Abstract :
Summary form only given. 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 diseased 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 systems-based approach to bioinformatics as it evolve´s towards biomedical informatics.
Keywords :
biocybernetics; cancer; cellular biophysics; diseases; genetics; graph theory; molecular biophysics; patient diagnosis; patient treatment; physiological models; stochastic processes; bioinformatics; biological pathways; biological processes; biomedical informatics; cancer; cell division; cells; clinical disease; coagulation disorders; complex systems; component parts; computational approaches; computer scientists; deterministic modeling; diabetes; diagnosis; diagnostic target design; diseased states; engineers; gene expression; gene identification; gene products; genomic data; graph theoretical methods; mathematicians; medical areas; molecular basis; multiple modeling hierarchies; normal states; pharmaceutical areas; physicists; predisposition; stochastic modeling; systems-based perspective; therapeutic target design; treatment; unresolved molecular interactions; Bioinformatics; Biological system modeling; Biology computing; Biomedical informatics; Cells (biology); Diseases; Evolution (biology); Gene expression; Medical diagnostic imaging; Physics computing;