Computational framework for modelling immune cell migration in tissue repair and the origins of cancer

I am interested in how immune cells move through tissue responding to signals from injury or tumours.

I will use data from multiple biological systems and Bayesian statistics to mathematically model immune cell migration to better predict how and why they move.

My results will feedback into the biological models iteratively to test predictions and learn when and how to manipulate immune cell migration in animal models, which can help or hinder tissue repair and cancer.

I hypothesise that mathematical modelling will help better understand immune cell migration in response to tissue damage.

My team is developing computational tools which we will apply to zebrafish data (an easy-to-image vertebrate) from collaborators before obtaining pilot results for follow-on funding using mouse data from collaborators, which carries greater relevance to human health.

My aim is to model immune cell migration in three applications where better control over their migration may enable new therapies: (1) Understand leukocyte recruitment and retention to wounds in zebrafish, by calculating how chemoattractant gradients spread through the tissue, and inferring how these control leukocyte migration. (2) Reveal how neutrophils are recruited to early tumours through chemoattractants secreted by pre-neoplastic cells (PNCs, by extending our model to include PNCs as signal sources and parameterise it from existing data. (3) Determine how microglia act in response to neural demyelination injury.

We will analyse pilot data of in vitro migration in chemokine gradients to predict how much data we will need for follow-on grant applications and how to optimise experimental design.