COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 145
Endy and Yin in using their T7 model to propose a pharmaceutical strategy for preventing both T7
propagation and the development of drug resistance through mutation.^57
Given observed cell behavior, simulation models can be used to suggest the necessity of a given
regulatory motif or the sufficiency of known interactions to produce the phenomenon. For example, Qi
et al. demonstrate the sufficiency of membrane energetics, protein diffusion, and receptor-binding
kinetics to generate a particular dynamic pattern of protein location at the synapse between two im-
mune cells.^58
The following sections describe several simulation studies in more detail.
Box 5.8
Escherichia coli Constraint-based Models
A. In Silico Model^1
The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical infor-
mation, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for
each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric
matrix. The E. coli stoichiometric matrix was used to define the system’s characteristics and the capabilities of E. coli
metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic
network to support growth were assessed, and the in silico predictions were compared with experimental observa-
tions. It was shown that based on stoichiometric and capacity constraints the in-silico analysis was able to qualita-
tively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the
synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possi-
ble, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.B. Genome-scale Model^2
An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes
904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and
charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to
protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions
made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances.
Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important
for maximizing cellular growth.... E. coli iJR904 has improved capabilities over iJE660a [a model that accounted for
660 genes and 627 unique biochemical reactions and was itself a slight modification of the original model described
in the above paragraph]. iJR904 is a more complete and chemically accurate description of E. coli metabolism than
iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will
help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic,
proteomic and fluxomic data simultaneously.(^1) Reprinted from J.S. Edwards and B.O. Palsson, “The Escherichia coli MG1655 in Silico Metabolic Genotype: Its Definition, Characteristics, and
Capabilities,” Proceedings of the National Academy of Sciences 97(10): 5528-5533, 2000. Copyright 2000 National Academy of Sciences.
(^2) J.L. Reed, T.D. Vo, C.H. Schilling, and B.O. Palsson, “An Expanded Genome-scale Model of Escherichia coli K-12 (iJR904 GSM/GPR),”
Genome Biology 4(9): Article R54, 2003, available at http://genomebiology.com/2003/4/9/R54. Reprinted by permission of the authors.
(^57) D. Endy and J. Yin, “Toward Antiviral Strategies That Resist Viral Escape,” Antimicrobial Agents and Chemotherapy 44(4):1097-
1099, 2000.
(^58) S.Y. Qi, J.T. Groves, and A.K. Chakraborty, “Synaptic Pattern Formation During Cellular Recognition,” Proceedings of the
National Academy of Sciences 98(12):6548-6553, 2001.