ILAR Journal 38(2) 1997: The Role of Computational Models in Animal Research

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ILAR Journal 38(2)
The Role of Computational Models in Animal Research

Introduction
Bennett Dyke

The idea for a computational modeling issue of ILAR Journal came about largely because of the concern among the members of the Institute for Laboratory Animal Research (ILAR) Council that among some nonspecialists, expectations of computer simulation as an important and unrealized alternative to animal experimentation seem to persist. Although this concern has been discussed critically in scientific circles, we felt that a review in the ILAR Journal would reach a particularly important audience of those who are involved in a variety of ways with laboratory animals.

Another motivation was simply to reacquaint the readership with some examples of modeling applications in fields related to biomedical and laboratory animal science. ILAR News (32[2]:2-3) contained a summary report of a 1989 National Institutes of Health conference on modeling in biomedical research. The report consisted of some general recommendations about advantages of models in cardiovascular and diabetes research, but contained no concrete illustrations of their application. Although it would not be possible to cover all uses of computer modeling, we selected some representative areas primarily because of their relevance to the alternatives issue. We asked the authors to summarize the strengths and limitations of what they believe to be important applications of modeling in the fields they covered and to address the issue of the use of animals in relation to the modeling they describe.

In this issue, Foster and Boston describe considerations underlying the construction and use of metabolic models, with examples based on experiments with their SAAM computer modeling system. Ballou shows how demographic and population genetic models are used to optimize long-term health and viability of captive breeding populations. Dyke and Mahaney describe statistical genetic methods applied to the understanding of genetic factors contributing to precursors of common disease in animal models. Smith, Fosse, Dewhurst, and Smith report on the use of models in training and education, drawing examples from the Norwegian alternatives database (NORINA).

It is apparent that computer modeling is an integral part of important segments of biomedical science, and that its role is primarily one that complements more traditional experimentation and analysis. A common theme underlying these papers is that while models make it possible to understand and manipulate experimental data, they are consumers rather than producers of data. This means that modeling can reduce numbers of animals used in research only in a limited way. It is evident that the educational use of modeling, where the intent is to disseminate prior knowledge, is the only clear-cut case in which the use of animals can be reduced. Although we do not expect to hear the end of claims that modeling can substitute for experimentation, we hope that these papers will provide some insights and examples as to why this is not the case.