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 Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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.

scholarly journals FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

2019 ◽  
Author(s):  
Hongzhong Lu ◽  
Zhengming Zhu ◽  
Eduard J Kerkhoven ◽  
Jens Nielsen
Keyword(s):  
Metabolic Networks ◽  
R Package ◽  
Network Size ◽  
Research Community ◽  
Supplementary Information ◽  
Large Network ◽  
Strain Design ◽  
Scale Models ◽  
Genome Scale ◽  
Integrative Omics

AbstractSummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.MotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.ResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.Availability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT [email protected] informationSupplementary data are available online.

scholarly journals Quantifying the entropic cost of cellular growth control

2017 ◽  
Author(s):  
Daniele De Martino ◽  
Fabrizio Capuani ◽  
Andrea De Martino
Keyword(s):  
Growth Control ◽  
Growth Dynamics ◽  
Cellular Growth ◽  
Initial Size ◽  
E Coli ◽  
Regulatory Strategies ◽  
Control Growth ◽  
Metabolic States ◽  
Scale Models ◽  
Genome Scale

We quantify the amount of regulation required to control growth in living cells by a Maximum Entropy approach to the space of underlying metabolic states described by genome-scale models. Results obtained forE. coliand human cells are consistent with experiments and point to different regulatory strategies by which growth can be fostered or repressed. Moreover we explicitly connect the ‘inverse temperature’ that controls MaxEnt distributions to the growth dynamics, showing that the initial size of a colony may be crucial in determining how an exponentially growing population organizes the phenotypic space.

scholarly journals redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
S Tsouka ◽  
V Hatzimanikatis
Keyword(s):  
Lipid Metabolism ◽  
Metabolic Networks ◽  
Ad Hoc ◽  
Mechanistic Model ◽  
Model Organism ◽  
Metabolic Model ◽  
Lipid Biochemistry ◽  
Large Size ◽  
Scale Models ◽  
Genome Scale

ABSTRACT Over the last decades, yeast has become a key model organism for the study of lipid biochemistry. Because the regulation of lipids has been closely linked to various physiopathologies, the study of these biomolecules could lead to new diagnostics and treatments. Before the field can reach this point, however, sufficient tools for integrating and analyzing the ever-growing availability of lipidomics data will need to be developed. To this end, genome-scale models (GEMs) of metabolic networks are useful tools, though their large size and complexity introduces too much uncertainty in the accuracy of predicted outcomes. Ideally, therefore, a model for studying lipids would contain only the pathways required for the proper analysis of these biomolecules, but would not be an ad hoc reduction. We hereby present a metabolic model that focuses on lipid metabolism constructed through the integration of detailed lipid pathways into an already existing GEM of Saccharomyces cerevisiae. Our model was then systematically reduced around the subsystems defined by these pathways to provide a more manageable model size for complex studies. We show that this model is as consistent and inclusive as other yeast GEMs regarding the focus and detail on the lipid metabolism, and can be used as a scaffold for integrating lipidomics data to improve predictions in studies of lipid-related biological functions.

scholarly journals Omic data from evolved E. coli are consistent with computed optimal growth from genome‐scale models

2010 ◽  
Vol 6 (1) ◽  
pp. 390 ◽  
Author(s):  
Nathan E Lewis ◽  
Kim K Hixson ◽  
Tom M Conrad ◽  
Joshua A Lerman ◽  
Pep Charusanti ◽  
...  
Keyword(s):  
Optimal Growth ◽  
E Coli ◽  
Scale Models ◽  
Genome Scale ◽  
Omic Data

scholarly journals Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Parizad Babaei ◽  
Tahereh Ghasemi-Kahrizsangi ◽  
Sayed-Amir Marashi
Keyword(s):  
In Silico ◽  
Metabolic Networks ◽  
Closely Related Species ◽  
Systematic Analysis ◽  
Reconstruction Process ◽  
Flux Balance ◽  
Wide Range ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

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

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.

FCDECOMP: Decomposition of metabolic networks based on flux coupling relations

2014 ◽  
Vol 12 (05) ◽  
pp. 1450028 ◽  
Author(s):  
Abolfazl Rezvan ◽  
Sayed-Amir Marashi ◽  
Changiz Eslahchi
Keyword(s):  
Metabolic Networks ◽  
System Level ◽  
Computational Framework ◽  
Flux Coupling ◽  
Metabolic Functions ◽  
Original Genome ◽  
A Cell ◽  
Scale Network ◽  
Genome Scale

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

scholarly journals RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

2018 ◽  
Author(s):  
Hao Wang ◽  
Simonas Marcišauskas ◽  
Benjamín J Sánchez ◽  
Iván Domenzain ◽  
Daniel Hermansson ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
De Novo ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Primary And Secondary Metabolism ◽  
Scale Models ◽  
Improved Performance ◽  
Metabolites Production ◽  
Genome Scale

AbstractRAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.Author summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVEN’s functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

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