Kinetic models are critical to predict the dynamic behaviour of metabolic networks.
Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting …

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 …

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior
distributions without having to calculate likelihoods. In this paper, we discuss and apply an …

A central challenge in computational modeling of biological systems is the determination of
the model parameters. Typically, only a fraction of the parameters (such as kinetic rate …

Abstract Models of biochemical regulation in prokaryotes and eukaryotes, typically
consisting of a set of first-order nonlinear ordinary differential equations, have become …

The inherent complexity of cellular signaling networks and their importance to a wide range
of cellular functions necessitates the development of modeling methods that can be applied …

A major goal of biophysics is to understand the physical mechanisms of biological
molecules and systems. Mechanistic models are evaluated based on their ability to explain …

A First Course in Systems Biology is an introduction for advanced undergraduate and
graduate students to the growing field of systems biology. Its main focus is the development …

Motivation: Genetic networks regulate key processes in living cells. Various methods have
been suggested to reconstruct network architecture from gene expression data. However …

Using models to simulate and analyze biological networks requires principled approaches
to parameter estimation and model discrimination. We use Bayesian and Monte Carlo …