Computer Simulation of Biological Processes
University of Oulu
- Biocomputing CoordinatorAndré Juffer
Computer simulation techniques have become important tools to study the properties of biomolecules and their interactions with other molecules. These techniques are employed with the general objective of predicting and explaining physical quantities and phenomena, such as protein-ligand associations, reaction rate constants of enzyme-catalyzed reactions, acid dissociation constants of proteins, protein domain motion, protein folding and stability, and the like. The underlying computational model provides direct insight into the workings of these molecules, such that statements about their functional properties can be offered. Such knowledge is frequently relied upon, for instance, in the drug design industry to speed up the process of drug finding and drug target analysis.
Computer simulation and modeling are not limited to the study of proteins, but can be and are being applied to many different domains of science. This project is specifically concerned with the study of biological processes. Whereas bioinformatics is largely concerned with the generation and analysis of biological data, computer simulation and modeling are applied with the objective of explaining the behavior of a given biological process. Each simulation model typically deals with different time- and length scales. Chemical reactions at the active site of an enzyme occur in very short timescales (fs), protein dynamics generally take place in the range of ps to ns or even longer timescales, interfacial processes (e.g. protein diffusion at membrane surfaces) cover the ms to ms timescale, protein translation falls in the ms to s range, and the growth of tumors occurs over months to years. Each model must therefore involve different simulation and modeling methods.
Simulation and modeling methods rely on the application of techniques that are based in physics (theoretical biophysics), mathematics and chemistry. This project is predominantly concerned with the development and application of such techniques. These range from very detailed QM/MM (quantum mechanics/molecular mechanics) and dynamic models of proteins to multiple-scale approaches that combine, for instance, diffusion models with cellular automata (CA) in the case of brain tumor simulation.