Speaker
Description
Mathematical models of prostate cancer progression and treatment response often rely on deterministic dynamics, yet in vivo behaviour reflects stochastic variation across scales. Randomness may arise from clonal heterogeneity, phenotype switching, metabolic plasticity, tumour micro-environment structure, and fluctuating host–microbiome interactions. These factors shape treatment response and resistance, supporting models in which noise is treated as a structural component rather than a perturbation.
This talk examines how stochasticity can be incorporated into dynamical systems for prostate cancer under phytocannabinoid-based therapy [1], aiming to identify which sources of variability best explain experimental observations. Stochastic and hybrid models [2] are introduced to represent tumour–microbiome–therapy interactions, informed by experimental data from phytocannabinoid treatments and accounting for various responses under distinct dietary and metabolic contexts.
Numerical simulations allow comparison between intrinsic cellular variability, environmental fluctuations and therapy-related effects. The aim is to assess how different sources of stochasticity influence simulated treatment response and resistance under mono- and combination phytocannabinoid therapies. By isolating and quantifying distinct noise mechanisms, the framework aids interpretation of heterogeneous experimental outcomes and supports data-informed stochastic models of refractory prostate cancer.
Bibliography
[1] Mahmoud, A.M.; Kostrzewa, M.; Marolda, V.; Cerasuolo, M.; Maccarinelli, F.; Coltrini, D.; Rezzola, S.; Giacomini, A.; Mollica, M.P.; Motta, A.; et al. Cannabidiol alters mitochondrial bioenergetics via VDAC1 and triggers cell death in hormone-refractory prostate cancer. Pharmacol. Res., 189, 106683, 2023.
[2] Burbanks, A.; Cerasuolo, M.; Ronca, R.; and Turner, L. A hybrid spatiotemporal model of PCa dynamics and insights into optimal therapeutic strategies. Mathematical Biosciences, 355, 108940, 2023.
