State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

Abstract

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

Time-Delayed Models of Gene Regulatory Networks

“…Approaches based on an artificial bee colony search algorithm have allowed the reconstruction of a GRN in Escherichia coli [33]. A very recent review by Al Qazlan et al [34] gives an overview of different fuzzy methods, as well as their combinations with other approaches, such as ordinary differential equations, with the purpose of optimising data mining of gene expression and microarray datasets to recover GRNs.…”

Computational modeling of aging-related gene networks: a review

“…Overall, the networks generated from hematopoietic stem cell data from young patients displayed a smaller discrepancy between perturbed and unperturbed dynamics than those generated from aged individuals. Fuzzy logic extends Boolean networks by introducing more than two categories, like 'low, ' 'medium, ' and 'high' (26,27). Gene expression levels map to these states through a 'membership function' and regulatory interactions, executed by fuzzy logic operators (OR, AND, or NOT), output states with intermediary values for each category.…”

A Multistaged Hyperparallel Optimization of the Fuzzy-Logic Mechanistic Model of Molecular Regulation

“…two inputs and one output. Without improvement, the algorithm may only model simple regulation patterns and be unable to scale well to more complex models whose implementation time would be on the scale of years instead of hours [23]. Extending preliminary works of Reynolds [24], and by modifying the data preprocessing steps, Ressom and others did improve Woolf and Wang's approach [25].…”