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.