Heterogeneity-driven cell signalling

It is often thought that when cells are genetically identical, they behave in the same manner. However, recent experiments have convincingly demonstrated that this is far from the truth. For example, stimulating the same cell multiple times with the same stimulus may lead to very different responses. Given that these responses are crucial for healthy well-being (e.g. insulin release after eating cake) a key question in cell signalling is to understand and predict these heterogeneous cell dynamics.

In this project, we will use stochastic point-processes to describe such inhomogeneous cellular behaviour and use Bayesian methods to fit them to data. As a test case, we will investigate the dynamics of one of the most important cellular messengers: intracellular calcium. More precisely, we will study so-called calcium spike sequences and aim to ascertain what statistical features best describe the observed heterogeneity. The insights that we will gain will be both of practical and conceptual importance. As for the former, a key goal in computational cell physiology is to model cell behaviour in a computationally inexpensive, yet accurate manner. A Bayesian approach offers a natural way for doing this in conjunction with the quantification of uncertainty of the dynamics of the process. In terms of conceptual advances, the statistical details of calcium spike sequences may point towards the mechanisms that generate them. In turn, this is crucial information when we need to tailor signalling cascades for biomedical applications.