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Mathematical and computational modeling provide a natural infrastructure for systematically bringing together the wealth of data and knowledge obtained from biological, physiological, and clinical research. Furthermore, the power of this infrastructure is the ability for the model to simulate pathophysiology and predict how, when, and in whom diseases will develop. Historically, the majority of models in biology and physiology are created to understand a particular process or phenomenon by putting together known biological constructs, using modeling to fill in the gaps of the unknown, creating new hypotheses, and redesigning the models based on experimental outcomes and measures. These models are often used to supplement research efforts focused on specific biological questions and domain areas to provide new insights in understanding data.
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White et al. (2009) studied this question.
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