ISMB99 - Tutorial 10
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Recent technological developments, such as cDNA microarray based RNA quantitation and proteomics, have opened the opportunity for massively parallel biological data acquisition and thus have shifted our attention towards a more complex understanding of molecular biology. In addition to determining the roles of individual genes, we can now start to study cells as a complex network of biochemical factors. The aim of this tutorial is to provide an overview of the experimental, theoretical and computational foundations of genetic network analysis. We will discuss the following issues: The nature, precision and information content of massively parallel biological data sets. Principles of genetic network modeling based on Boolean, continuous and stochastic nets. The potential use of genetic network modeling in (1) predictive forward modeling, (2) predictive system characterization and (3) reverse engineering of genetic networks. Reverse engineering of time dependent gene expression network matrixes based on Boolean networks, correlation matrices, continuous additive models and dynamic Bayesian nets. Cluster analysis of time-dependent gene expression matrices and its potential uses. By the end of the tutorial attendants can expect the following: to be able to make informed decisions about the appropriate experimental technologies they need to apply to solve a particular problem.; to be able to estimate the amount and nature of information they can obtain in large scale molecular biology; to be able to locate literature, softwares or collaborators for computational analysis of massively parallel experimental data sets. Participants will also understand the potential practical use of modeling and theory in experimental biology. The tutorial is designed to benefit both molecular biologists who need a good intuitive understanding of how theory and computer science can help their work AND computer scientists who need a better understanding of how data are generated in molecular biology and what sort of help do biologists expect in genetic network analysis. |