Understanding genetic regulatory networks

In this paper, the problem of genetic regulatory network inference from time series microarray experiment data is considered. A noisy sigmoidal model is proposed to include both system noise and measurement noise. In order to solve this nonlinear…

A genetic regulatory network (GRN) is a dynamic system to describe interactions among a large number of different substances in a living biological cell. There have been numerous attempts to model the dynamical behaviour of genetic regulatory…

In this paper we introduce a system that combines ontogenetic development and artificial evolution to automatically design robots in a physics-based, virtual environment. Through lesion experiments on the evolved agents, we demonstrate that the…

Multistability is found to be an important recurring theme in synthesis biology. In this article, the multistability analysis problem is investigated by applying control theory and mathematical tools. Both the modelling and analysis issues are…

In order to efficiently analyze the complicated regulatory systems often encountered in biological settings, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small-scale complexity…

. Biological systems form complex networks of interaction on several scales, ranging from the molecular to the ecosystem level. On the subcellular scale, interaction between genes and gene products (mRNAs, proteins) forms the basis of essential…

Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene…

Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multi-layered, and organized in a nested, modular hierarchy of regulatory network kernels, function-specific building blocks,…

We present an approximation scheme for deriving reaction rate equations of genetic regulatory networks. This scheme predicts the timescales of transient dynamics of such networks more accurately than does standard quasi-steady state analysis by…

Due to the randomness of the biochemical process at the molecular level, genetic regulatory networks are inherently noisy. They are also subjected to extrinsic noises which are external to the gene expression. In this paper, we consider genetic…