Catalyzing Inquiry at the Interface of Computing and Biology

COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 143

pathway involved in the circadian rhythm of Synechococcus sp. PCC 7942,^50 and to model mitochondrial
energy metabolism and metabolic pathways in rice.^51
Another cellular simulation environment is the Virtual Cell, developed at the University of Connecti-
cut Health Center.^52 The Virtual Cell is a tool for experimentalists and theoreticians for computationally
testing hypotheses and models. To address a particular question, these mechanisms (chemical kinetics,
membrane fluxes and reactions, ionic currents, and diffusion) are combined with a specific set of experi-
mental conditions (geometry, spatial scale, time scale, stimuli) and applicable conservation laws to specify

Box 5.7

Pathway Reconstruction: A Systems Approach

On Topology.

In this level, we are only concerned with identifying the interaction between nodes (genes, proteins, metabolites,

etc.) in the system. The goal is the generation of a diagram of non-directional connections between all interacting

nodes. For example, many have sought to develop large-scale maps of protein–protein interactions derived from

various sources. Two-hybrid studies have produced genome-wide interaction maps for E. coli bacteriophage T7,

yeast, Drosophila, and C. elegans. Although this approach can be comprehensive in regard to being genome wide,

many interactions are not reproducible (a potential source of false negatives) and putative interactions occur be-

tween unlikely protein combinations (a potential source of false positives).... Another approach to constructing

large-scale connection maps is by mining databases. Specific databases of protein interactions are being developed,

the largest of which are DIP and BIND. These databases combine data from many high-throughput experiments

along with data from other sources, such as published literature.... Along other lines, investigators have attempted

to identify topological links by analyzing the dynamic behavior of networks. Pioneering work in this area shows that

metabolic network topologies can be derived from correlation of time-series measurements of species concentra-

tions. The method is further refined to better identify connections in non-linear systems using mutual information

instead of correlation. In another method, pair-wise correlation of gene expression data is used to predict functional

connections that could then be combined into “relevance networks” of linked genes. Other methods may seek to use

some combination of data sources, although this may not be completely straightforward.

On Inferring Qualitative Connections.

In this level, we include not only associations between cellular entities but also the causal relations of such associ-

ations, such as which entities serve as input to others.... Researchers have proposed methods that infer connectiv-

ities from the estimations of the Jacobian matrix for metabolic, signaling, and genetic networks. Ross and co-workers

have proposed a method based on propagated perturbations of chemical species that can reconstruct causal se-

quences of reactions from synthetic and experimental data. To reconstruct gene regulatory systems, methods include

fuzzy logic analysis of facilitator/repressor groups in the yeast cell cycle and reconstruction of binary networks.

However, the wide application of such methods is often limited because the continuous nature of many biological

systems prevents easy abstractions into coarser signals. Recently, there has been considerable work using Bayesian

network inference. Examples include inferring gene regulation using gene expression data from the yeast cell cycle

or using data from synthetic gene networks.

SOURCE: Reprinted by permission from J.J. Rice and G. Stolovitzky, “Making the Most of It: Pathway Reconstruction and Integrative

Simulation Using the Data at Hand,” Biosilico 2(2):70-77. Copyright 2004 Elsevier. (References omitted.)

(^50) F. Miyoshi et al., “Estimation of Genetic Networks of the Circadian Rhythm in Cyanobacterium Using the E-CELL system,”
poster session, presented at US-Japan Joint Workshop on Systems Biology of Useful Microorganisms, September 6-18, 2002, Keio
University, Yamagata, Japan, available at http://nedo-doe.jtbcom.co.jp/abstracts/35.pdf.
(^51) E. Wang et al., “e-Rice Project: Reconstructing Plant Cell Metabolism Using E-CELL System,” poster session presented at
Systems Biology: The Logic of Life—3rd International Conference on Systems Biology, December 13-15, 2002, Karolinska
Institutet, Stockholm, available at http://www.ki.se/icsb2002/pdf/ICSB_222.pdf.
(^52) L.M. Loew and J.C. Schaff, “The Virtual Cell: A Software Environment for Computational Cell Biology,” Trends in Biotechnol-
ogy 19(10):401-406, 2001.