Complex models of biochemical reaction systems have become increasingly common in the
systems biology literature. The complexity of such models can present a number of …

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

In this work we present new scalable, information theory-based variational methods for the
efficient model reduction of high-dimensional deterministic and stochastic reaction networks …

Abstract Model reduction is a central problem in mathematical biology. Reduced order
models enable modeling of a biological system at different levels of complexity and the …

Many complex kinetic models in the field of biochemical reactions contain a large number of
species and reactions. These models often require a huge array of computational tools to …

A stochastic reaction network model of Ca 2+ dynamics in synapses (Pepke et al PLoS
Comput. Biol. 6 e1000675) is expressed and simulated using rule-based reaction modeling …

Biological systems such as mammalian cell cycle are complex systems consisting of a large
number of molecular species interacting in ways that produce complex nonlinear systems …

We present an automated model reduction algorithm that uses quasi-steady state
approximation based reduction to minimize the error between the desired outputs …

Background Understanding the dynamical behaviour of biological systems is challenged by
their large number of components and interactions. While efforts have been made in this …

Often, the time evolution of a biochemical reaction network is crucial for determining the
effects of combining multiple pharmaceuticals. Here we illustrate a mathematical framework …