Discriminating Significant From Insignificant Model Parameters: The Case of a Dynamic CHO Cell Model.
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. ... read moreInvestigators 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
Thesis (M.S.)--Tufts University, 2013.
Submitted to the Dept. of Chemical and Biological Engineering.
Advisors: Christos Georgakis, and Kyongbum Lee.
Committee: Christos Georgakis, Kyongbum Lee, and Ryan Nolan.
Keyword: Chemical engineering.read less