Im Sommersemester 2006 findet im Rahmen des Informatik-Kolloquiums folgender Vortrag statt:
Freitag, 12. Mai 2006, 15:00 Uhr, Turing-Hörsaal
Prof. Kalyan Basu (University of Texas at Arlington)
"Modeling Biological Network"
The success of Genome project, the tremendous advancement in the Micro array technique and large scale assay technologies like cDNA array are generating large volumes of scientific data for the life system, from microbes to the Homo-sapiens. The collected information is added at a very rapid speed to the different databases like Genome DB, Protein DB (PDB), and PubMed DBs etc. We are now in an era where our capability to generate biological data is less of an obstacle to our understanding of the biological system. The key challenge is to harvest such data and create a higher-level system understanding of the complexities of different organisms and the behavior of networks, rather than the operation of single genes governs species as Phenotype. Thus the research in Genomics and Proteomics alone is not sufficient to understand the biological complexities. Indeed, knowledge of cell dynamics and signaling networks is becoming essential to understand complex biological processes. The GTL (Genome to Life) project of Department of Energy (DOE) has started using this idea for the solution of many problems like secure energy supply, cleaning up of environmental carbon dioxide that contributes to global warming. The modeling challenge is to develop comprehensive technique integrating molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple levels of abstraction starting from the cell, to the tissues and finally to the whole organism. The major tasks are (1) close linking of mathematical models to biological functions and experimental data; (2) integration of knowledge from different sources; and (3) the speed up of computation to handle very large computing of multi cellular complexity. The stochastic resonance is a new observation in the biological system and that motivates us to look to the networking techniques. In addition the inherent stiffness of biological process simulation is also addressed in our research. For long time, the networking traffic research community was involved on complex stochastic system modeling for telecommunication, wireless and internet networks. The applied probability and statically modeling of networks is a mature research area. We like to take advantage of this mature research field to model the biological process. This required statistical model of the different biological functions as observed by biological experiments. This opens up new research direction of System Biology.
At University of Texas at Arlington, we started the research on Modeling Biological Networks in 2004 in collaboration with UNT Health Science and Mt Sinai Medical School faculties. We also started offering a Graduate course on Modeling Biological Network in spring 2006. This talk will define the scope of Biological Network and how the modeling can obtain the dynamic performance of the biological system. It will give a short overview of the current system Biology techniques to model the Biological systems. After this review, we will present selected results of our Biological function modeling using stochastic techniques. Finally we will present the discrete event simulation of biological process of PhoPQ regulation in Salmonella Typhimurium . Finally we will give a short description of iSimBioSys, the Hybrid simulator that we are developing for Genome Scale Cell simulation.