Literature DB >> 31602620

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges.

Yu Chen1,2, Gang Li1,2, Jens Nielsen3,4,5.   

Abstract

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Entities:  

Keywords:  Biomarker; Drug target; Genome-scale metabolic models; Human cells; Metabolic engineering; Saccharomyces cerevisiae; Systems biology; Yeast

Mesh：

Substances：

Year:  2019        PMID： 31602620     DOI： 10.1007/978-1-4939-9736-7_19

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


  64 in total

Review 1.  Using Genome-scale Models to Predict Biological Capabilities.

Authors:  Edward J O'Brien; Jonathan M Monk; Bernhard O Palsson
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

Review 2.  Constraint-based models predict metabolic and associated cellular functions.

Authors:  Aarash Bordbar; Jonathan M Monk; Zachary A King; Bernhard O Palsson
Journal:  Nat Rev Genet       Date:  2014-01-16       Impact factor: 53.242

3.  A protocol for generating a high-quality genome-scale metabolic reconstruction.

Authors:  Ines Thiele; Bernhard Ø Palsson
Journal:  Nat Protoc       Date:  2010-01-07       Impact factor: 13.491

4.  Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.

Authors:  Jochen Förster; Iman Famili; Patrick Fu; Bernhard Ø Palsson; Jens Nielsen
Journal:  Genome Res       Date:  2003-02       Impact factor: 9.043

5.  Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction.

Authors:  Benjamin D Heavner; Nathan D Price
Journal:  PLoS Comput Biol       Date:  2015-11-13       Impact factor: 4.475

6.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

Authors:  Zachary A King; Justin Lu; Andreas Dräger; Philip Miller; Stephen Federowicz; Joshua A Lerman; Ali Ebrahim; Bernhard O Palsson; Nathan E Lewis
Journal:  Nucleic Acids Res       Date:  2015-10-17       Impact factor: 16.971

7.  KEGG: new perspectives on genomes, pathways, diseases and drugs.

Authors:  Minoru Kanehisa; Miho Furumichi; Mao Tanabe; Yoko Sato; Kanae Morishima
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

8.  The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum.

Authors:  Rasmus Agren; Liming Liu; Saeed Shoaie; Wanwipa Vongsangnak; Intawat Nookaew; Jens Nielsen
Journal:  PLoS Comput Biol       Date:  2013-03-21       Impact factor: 4.475

9.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).

Authors:  Jennifer L Reed; Thuy D Vo; Christophe H Schilling; Bernhard O Palsson
Journal:  Genome Biol       Date:  2003-08-28       Impact factor: 13.583

Review 10.  Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine.

Authors:  Cheng Zhang; Qiang Hua
Journal:  Front Physiol       Date:  2016-01-07       Impact factor: 4.566
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  6 in total

1.  A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Authors:  Christopher Culley; Supreeta Vijayakumar; Guido Zampieri; Claudio Angione
Journal:  Proc Natl Acad Sci U S A       Date:  2020-07-16       Impact factor: 11.205

2.  Information Theory in Computational Biology: Where We Stand Today.

Authors:  Pritam Chanda; Eduardo Costa; Jie Hu; Shravan Sukumar; John Van Hemert; Rasna Walia
Journal:  Entropy (Basel)       Date:  2020-06-06       Impact factor: 2.524

3.  Yeast optimizes metal utilization based on metabolic network and enzyme kinetics.

Authors:  Yu Chen; Feiran Li; Jiwei Mao; Yun Chen; Jens Nielsen
Journal:  Proc Natl Acad Sci U S A       Date:  2021-03-23       Impact factor: 12.779

4.  Bayesian genome scale modelling identifies thermal determinants of yeast metabolism.

Authors:  Gang Li; Yating Hu; Hao Luo; Hao Wang; Aleksej Zelezniak; Boyang Ji; Jens Nielsen
Journal:  Nat Commun       Date:  2021-01-08       Impact factor: 14.919

5.  Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species.

Authors:  Iván Domenzain; Feiran Li; Eduard J Kerkhoven; Verena Siewers
Journal:  FEMS Yeast Res       Date:  2021-03-04       Impact factor: 2.796

Review 6.  Genome-scale modeling of yeast metabolism: retrospectives and perspectives.

Authors:  Yu Chen; Feiran Li; Jens Nielsen
Journal:  FEMS Yeast Res       Date:  2022-02-22       Impact factor: 2.796
  6 in total

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