Literature DB >> 25156867

Applications of computational modeling in metabolic engineering of yeast.

Eduard J Kerkhoven1, Petri-Jaan Lahtvee1,2, Jens Nielsen3,2,2.   

Abstract

Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology the aim is often to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox, and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression; however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally nonexistent, but well-characterized components into a living organism. To describe these systems kinetic models are often used and to integrate these systems with the standard metabolic engineering approach, it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.

Entities:  

Keywords:  biotechnology; genome-scale model; kinetic model; synthetic biology

Mesh:

Year:  2015        PMID: 25156867     DOI: 10.1111/1567-1364.12199

Source DB:  PubMed          Journal:  FEMS Yeast Res        ISSN: 1567-1356            Impact factor:   2.796


  14 in total

1.  Model-based biotechnological potential analysis of Kluyveromyces marxianus central metabolism.

Authors:  A Pentjuss; E Stalidzans; J Liepins; A Kokina; J Martynova; P Zikmanis; I Mozga; R Scherbaka; H Hartman; M G Poolman; D A Fell; A Vigants
Journal:  J Ind Microbiol Biotechnol       Date:  2017-04-25       Impact factor: 3.346

2.  Reaction kinetic analysis of the 3-hydroxypropionate/4-hydroxybutyrate CO2 fixation cycle in extremely thermoacidophilic archaea.

Authors:  Andrew J Loder; Yejun Han; Aaron B Hawkins; Hong Lian; Gina L Lipscomb; Gerrit J Schut; Matthew W Keller; Michael W W Adams; Robert M Kelly
Journal:  Metab Eng       Date:  2016-10-19       Impact factor: 9.783

3.  Alcohol Selectivity in a Synthetic Thermophilic n-Butanol Pathway Is Driven by Biocatalytic and Thermostability Characteristics of Constituent Enzymes.

Authors:  Andrew J Loder; Benjamin M Zeldes; G Dale Garrison; Gina L Lipscomb; Michael W W Adams; Robert M Kelly
Journal:  Appl Environ Microbiol       Date:  2015-08-07       Impact factor: 4.792

Review 4.  Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.

Authors:  Fei He; Ettore Murabito; Hans V Westerhoff
Journal:  J R Soc Interface       Date:  2016-04-13       Impact factor: 4.118

5.  Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica.

Authors:  Pauline Trébulle; Jean-Marc Nicaud; Christophe Leplat; Mohamed Elati
Journal:  NPJ Syst Biol Appl       Date:  2017-08-11

6.  Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints.

Authors:  Benjamín J Sánchez; Cheng Zhang; Avlant Nilsson; Petri-Jaan Lahtvee; Eduard J Kerkhoven; Jens Nielsen
Journal:  Mol Syst Biol       Date:  2017-08-03       Impact factor: 11.429

7.  Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements.

Authors:  Duygu Dikicioglu; Stephen G Oliver
Journal:  Biotechnol Bioeng       Date:  2019-01-12       Impact factor: 4.530

8.  Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.

Authors:  Ljubisa Miskovic; Jonas Béal; Michael Moret; Vassily Hatzimanikatis
Journal:  PLoS Comput Biol       Date:  2019-08-20       Impact factor: 4.475

9.  Diversity of flux distribution in central carbon metabolism of S. cerevisiae strains from diverse environments.

Authors:  Thibault Nidelet; Pascale Brial; Carole Camarasa; Sylvie Dequin
Journal:  Microb Cell Fact       Date:  2016-04-05       Impact factor: 5.328

10.  Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica.

Authors:  Eduard J Kerkhoven; Kyle R Pomraning; Scott E Baker; Jens Nielsen
Journal:  NPJ Syst Biol Appl       Date:  2016-03-03
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