(656e) A Computational Approach to Identify Optimal Interventions to Bacterial Metabolism

Vital-Lopez, F. G. - Presenter, The Pennsylvania State University
Armaou, A. - Presenter, Pennsylvania State University

Microorganisms are extensively used for the synthesis of several industrially and important chemicals and pharmaceuticals. In spite of their inherent capabilities, the employed bioprocesses usually require strain modifications to improve their capabilities. The creation of these strains is a difficult endeavor due to the complexity of these systems. However, advances in genetics and molecular biology have allowed tuning the enzymatic repertoire of microorganisms at will, which motivates the question of which interventions are needed to maximize the production of a target metabolite. Knowledge of the metabolic reaction networks, expressed in the form of stoichiometric or kinetic models, is essential to answer this question. Hatzimanikatis et al. [1] addressed this issue by using linear in logarithms functions to approximate a kinetic metabolic model and formulated the problem as a mixed-integer linear (MILP) optimization one to design metabolic networks considering a pre-specified number of modifications. In this work, we present a computational framework to hierarchically identify all engineering interventions allowing for reaction eliminations and/or enzyme level modulations to enhance a given metabolic flux. The procedure relies on the generalized linearization of the kinetic model and the subsequent solution of a series of MILP formulations. The approach is illustrated through the identification of optimal engineering interventions (up to six enzyme manipulations) for serine overproduction, using a kinetic model of the central carbon metabolism of E. coli [2]. The proposed procedure can be a useful tool since it offers an answer to a key question in the design of metabolic systems.


1. Hatzimanikatis, V., C.A. Floudas, and J.E. Bailey, Analysis and design of metabolic reaction networks via mixed-integer linear optimization. Aiche Journal, 1996. 42(5): p. 1277-1292.

2. Chassagnole, C., N. Noisommit-Rizzi, J.W. Schmid, K. Mauch, and M. Reuss, Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnol Bioeng, 2002. 79(1): p. 53-73.