Fuzzy Logic in Genetic Regulatory Network Models


  • Carlos Muñoz Poblete University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile
  • Francisco Vargas Parra University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile
  • Jaime Bustos Gomez University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile
  • Millaray Curilem Saldias University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile
  • Sonia Salvo Garrido University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile
  • Horacio Miranda Vargas University of La Frontera Avenue Francisco Salazar 01145, Temuco, Chile


Genetic Regulatory Network, Fuzzy Logic, ANFIS, Differential Equations, Lac Operon


Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). Several models have been proposed to describe these interactions, been the most commonly used those based on ordinary differential equations (ODEs). Some approximations using piecewise linear differential equations (PLDEs), have been proposed to simplify the model non linearities. However they not allways give good results. In this context, it has been developed a model capable of representing small GRN, combining characteristics from the ODE’s models and fuzzy inference systems (FIS). The FIS is trained through an artificial neural network, which forms an Adaptive Nertwork-based Fuzzy Inference System (ANFIS). This network allows to adapt the membership and output functions from the FIS according to the training data, thus, reducing the previous knowledge needed to model the specific phenomenon.
In addition, Fuzzy Logic allows to express their rules through linguistic labels, which also allows to incorporate expert knowledge in a friendly way. The proposed model has been used to describe the Lac Operon in E. Coli and it has been compared with the models already mentioned. The outcome errors due to the training process of the ANFIS network are comparable with those of the models based on ODEs. Additionally, the fuzzy logic approach provides modeling flexibility and knowledge acquisition advantages.


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