Speaker
Description
Linking models to experimental data is a common challenge in mathematical biology. Several techniques have been developed to parameterize theoretical frameworks, including maximum likelihood estimation and Approximate Bayesian Computation. Previous work established that combining these approaches with spatial data analysis, specifically with statistics known as pairwise correlation functions (PCFs) that quantify the relative degree of clustering or exclusion among cells across multiple length scales, can recover parameters in models of cell movement in homogeneous populations. Yet it remains unclear whether PCFs are equally useful at parameterizing models in cases with multiple cell types. I will describe a simplistic mathematical framework created to address this gap, which is based on in vitro experiments of immune cell infiltration into tumor-dense space. The model tracks individual cell positions using an overdamped version of Newton’s law, with forces arising from short-range repulsion and long-range attraction between cells. I create multiple synthetic datasets in which tumor and immune cells interact with varying length scales and strengths of attraction, and apply Approximate Bayesian Computation-Sequential Monte Carlo to determine how well the posterior parameter distribution recovers the “ground truth”. I demonstrate that the attractive forces between cells are practically identifiable, even when their underlying parameters may not be.
