The effect of aging on combinatorial signal integration

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.

Evolution of 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 affect their evolution.

Harness deep learning to biomedical applications

During the last few years, deep learning and in particular deep convolution networks play a major role in computer vision. On the one hand, we deploy deep learning to provide tools for medical decision support systems while on the other hand, we develop ways to use it to infer new biological insight from big data.