We develop models of molecular process to discover the fundamental principles that govern how cells operate and maintain themselves as self-organizing life forms.

Molecular processes allow cells to respond to extracellular cues, allowing them to adapt to their environment. For signal transduction pathways, the composition and architecture is defined at the level of protein structure, which dictates which proteins interact with each other, how these interactions are coupled and how they are influenced by post-translational modifications. Yet, how and which extracellular cues are processed in individual cells depends on their molecular make-up, i.e., their cell identity – a systems level property. This cell-to-cell variability drives various biological phenomena including cell fate decisions and resistance to anti-cancer drugs and is, thus, of relevance to basic biology and human health.

Our lab combines dynamic mathematical models with machine learning to connect structural, process and systems level dynamics and states. We apply such hybrid models to, for example, study how the regulator rules governing molecular processes emerge from protein structure or how cell states can be used to predict cell-to-cell variability in dynamic process-level responses. We develop the computational tools to build and train these models and apply them to biological problems in cancer, developmental, and fundamental biology.