Kinetic models in industrial biotechnology - Improving cell factory performance
Journal article, 2014

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Author

Joachim Almquist

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Marija Cvijovic

Chalmers, Mathematical Sciences, Mathematics

University of Gothenburg

V. Hatzimanikatis

Ecole Polytechnique Federale De Lausanne

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Mats Jirstrand

Fraunhofer-Chalmers Centre

Metabolic Engineering

1096-7176 (ISSN) 1096-7184 (eISSN)

Vol. 24 38-60

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Chemical Sciences

DOI

10.1016/j.ymben.2014.03.007

More information

Latest update

5/3/2018 1