Stochastic Reaction Networks
Cells proliferate by constantly producing molecules that carry out essential functions. Many of these molecules are present in such low numbers that reaction rates fluctuate from cell to cell and over time. We develop stochastic methods based on the Chemical Master Equation to quantify how cells can function reliably despite this stochasticity and how they diversify in response to external stimuli.
Agent-based Cell Models
Cells constantly make decisions such as when and at what size to divide. These decision processes may appear random without investigating which internal and external signals cells may respond to. We develop agent-based stochastic models that describe how and why cells make decisions. Using these models we infer which molecular networks are at play and identify environmental factors affecting these decisions.
Cell Lineage Dynamics
Cell divisions generate ancestral relationships between cells in a population, which are called lineages, and can be observed under a microscope. Disentangling these genealogical relationships allows us to make sense of cell fate decision and cellular heterogeneity. To do this, we develop mathematical methods to predict cellular histories from agent-based models of growing and dividing cell populations.
In the News
- Our group received generous funding through a UKRI Future Leaders Fellowship to advance agent-based modelling approaches for single-cell dynamics.
- A feature on our work on single-cell growth and its implications for antibiotic tolerance.
- Two recent news articles (and here) describe our findings about how cells coordinate divisions with their internal clocks and the environment.