Abstract: Understanding and predicting how cells sense and respond to their environments is a key goal of systems biology. New experimental and computational methodologies have helped elucidate many signal transduction pathways and gene regulation mechanisms, but it remains difficult to predict the phenotypic diversity of cellular dynamics. To better understand and predict these complex networks, we propose a comprehensive approach for the identification of gene regulation systems. First, we developed a quantitative assay to measure single-molecule expression of endogenous mRNA at fast temporal resolution in individual cells. Second, we developed an efficient and flexible computational approach to analyze data from such experiments. Third, we integrated these experimental and computational approaches within a novel hierarchical network identification framework, which involves several clearly defined rounds of analysis, prediction, experiment design, and validation. Finally, we show that our semi-automated hierarchical approach can select a single, predictive gene regulatory model from out of several thousand automatically-generated hypotheses. In addition the identified model provides accurate predictions at diverse environmental and genetic conditions, which extend well beyond its training data. Since our approach is not specific to any gene, pathway or organism, it can lead to new insight into complex cellular networks from yeast to human.
Bio: Gregor Neuert completed his Diplom in Technical/Engineering Physics at Ilmenau University of Technology (Germany), then completed his Ph.D. in Physics/Biophysics at Ludwig Maximilians University (Munich) with Hermann Gaub. He is currently a postdoc at MIT in the Departments of Physics and Biology and the Koch Center for Integrative Cancer Research under the supervision of Alexander van Oudenaarden. His research focuses on understanding dynamic information processing (for example, in gene regulation or signal transduction) in uni- and multicellular organisms using quantitative single-molecule experiments, genetics, and mathematical models. Website: http://www.mit.edu/~gneuert/
Host: Megan Valentine, Mechanical Engineering