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SUMMARY:Data-driven and multi-scale modelling of prostate cancer progressi
 on and therapeutic resistance
DTSTART;TZID=Europe/London:20260508T110000
DTEND;TZID=Europe/London:20260508T120000
DTSTAMP:20260507T122413Z
UID:fcf94e7a-9144-f111-bec7-7c1e52046848
CREATED:20260430T123830Z
DESCRIPTION:Prostate cancer progression and therapeutic resistance present
  significant clinical challenges\, particularly in the transition to castr
 ation-resistant disease. Although androgen deprivation therapy and second-
 generation drugs have improved patient outcomes\, resistance frequently de
 velops\, reflecting tumour heterogeneity and the influence of its microenv
 ironment. This talk presents two interdisciplinary studies that address th
 ese issues through data-driven mathematical approaches. We show how integr
 ating experimental data with mathematical and statistical modelling can im
 prove our understanding of prostate cancer dynamics and inform more effect
 ive\, context-specific therapeutic strategies. The first study examines dr
 ug resistance and tumour evolution under treatment. We develop a multi-sca
 le hybrid modelling framework to capture processes occurring across differ
 ent temporal scales. Partial differential equations describe the behaviour
  of drugs and other chemicals in the tumour microenvironment (over the ‘
 fast’ timescale)\, while a cellular automaton captures the dynamics of t
 umour cells (over the ‘slow’ timescale). Through computational analysi
 s of the model solutions\, we examine the spatial dynamics of tumour cells
 \, assess the efficacy of different drug therapies in inhibiting prostate 
 cancer growth\, and identify effective drug combinations and treatment sch
 edules to limit tumour progression and prevent metastasis. The second stud
 y focuses on the role of host–microbiome interactions in obesity-associa
 ted prostate cancer. Using data from experiments with the TRAMP mouse mode
 l\, we apply statistical and machine learning methods\, including generali
 sed linear models\, Granger causality\, and support vector regression\, to
  characterise microbial dynamics and their responses to treatment. These f
 indings are incorporated into a dynamical systems framework that captures 
 microbiome–tumour co-evolution under therapeutic and dietary perturbatio
 ns\, providing insight into how dietary fat and combination therapies invo
 lving enzalutamide and phytocannabinoids influence tumour progression and 
 gut microbiota composition.
LAST-MODIFIED:20260430T124053Z
LOCATION:Mathematical Institute - L4\, L4 Mathematical Institute Woodstock
  Road Oxford Oxfordshire OX2 6GG United Kingdom
SPEAKER:Dr Marianna Cerasuolo (Department of Mathematics\, University of S
 ussex)
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