High level properties of genetic regulatory network

Kai Willadsen1, Janet Wiles2
1kaiw@itee.uq.edu.au, University of Queensland; 2, University of Queensland

Abstract approaches to modeling gene regulation date back to the development of the Random Boolean Network model in 1969. To this day, the Random Boolean Network model remains one of the most influential complex-systems models of gene regulation. Recently, there has been renewed interest in providing this model with stronger biological grounding, in order to be able to use the model in real-world situations.

While being less realistic, simplistic models such as the Random Boolean Network model have several advantages over continuous models in terms of their computational complexity, analytic tractability and human comprehension. These models demonstrate a range of interesting properties, including developmental robustness and homeostasis.

Traditional Boolean models ignore many important properties of real-world genetic regulatory systems: the interactions between genes in the simulated networks are often implausible, the network structure is usually randomly generated, and the discrete representations of time within the models has been shown to cause certain artifacts. In addition, there has been limited application of these models to solving real-world problems. This poster will discuss issues associated with providing the Random Boolean Network model with a biological grounding.