Applications of computational modeling in metabolic engineering of yeast
Journal article, 2015

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.

synthetic biology

biotechnology

genome-scale model

kinetic model

Author

Eduard Kerkhoven

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Petri-Jaan Lahtvee

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

FEMS Yeast Research

1567-1356 (ISSN) 1567-1364 (eISSN)

Vol. 15 1 12199

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering

Subject Categories

Bioinformatics and Systems Biology

DOI

10.1111/1567-1364.12199

More information

Created

10/7/2017