Biochemical systems involving a high number of components with intricate interactions often
lead to complex models containing a large number of parameters. Although a large model …

Abstract Background Systems Biology continues to produce increasingly large models of
complex biochemical reaction networks. In applications requiring, for example, parameter …

Background Models of biochemical systems are typically complex, which may complicate
the discovery of cardinal biochemical principles. It is therefore important to single out the …

Mathematical models of biological processes have various applications: to assist in
understanding the functioning of a system, to simulate experiments before actually …

Background Striking a balance between the degree of model complexity and parameter
identifiability, while still producing biologically feasible simulations using modelling is a …

Background The dynamics of biochemical networks can be modelled by systems of ordinary
differential equations. However, these networks are typically large and contain many …

Background Mathematical models provide abstract representations of the information
gained from experimental observations on the structure and function of a particular …

Developing mechanistic models has become an integral aspect of systems biology, as has
the need to differentiate between alternative models. Parameterizing mathematical models …

The complexity of full‐scale metabolic models is a major obstacle for their effective use in
computational systems biology. The aim of model reduction is to circumvent this problem by …

Motivation: To obtain meaningful predictions from dynamic computational models, their
uncertain parameter values need to be estimated from experimental data. Due to the usually …