Computational Modeling of Biological Systems: from Gene to Organ, Hilton Head, South Carolina, February 23-26, 2000

Johns Hopkins University, Baltimore MD

Investigators

Abstract

0000172 Popel The amount and rate of accumulation of biological information is increasing exponentially. The importance of this information explosion cannot be understated - it has transformed the nature of both biology and medicine, and has led many to claim that biology has become an information science. There is, however, growing recognition that the tabulation of genetic and molecular building blocks from which biological systems are composed is not sufficient for understanding the functions of these complex systems. Rather, it is becoming clear that the emergent, integrative behaviors of biological systems are a result of regulated interactions among all the components that form complex structures with specific properties, and that knowledge of each system component, however detailed, is not sufficient by itself to understand these integrative behaviors. Achieving an understanding of molecules, cells, tissues and organs as integrative entities is the next major frontier of biomedical science. Because of the inherent complexity of real biological systems, the development and analysis of information-based computational models is necessary to achieve this understanding. The objective of the workshop is to bring together researchers who are developing experimentally-based computational models of biological systems spanning hierarchical levels of analysis that include: a) regulation of gene expression; b) sub-cellular and cellular dynamics; and c) tissue and organ function. The meeting will provide a forum for presentation of the state-of-the-art in modeling research, allow for assessment of the degree to which an integrative understanding of system function is being achieved by existing models, and help to identify new areas of high growth potential for the next generation of models. The meeting will also include a panel discussion "Using Computational Models in Education."