Models of Gene Regulation: Integrating Modern Knowledge into the Random Boolean Network Framework

Kauffman’s random Boolean networks are abstract, high level models for dynamical behavior of gene regulatory networks. They simulate the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by its simplicity, suffered from fundamental shortcomings unveiled by the recent advances in genetics and biology. Using these new discoveries, the model can be improved to reflect current knowledge. Artificial topologies, such as scale-free or hierarchical, are now believed to be closer to that of gene regulatory networks. We have studied actual biological organisms and used parts of their genetic regulatory networks in our models. We also have addressed the improbable full synchronicity of the event taking place on Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.