Distributed Information Processing in Biological and Computational Systems

ABSTRACT: Computer science and biology have enjoyed a long and fruitful relationship for decades. Computational methods are widely used to analyze and integrate large biological data sets, while several algorithms were inspired by the high-level design principles of biological systems. In this talk I will discuss similarities and differences between assumptions, requirements and goals of distributed biological and computational systems. To illustrate the mutual benefits I will present examples from two recent studies. The first models bacterial food search as an application of probabilistic belief propagation while the second looks at epigenetics as a process implementing a shared memory communication model.

BIO: Ziv Bar-Joseph is the FORE Systems Professor of Computational Biology and Machine Learning at the School of Computer Science at Carnegie Mellon University. His work focuses on the analysis, integration and modeling of high throughput biological data and on improving algorithms for distributed computational networks by relying on our increased understanding of how biological systems operate. Dr. Bar-Joseph received his Ph.D. from MIT in 2003. He is the director of the joint CMU-Pitt PhD program in Computational Biology and the PI of a number of large, multi-university centers including the HuBMAP Computational Tools Center. He was the recipient of the DIMACS-Celera Genomics Graduate Student Award in Computational Biology, the NSF CAREER award and Overton prize in computational biology.

Hosted by: Paul Bogdan