132 CATALYZING INQUIRY
time scale). In some cases, leaving out some of the interacting components (e.g., those whose interactions
are weakest or least significant) may be a workable method. In other cases, lumping together families or
groups of substances to form aggregate components or compartments works best. Sensitivity analysis of
alternative model structures and parameters can be performed using likelihood and significance mea-
sures. Sensitivity analysis is important to inform a model builder of the essential components of the model
and to attempt to reduce model complexity without loss of explanatory power.
Model evaluation can be complicated by the robustness of the biological organism being repre-
sented. Robustness generally means that the organism will endure and even prosper under a wide
range of conditions—which means that its behavior and responses are relatively insensitive to varia-
tions in detail.^33 That is, such differences are unlikely to matter much for survival. (For example, the
modeling of genetic regulatory networks can be complicated by the fact that although the data may
show that a certain gene is expressed under certain circumstances, the biological function being served
may not depend on the expression of that gene.) On the other hand, this robustness may also mean that
a flawed understanding of detailed processes incorporated into a model that does explain survival
responses and behavior will not be reflected in the model’s output.^34
Simulation models are essentially computer programs and hence suffer from all of the problems
that plague software development. Normal practice in software development calls for extensive testing
to see that a program returns the correct results when given test data for which the appropriate results
are known independently of the program as well as for independent code reviews. In principle, simula-
tion models of biological systems could be subject to such practices. Yet the fact that a given simulation
model returns results that are at variance with experimental data may be attributable to an inadequacy
of the underlying model or to an error in programming.^35 Note also that public code reviews are
impossible if the simulation models are proprietary, as they often are when they are created by firms
seeking to obtain competitive advantage in the marketplace.
These points suggest a number of key questions in the development of a model.
- How much is given up by looking at simplified versions?
- How much poorer, and in what ways poorer, is a simplified model in its ability to describe the system?
- Are there other, new ways of simplifying and extracting salient features?
- Once the simplified representation is understood, how can the details originally left out be
reincorporated into a model of higher fidelity?
Finally, another approach to model evaluation is based on notions of logical consistency. This
approach uses program verification tools originally developed by computer scientists to determine
whether a given program is consistent with a given formal specification or property. In the biological
context, these tools are used to check the consistency and completeness of a model’s description of the
biological system’s processes. These descriptions are dynamic and thus permit “running” a model to
observe developments in time. Specifically, Kam et al. have demonstrated this approach using the
languages, methods, and tools of scenario-based reactive system design and applied it to modeling the
well-characterized process of cell fate acquisition during Caenorhabditis elegans vulval development.
(Box 5.4 describes the intellectual approach in more detail.^36 )
(^33) L.A. Segel, “Computing an Organism,” Proceedings of the National Academy of Sciences 98(7):3639-3640, 2001.
(^34) On the basis of other work, Segel argues that a biological model enjoys robustness only if it is “correct’’ in certain essential features.
(^35) Note also the well-known psychological phenomenon in programming—being a captive of one’s test data. Programming
errors that prevent the model from accounting for the data tend to be hunted down and fixed. However, if the model does
account for the data, there is a tendency to assume that the program is correct.
(^36) N. Kam, D. Harel, H. Kugler, R. Marelly, A. Penueli, J. Hubbard, et al., “Formal Modeling of C. elegans Development: A
Scenario-based Approach,” pp. 4-20 in Proceedings of the First International Workshop on Computational Methods in Systems Biology
(CMSB03; Rovereto, Italy, February 2003), Vol. 2602, Lecture Notes in Computer Science, Springer-Verlag, Berlin, Heidelberg,
- This material is scheduled to appear in the following book: G. Ciobanu, ed., Modeling in Molecular Biology, Natural Comput-
ing Series, Springer, available at http://www.wisdom.weizmann.ac.il/~kam/CelegansModel/Publications/MMB_Celegans.pdf.