(693g) In Silico Optimization of the Ethanol Production in Escherichia Coli in a Glycerol Media Implementing Dynamic Flux Balance Analysis and Experimental Validation

Barreto, C. M., Universidad de los Andes
Gomez Ramirez, J. M., Universidad de los Andes
González Barrios, A. F., Universidad de los Andes
Ramirez Angulo, J. P., Grupo de Diseño de Productos y Procesos (GDPP), Universidad de los Andes
Achenie, L., Virginia Tech

Cells have been employed as miniaturized chemical plants that produce various chemicals towards our benefits. However, the bio-based processes are generally inefficient due to the limited metabolic capacity of the cell towards the production of a desired product. The reason is because the objective of microbial metabolism is different from that of ours. One technique implemented to analyze microbial metabolic networks in metabolic engineering is known as Flux Balance Analysis (FBA). Usually, FBA is employed to study the metabolic flux at a particular steady state of the system. Recently, it has been noticed that the dynamics of these metabolic networks have to be studied. As a result, FBA has been extended in order to account for the network dynamics [1]. On the other hand, since glycerol has become an inexpensive and abundant carbon source due to its generation as an inevitable by-product of biodiesel fuel production, the development of processes to convert crude glycerol into higher-value products is imperative. The use of glycerol as feedstock in fermentation processes can produce fuels and reduced chemicals at higher yields than those obtained from common sugars such as glucose or xylose. Herein, an optimization framework is developed capable of maximize ethanol production in E.coli. This framework is a bi-level optimization platform in which the inner level will be in charge of the maximization of cellular growth and the outer level will maximize ethanol production. The maximization of cellular growth is done implementing Dynamic Flux Balance Analysis. This optimization problem is a Non Linear Programming (NLP) problem type and is solved using the Orthogonal Collocation algorithm.


[1] Mahadevan, R., Edwards, J. S., and Doyle, III F. J. (2002). Dynamic Flux Balance Analysis of Diauxic Growth in Escherichia coli. Biophys. J., 83, 1331 ? 1340.