Agent-based models provide a flexible framework that is frequently used for modelling many
biological systems, including cell migration, molecular dynamics, ecology and epidemiology …

Developing an accurate first-principle model is an important step in employing systems
biology approaches to analyze an intracellular signaling pathway. However, an accurate first …

Biologically-informed neural networks (BINNs), an extension of physics-informed neural
networks, are introduced and used to discover the underlying dynamics of biological …

Complex nonlinear dynamics are ubiquitous in marine ecology. Empirical dynamic
modelling can be used to infer ecosystem dynamics and species interactions while making …

Intra-tumor and inter-patient heterogeneity are two challenges in developing mathematical
models for precision medicine diagnostics. Here we review several techniques that can be …

Data assimilation, which relies on explicit knowledge of dynamical models, is a well‐known
approach that addresses models' limitations due to various reasons, such as errors in input …

Development of satellite remote sensing data has provided a unique opportunity to improve
our understanding of Earth and its hydrological processes. This is especially of interest due …

Forecasting methods are notoriously difficult to interpret, particularly when the relationship
between the data and the resulting forecasts is not obvious. Interpretability is an important …

I. Abstract The Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate
forecasts of infection and mortality rates are essential for informing healthcare resource …

Demand for computer models that simulate insect population dynamics is growing due to
many factors including increased pressure on natural resources and climate change …