Modelling and optimization of metabolic pathways in bacteria

The rational bacterial strain design is a major challenge in synthetic biology. This paper deals with the optimization of a bacterial strain for specific processes taking place in a bioreactor. Such problems are namely maximizing the growth and the production of a product of interest. First, a model combining the internal behavior of the cells with a bioreactor environment is developed assuming mass balance and biological constraints. This model assumes that the production of proteins can be controlled. The problem is then solved with constant optimization variables and returns an optimal strategy for synthetic strains.

[1]  Jay D. Keasling,et al.  Engineering Static and Dynamic Control of Synthetic Pathways , 2010, Cell.

[2]  V. Fromion,et al.  Quantitative prediction of genome-wide resource allocation in bacteria. , 2015, Metabolic engineering.

[3]  D. Oyarzún,et al.  Dynamic optimization of metabolic networks coupled with gene expression. , 2013, Journal of theoretical biology.

[4]  Denis Dochain,et al.  ADAPTIVE CONTROL OF BIOREACTORS , 1990 .

[5]  P. Swain,et al.  Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.

[6]  C. J. Franzén,et al.  Dynamic flux balancing elucidates NAD(P)H production as limiting response to furfural inhibition in Saccharomyces cerevisiae. , 2015, Biotechnology journal.

[7]  Xiaomei Lv,et al.  Sequential control of biosynthetic pathways for balanced utilization of metabolic intermediates in Saccharomyces cerevisiae. , 2015, Metabolic engineering.

[8]  R. Mahadevan,et al.  Estimating optimal profiles of genetic alterations using constraint-based models. , 2005, Biotechnology and bioengineering.

[9]  H. E. Kubitschek,et al.  Independence of buoyant cell density and growth rate in Escherichia coli , 1984, Journal of bacteriology.

[10]  Vincent Fromion,et al.  Cell design in bacteria as a convex optimization problem , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[11]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[12]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

[13]  H. Bremer.  Modulation of Chemical Composition and Other Parameters of the Cell by Growth Rate , 1999 .

[14]  Dominique Bonvin,et al.  Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty , 2003, Comput. Chem. Eng..