Literature DB >> 18392985

Design and application of genome-scale reconstructed metabolic models.

Isabel Rocha1, Jochen Förster, Jens Nielsen.   

Abstract

In this chapter, the process for the reconstruction of genome-scale metabolic networks is described, and some of the main applications of such models are illustrated. The reconstruction process can be viewed as an iterative process where information obtained from several sources is combined to construct a preliminary set of reactions and constraints. This involves steps such as genome annotation; identification of the reactions from the annotated genome sequence and available literature; determination of the reaction stoichiometry; definition of compartmentation and assignment of localization; determination of the biomass composition; measurement, calculation, or fitting of energy requirements; and definition of additional constraints. The reaction and constraint sets, after debugging, may be integrated into a stoichiometric model that can be used for simulation using tools such as Flux Balance Analysis (Section 3.8). From the flux distributions obtained, physiologic parameters such as growth yields or minimal medium components can be calculated, and their distance from similar experimental data provides a basis from where the model may need to be improved.

Mesh:

Substances:

Year:  2008        PMID: 18392985     DOI: 10.1007/978-1-59745-321-9_29

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  13 in total

1.  Reconstructing genome-scale metabolic models with merlin.

Authors:  Oscar Dias; Miguel Rocha; Eugénio C Ferreira; Isabel Rocha
Journal:  Nucleic Acids Res       Date:  2015-04-06       Impact factor: 16.971

2.  Whole-genome metabolic network reconstruction and constraint-based modeling.

Authors:  Charles R Haggart; Jennifer A Bartell; Jeffrey J Saucerman; Jason A Papin
Journal:  Methods Enzymol       Date:  2011       Impact factor: 1.600

Review 3.  A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).

Authors:  Ilija Dukovski; Djordje Bajić; Jeremy M Chacón; Michael Quintin; Jean C C Vila; Snorre Sulheim; Alan R Pacheco; David B Bernstein; William J Riehl; Kirill S Korolev; Alvaro Sanchez; William R Harcombe; Daniel Segrè
Journal:  Nat Protoc       Date:  2021-10-11       Impact factor: 13.491

4.  OptFlux: an open-source software platform for in silico metabolic engineering.

Authors:  Isabel Rocha; Paulo Maia; Pedro Evangelista; Paulo Vilaça; Simão Soares; José P Pinto; Jens Nielsen; Kiran R Patil; Eugénio C Ferreira; Miguel Rocha
Journal:  BMC Syst Biol       Date:  2010-04-19

5.  Genome-wide metabolic (re-) annotation of Kluyveromyces lactis.

Authors:  Oscar Dias; Andreas K Gombert; Eugénio C Ferreira; Isabel Rocha
Journal:  BMC Genomics       Date:  2012-10-01       Impact factor: 3.969

6.  Semantic annotation of biological concepts interplaying microbial cellular responses.

Authors:  Rafael Carreira; Sónia Carneiro; Rui Pereira; Miguel Rocha; Isabel Rocha; Eugénio C Ferreira; Anália Lourenço
Journal:  BMC Bioinformatics       Date:  2011-11-28       Impact factor: 3.169

7.  Individualized therapy of HHT driven by network analysis of metabolomic profiles.

Authors:  Neema Jamshidi; Franklin J Miller; Jess Mandel; Timothy Evans; Michael D Kuo
Journal:  BMC Syst Biol       Date:  2011-12-20

8.  Reconstruction and in silico analysis of an Actinoplanes sp. SE50/110 genome-scale metabolic model for acarbose production.

Authors:  Yali Wang; Nan Xu; Chao Ye; Liming Liu; Zhongping Shi; Jing Wu
Journal:  Front Microbiol       Date:  2015-06-25       Impact factor: 5.640

9.  Genome-scale modeling of the protein secretory machinery in yeast.

Authors:  Amir Feizi; Tobias Österlund; Dina Petranovic; Sergio Bordel; Jens Nielsen
Journal:  PLoS One       Date:  2013-05-07       Impact factor: 3.240

10.  Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum.

Authors:  Eddy J Bautista; Joseph Zinski; Steven M Szczepanek; Erik L Johnson; Edan R Tulman; Wei-Mei Ching; Steven J Geary; Ranjan Srivastava
Journal:  PLoS Comput Biol       Date:  2013-09-05       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.