KEYNOTE SPEAKER

Machine Learning Approaches for Systems Biology and Drug Discovery

Prof. Robert F. Murphy
Carnegie Mellon University, USA

Abstract

Recognition of cells, tissues and organs as complex systems with emergent properties has led to the creation of the field of systems biology, and this complexity has also been manifested in a number of prominent drug recalls due to unanticipated side effects. Cutting-edge machine-learning methods have an important role to play in understanding biological systems and aiding drug development. Cell imaging assays are widely used in drug development and systems biology, and improved methods to extract detailed information from imaging assays are needed. The CellOrganizer project provides tools for learning generative models of cell organization directly from images and for synthesizing cell images (or other representations) from one or more models. Model learning captures variation among cells and inputs can be two- or three-dimensional static images or movies. Current components of CellOrganizer can learn models of cell shape, nuclear shape, chromatin texture, vesicular organelle size, shape and position, and microtubule distribution. These models can be conditional upon each other: for example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance. Major advantages of the generative model approach are that models learned from separate experiments can be combined into one synthetic cell instance, and that results from different microscope systems and different experimental conditions can be compared through the framework of the generative model parameters that describe them. This will be especially important for integrating results from diverse studies of the effects of drugs and other perturbagens. However, this leads to a second machine learning challenge. Since the number of proteins that can be affected is in the tens of thousands, and the number of potential therapeutics whose effects we would like to know is at least in the hundreds of thousands, exhaustive testing of all compounds on all proteins is not feasible. Active machine learning methods, combined with generative models, can provide a framework for exploring large perturbagen spaces to find potential therapeutics with high desired activity on a specific target while minimizing activity on other targets.

Biography of the Keynote Speaker

Robert F. Murphy is the Ray and Stephanie Lane Professor of Computational Biology and Director (Department Head) of the Lane Center for Computational Biology in the School of Computer Science at Carnegie Mellon University. He also is Professor of Biological Sciences, Biomedical Engineering, and Machine Learning. He is a Fellow of the American Institute for Medical and Biological Engineering, and received an Alexander von Humboldt Foundation Senior Research Award in 2008. Dr. Murphy has co-edited two books and three special journal issues on cell imaging, and has published over 180 research papers. He is Past-President of the International Society for Advancement of Cytometry, was named as the first External Senior Fellow of the School of Life Sciences in the Freiburg (Germany) Institute for Advanced Studies, and is a member of the National Advisory General Medical Sciences Council.
Dr. Murphy’s career has centered on combining fluorescence-based cell measurement methods with quantitative and computational methods. In the mid 1990’s, his group pioneered the application of machine learning methods to high-resolution fluorescence microscope images depicting subcellular location patterns. His current research interests include image-derived models of cell organization and active machine learning approaches to experimental biology.