Global sensitivity analysis further reduced the number of FBA modes required to describe the aerobicE. colidata, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks where the generation of elementary modes is infeasible.

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The Response to 2-Aminoacrylate Differs in Escherichia coli and Salmonella enterica, despite Shared Metabolic Components

Journal of Bacteriology ◽

10.1128/jb.00140-17 ◽

2017 ◽

Vol 199 (14) ◽

Cited By ~ 13

Author(s):

Andrew J. Borchert ◽

Diana M. Downs

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Salmonella Enterica ◽

Metabolic Networks ◽

Metabolic Potential ◽

Content Type ◽

E Coli ◽

Sense And Respond ◽

External Stimuli

ABSTRACT The metabolic network of an organism includes the sum total of the biochemical reactions present. In microbes, this network has an impeccable ability to sense and respond to perturbations caused by internal or external stimuli. The metabolic potential (i.e., network structure) of an organism is often drawn from the genome sequence, based on the presence of enzymes deemed to indicate specific pathways. Escherichia coli and Salmonella enterica are members of the Enterobacteriaceae family of Gram-negative bacteria that share the majority of their metabolic components and regulatory machinery as the “core genome.” In S. enterica, the ability of the enamine intermediate 2-aminoacrylate (2AA) to inactivate a number of pyridoxal 5′-phosphate (PLP)-dependent enzymes has been established in vivo. In this study, 2AA metabolism and the consequences of its accumulation were investigated in E. coli. The data showed that despite the conservation of all relevant enzymes, S. enterica and E. coli differed in both the generation and detrimental consequences of 2AA. In total, these findings suggest that the structure of the metabolic network surrounding the generation and response to endogenous 2AA stress differs between S. enterica and E. coli. IMPORTANCE This work compared the metabolic networks surrounding the endogenous stressor 2-aminoacrylate in two closely related members of the Enterobacteriaceae. The data showed that despite the conservation of all relevant enzymes in this metabolic node, the two closely related organisms diverged in their metabolic network structures. This work highlights how a set of conserved components can generate distinct network architectures and how this can impact the physiology of an organism. This work defines a model to expand our understanding of the 2-aminoacrylate stress response and the differences in metabolic structures and cellular milieus between S. enterica and E. coli.

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