Throughout the life sciences, we routinely seek to interpret measurements and observations
using parametrized mechanistic mathematical models. A fundamental and often overlooked …

Mathematical modelling and statistical inference provide a framework to evaluate different
non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has …

Background Autophagy refers to the conserved cellular catabolic process relevant to
lysosome activity and plays a vital role in maintaining the dynamic equilibrium of intracellular …

Sigmoid growth models, such as the logistic, Gompertz and Richards' models, are widely
used to study population dynamics ranging from microscopic populations of cancer cells, to …

Ordinary differential equation models are nowadays widely used for the mechanistic
description of biological processes and their temporal evolution. These models typically …

In this article, we employ a collection of stochastic differential equations with drift and
diffusion coefficients approximated by neural networks to predict the trend of chaotic time …

Accurate inference of gene regulatory networks (GRNs) is an essential premise for
understanding pathogenesis and curing diseases. Various computational methods have …

In this paper, we present conditions for identifying the generator of a linear stochastic
differential equation (SDE) from the distribution of its solution process with a given fixed …

Interpreting data using mechanistic mathematical models provides a foundation for
discovery and decision-making in all areas of science and engineering. Developing …

Computational models are becoming an increasingly valuable tool in biomedical research.
Their accuracy and effectiveness, however, rely on the identification of suitable parameters …