Cell fate simulation model of gustatory neurons with MicroRNAs double-negative feedback loop by hybrid functional Petri net with extension

Genome Inform. 2006;17(1):100-11.

Abstract

Biological regulatory networks have been extensively researched. Recently, the microRNA regulation has been analyzed and its importance has increasingly emerged. We have applied the Hybrid Functional Petri net with extension (HFPNe) model and succeeded in creating model biological pathways, e.g. metabolic pathways, gene regulatory networks, cell signaling networks, and cell-cell interaction models with one of the HFPNe implementations Cell Illustrator. Thus, we have applied HFPNe to model regulatory networks that involve a new key regulator microRNA. As a test case, we selected the cell fate determination model of two gustatory neurons of Caenorhabditis elegans-ASE left (ASEL) and ASE right (ASER). These neurons are morphologically bilaterally symmetric but physically asymmetric in function. Johnston et al. have suggested that their cell fate is determined by the double-negative feedback loop involving the lsy-6 and mir-273 microRNAs. Our simulation model confirms their hypothesis. In addition, other well-known mutants that are related with the double-negative feedback loop are also well-modeled. The new upstream regulator of lsy-6 (lsy-2) that is mentioned in another paper is also integrated into this model for the mechanism of switching between ASEL and ASER without any contradictions. Therefore, the HFPNe-based modeling will be one of the promising modeling methods and simulation architectures that illustrate microRNA regulatory networks.

MeSH terms

  • Cell Differentiation / genetics
  • Computer Simulation*
  • Feedback, Physiological / genetics*
  • Gene Expression Regulation / physiology
  • MicroRNAs / chemistry*
  • MicroRNAs / physiology*
  • Models, Biological*
  • Mutation
  • Neurons, Afferent / chemistry*
  • Neurons, Afferent / cytology
  • Neurons, Afferent / physiology*
  • Signal Transduction / genetics
  • Taste / genetics*

Substances

  • MicroRNAs