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COMPUTATIONAL MODELING OF BIOLOGICAL SYSTEMS: IMPLICATIONS FOR USE OF LABORATORY ANIMALS IN TOXICOLOGICAL RESEARCH AND TESTING.
Citation:
CONOLLY, R. COMPUTATIONAL MODELING OF BIOLOGICAL SYSTEMS: IMPLICATIONS FOR USE OF LABORATORY ANIMALS IN TOXICOLOGICAL RESEARCH AND TESTING. Presented at Fifth World Congress on Alternatives and Animals in the Life Sciences, Berlin, GERMANY, August 21 - 25, 2005.
Impact/Purpose:
Living organisms from single cells to people can be thought of as “biological machines” - feedback control systems following genetically-determined developmental programs and in adulthood focusing on homeostasis and reproduction. Systems engineering principles that define the control circuits in man-made machines are also applicable to living systems. In fact, striking parallels exist between control circuits in complex machines and in biological cells and tissues (Carlson and Doyle, PNAS, 99, Suppl. 1, 2538-2545, 2002). Regulatory networks exist at all levels of biological organization – molecular, cellular, tissue and organism – and a systems engineering approach to characterizing their structure and function appears to be possible. We can ask if and when computational models will be ready to replace laboratory animals in toxicological research and testing. First, however, we should recall a cardinal rule of computer programming – garbage in - garbage out. In other words, a robust, predictive computational model of a biological system must be based on a sound understanding of that system. The rate-limiting step in the development of these models is the rate of our progress in understanding the relevant biology.
Description:
Computational models have important roles to play as adjuncts to classical toxicological methods.