Smallbone2013 - E.coli metabolic model with modular rate law
Ontology highlight
ABSTRACT:
SUBMITTER: 
Kieran Smallbone
PROVIDER: BIOMD0000000470 | BioModels | 2024-09-02
REPOSITORIES: BioModels
Dataset's files
Items per page: 5 1 - 5 of 16 |
Similar Datasets
Project description:Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).
Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.
2024-09-02 | BIOMD0000000469 | BioModels
Project description:Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).
Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.
2024-09-02 | BIOMD0000000471 | BioModels
Project description:Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).
Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.
2024-09-02 | BIOMD0000000472 | BioModels
Project description:Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).
Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.
2024-09-02 | BIOMD0000000473 | BioModels
Project description:The filamentous fungus Aspergillus oryzae is an important microbial cell factory for industrial production of useful enzymes, such as α-amylase. In order to optimize the industrial enzyme production process, there is a need to understand fundamental processes underlying protein production, here under how protein production links to metabolism through global regulatory structures. In this study, two α-amylase-producing strains of A. oryzae, a wild type strain and a transformant strain containing additional copies of the α-amylase gene, were characterized at a systematic level. Based on integrated analysis of ome-data together with genome-scale metabolic network and flux calculation, we identified key genes, key enzymes, key proteins, and key metabolites involved in the processes of protein synthesis and secretion, nucleotide metabolism, and amino acid metabolism that can be the potential targets for improving industrial protein production. Keywords: Two Aspergillus oryzae strains and two different carbon sources Two carbon sources (glucose, maltose) with three biological replicates for A. oryzae strain A1560 and strain CF1.1
2012-02-12 | E-GEOD-22566 | biostudies-arrayexpress
Project description:The filamentous fungus Aspergillus oryzae is an important microbial cell factory for industrial production of useful enzymes, such as α-amylase. In order to optimize the industrial enzyme production process, there is a need to understand fundamental processes underlying protein production, here under how protein production links to metabolism through global regulatory structures. In this study, two α-amylase-producing strains of A. oryzae, a wild type strain and a transformant strain containing additional copies of the α-amylase gene, were characterized at a systematic level. Based on integrated analysis of ome-data together with genome-scale metabolic network and flux calculation, we identified key genes, key enzymes, key proteins, and key metabolites involved in the processes of protein synthesis and secretion, nucleotide metabolism, and amino acid metabolism that can be the potential targets for improving industrial protein production. Keywords: Two Aspergillus oryzae strains and two different carbon sources
2012-02-12 | GSE22566 | GEO
Project description:E. coli frequently encounters oxidative stress both in its natural environment or in industrial biotechnology. Elucidating the mechanisms behind tolerance to oxidative stress would be beneficial for understanding pathogenesis as well as improving production strain fitness. We make use of adaptive laboratory evolution to develop two strains of E. coli which exhibit 500% increased tolerance to paraquat stress compared to wild type. Evolved strains tolerate oxidative stress by reduction of flux through TCA, dyregulation of iron-uptake genes, and up-regulation of cell motility or iron-sulfur cluster repair genes.
2022-07-07 | GSE134256 | GEO
Project description:Pitkanen2014 - Metabolic reconstruction of Magnaporthe grisea using CoReCo
This model was reconstructed with the CoReCo method (Comparative ReConstruction of genome-scale metabolic networks) from protein sequence and phylogeny data.
This model is described in the article:
Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species
Esa Pitkänen, Paula Jouhten, Jian Hou, Muhammad Fahad Syed, Peter Blomberg, Jana Kludas, Merja Oja, Liisa Holm, Merja Penttilä, Juho Rousu, Mikko Arvas
PLoS Comput Biol. 2014 Feb 6;10(2):e1003465
Abstract:
We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
This model is hosted on BioModels Database
and identified by: MODEL1302010009
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
2014-03-02 | MODEL1302010009 | BioModels
Project description:Pitkanen2014 - Metabolic reconstruction of Postia placenta using CoReCo
This model was reconstructed with the CoReCo method (Comparative ReConstruction of genome-scale metabolic networks) from protein sequence and phylogeny data.
This model is described in the article:
Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species
Esa Pitkänen, Paula Jouhten, Jian Hou, Muhammad Fahad Syed, Peter Blomberg, Jana Kludas, Merja Oja, Liisa Holm, Merja Penttilä, Juho Rousu, Mikko Arvas
PLoS Comput Biol. 2014 Feb 6;10(2):e1003465
Abstract:
We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
This model is hosted on BioModels Database
and identified by: MODEL1302010032
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
2014-03-02 | MODEL1302010032 | BioModels
Project description:Pitkanen2014 - Metabolic reconstruction of Trichoderma reesei using CoReCo
This model was reconstructed with the CoReCo method (Comparative ReConstruction of genome-scale metabolic networks) from protein sequence and phylogeny data.
This model is described in the article:
Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species
Esa Pitkänen, Paula Jouhten, Jian Hou, Muhammad Fahad Syed, Peter Blomberg, Jana Kludas, Merja Oja, Liisa Holm, Merja Penttilä, Juho Rousu, Mikko Arvas
PLoS Comput Biol. 2014 Feb 6;10(2):e1003465
Abstract:
We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
This model is hosted on BioModels Database
and identified by: MODEL1302010019
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information.
2014-03-02 | MODEL1302010019 | BioModels