Savir Lab

Dept. of Physiology,
Biophysics & Systems Biology

About us

One of the main determinants of the fitness of biological systems is their ability to sense multiple cues from the environment, interpret them correctly and respond accordingly. At the Savir lab, our goal is to study, both experimentally and theoretically, Information processing in biological systems and its failure in aged cells.


Why do many organisms have similar aging rules? Why do many organisms fail in s similar way?

Why do we age? is aging the result of evolution or an inevitable constraint?

What is the difference between the aging of biological and synthetic systems?

How do the basic properties of biological systems such as self-replication and self-renewal interplay with aging?

Does AI age as biological systems?

To answer all these questions, we take an interdisciplinary approach and use tools from experimental biology (high throughput setups, microfluidics, genetics), Biophysics and applied math (stochastic processes, mathematical biology), and Engineering (signal processing, deep learning).


Aging & Failure of Complex and Biological Systems

Despite the fact that aging is one of the most prominent biological processes, many fundamental questions regarding its role remain unanswered.

We study how age affects the ability of systems, such as microorganisms, immune cells, and the mammalian embryo, to sense their environment and responds accordingly.

Information processing

Information processing in living systems

Practically all biological systems rely on the ability of bio-molecules to specifically recognize each other. Examples are antibodies targeting antigens, regulatory proteins binding DNA and enzymes catalyzing their substrates. 

This task is further complicated by the inherent noise in the biochemical environment. We quantify the constraints and limits on the way biological systems can process information and how it affects their evolution.

harnessing Ai

Harnessing AI for biomedical applications

Gaining insights and actionable decisions from vast digital medical data sources is a key challenge in implementing personalized medicine and next-generation healthcare.

We develop machine-learning approaches that allow inferring novel biological insights and creating medical decision-support systems. We particularly focus on multimodal networks that can cope with multiple data sources such as images, omics (tabular data, network data), and chemical structures (graphs).

Join Us!

We are always looking for talented people to join us!
If you are interested, send an email with CV and your research interests to