Fuzzy Logic in Genetic Regulatory Network Models

Abstract

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.

Computational modeling of aging-related gene networks: a review

“…Aldridge et al (28) manually constructed a fuzzy network to model colon cancer response to membrane receptors, yielding less deviation from validation data than a discrete model with equivalent links. Poblete et al (29) employed a neural network to optimize parameters for a model with fuzzy membership functions describing gene regulation. Fuzzy networks offer enhanced flexibility and realism in modeling GRNs compared to Boolean networks.…”

MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks

“…A hybrid fuzzy model introduced by Sokhansanj and Fitch [40], applied Union Rule Configuration (URC) together with fuzzy logic to avoid the combinatorial explosion issue in fuzzy rules. Another hybrid model by Poblete and colleagues [41], combines fuzzy inference systems with ODEs to produce an accurate model by reducing the need of prior knowledge. However, this approach is limited to the inference of small-scale networks.…”

Global dissipativity of fuzzy genetic regulatory networks with mixed delays

“…For example: In article Ali, Gunasekaran, Ahn, & Shi (2016), Ali, M. S. et al, investigated the sampled-data stabilization problem for Takagi-Sugeno (T-S) FGRNs subject to leakage delays. In paper Poblete,Parra,Gomez,Saldias,Garrido,& Vargas (2009), Poblete, C. M. et al, presented the fuzzy logic in GRN Models. In article Ram, Chetty,& Dix (2006), Ram, R. et al, proposed the fuzzy model for GRN to search microarray datasets for activator/repressor regulatory relationship.…”