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, through …

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 model is …

Background Optimization is the key to solving many problems in computational biology.
Global optimization methods, which provide a robust methodology, and metaheuristics in …

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 …

The prediction of links among variables from a given dataset is a task referred to as network
inference or reverse engineering. It is an open problem in bioinformatics and systems …

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

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public
awareness and appreciation of the utility of dynamic models. At the same time, the …

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, …

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 inferring …

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