scholarly journals Essentiality of local topology and regulation in kinetic metabolic modeling

2019 ◽  
Author(s):  
Gaoyang Li ◽  
Wei Du ◽  
Huansheng Cao
Keyword(s):  
Metabolic Networks ◽  
Functional Organization ◽  
Dynamic Regulation ◽  
E Coli ◽  
Metabolic Fluxes ◽  
Regulation Of Metabolism ◽  
Coupled Reactions ◽  
Highly Correlated ◽  
Genome Scale ◽  
Local Topology

AbstractGenome-scale metabolic networks (GSMs) are mathematic representation of a set of stoichiometrically balanced reactions. However, such static GSMs do not reflect or incorporate functional organization of genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; and downstream metabolites often dynamically regulate the gene expression of their reactions via feedback. Here, we present a method which reconstructs GSMs with locally coupled reactions and transcriptional regulation of metabolism by key metabolites. The proposed method has outstanding performance in phenotype prediction of wild-type and mutants in Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and Bacillus subtilis (B. subtilis) growing in various conditions, outperforming existing methods. The predicted growth rate and metabolic fluxes are highly correlated with those experimentally measured. More importantly, our method can also explain the observed growth rates by capturing the ‘real’ (experimentally measured) changes in flux between the wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology and microbial pathology.

scholarly journals Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139507 ◽  
Author(s):  
Ahmad A. Mannan ◽  
Yoshihiro Toya ◽  
Kazuyuki Shimizu ◽  
Johnjoe McFadden ◽  
Andrzej M. Kierzek ◽  
...  
Keyword(s):  
Kinetic Model ◽  
Open System ◽  
Central Metabolism ◽  
E Coli ◽  
Metabolic Fluxes ◽  
Genome Scale

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.

Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes

2018 ◽  
Vol 20 (4) ◽  
pp. 1590-1603 ◽  
Author(s):  
Gaoyang Li ◽  
Huansheng Cao ◽  
Ying Xu
Keyword(s):  
Metabolic Networks ◽  
Reaction Network ◽  
Clustering Coefficient ◽  
Integrated Analysis ◽  
Node Degree ◽  
Power Law Distribution ◽  
Metabolic Fluxes ◽  
Reaction Cycles ◽  
Functional Analyses ◽  
Genome Scale

Abstract We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average ∼56, ∼31 and ∼13% of a network’s nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.

scholarly journals Probabilistic Thermodynamic Analysis of Metabolic Networks

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.

scholarly journals In Silico Analysis of Bioethanol Overproduction by Genetically Modified Microorganisms in Coculture Fermentation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Lisha K. Parambil ◽  
Debasis Sarkar
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Genetically Modified ◽  
In Silico Analysis ◽  
Ethanol Productivity ◽  
Microbial Consortia ◽  
Bioethanol Production ◽  
E Coli ◽  
Negative Impacts ◽  
Genome Scale

Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective Escherichia coli strain ZSC113 and glucose-selective wild-type Saccharomyces cerevisiae is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by E. coli strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified E. coli strain ZSC113 and S. cerevisiae that significantly enhance ethanol productivity in batch coculture fermentation.

scholarly journals Probabilistic Thermodynamic Analysis of Metabolic Networks

2020 ◽  
Author(s):  
Mattia G. Gollub ◽  
Hans-Michael Kaltenbach ◽  
Jörg Stelling
Keyword(s):  
Metabolic Network ◽  
Metabolic Networks ◽  
Thermodynamic Quantities ◽  
Probabilistic Framework ◽  
Substrate Channeling ◽  
E Coli ◽  
Metabolic Fluxes ◽  
Steady State Flux ◽  
Metabolite Concentrations ◽  
Current Sampling

AbstractRandom sampling of metabolic fluxes can provide an unbiased 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. 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. C++ source code with MATLAB interface available at https://gitlab.com/csb.ethz/pta.

scholarly journals Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

2016 ◽  
Author(s):  
Zachary A. King ◽  
Edward J. O’Brien ◽  
Adam M. Feist ◽  
Bernhard O. Palsson
Keyword(s):  
Cancer Biology ◽  
Metabolic Networks ◽  
Predictive Power ◽  
Literature Mining ◽  
Next Generation ◽  
E Coli ◽  
Wide Range ◽  
Scale Models ◽  
A Cell ◽  
Genome Scale

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology1, cancer biology2, and biotechnology3. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. Next-generation models of metabolism and gene expression are even more capable than previous models, but parameterization poses new challenges.

scholarly journals Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
You‐Tyun Wang ◽  
Min‐Ru Lin ◽  
Wei‐Chen Chen ◽  
Wu‐Hsiung Wu ◽  
Feng‐Sheng Wang
Keyword(s):  
Metabolic Networks ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancers ◽  
Genome Scale

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

scholarly journals F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Abdelhalim Larhlimi ◽  
Laszlo David ◽  
Joachim Selbig ◽  
Alexander Bockmayr
Keyword(s):  
Metabolic Networks ◽  
Flux Coupling ◽  
Genome Scale
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