fuzzy logic model

Interactions of many key proteins or genes in signalling pathway have been studied qualitatively in the literature, but only little quantitative information is available.

Although much has been done to clarify the biochemistry of transcriptional dynamics in signalling pathway, it remains difficult to find out and predict quantitative responses. The aim of this study is to construct a computational model of epidermal growth factor receptor (EGFR) signalling pathway as one of hallmarks of cancer so as to predict quantitative responses.

In this analytical study, we presented a computational model to investigate EGFR signalling pathway. Interaction of Arsenic trioxide (ATO) with EGFR signalling pathway factors has been elicited by systematic search in data bases, as ATO is one of the mysterious chemotherapy agents that control EGFR expression in cancer. ATO has dichotomous manner in vivo, dependent on its concentration. According to fuzzy rules based upon qualitative knowledge and Petri Net, we can construct a quantitative model to describe ATO mechanism in EGFR signalling pathway.

By Fuzzy Logic models that have the potential to trade with the loss of quantitative information on how different species interact, along with Petri net quantitatively describe the dynamics of EGFR signalling pathway. By this model the dynamic of different factors in EGFR signalling pathway is achieved.

The use of Fuzzy Logic and PNs in biological network modelling causes a deeper understanding and comprehensive analysis of the biological networks.

Crisis management is of critical importance in the oil and gas industries due to the increasing occurrence of accidents in these areas. One of the most important issues regarding crisis management in such industries is the identification of safety assembly points where employees should gather in emergencies. This study aimed to identify the safe points in a refinery using geographic information system (GIS) and fuzzy logic for emergency assembly.

Regarding the aim of the study purpose, the required data were collected, and a focus group meeting was held with experts to determine the criteria influencing the safety point zoning as well as high-risk units using the HAZOP method. After the identification of the criteria and sub-criteria affecting the zoning, the weight of each zoning parameter was calculated, and the safety zones were determined using the fuzzy logic model and its operators in the GIS environment.

According to the results of the risk assessment, the criteria and sub-criteria affecting zoning were divided into three categories of inconsistent (layer weight: 0.740), consistent (layer weight: 0.094), and access to exit routes (layer weight: 0.167). Moreover, the map results based on the fuzzy logic model revealed three safe points, including the vicinity of the fire station, clinic, and wastewater treatment plant in this refinery where the employees should gather in the event of emergencies.

The results of this study showed that the selection of appropriate criteria in safe point zoning is of great importance in the emergencies in the industries. Moreover, an initial risk assessment can be effective in determining these criteria and sub-criteria. In addition, the fuzzy logic model has high accuracy and precision in determining the appropriate safe places.