Abstract

The development of hybrid methodologies is of current interest in both multi-scale modelling and stochastic reaction–diffusion systems regarding their applications to biology. We formulate a hybrid method for stochastic multi-scale models of cells populations that extends the remit of existing hybrid methods for reaction–diffusion systems. Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics. In order to formulate this method, we develop a coarse-grained approximation for both the full stochastic model and its mean-field limit. Such approximation involves averaging out the age-structure (which accounts for the multi-scale nature of the model) by assuming that the age distribution of the population settles onto equilibrium very fast. We then couple the coarse-grained mean-field model to the full stochastic multi-scale model. By doing so, within the mean-field region, we are neglecting noise in both cell numbers (population) and their birth rates (structure). This implies that, in addition to the issues that arise in stochastic-reaction diffusion systems, we need to account for the age-structure of the population when attempting to couple both descriptions. We exploit our coarse-graining model so that, within the mean-field region, the age-distribution is in equilibrium and we know its explicit form. This allows us to couple both domains consistently, as upon transference of cells from the mean-field to the stochastic region, we sample the equilibrium age distribution. Furthermore, our method allows us to investigate the effects of intracellular noise, i.e. fluctuations of the birth rate, on collective properties such as travelling wave velocity. We show that the combination of population and birth-rate noise gives rise to large fluctuations of the birth rate in the region at the leading edge of front, which cannot be accounted for by the coarse-grained model. Such fluctuations have non-trivial effects on the wave velocity. Beyond the development of a new hybrid method, we thus conclude that birth-rate fluctuations are central to a quantitatively accurate description of invasive phenomena such as tumour growth.

Highlights

  • Cells behaviour within tissues respond to a number of stimuli

  • We extend and further develop the hybrid method formulated by Spill et al [67] for stochastic reaction– diffusion systems to stochastic multi-scale models of tumour growth

  • We are interested on the mean first-passage time (MFPT) problem associated to the activation of CycB, which determines the onset of the S-phase through the G1/S transition

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Summary

Introduction

Cells behaviour within tissues respond to a number of stimuli. Their behaviour result from a complex network of interactions between genes and gene products which regulates gene expression. When the coarse-grained mean-field model is coupled to the full stochastic multiscale population-dynamical model, the deviation travelling wave speed is very much rectified and a much more accurate result is obtained This result demonstrates the usefulness of such hybrid approaches: they can recover accurately the behaviour predicted by the more detailed models whilst, by averaging out some of those details in regions where they are not necessary, their computational performance is much improved. We take as a benchmark the full stochastic multi-scale model solved by means of the age-structured Gillespie algorithm [17] During this analysis we conclude that failure of the coarse-grained growth rate to describe the population dynamics at the leading edge of the front is responsible for the discrepancies between the travelling wave speeds.

Summary of the stochastic multi-scale model
General setting
Resource layer: dynamics of diffusible substances
Intracellular layer: oxygen-dependent birth rate
Cellular scale: age-structured birth-and-death with diffusion
Linking scales together
Separation of time scales and coarse-graining of the age structure
Mean-field model
Early evolution
Intermediate regime
Long-time behaviour
Stochastic system
Stochastic coarse-grained model
Hybrid method for stochastic multi-scale models of tumour growth
Moving the interface
Discussion and conclusions
Methods
Full Text
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