c56b1501a796d310VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/News & Events/Archived Events/2012-2013 EventsJames Henderson###@###(Fri, 11 Jan 2013)James Henderson###@###(Fri, 11 Jan 2013)438 West HallReconstruction of Biological Regulatory Networks Using Differential Equation Modelsstats1357924500000135792450000012:15 PM<p><b>Title: </b>Reconstruction of Biological Regulatory Networks Using Differential Equation Models<br> <b>Advisor: </b>Professor George Michailidis&nbsp;<br> <b>Committee Members: </b>Associate Professor Edward Ionides, Associate Professor Moulinath Banerjee</p> <p>Abstract: High-throughput data technologies have led to an explosion of interest in reconstructing biological networks. Dynamic models based on systems of coupled ordinary differential equations (ODEs) not only allow for reconstruction from time-course data, but also provide additional insight into the underlying biological mechanisms relative to methods based on steady state data. In this work I formalize the network reconstruction problem from a dynamic systems point of view and propose a novel coupling metric for quantifying the relationship between coupled nonlinear ODEs. Combining existing techniques in a novel way, methodology is developed for non-parametric estimation of a dynamic system. The methodology is illustrated using data from an in silico networks describing the regulatory relationships among transcription factors in mouse-embryonic stem cells. In a second example using an in silico model of an E. coli &nbsp;subnetwork I show how the model can be modified to accommodate varied experimental conditions.&nbsp;</p>Njjsantosjjsantos1366829296897556b1501a796d310VgnVCM100000c2b1d38d____once11112newnewEvent Flyer/UMICH/stats/Home/Events/Dissertations and Oral Preliminary Examinations/James Henderson PreLim Flyer.pdfJames Henderson