We combine nonlinear dynamics, biophysics, and data science to understand how biochemical networks make decisions and how cells self-organize into multicellular communities. We work closely with experimental labs, so every model we build is tested against data.
From single enzymes to bacterial communities, we look for the design principles behind how living systems sense, decide, and self-organize.

How starving Myxococcus xanthus cells aggregate into fruiting bodies, and how collective behavior emerges from genetic networks.

Design principles of sigma-factor and two-component networks, from Bacillus subtilis to Mycobacterium tuberculosis.

How B. subtilis chooses between sporulation and biofilm formation, and how master-regulator dynamics encode that choice.

Speed, accuracy, and energy dissipation in replication, transcription, translation, and CRISPR-Cas9.

How cells infected by phage lambda “count” viral copies to choose between lysis and lysogeny.

Predictive models for protein therapeutics: immunotherapy, molecular glues, and GLP-1 receptor agonists.
Our group brings together people trained in physics, mathematics, chemistry, engineering, and biology. Lab alumni now lead research groups and work in industry around the world.