Elhanan Borenstein, Ph.D.


Associate Professor

Blavatnik School of Computer Science

and

Sackler Faculty of Medicine

Tel Aviv University

Associate Professor

Dept of Genome Sciences

and

Computer Science & Eng.

University of Washington

External Professor
Santa Fe Institute
Research
The human microbiome - the complex ensemble of microorganisms that populate the human body - has a tremendous impact on our health. World-wide research initiatives and novel metagenomic studies now provide exciting insights into the previously uncharted composition of the microbiome, and reveal marked compositional changes associated with a wide range of diseases. Yet, a system-level understanding of the human microbiome and its impact on the host is still lacking.

To address this challenge, we combine a variety of computational approaches, including systems biology, modeling, machine learning, data science, and networks analysis with multi-omic microbiome data, to develop novel computational methods for studying the human microbiome as a complex ecosystem and for going beyond simple comparative microbiome analyses. This system-level approach is crucial to resolving fundamental questions concerning the human microbiome and its role in human health, with numerous biomedical applications.

Research in the lab is multidisciplinary in nature and spans several levels of abstraction, ranging from state of the art computational methods for analyzing microbiome big data to theoretical studies of mathematical and computational models.

Specific research themes include (and visit aslo our Publications page):

Metagenomic systems biology, computational modeling, and rational design of the human microbiome

The human microbiome is a complex biological system with numerous interacting components across multiple organizational levels. To date, however, most studies of the microbiome have focused on profiling its composition and on comparative analyses, whereas significantly less effort has been directed at elucidating, characterizing and modeling these interactions and on studying the microbiome as a complex and interconnected. To fill this gap, our lab develops a variety of computational frameworks for in-silico modeling of the microbiome, allowing us to systematically predict and characterize the complex web of metabolic dependencies between the species comprising the microbiome and between the microbiome and its host. These models provide an improved mechanistic understanding of the microbiome and an innovative approach for rationally designing clinical microbiome interventions and microbiome-based therapy. We are especially interested in developing a metabolic model-based framework for predicting the relationship between the gut microbiome, its metabolome, and the host diet.

Computational methods, machine learning, and data science for multi-omic analysis of microbiome data

Systems thinking, analysis, and modeling are not limited to the study of interactions among various microbiome components but should also be applied to studying the links between different facets of the microbiome. Indeed, much of the effort in modern microbiome research focuses on generating multiple types of omic data, including, most notably, metagenomics, metatranscriptomics, metaproteomics, and metametabolomics. Our lab develops novel computational methods for integrating these multiple meta-omic datasets, going beyond the identification of statistical associations and putting forward systems-level and data-science frameworks that link such data based on a comprehensive understanding of the microbiome. Such methods are crucial for successfully exploring highly multi-dimensional microbiome data, identifying drivers of disease, and detecting putative intervention targets.

To explore some of the tools developed by our lab, visit our Software page.

Computational metagenomics and analysis of microbiome taxonomic and functional variation across health and disease

In collaboration with multiple experimental and clinical laboratories, we are studying taxonomic and functional shifts in the human microbiome that are associated with a variety of disease states. We specifically utilize our expertise in shotgun metagenomics processing and annotation, machine learning, data analysis, and modeling to provide a rigorous and accurate profiling of the microbiome across samples and to reliably identify specific taxa, pathways, and functional modules that may be linked to disease.

A few examples of such projects include:
  • The gut microbiome and its relationship to nutritional status in children with cystic fibrosis
  • The impact of the microbiome on aging and on aging-related diseases
  • The microbiome of diary workers and its role in disease and infection susceptibility
  • Microbiome multi-omic analysis in autism, epilepsy, inflammatory bowel disease, and HIV
  • The role of type VI secretion system in microbiome assembly within and between species
Large scale computational study of complex biological and ecological networks

Biology is all about interactions and many biological and ecological systems are best represented as networks of interacting components, whose structures and topologies are an important determinant of system function and dynamics. We study the topological properties of complex biological networks, with an emphasis on metabolic networks and the evolutionary forces that forge their structure. We focus on the modularity, robustness, and capacity for innovation of such networks, and are specifically interested in the interplay between the organization of various networks and the environments in which they evolved and prevail.