From bacteria to mammalian cells: Interrogating single-cell dynamics using agent-based stochastic models

One of the most pressing challenges for healthcare is that many patients do not respond to treatment, which produces physical, social, and economic suffering. Moreover, variable treatment response contributes to the cost of drug development. A major driver of these inefficiencies is the cellular heterogeneity existing within and between patients in complex diseases such as cancer.

Altered behaviours can involve only a few cells, but to-date such changes are often profiled at the population level, which masks functionally and clinically relevant intercellular variations.

Modern single-cell technologies allow the tracking growth and intracellular concentrations in hundreds of cells simultaneously. The outcomes of these experiments are difficult to interpret because they vary drastically from cell to cell, even within genetically identical cell populations grown under the same conditions. Predictive models are needed to make sense of these experiments and to understand how cells exploit this heterogeneity, for instance, to survive drug treatment.

It will be crucial to address this question with the wealth of single-cell data becoming available to tackle problems of drug tolerance and diseases, as well as to improve therapies for the health sector.

Current mathematical approaches quantify the stochasticity inherent in reactions by which molecules are synthesised in the cell, but they cannot predict how these heterogeneous affect cell growth, division, and death. To understand this effect, I will develop new mathematics and models that enable us to understand the complex interplay between cell growth and the reactions in single cells.

These models treat cells as individuals and allow tracking the state of every cell and their histories in a growing cell population. They thus provide a quantitative understanding of biological data at the experimental single-cell resolution.

Using these mathematical methods, the project will uncover the causes and consequences of heterogeneity in bacterial and cancer cell populations, which has important implications in a range of biotechnological and medical applications. Using a combination of theory and experiment, we will explain how cellular heterogeneity affects essential cellular functions such as cell division, growth and the cell cycle that ultimately drive proliferation and cell survival.

In particular, we will investigate cell division and cell cycle kinetics to explore how heterogeneity allows bacteria to cope with stress and cancer cells to evade chemotherapeutic treatment. The project thus presents a transformative and quantitative single-cell perspective on the role of cellular heterogeneity in cell proliferation and its implications for disease.