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

Biological processes are often modeled by ordinary differential equations with unknown
parameters. The unknown parameters are usually estimated from experimental data. In …

Mathematical description of biological processes such as gene regulatory networks or
signalling pathways by dynamic models utilising ordinary differential equations faces …

Motivation Mathematical modeling using ordinary differential equations is used in systems
biology to improve the understanding of dynamic biological processes. The parameters of …

Parameter identifiability problems can plague biomodelers when they reach the
quantification stage of development, even for relatively simple models. Structural …

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 …

Background Very frequently the same biological system is described by several, sometimes
competing mathematical models. This usually creates confusion around their validity, ie …

Successful mathematical modeling of biological processes relies on the expertise of the
modeler to capture the essential mechanisms in the process at hand and on the ability to …

Throughout the life sciences, we routinely seek to interpret measurements and observations
using parametrized mechanistic mathematical models. A fundamental and often overlooked …

Motivation: Mathematical description of biological reaction networks by differential equations
leads to large models whose parameters are calibrated in order to optimally explain …