A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

H∞ Sampled-Data Controller Design for Stochastic Genetic Regulatory Networks

Artificially regulating gene expression is an important step in developing new treatment for system-level disease such as cancer. In this paper, we propose a method to regulate gene expression based on sampled-data measurements of gene products concentrations. Inherent noisy behaviour of Gene regulatory networks are modeled with stochastic nonlinear differential equation. To synthesize feed...

Boolean models for genetic regulatory networks

Boolean Modeling of Genetic Regulatory Networks

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 processes like signal transduction, cell metabolism or embryonic development. Recent experimental advances helped uncover the qualitative structure of man...

Spatial Information and Boolean Genetic Regulatory Networks

In the course of understanding the functioning of cellular processes, modelling frameworks for biological networks are mandatory in order to reason on the models and their properties. One of the main problems with such modelling frameworks is to determine the dynamics of gene regulatory networks (GRN). Formal techniques, most of them based on model checking, have been applied to select valid dy...

On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior

Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation whi...