DIMACS Mini-Workshop on System Based Modeling in Informatics

February 19 - 20, 2001
DIMACS Center, Rutgers University, Piscataway, New Jersey

Michael Liebman, Abramson Family Cancer Center, University of Pennsylvania, School of Medicine, liebmanm@mail.med.upenn.edu
Richard L.X. Ho, R.W. Johnson Pharmaceutical Research Institute, RHo@prius.jnj.com
Presented under the auspices of the Special Year on Computational Molecular Biology.

As biologists model complex systems whose properties are not fully explained by the properties of their component parts, they have long understood that it is important to investigate the interactions of those component parts, interactions such as those in which different cells work together in such tasks as determining when a cell divides and how gene expression is regulated. Bioinformatics, with an expansion to a systems-based perspective taking advantage of the expertise of mathematicians, computer scientists, engineers, and physicists, is well positioned to play a major role in achieving this.

Bioinformatics has evolved to focus on the molecular basis of genomic data, attempting to identify, qualify and quantify genes and gene products. The ultimate goal for the application of bioinformatics in practice, for example in the pharmaceutical and medical areas, is in the development of knowledge to impact the practice of medicine (i.e., diagnosis and treatment of predisposition and disease). Biomedical Informatics is relatively early in its evolution in that it examines the bioinformatic data from this systems-based perspective and attempts to integrate observations and knowledge about clinical disease to analyze the underlying biological processes. Success in these separate developments will come from their convergent evolution. To enable the interface between computation and experiment, stochastic and deterministic modeling including graph theoretical methods are being applied to the representation and evaluation of biological pathways and processes in normal and disease states. These computational approaches attempt to deal with incomplete information, unresolved molecular interactions and multiple modeling hierarchies. We hope that progress on them will result in their application in the analysis and interpretation of clinical disease, e.g., cancer, coagulation disorders, diabetes, in terms of gene identification for use in diagnostic and therapeutic target design. This workshop will investigate these computational approaches and explore the system-based approach to bioinformatics.

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Document last modified on January 8, 2001.