We discuss issues of structural and practical identifiability of partially observed differential
equations which are often applied in systems biology. The development of mathematical …

An increasing number of industrial bioprocesses capitalize on living cells by using them as
cell factories that convert sugars into chemicals. These processes range from the production …

Mathematical models of biological reactions at the system-level lead to a set of ordinary
differential equations with many unknown parameters that need to be inferred using …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …

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 …

Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …

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 …

Observability is a modelling property that describes the possibility of inferring the internal
state of a system from observations of its output. A related property, structural identifiability …

The interplay of mathematical modelling with experiments is one of the central elements in
systems biology. The aim of reverse engineering is to infer, analyse and understand …

In this paper, we address the system identification problem in the context of biological
modelling. We present and demonstrate a methodology for (i) assessing the possibility of …