(110e) A General Hybrid Optimization Framework for the Optimal Modulation of Enzyme Levels Using Large-Scale Kinetic Models of Bacterial Metabolism

The intrinsic complexity of cellular systems has necessitated the
use of various modeling approaches to address specific problems of cellular
organization and function. To this end, we have developed a general
optimization framework to identify which enzyme level should be modulated up or
down in response to overproduction requirements using large-scale mechanistic
models of bacterial systems. The framework is demonstrated on a kinetic model
of carbon metabolism of Escherichia coli for serine biosynthesis (Chassagnole et al. 2002).  An efficient hybrid deterministic/stochastic
solution strategy is devised to solve the resulting general mixed integer
nonlinear problem (MINLP) problems. Specifically, a customized simulated
annealing algorithm is used to identify which enzyme levels to change while
gradient-based algorithms (i.e., SQP) are employed to identify the
corresponding optimal enzyme levels. Computational results show that by
optimally manipulating relatively small enzyme sets, a substantial
increase in serine production can be achieved. For example, the modulation of
only three enzymes results in a flux increase which matches approximately 50%
of the best predictions obtained by manipulating all the thirty enzymes in the
model. Importantly, by manipulating ten
enzymes the organism's maximum overproduction capability is reached. To get quantitative insights into how successive small
enzyme sets can be chosen, flux control coefficients (FCCs) (Kacser and Burns 1973) are calculated to compare FCC-based predictions with global
optimization results. The proposed
approach thus provides a versatile tool for the elucidation of controlling
enzymes with implications in biotechnology.

References

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

Kacser, H. and J. A. Burns (1973).
"The Control of flux." Symp Soc Exp Biol 27: 65-104.