Programmable models of growth and mutation of cancer-cell populations

TL;DR

This paper introduces a systematic stochastic modeling approach using Concurrent Constraint Programming to describe cancer-cell population growth and mutation, demonstrated through prostate cancer development and treatment interactions.

Contribution

It presents a novel methodology for constructing detailed mathematical models of cancer progression and therapy interactions using stochastic programming techniques.

Findings

Successfully reconstructs prostate cancer growth models

Demonstrates modeling of hormone therapy interactions

Shows flexibility of the proposed stochastic approach

Abstract

In this paper we propose a systematic approach to construct mathematical models describing populations of cancer-cells at different stages of disease development. The methodology we propose is based on stochastic Concurrent Constraint Programming, a flexible stochastic modelling language. The methodology is tested on (and partially motivated by) the study of prostate cancer. In particular, we prove how our method is suitable to systematically reconstruct different mathematical models of prostate cancer growth - together with interactions with different kinds of hormone therapy - at different levels of refinement.