Generating the Data Needed for Prediction

Hans Scholten, Session Chair

Generating the Data Needed for Prediction will highlight the important differences in experimental design required for a systems biology approach, as compared to a more reductionist approach. The need for large datasets to feed the development of mathematical models will be discussed, as well as some of the problems associated with generating and managing large datasets.

Session 1 Speakers

David Galbraith, University of Arizona, “Extending the Capabilities of Microarrays for Uncovering Functional Linkages Between Genes”
Gill Geesey, Center for Biofilm Engineering, Montana State University, "What Biofilm Parameters Are Useful for System Modeling?"
Peter Nelson, Fred Hutchinson Cancer Research Center, “Defining the Extent of Genetic Variability”
Brian Thrall, Pacific Northwest National Laboratory, “Systems Analysis of the Response of Human Mammary Epithelial Cells to Epidermal Growth Factor Stimulation”