COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 125
Lander explains the intellectual paradigm for determining function as follows:
By investigating how such behaviors change for different parameter sets—an exercise referred to as
“exploring the parameter space”—one starts to assemble a comprehensive picture of all the kinds of
behaviors a network can produce. If one such behavior seems useful (to the organism), it becomes a
candidate for explaining why the network itself was selected; i.e., it is seen as a potential purpose for the
network. If experiments subsequently support assignments of actual parameter values to the range of
parameter space that produces such behavior, then the potential purpose becomes a likely one.5.3 TYPES OF MODELS^13
5.3.1 From Qualitative Model to Computational Simulation,
Biology makes use of many different types of models. In some cases, biological models are qualita-
tive or semiquantitative. For example, graphical models show directional connections between compo-
nents, with the directionality indicating influence. Such models generally summarize a great deal of
known information about a pathway and facilitate the formation of hypotheses about network function.
Moreover, the use of graphical models allows researchers to circumvent data deficiencies that might be
encountered in the development of more quantitative (and thus data-intensive) models. (It has also
been argued that probabilistic graphical models provide a coherent, statistically sound framework that
can be applied to many problems, and that certain models used by biologists, such as hidden Markov
models or Bayesian Networks), can be regarded as special cases of graphical models.^14 )
On the other hand, the forms and structures of graphical models are generally inadequate to express
much detail, which might well be necessary for mechanistic models. In general, qualitative models do not
account for mechanisms, but they can sometimes be developed or analyzed in an automated manner.
Some attempts have been made to develop formal schemes for annotating graphical models (Box 5.2).^15
Qualitative models can be logical or statistical as well. For example, statistical properties of a graph
of protein-protein interaction have been used to infer the stability of a network’s function against most
“deletions” in the graph.^16 Logical models can be used when data regarding mechanism are unavail-
able and have been developed as Boolean, fuzzy logical, or rule-based systems that model complex
networks^17 or genetic and developmental systems.
In some cases, greater availability of data (specifically, perturbation response or time-series data)
enables the use of statistical influence models. Linear,^18 neural network-like,^19 and Bayesian^20 models
have all been used to deduce both the topology of gene expression networks and their dynamics. On the
(^13) Section 5.3 is adapted from A.P. Arkin, “Synthetic Cell Biology,” Current Opinion in Biotechnology 12(6):638-644, 2001.
(^14) See, for example, Y. Moreau, P. Antal, G. Fannes, and B. De Moor, “Probabilistic Graphical Models for Computational
Biomedicine, Methods of Information in Medicine 42(2):161-168, 2003.
(^15) K.W. Kohn, “Molecular Interaction Map of the Mammalian Cell Cycle: Control and DNA Repair Systems,” Molecular Biology
of the Cell 10(8):2703-2734, 1999; I. Pirson, N. Fortemaison, C. Jacobs, S. Dremier, J.E. Dumont, and C. Maenhaut, “The Visual
Display of Regulatory Information and Networks,” Trends in Cell Biology 10(10):404-408, 2000. (Both cited in Arkin, 2001.)
(^16) H. Jeong, S.P. Mason, A.L. Barabasi, and Z.N. Oltvai, “Lethality and Centrality in Protein Networks,” Nature 411(6833):41-42,
2001; H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, “The Largescale Organization of Metabolic Networks,”
Nature 407(6804):651-654, 2000. (Cited in Arkin, 2001.)
(^17) D. Thieffry and R. Thomas, “Qualitative Analysis of Gene Networks,” pp. 77-88 in Pacific Symposium on Biocomputing, 1998.
(Cited in Arkin, 2001.)
(^18) P. D’Haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, “Linear Modeling of mRNA Expression Levels During CNS Develop-
ment and Injury,” pp. 41-52 in Pacific Symposium on Biocomputing, 1999. (Cited in Arkin, 2001.)
(^19) E. Mjolsness, D.H. Sharp, and J. Reinitz, “A Connectionist Model of Development,” Journal of Theoretical Biology 152(4):429-
453, 1999. (Cited in Arkin, 2001.)
(^20) N. Friedman, M. Linial, I. Nachman, and D. Pe’er, “Using Bayesian Networks to Analyze Expression Data,” Journal of
Computational Biology 7(3-4):601-620, 2000. (Cited in Arkin, 2001.)