Advancing Scalable Particle-Based Reaction-Diffusion (PBRD) for the Whole-Cell Era.

Whole-cell modelling (WCM) is a grand challenge for 21st-century science,
demanding an interdisciplinary approach to create predictive tools that bridge the gap from
fundamental molecular structures to the emergent behaviours of life. While a spatially-
resolved model of a minimal cell has recently been proposed, its scalability to more complex
cells remains a major hurdle. The most formidable computational bottleneck of these multi-
physics, modular, WCMs lies in simulating stochastic reaction-diffusion processes. Reaction-
Diffusion Master Equation (RDME) methods, for example the Spatial Gillespie Algorithm,
are the most widely used approach but are fundamentally constrained by their reliance on
discrete spatial domains. In contrast, Particle-Based Reaction-Diffusion (PBRD) simulations,
while offering high spatial resolution and molecular-level stochasticity, have been historically
overlooked in favour of RDME methods because the more significant scalability limitations
of PBRD methods have typically been considered an insurmountable barrier to advancing
WCMs.
This talk will shed light on this scientific blind spot by introducing recent
breakthroughs in PBRD methodologies that directly address and overcome its
traditional scalability limitations. By exploring the mathematical foundations for how
PBRD can efficiently simulate the molecular scale kinetics of increasingly complex
cell types, this work pushes the boundaries of what is computationally feasible and
paves the way for the next generation of whole-cell simulations.