Boolean network models are one of the simplest models to study complex dynamic behavior
in biological systems. They can be applied to unravel the mechanisms regulating the …

Twenty years ago, we proposed a model of developmental control based on tensegrity
architecture, in which tissue pattern formation in the embryo is controlled through …

Inspired by empirical studies of networked systems such as the Internet, social networks,
and biological networks, researchers have in recent years developed a variety of techniques …

The availability of large data sets has allowed researchers to uncover complex properties
such as large-scale fluctuations and heterogeneities in many networks, leading to the …

Cells in multicellular organisms switch between distinct cell fates, such as proliferation or
differentiation into specialized cell types. Genome-wide gene regulatory networks govern …

The dynamics of Boolean networks with scale-free topology are studied. The existence of a
phase transition from ordered to chaotic dynamics, governed by the value of the scale-free …

We determine stability and attractor properties of random Boolean genetic network models
with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in …

We study how the notions of importance of variables in Boolean functions as well as the
sensitivities of the functions to changes in these variables impact the dynamical behavior of …

Background New techniques for determining relationships between biomolecules of all
types–genes, proteins, noncoding DNA, metabolites and small molecules–are now making …

The Kauffman model describes a particularly simple class of random Boolean networks.
Despite the simplicity of the model, it exhibits complex behavior and has been suggested as …