Events Calendar for Drexel UniversityClick here to Print
Event Details
Notify me if this event changes.Add this event to my personal calendar.
Go Back
Building Models of Cell Differentiation and Perturbation Directly from Microscope Images
Start Date: 12/5/2014Start Time: 4:00 PM
End Date: 12/5/2014End Time: 5:30 PM
Event Description
Robert F. Murphy, PhD, Ray and Stephanie Lane Professor of Computational Biology at Carnegie Mellon University, will discuss how, given the complexity of biological systems, machine learning methods are critically needed for building systems models of cell and tissue behavior and for studying their perturbations. Such models require accurate information about the subcellular distributions of proteins, RNAs, and other macromolecules to be able to capture and simulate their spatiotemporal dynamics. Microscope images provide the best source of this information, and we have developed tools to build generative models of cell organization directly from such images. Generative models are capable of producing new instances of a pattern that are expected to be drawn from the same underlying distribution as those it was trained with. Our open source system, CellOrganizer [www.CellOrganizer.org], currently contains components that can build probabilistic generative models of cell, nuclear and organelle shape, organelle position, and microtubule distribution. These models capture heterogeneity within cell populations, and can be dependent upon each other and can be combined to create new higher level models. The parameters of these models can be used as a highly interpretable basis for analyzing perturbations (e.g., induced by drug addiction), and generative models of cell organization can be used as a basis for cell simulations to identify mechanisms underlying cell behavior. Once a common framework for representing the effects of perturbagens can be created, the next step is to learn a comprehensive model that describes the effect of large numbers of potential perturbagens (e.g., small molecule compounds). We have developed “active” machine learning approaches that iteratively select experiments to perform to improve the best model currently available. Results in test cases show that very accurate models can be built with significantly fewer measurements than exhaustive screening. For more info, please visit www.biomed.drexel.edu.
Contact Information:
Name: Ken Barbee
Phone: 215-895-1335
Email: barbee@drexel.edu
Biomed DEC.jpg
Location:
Papadakis Integrated Sciences Building (PISB), Room 120, located at the corner of 33rd and Chestnut Streets.
Audience:
  • Everyone

  • Select item(s) to Search
    Select item(s) to Search
    Select item(s) to Search
    Select item(s) to Search