Abstract: The human microbiome – the diverse ensemble of microorganisms that populate the human body – represents a vastly complex ecosystem that is tightly linked to our health. Multiple molecular assays now enable high-throughput profiling of this system, providing large-scale and comprehensive characterization of its ecology, functional capacity, and metabolic activity. To date, however, analyses of such multi-omic data typically focus on statistical associations, often ignoring extensive prior knowledge of the mechanisms, dependencies, and regularities linking these various facets of the microbiome. In this talk, I will highlight the pressing need for the development of predictive systems-level models of the microbiome and of model-based computational methods for integrating and analyzing microbiome multi-omic data. I will introduce several novel computational frameworks for linking taxonomic, genomic, metagenomic, and metabolomic information about the microbiome. Combined, such frameworks lead to an improved comprehensive, multi-scale, and mechanistic understanding of the microbiome in health and disease, informing efforts for personalized microbiome-based therapy.

Prof. Borenstein is a full professor at the Blavatnik School of Computer Science and at the Sackler Faculty of Medicine at Tel Aviv University. He is also an external professor at the Santa Fe Institute and a faculty fellow of TAU’s Edmond J. Safra Center for Bioinformatics. Prof. Borenstein received his BSc in physics and computer science and his PhD in computer science from Tel-Aviv University, and held a joint postdoctoral position at Stanford University and the Santa Fe Institute. In 2010, he joined the Department of Genome Sciences at the University of Washington as a faculty member, and in 2018, moved to Tel Aviv University with a joint appointment in Medicine and in Computer Science. Prof. Borenstein is the recipient of various prestigious awards including the Alfred P. Sloan Fellowship and the NIH New Innovator Award.