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

  • Christian Darabos
  • Mario Giacobini
  • Jason H. Moore
  • Marco Tomassini
Chapter

Abstract

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.

Keywords

Boolean Function Degree Distribution Gene Regulatory Network Mouse Embryonic Stem Cell Boolean Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

This work was partially supported by NIH grants LM009012, LM010098, AI59694, by the Swiss National Science Foundation grant PBLAP3-136923, and by Neuroscience Program of the Compagnia di San Paolo in Torino. The authors are grateful to Luca Ferreri for his precious help with statistical elaborations and the corresponding figures, and to Joshua L. Payne for his invaluable contribution to the discussions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Christian Darabos
    • 1
  • Mario Giacobini
    • 2
  • Jason H. Moore
    • 1
  • Marco Tomassini
    • 3
  1. 1.Computational Genetics LaboratoryGeisel School of Medicine at Dartmouth CollegeHanoverUSA
  2. 2.Computational Epidemiology Group, Department of Veterinary Sciences, and Complex Systems UnitMolecular Biotechnology Center, University of TorinoTorinoItaly
  3. 3.Information Systems Department, Faculty of Business and EconomicsUniversity of LausanneLausanneSwitzerland

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