On a Stochastic PDE Model for Epigenetic Dynamics
We propose a stochastic model to investigate epigenetic mutations, i.e., modifications of the genetic information that control gene expression patterns in a cell but do not alter the DNA sequence. Epigenetic mutations are related to environmental fluctuations, which leads us to consider (additive) noise as the driving element for such mutations (noise-induced transitions in Waddington's epigenetic landscape). We focus on two applications: firstly, molecular biochemistry of cancer immunology involving macrophages' epigenetic modifications, where we show the relevance of random perturbations in the tumor microenvironment, and secondly, cell fate determination and mutation of the flower Arabidopsis thaliana. Due to the complexities of cancer biology for the first case, we present the details in [1] since our principal objective here is to validate our system as an appropriate epigenetic model for more general biological applications, with emphasis on mathematical oncology and developmental biology; for such results, we rely on the theory of Stochastic PDE, theory of large deviations, and ergodic theory. Moreover, since epigenetic mutations are reversible, a fact currently exploited to develop so-called epi-drugs to treat diseases such as cancer, we also investigate an optimal control problem for our system to study the reversal of epigenetic mutations; our control problem is also relevant for studying epigenetic stabilizers and transcription factors in immunotherapies for cancer [1].