The field of machine learning is comprised of techniques, which have proven powerful
approaches to knowledge discovery and construction of 'digital twins' in the highly …

Cardiac electrophysiology models are among the most mature and well‐studied
mathematical models of biological systems. This maturity is bringing new challenges as …

In this research, we study the propagation patterns of epidemic diseases such as the COVID-
19 coronavirus, from a mathematical modeling perspective. The study is based on an …

Microkinetic, in situ analytics, and empirical modeling approaches for developing intrinsic
CO2 photoreduction kinetics are presented in this Perspective. Intrinsic kinetic models that …

Motivation Kinetic models contain unknown parameters that are estimated by optimizing the
fit to experimental data. This task can be computationally challenging due to the presence of …

Motivation: Many problems of interest in dynamic modeling and control of biological systems
can be posed as non-linear optimization problems subject to algebraic and dynamic …

Background The past decades of research have seen an increase in statistical tools to
explore the complex dynamics of mental health from patient data, yet the application of these …

Mechanistic mathematical modeling of biochemical reaction networks using ordinary
differential equation (ODE) models has improved our understanding of small-and medium …

Background Kinetic models of biochemical systems usually consist of ordinary differential
equations that have many unknown parameters. Some of these parameters are often …

Life-like systems need to maintain a basal metabolism, which includes importing a variety of
building blocks required for macromolecule synthesis, exporting dead-end products, and …