Prediction is the attempt to use existing knowledge to foretell an event before it happens. The recent progress in the field of genetics, biochemistry, and molecular biology has increased our ability to understand and predict the behavior of biological systems. Computational modeling, based on biological information, has been used to extend our understanding thus enhancing predictive accuracy. Models used to simulate cellular or human biology produce reliable data and new hypotheses and attempts to translate knowledge and information across different levels, from "in vitro" screens to cell-based arrays and, ultimately, to patients. Prediction made using models plays a vital role in the pharmaceutical industry where knowledge extension is valuable to generate novel products and can save millions of dollars based on increased efficiency. In pharmaceutical research and development computational modeling has potential to substantially impact on efficiency and development in different areas: from cell-signal and signal-response behavior to physiology, in which models are used to simulate clinical outcomes. In addition to specific applications, there is also a natural role for modeling to link traditional biology and high-throughput informatics analysis. Data from high-throughput datasets are integrated with data from other in-house biological experiments and from literature. A model is constructed from a subset of these data and is then validated using traditional biology experiments. When the model captures experimental behavior the it is used to generate predictions or hypotheses that suggest new biological experiments. From these new models will be generated rising the knowledge enhancing loop: experiment – data – model – simulation – experiment. Increasing biological complexity translates into theoretical and computational complexity of the models. To achieve results in reasonably short time increasing computer power is required. This today is achieved via the use of computational platforms which allow to share the computation in a large number of single or multiple processors allocated in different places: Grid Computing. During this workshop experts from different areas will discuss aspects of positive integration of modeling with Grid Computing to improve the research and development in drug discovery. The tutorial (23-01-2009) will be devoted to the use of the Immune System Simulator which has been developed in the framework of the EU-funded ImmunoGrid project. Other software, implemented during the PI2S2 project managed by the Consorzio COMETA, will also be presented. For further information please feel free to contact the workshop Organizers: Prof. S. Motta, motta at dmi dot unict dot it and Dr. F. Pappalardo, francesco at dmi dot unict dot it.
Starts 22 Jan 2009 09:00
Ends 23 Jan 2009 15:00
Europe/Rome
Catania
Science and Technology Park of Sicily Zona Industriale, Blocco Palma I, stradale V. Lancia, 57