Computational Biology

The interface between computer science, biology and medicine is among the most vibrant and active interdisciplinary domains in modern science. Biological systems are natively processing information: from genome sequences, through protein structures and interaction and all the way up to complex physiology or dynamics in ecological niches. So in order to truly understand biology, we must develop models for such information processing and in essence learn how cells, animals and ecologies “compute”.  Medical applications, from diagnostics to treatment, also involve inherent computation as physicians must consider rapidly expanding data sources and modalities and an underlying biological model to derive the optimal decisions for their patients. Biology and medicine were for many years much less amenable for mathematical models than physics. But with the dramatic development of experimental techniques in genomics and imaging, and with the standard accumulation of electronic health data, computer science became fundamentally important in the biomedical sciences of the 21st century.

Weizmann groups are at the forefront of the search for a new computational language for biology and medicine. They are synthesizing new methods and ideas from machine learning and data science, with deep understanding of the underlying biology or medical challenges.  The combination of theoretical, computational and experimental work is frequently achieved within the same lab and by researchers and students that become well immersed in multiple disciplines. The highly flexible scientific culture at Weizmann allows for interactions across all faculties, with physicists, neuroscientists, chemists and environmental scientists all sharing and exchanging techniques and strategies for handling very large-scale data in order to understand how biology is working.

The core CS/biology groups of Eran Segal, Udi Shaprio and Amos Tanay are international leaders in biomedical signal processing, microbiome and nutrition, data-rich patient models, cell lineages, single cell genomics, genome regulation and more. They are part of the world class interdisciplinary group of systems biology at Weizmann including over 20 groups integrated into all Weizmann faculties. CS/biology groups are also central for the Weizmann AI hub, in which methods developers and biomedical researchers define and solve together the most exciting problem in CS-driven biology and medicine.

Recurring seminars and meetings

Research centers 

Faculty Members

Computer Science and Applied Mathematics

Eran Segal

Computational Biology, Machine Learning
Computer Science and Applied Mathematics

Ehud Shapiro

Computational foundations for digital democracy,  grassroots distributed systems
Computer Science and Applied Mathematics

Amos Tanay

Computational biology, Single cell genomics in stem cells and ageing