Network and state space models: science and science fiction approaches to cell fate predictions

2 Mar 2012

John Quackenbush
Computational Biology and Functional Genomics Laboratory
Dana Farber Cancer Institute and Harvard School of Public Health


Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. 'Omic technologies were once heralded as providing a window into those networks, but so far their success has been limited, in large part because the high-dimensional they produce cannot be fully constrained by the limited number of measurements and in part because the data themselves represent only a small part of the complete story. To circumvent these limitations, we have developed methods that combine 'omic data with other sources of information in an effort to leverage, more completely, the compendium of information that we have been able to amass. Here we will present a number of approaches we have developed, with an emphasis on how those methods have provided insights into the role that particular cellular pathways play in driving differentiation, and the role that variation in gene expression patterns influences the development of disease states. Looking forward, we will examine more abstract state-space models that may have potential to lead us to a more general predictive, theoretical biology.

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