arXiv subjects

Colin Gillespie

Publications and source records attributed to Colin Gillespie.

3 recordsLinked to original sources

arXiv2023

MATILDA.FT, a Mesoscale Simulation Package for Inhomogeneous Soft Matter

In this paper we announce the public release of a massively-parallel, GPU-accelerated software, which is the first to combine both coarse-grained molecular dynamics and field-theoretical simulations in one simulation package. MATILDA.FT (Mesoscale, Accelerated, Theoretically-Informed, Langevin, Dissipative particle dynamics, and Field Theory) was designed from the ground-up to run on CUDA-enabled GPUs, with the Thrust library acceleration, enabling it to harness the possibility of massive parallelism to efficiently simulate systems on a mesoscopic scale. MATILDA.FT is a versatile software, enabling the users to use either Langevin dynamics or Field Theory to model their systems - all within the same software. It has been used to model a variety of systems, from polymer solutions, and nanoparticle-polymer interfaces, to coarse-grained peptide models, and liquid crystals. MATILDA.FT is written in CUDA/C++ and is object oriented, making its source-code easy to understand and extend. The software comes with dedicated post-processing and analysis tools, as well as the detailed documentation and relevant examples. Below, we present an overview of currently available features. We explain in detail the logic of parallel algorithms and methods. We provide necessary theoretical background, and present examples of recent research projects which utilized MATILDA.FT as the simulation engine. We also demonstrate how the code can be easily extended, and present the plan for the future development. The source code, along with the documentation, additional tools and examples can be found on GitHub repository.

cond-mat.soft↗

arXiv2018

Building Reality Checks into the Translational Pathway for Diagnostic and Prognostic Models

There has been a significant increase in the number of diagnostic and prognostic models published in the last decade. Testing such models in an independent, external validation cohort gives some assurance the model will transfer to a naturalistic, healthcare setting. Of 2,147 published models in the PubMed database, we found just 120 included some kind of separate external validation cohort. Of these studies not all were sufficiently well documented to allow a judgement about whether that model was likely to transfer to other centres, with other patients, treated by other clinicians, using data scored or analysed by other laboratories. We offer a solution to better characterizing the validation cohort and identify the key steps on the translational pathway for diagnostic and prognostic models.

stat.AP↗

arXiv2014

Bayesian Inference for Hybrid Discrete-Continuous Stochastic Kinetic Models

We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process, computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either "fast" or "slow" with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a Markov jump process with time dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model.

stat.CO↗