Steps of modeling complex biological systems.
A disease like schizophrenia results from the malfunctioning of a complex, multi-faceted biological system. As a consequence, the root causes of such a disease and the trajectories from health toward the disease are very difficult to comprehend with simple cause-and-effect reasoning. Similarly, reductionistic investigations are crucial for the discovery of specific disease mechanisms, but they are not sufficient for comprehensive assessments and explanations. A promising option for advancing the field is the utilization of mathematical models that can quantitatively account for hundreds of components and their interactions and thus have the potential of truly explaining complex diseases. While the potential of mathematical models is quite evident in principle, their practical implementation is a daunting task. On the one hand, many distinctly different approaches are possible. For instance, in the case of schizophrenia, models could focus on neurological aspects, physiological features, or the biochemical malfunctioning within some cell complexes in the brain, and each model would ultimately be very different. On the other hand, it seems that there are no rules or recommendations that guide the development of a new mathematical model from scratch. We discuss here that, even though mathematical models in biology and medicine may ultimately have a very different appearance, their development can be structured as a sequence of generic steps. Major drivers for many of the details of model development are the goals and objectives of the modeling task and the availability and quality of data that can be used for model design and validation.
Voit
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Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0535, USA. eberhard.voit@bme.gatech.edu
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Miller
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