- Rajakaruna, H., O’Connor, J. H., Cockburn, I. A. & Ganusov, V. V. 2022 Liver environment- imposed constraints diversify movement strategies of liver-localized CD8 T cells. J Immunol, 208, 1292–1304. doi:10.4049/jimmunol.2100842.
What regulates specific movement patterns of T cells in tissues remains poorly understood. Detection of some exotic movements such as Levy walks has been attributed to existence of cell-intrinsic programs of search for infection, thought to be evolutionary optimal. In this paper we showed that for CD8 T cells in the liver the observed movement patters of cells (Levy walks or Brownian walks) can be easily explained by environmental details of the liver such as straightness of the liver sinusoids and blood flow. With this paper we provide a strong argument that experimental data on T cell movement in tissues must be understood by taking the tissue details into account.
- Kelemen, R. K., Rajakaruna, H., Cockburn, I.A., and Ganusov, V. V. (2019) Clustering of activated CD8 T cells around malaria-infected hepatocytes is rapid and is driven by antigen-specific cells. Front Immunol, 10:2153. doi: 10.3389/fimmu.2019.02153
In this paper we developed quantitative framework to understand how activated CD8 T cells form clusters around Plasmodium liver stages. We showed that several alternative mathematical models are fully compatible with experimental data, and that additionally designed experiments were needed to discriminate between alternatives (e.g., reject fixed environment model and support density-dependent recruitment model). This paper represents my personal example of using strong inference in mathematical modeling.
- Ganusov, V. V. (2016) Strong Inference in Mathematical Modeling: A Method for Robust Science in the Twenty-First Century. Front Microbiol, 7, 1131
In this paper I outline the basic philosophy behind mathematical modeling and how modeling can help us understand biological processes. I highlight several pitfalls in mathematical modeling nowadays (e.g., focus on single hypothesis/mechanism, confirmation and not falsification of models), and how the use of strong inference may improve inference of biological mechanisms from experimental data.