scholarly journals Effective Dynamic Models of Metabolic Networks

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.

scholarly journals Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: GridProd

2017 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Growth Rate ◽  
Metabolic Network ◽  
Flux Balance Analysis ◽  
Efficient Method ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Flux Distribution ◽  
Production Rates ◽  
Flux Balance ◽  
Balance Analysis

AbstractConstraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production. Accordingly, we have developed a computational method where parsimonious metabolic flux distribution is computed for designated constraints on growth and production rates which are represented by grids. When the growth rate of this obtained parsimonious metabolic network is maximized, higher production rates compared to those noted using existing methods are observed for many target metabolites. The set of reactions used in this parsimonious flux distribution consists of reactions included in the original genome scale model iAF1260. The computational experiments show that the grid size affects the obtained production rates. Under the conditions that the growth rate is maximized and the minimum cases of flux variability analysis are considered, the developed method produced more than 90% of metabolites, while the existing methods produced less than 50%. Mathematical explanations using examples are provided to demonstrate potential reasons for the ability of the proposed algorithm to identify design strategies that the existing methods could not identify. The source code is freely available, and is implemented in MATLAB and COBRA toolbox.Author summaryMetabolic networks represent the relationships between biochemical reactions and compounds in living cells. By computationally modifying a given metabolic network of microbes, we can simulate the effect of knockouts and estimate the production of valuable metabolites. A common mathematical model of metabolic networks is the constraint-based flux model. In constraint-based flux balance analysis, a pseudo-steady state is assumed to predict the metabolic profile where the sum of all incoming fluxes is equal to the sum of all outgoing fluxes for each internal metabolite. Based on these constraints, the biomass objective function, written as a linear combination of fluxes, is maximized. In this study, we developed an efficient method for computing the design of minimum metabolic networks by using constraint-based flux balance analysis to simulate the production of useful metabolites.

scholarly journals A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools

2019 ◽  
Vol 105 ◽  
pp. 64-71 ◽  
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Edik M. Blais ◽  
Ethan Stancliffe ◽  
Glynis L. Kolling ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Flux Balance Analysis ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Flux Balance ◽  
Analysis Tools ◽  
Balance Analysis

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

2012 ◽  
pp. 774-791
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.

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

mBio ◽  
2016 ◽  
Vol 7 (1) ◽  
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.

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

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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