34 citations · 202 across the 25 of their papers we have counts for
15 papers · 1 filter
The mass-conversion method: a hybrid technique for simulating well-mixed chemical reaction networks
Joshua C. Kynaston, Christian A Yates, Anna Hekkink +1
There exist several methods for simulating biological and physical systems as represented by chemical reaction networks. Systems with low numbers of particles are frequently modell…
Stochastic drift in discrete waves of non-locally interacting-particles
Andrei Sontag, Tim Rogers, Christian A. Yates
In this paper, we investigate a generalised model of particles undergoing second-order non-local interactions on a lattice. Our results have applications across many research a…
Critical weaknesses in shielding strategies for COVID-19
Cameron A. Smith, Christian A. Yates, Ben Ashby
The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, has led to a wide range of non-pharmaceutical interventions being implemented around the world to curb transmission. Ho…
Incorporating domain growth into hybrid methods for reaction-diffusion systems
Cameron A. Smith, Christian A. Yates
Reaction--diffusion mechanism are a robust paradigm that can be used to represent many biological and physical phenomena over multiple spatial scales. Applications include intracel…
The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes
Christian A. Yates, Adam George, Armand Jordana +3
The simulation of stochastic reaction-diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle nu…
Efficiently simulating discrete-state models with binary decision trees
Christopher Lester, Ruth E. Baker, Christian A. Yates
Stochastic simulation algorithms (SSAs) are widely used to numerically investigate the properties of stochastic, discrete-state models. The Gillespie Direct Method is the pre-emine…
Unbiased on-lattice domain growth
Cameron A. Smith, Cécile Mailler, Christian A. Yates
Domain growth is a key process in many areas of biology, including embryonic development, the growth of tissue, and limb regeneration. As a result, mechanisms for incorporating it…
Spatially-extended hybrid methods: a review
Cameron A. Smith, Christian A. Yates
Many biological and physical systems exhibit behaviour at multiple spatial, temporal or population scales. Multiscale processes provide challenges when they are to be simulated usi…
The auxiliary region method: A hybrid method for coupling PDE- and Brownian-based dynamics for reaction-diffusion systems
Cameron A. Smith, Christian A. Yates
Reaction-diffusion systems are used to represent many biological and physical phenomena. They model the random motion of particles (diffusion) and interactions between them (reacti…
Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches
Christopher Lester, Christian A. Yates, Ruth E. Baker
In this work, we consider the problem of estimating summary statistics to characterise biochemical reaction networks of interest. Such networks are often described using the framew…
A multi-stage representation of cell proliferation as a Markov process
Christian A Yates, Matthew J Ford, Richard L Mort
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 im…
A hybrid algorithm for coupling PDE and compartment-based dynamics
Jonathan U. Harrison, Christian A. Yates
Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly be…
The pseudo-compartment method for coupling PDE and compartment-based models of diffusion
Christian A. Yates, Mark B. Flegg
Spatial reaction-diffusion models have been employed to describe many emergent phenomena in biological systems. The modelling technique most commonly adopted in the literature impl…
Extending the multi-level method for the simulation of stochastic biological systems
Christopher Lester, Ruth E. Baker, Michael B. Giles +1
The multi-level method for discrete state systems, first introduced by Anderson and Higham [Multiscale Model. Simul. 10:146--179, 2012], is a highly efficient simulation technique…
An adaptive multi-level simulation algorithm for stochastic biological systems
Christopher Lester, Christian A. Yates, Michael B. Giles +1
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on s…