ORCID Profile
0000-0002-4569-4312
Current Organisation
University of Nottingham
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Biological Mathematics | Pharmaceutical Sciences | Applied Mathematics | Human Biophysics |
Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Biological Sciences
Publisher: Wiley
Date: 10-11-2019
DOI: 10.1002/CPT.1647
Publisher: IOP Publishing
Date: 15-04-2009
DOI: 10.1088/1478-3975/6/3/036001
Abstract: A computational study of discrete 'cell-centre' approaches to modelling the evolution of a collection of cells is undertaken. The study focuses on the mechanical aspects of the tissue, in order to separate the passive mechanical response of the model from active effects such as cell-growth and cell ision. Issues which arise when implementing these models are described, and a series of numerical mechanical experiments is performed. It is shown that discrete tissues modelled this way typically exhibit elastic-plastic behaviour under slow compression, and act as a brittle linear elastic solid under slow tension. Both overlapping spheres and Voronoi-tessellation-based models are examined, and the effect of different cell-cell interaction force laws on the bulk mechanical properties of the tissue is determined. This correspondence allows parameters in the cell-based model to be chosen to be compatible with bulk tissue measurements.
Publisher: Wiley
Date: 28-08-2009
Publisher: Public Library of Science (PLoS)
Date: 27-03-2014
Publisher: Public Library of Science (PLoS)
Date: 20-02-2013
Publisher: The Open Journal
Date: 13-03-2020
DOI: 10.21105/JOSS.01848
Publisher: Elsevier BV
Date: 2015
Publisher: The Royal Society
Date: 13-11-2010
Abstract: In this paper, we review multi-scale models of solid tumour growth and discuss a middle-out framework that tracks in idual cells. By focusing on the cellular dynamics of a healthy colorectal crypt and its invasion by mutant, cancerous cells, we compare a cell-centre, a cell-vertex and a continuum model of cell proliferation and movement. All models reproduce the basic features of a healthy crypt: cells proliferate near the crypt base, they migrate upwards and are sloughed off near the top. The models are used to establish conditions under which mutant cells are able to colonize the crypt either by top-down or by bottom-up invasion. While the continuum model is quicker and easier to implement, it can be difficult to relate system parameters to measurable biophysical quantities. Conversely, the greater detail inherent in the multi-scale models means that experimentally derived parameters can be incorporated and, therefore, these models offer greater scope for understanding normal and diseased crypts, for testing and identifying new therapeutic targets and for predicting their impacts.
Publisher: Elsevier BV
Date: 02-2023
Publisher: IEEE
Date: 08-2010
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 02-2013
Publisher: Cold Spring Harbor Laboratory
Date: 13-03-2017
DOI: 10.1101/100677
Abstract: Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics — the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an ex le, with the aim of generating a quantitatively predictive representation of the ion current. We fit a mathematical model to currents evoked by a novel 8 second sinusoidal voltage cl in CHO cells over-expressing hERG1a. The model is then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage cl s, and also a novel voltage cl comprised of a collection of physiologically-relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches. Techniques for Physiology 1 Ion current kinetics are commonly represented by current-voltage relationships, time-constant voltage relationships, and subsequently mathematical models fitted to these. These experiments take substantial time which means they are rarely performed in the same cell. Rather than traditional square-wave voltage cl s, we fit a model to the current evoked by a novel sum-of-sinusoids voltage cl that is only 8 seconds long. Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions. The new model predicts the current under traditional square-wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically-relevant series of action potential cl s is predicted extremely well. The short sinusoidal protocols allow a model to be fully fitted to in idual cells, allowing us to examine cell-cell variability in current kinetics for the first time.
Publisher: Cold Spring Harbor Laboratory
Date: 02-07-2022
DOI: 10.1101/2022.07.01.22277134
Abstract: During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. We introduce an SIR -type model with the infected population structured by ‘infected age’, i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb–14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty.
Publisher: Elsevier BV
Date: 08-2010
Publisher: Wiley
Date: 17-04-2018
DOI: 10.1113/JP275733
Publisher: Public Library of Science (PLoS)
Date: 14-03-2013
Publisher: Elsevier BV
Date: 12-2009
Publisher: Springer Berlin Heidelberg
Date: 2013
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2019
End Date: 06-2023
Amount: $409,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2019
End Date: 2021
Funder: Australian Research Council
View Funded Activity