Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies

Abstract

Background: To understand the dynamic behavior of cellular systems, mathematical modeling is often necessary and comprises three steps: (1) experimental measurement of participating molecules, (2) assignment of rate laws to each reaction, and (3) parameter calibration with respect to the measurements. In each of these steps the modeler is confronted with a plethora of alternative approaches, e. g., the selection of approximative rate laws in step two as specific equations are often unknown, or the choice of an estimation procedure with its specific settings in step three. This overall process with its numerous choices and the mutual influence between them makes it hard to single out the best modeling approach for a given problem.

Comparison of GA and PSO performance in parameter estimation of microbial growth models: A case-study using experimental data

“…The overall conclusion of the tuning experiments was that the PSO is more appropriate than the GA for solving this problem, since, according to the criteria examined, and for the best tuning settings, the former yielded significantly better results than the latter. This conclusion agrees with the results obtained by Drager et al [8], for the estimation of parameters of similar models (metabolic models described by systems of ODEs with a comparable number of parameters), as they also favor the use of the PSO (albeit in a slightly different variant) to the use of the GA (with settings similar to the present ones). We think that this may derive from the simpler, more straightforward manner in which the PSO is formulated in relation to this kind of problem.…”

“…A fairly recent and exhaustive is that by Drager et al [8], in which the performance of eight different optimization strategies (including the GA and the PSO, as well as other metaheuristics) was compared when estimating the parameters of complex metabolic models for C. glutamicum. Mathematically, our model closely resembles some of the ones studied by Drager, highly non-linear and with a comparable number of parameters; however, the phenomena modeled in [8] refers to a well-characterized metabolic pathway, one for which all relevant chemical reactions are known. On the other hand, ours is more of a physiological model, in which we model the overall macroscopic effects deriving from the myriad of microscopic phenomena occuring inside cells.…”

“…In recent years, both the GA and the PSO, as well as hybrid algorithms combining the two, have been applied to parameter estimation problems, in the context of kinetic models of microorganisms and plants [5][6] [7], metabolic models [8], models of chemical reactions [9] [10], and models of biological reactors [11]. A fairly recent and exhaustive is that by Drager et al [8], in which the performance of eight different optimization strategies (including the GA and the PSO, as well as other metaheuristics) was compared when estimating the parameters of complex metabolic models for C. glutamicum.…”

Biochemical Systems Theory: A Review

“…Similarly, Dräger et al 2009 compared various rate laws, especially with respect to their optimization potential and obtained mixed results. Depending on the situation, a GMA model or a combination of MM with so-called convenience kinetics produced the best results [691]. Costa et al considered a combination of lin-log and MM models best, unless the concentrations in the system were small [167].…”

A probabilistic approach to identify putative drug targets in biochemical networks

“…To describe the kinetics of enzyme catalysed reactions for which the true kinetics is unknown, several possible heuristic approximate rate equations have been proposed in the literature [33][34][35]. In general, good results are obtained for functional forms that follow generalized Michaelis-Menten equations.…”