An integrated approach for inference and mechanistic modeling to advance drug development

24 March 2005

Rina Gendelman


An important challenge facing researchers in drug development is how to translate multiple types of measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. Progress in drug development efforts requires a two-pronged, yet intertwined computational biology strategy that can: (1) infer new (and confirm or negate existing) biological relations using approaches based on reverse-engineering, learning algorithms, and data-mining techniques applied to large-scale high-throughput data; and (2) account for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue-level models. Application of this approach to the study of the cell cycle progression can lead to better understanding and potential target identification for future treatments

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