BPS2026 – Facilitated diffusion, human aging, and why every student must learn the Gillespie algorithm

Both undergraduate and PhD students of physical science are well trained to solve various standard kinds of equations, but it often comes as a shock that biophysical problems generally have a large stochastic element. Moreover, many biophysical problems do not have the “forward” character of elementary Physics (predict the future from the past) but rather demand distributions of first-passage times (satisfy an endpoint condition from generic initial conditions). Life-science students often appreciate such problems but have no background for approaching them quantitatively. For all these reasons, Gillespie simulation of stochastic processes is a general-purpose tool that should be in every student's toolkit, but generally is not. I will outline how I introduce this tool to every Biophysics undergraduate major at my institution, following a recent textbook. As examples, I will give details on two new publicly disseminated course modules involving current topics that I have asked my students to pursue (1) facilitated diffusion (hybrid 1D/3D) for gene transcription factor binding and (2) a first-passage framework that recapitulates experimental data on human aging.