A stochastic hierarchical model for low-grade glioma evolution.
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
14th International Conference on Monte Carlo Methods and Applications (MCM23).
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Abstract: A stochastic hierarchical model for the evolution of low-grade gliomas is proposed. Starting with the description of
cell motion using a piecewise diffusion Markov process (PDifMP) at the cellular level, we derive an equation for the density of
the transition probability of this Markov process based on the generalised Fokker-Planck equation. Then, a macroscopic model is
derived via parabolic limit and Hilbert expansions in the moment equations. After setting up the model, we perform several
numerical tests to study the role of the local characteristics and the extended generator of the PDifMP in the process of tumour
progression. The main aim focuses on understanding how the variations of the jump rate function of this process at the microscopic
scale and the diffusion coefficient at the macroscopic scale are related to the diffusive behaviour of the glioma cells and to the
onset of malignancy, i.e., the transition from low-grade to high-grade gliomas.
Forschungs-