Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Building on a strong foundation of philosophy, theory, methods and computation over the
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
past three decades, Bayesian approaches are now an integral part of the toolkit for most …
Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation
Computational models are not just appealing because they can simulate and predict the
development of biological phenomena across multiple spatial and temporal scales, but also …
development of biological phenomena across multiple spatial and temporal scales, but also …
Designing and interpreting 4D tumour spheroid experiments
RJ Murphy, AP Browning, G Gunasingh… - Communications …, 2022 - nature.com
Tumour spheroid experiments are routinely used to study cancer progression and treatment.
Various and inconsistent experimental designs are used, leading to challenges in …
Various and inconsistent experimental designs are used, leading to challenges in …
A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling
In vitro tumour spheroids have been used to study avascular tumour growth and drug design
for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that …
for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that …
Structural identifiability analysis of linear reaction–advection–diffusion processes in mathematical biology
AP Browning, M Taşcă, C Falcó… - Proceedings of the …, 2024 - royalsocietypublishing.org
Effective application of mathematical models to interpret biological data and make accurate
predictions often requires that model parameters are identifiable. Approaches to assess the …
predictions often requires that model parameters are identifiable. Approaches to assess the …
Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates
Epidemiological models range in complexity from relatively simple statistical models that
make minimal assumptions about the variables driving epidemic dynamics to more …
make minimal assumptions about the variables driving epidemic dynamics to more …
Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour
evolution; however, these models are often challenging to validate with experimental …
evolution; however, these models are often challenging to validate with experimental …
Structural identifiability analysis of linear reaction-advection-diffusion processes in mathematical biology
Effective application of mathematical models to interpret biological data and make accurate
predictions often requires that model parameters are identifiable. Approaches to assess the …
predictions often requires that model parameters are identifiable. Approaches to assess the …
Fitting Stochastic Lattice Models Using Approximate Gradients
Stochastic lattice models (sLMs) are computational tools for simulating spatiotemporal
dynamics in physics, computational biology, chemistry, ecology, and other fields. Despite …
dynamics in physics, computational biology, chemistry, ecology, and other fields. Despite …
[PDF][PDF] Calibration of a Voronoi cell-based model for tumour growth using approximate Bayesian computation
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour
evolution; however, these models can pose a challenge in terms of their ability to be …
evolution; however, these models can pose a challenge in terms of their ability to be …