| Kinetic modeling for metabolic networks, formulated as a set of ordinary differential equations for intracellular species concentrations, provides the ability to simulate the dynamic behavior of cellular metabolism. Such models aim to predict cellular response to various external stimuli, allowing an investigator to develop a detailed fundamental understanding of the phenomena studied. Investigators often choose to include more than the necessary model details rather than risk the error of including less than necessary details. However, this increases the number of parameters that need to be identified from experimental data and introduces a substantial challenge in the identification of the important model parameters. As more details are added to the model, the increased number of parameters implies the necessity of an increased amount of experimental data. However, more experimental data does not imply that the values of the insignificant parameters can be easily determined. Most importantly, it is not clear whether all the model details and the corresponding parameters are necessary for a desired set of model predictions. The present paper presents a computationally efficient methodology to identify the model parameters that are highly significant for the model predictions and thus distinguish them from the insignificant ones.;The proposed approach is inspired by the classic Design of Experiments (DOE) techniques, performed in silico using a preliminary model. We start by defining the possible ranges of each of the unknown model parameters, design a set of in-silico experiments or, equivalently, a set of selected calculations that are simulated through the preliminary model. Utilizing analysis of variance (ANOVA) and response surface model (RSM) tools we develop a simplified nonlinear meta-model in which only the significant parameters are retained. We applied this methodology to a dynamic model of Chinese hamster ovary (CHO) cell metabolism (Nolan, 2011). This model, comprising 51 parameters and 34 reaction fluxes, was able to provide a reliable preliminary prediction of the effects of fed-batch process variables such as temperature shift, specific productivity, and nutrient concentrations. A D-optimal design of experiments was used to sample the parameters across their ranges, and a RSM was obtained with antibody flux as the output. Investigating linear, linearly interactive, and quadratic RSMs, we efficiently eliminated approximately 90% of the terms as being not highly significant, shedding light on the importance of each of the 51 original model parameters in the predictions of the metabolic model.;Through this parameter significance methodology, we were able to discriminate the highly significant parameters from the highly insignificant parameters. We demonstrate the utility of parameter significance discrimination as applied to parameter estimation. Refitting the 6 highly significant terms yields a 55% improvement in the objective function from the original model fitting, as compared to refitting the 12 highly insignificant terms which results in just a 6% improvement in the objective function. |
