24 citations · 25 across the 9 of their papers we have counts for
13 papers
An efficient and flexible generalization of transit compartment models for delayed biological processes
Giulia Belluccini, Christiaan H. van Dorp, Linjie Huang +3
Transit compartment models are widely used in pharmacokinetics/pharmacodynamics to represent delayed biological processes such as absorption, maturation, cellular proliferation, an…
Frequentist and Bayesian population pharmacometrics: similarities, differences, and practical implications
Jodie A. Cochrane, Haini Wen, Liang Yang +2
Although the frequentist versus Bayesian debate has a long-standing history, and both are well-established approaches for inference, they are often considered separately in pharmac…
Hyperparameter selection for equation learning with biologically-informed neural networks
William Lavery, Jodie A. Cochrane, John T. Nardini +1
Biologically-informed neural networks (BINNs) have emerged as a flexible subclass of physics-informed neural networks (PINNs) for learning terms in partial differential equations f…
The MCMC convergence graph: a diagnostic for multimodal posteriors
Cici Chen Gu, Eszter Lakatos, Sara Hamis
Multimodality is common in many scientific and engineering applications. However, standard Markov chain Monte Carlo (MCMC) convergence diagnostics often penalise multimodality in p…
Adaptive therapy under parametric, structural, and measurement uncertainty
Alexander P Browning, Rebecca M Crossley, Ryan J Murphy +2
Adaptive therapy has emerged as a promising treatment strategy that exploits within-tumour competition to delay disease progression. Implementation, however, typically relies on in…
Physics-Informed Neural Networks for Biological Reaction-Diffusion Systems
William Lavery, Jodie A. Cochrane, Christian Olesen +3
Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs)…
A practical introduction to ODE modelling in Stan for biological systems
Sara Hamis, John Forslund, Cici Chen Gu +1
Integrating dynamical systems models with time series data is a central part of contemporary mathematical biology. With the rich variety of available models and data, numerous meth…
A nonparametric approach to practical identifiability of nonlinear mixed effects models
Tyler Cassidy, Stuart T. Johnston, Michael Plank +4
Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data…
A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics
Kira Pugh, Linnéa Gyllingberg, Stanislav Stratiev +1
Mathematical oncology is an interdisciplinary research field where the mathematical sciences meet cancer research. Being situated at the intersection of these two fields makes math…
Phenotypic heterogeneity in temporally fluctuating environments
Alexander P Browning, Sara Hamis
Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined…
Reducing phenotype-structured PDE models of cancer evolution to systems of ODEs: a generalised moment dynamics approach
Chiara Villa, Philip K Maini, Alexander P Browning +3
Intratumour phenotypic heterogeneity is nowadays understood to play a critical role in disease progression and treatment failure. Accordingly, there has been increasing interest in…
A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and the effects of anti-inflammatory intervention
Sara J Hamis, Fiona R Macfarlane
Pyroptosis is an inflammatory mode of cell death that can contribute to the cytokine storm associated with severe cases of coronavirus disease 2019 (COVID-19). The formation of the…
Uncertainty and Sensitivity Analyses Methods for Agent-Based Mathematical Models: An Introductory Review
Sara Hamis, Stanislav Stratiev, Gibin G Powathil
Multiscale, agent-based mathematical models of biological systems are often associated with model uncertainty and sensitivity to parameter perturbations. Here, three uncertainty an…