Designing genetic regulatory networks using fuzzy Petri nets approach

Abstract

In this paper, we have successfully presented a fuzzy Petri net (FPN) model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition (HFT) to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.

Fuzzy logic based approaches for gene regulatory network inference

“…where, HFT={h f t 1 , h f t 2 }. The model assumed the following fuzzy production rules based on initial degree α (Hamed et al, 2010b), Hamed et al (2010b). The example consists of nine places P={p 1 , p 2 , …, p 9 }, transitions T={t 1 , t 2 , …, t 9 }, the initial degree α={0, 0.48, 0.52, 0.4, 0.6, 0, 0, 0, 0} T , initial marking vector M 0 ={1, 1, 1, 1, 1, 1, 0, 0 datasets, such as knockout and knockdown mutations, multifactorial, and time course data, into the model.…”

“…FPN is a promising modeling technique for large and complex systems which has capabilities for fuzzy knowledge representation and reasoning. The FPNs have been applied for modeling and simulation of GRN (Hamed et al, 2010a;Hamed et al, 2010b;Küffner et al, 2010;Hamed, 2013;Hamed, 2017;Li et al, 2017). Hamed et al (2010a) proposed FPNs based GRN model for searching activator/repressor regulatory relationship under gene triplets framework in gene expression.…”

“…The example consists of nine places P={p 1 , p 2 , …, p 9 }, transitions T={t 1 , t 2 , …, t 9 }, the initial degree α={0, 0.44, 0.56, 0.38, 0.62, 0, 0, 0, 0} T , initial marking vector M 0 ={1, 1, 1, 1, 1, 1, 0, 0, 0} T , and certainty factor µ i = {0.8, 0.6, 0.8, 0.7, 0.9, 0.95, 0.8, 0.99, 0.9}. Hamed et al (2010b) extended their previous FPN model by incorporating the concept of hidden fuzzy transition (HFT) as a new type of transition which allows wider search spaces to infer regulatory relationship.…”

“…, 0} T , and certainty factor µ i = {0.8, 0.6, 0.8, 0.7, 0.9, 0.95, 0.8, 0.99, 0.9} Hamed et al (2010b). extended their previous FPN model by incorporating the concept of hidden fuzzy transition (HFT) as a new type of transition which allows wider search spaces to infer regulatory relationship.…”

Picture Fuzzy Petri Nets for Knowledge Representation and Acquisition in Considering Conflicting Opinions

“…An FPN is a marked graphical system containing places and transitions [5]. Due to the capacity to depict imprecise knowledge and support inference processes, FPNs have garnered an increasing interest in both academics and practitioners and have been used in a lot of fields, such as fault diagnosis [6,7], adaptive software systems modeling [8], reliability optimization design [9], genetic regulatory network design [10], and DNA sequencing prediction [11,12].…”