Estimating Treatment Resistance from Aggregate Tumor Volume Data

Heterogeneity in biological populations, from cancer to ecological systems, is a fundamental characteristic that can significantly affect outcomes. Despite this, many mathematical models in population biology do not account for inter- or intra-individual heterogeneity. In systems such as cancer, this means assuming cellular homogeneity and deterministic phenotypes, even though heterogeneity is thought to play a crucial role in therapy resistance. In this talk, I will discuss several innovative approaches towards incorporating and estimating cellular heterogeneity in models of tumor growth. I will focus on random differential equations to model treatment heterogeneity and the Prohorov metric framework for estimating parameter distributions from aggregate data (e.g., tumor volume). We validate our method on synthetic and in vitro tumor volume data.

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https://sites.ucmerced.edu/ruttere