Background Improving our understanding of cancer and other complex diseases requires
integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in …

Abstract Machine learning (ML) is becoming increasingly crucial in many fields of
engineering but has not yet played out its full potential in bioprocess engineering. While …

A powerful way of gaining insight into biological systems is by creating a nonlinear
differential equation model, which usually contains many unknown parameters. Such a …

Here we present Tellurium, a Python-based environment for model building, simulation, and
analysis that facilitates reproducibility of models in systems and synthetic biology. Tellurium …

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 …

Standard benchmarks are important repositories to establish comparisons between
competing model and control methods, especially when a new method is proposed. This …

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 …

Mechanistic models are essential to deepen the understanding of complex diseases at the
molecular level. Nowadays, high-throughput molecular and phenotypic characterizations …

Bistable autoactivation has been proposed as a mechanism for cells to adopt binary fates
during embryonic development. However, it is unclear whether the autoactivating modules …