Quantitative dynamical models facilitate the understanding of biological processes and the
prediction of their dynamics. These models usually comprise unknown parameters, which …

Motivation Unknown parameters of dynamical models are commonly estimated from
experimental data. However, while various efficient optimization and uncertainty analysis …

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 important tools to describe and understand biological processes.
However, they typically rely on unknown parameters, the estimation of which can be …

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

Mathematical modelling of signalling pathways aids experimental investigation in system
and synthetic biology. Ever increasing data availability prompts the development of large …

Due to the high complexity of biological data it is difficult to disentangle cellular processes
relying only on intuitive interpretation of measurements. A Systems Biology approach that …

Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …

We present a nonlinear programming (NLP) framework for the scalable solution of
parameter estimation problems that arise in dynamic modeling of biological systems. Such …

Systems Biology is an emerging research area, which considers mathematical
representations of inter-and intra-cellular dynamics. Among the many research problems …