### Abstract

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 as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from 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 the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

Original language | English |
---|---|

Pages (from-to) | 2905–2928 |

Number of pages | 24 |

Journal | Bulletin of Mathematical Biology |

Volume | 79 |

Issue number | 12 |

Early online date | 13 Oct 2017 |

DOIs | |

Publication status | Published - 1 Dec 2017 |

### Fingerprint

### Keywords

- Algorithms
- Animals
- Cell Cycle/physiology
- Cell Proliferation/physiology
- Computer Simulation
- Humans
- Markov Chains
- Mathematical Concepts
- Mice
- Models, Biological
- NIH 3T3 Cells
- Neoplastic Stem Cells/pathology
- Stochastic Processes
- Time Factors

### Cite this

*Bulletin of Mathematical Biology*,

*79*(12), 2905–2928. https://doi.org/10.1007/s11538-017-0356-4

**A multi-stage representation of cell proliferation as a Markov process.** / Yates, Christian; Ford, Matthew; Mort, Richard.

Research output: Contribution to journal › Article

*Bulletin of Mathematical Biology*, vol. 79, no. 12, pp. 2905–2928. https://doi.org/10.1007/s11538-017-0356-4

}

TY - JOUR

T1 - A multi-stage representation of cell proliferation as a Markov process

AU - Yates, Christian

AU - Ford, Matthew

AU - Mort, Richard

PY - 2017/12/1

Y1 - 2017/12/1

N2 - The stochastic simulation algorithm commonly known as Gillespie's algorithm (originally derived for modelling well-mixed systems of chemical reactions) 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 (e.g. embryonic development, cancer formation) 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 as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from 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 the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

AB - The stochastic simulation algorithm commonly known as Gillespie's algorithm (originally derived for modelling well-mixed systems of chemical reactions) 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 (e.g. embryonic development, cancer formation) 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 as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from 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 the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

KW - Algorithms

KW - Animals

KW - Cell Cycle/physiology

KW - Cell Proliferation/physiology

KW - Computer Simulation

KW - Humans

KW - Markov Chains

KW - Mathematical Concepts

KW - Mice

KW - Models, Biological

KW - NIH 3T3 Cells

KW - Neoplastic Stem Cells/pathology

KW - Stochastic Processes

KW - Time Factors

U2 - 10.1007/s11538-017-0356-4

DO - 10.1007/s11538-017-0356-4

M3 - Article

VL - 79

SP - 2905

EP - 2928

JO - Bulletin of Mathematical Biology

JF - Bulletin of Mathematical Biology

SN - 0092-8240

IS - 12

ER -