A multi-stage representation of cell proliferation as a Markov process

TL;DR

This paper introduces a multi-stage Markov model for cell proliferation that improves simulation accuracy by incorporating realistic cell cycle time distributions, enabling the use of Gillespie's algorithm in biological modeling.

Contribution

It proposes a novel multi-stage model that restores the Markov property for cell cycle simulation, improving upon naive Gillespie algorithm applications.

Findings

Restores Markov property in cell cycle modeling.

Shows impact of cell cycle distribution on model outcomes.

Provides analytical exploration of the model's implications.

Abstract

The stochastic simulation algorithm commonly known as Gillespie's algorithm is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well-mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespie's algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean. Here we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically. We demonstrate the importance of employing the correct cell cycle time distribution by considering two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to their outcomes. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation, whilst still being able to take advantage of the Gillespie algorithm.