Title: Computational framework for modelling immune cell migration in tissue repair and the origins of cancer
Funder: The Academy of Medical Sciences

Abstract: 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.

Lay abstract: I will use advanced statistics and computer simulations to understand how immune cells move during inflammation after tissue is wounded and in the early stages of cancer. This is important because a more quantitative understanding of inflammation may improve future therapies. Immune cells are known to accumulate near wounds, but analysis of their movement is often limited to relatively simple statistics that are not directly connected to ideas about how cells move towards chemical signals. Mathematical models are needed to complement analysis of experimental results. Understanding immune cell movements in laboratory animals, like fish and mice, can also make drug testing more efficient in the future. Cancer can start with a single mutation that makes cells behave differently even before a tumour grows. Such pre-cancer cells attract immune cells in similar ways to wounds, which can be bad for patient survival. I want to know what pre-cancer cells do to attract immune cells and how they benefit from this, for example by growing faster. Learning this will help to predict how to control this harmful aspect of inflammation. My team will develop computational tools to quantify immune cell movements and how these change near wounds and early tumours. We will (1) quantitatively answer how immune cells are kept at wounds in fish, (2) determine how immune cells are attracted by pre-cancer cells, and (3) test how well we can analyse immune cell movement in mice after nerve damage, as future research in mice may be more relevant to human health.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: CANCER AND NEOPLASMS;GENERIC HEALTH RELEVANCE