A New Approach for Modelling Gene Regulatory Networks Using Fuzzy Petri Nets

SummaryGene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.

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Topology estimation of gene regulatory networks with relative expression level variations

2012 American Control Conference (ACC) ◽

10.1109/acc.2012.6314870 ◽

2012 ◽

Author(s):

Yali Wang ◽

Tong Zhou

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Level ◽

Relative Expression ◽

Relative Expression Level ◽

Gene Regulatory ◽

Topology Estimation

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Bayesian Networks for Modeling and Inferring Gene Regulatory Networks

Handbook of Research on Computational Methodologies in Gene Regulatory Networks ◽

10.4018/978-1-60566-685-3.ch003 ◽

2010 ◽

pp. 57-78

Author(s):

Sebastian Bauer ◽

Peter Robinson

Keyword(s):

Bayesian Networks ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Search Space ◽

Dynamic Bayesian Networks ◽

Underlying Structure ◽

Mathematical Framework ◽

Bayesian Techniques ◽

Inference Algorithms ◽

Gene Regulatory

Bayesian networks have become a commonly used tool for inferring structure of gene regulatory networks from gene expression data. In this framework, genes are mapped to nodes of a graph, and Bayesian techniques are used to determine a set of edges that best explain the data, that is, to infer the underlying structure of the network. This chapter begins with an explanation of the mathematical framework of Bayesian networks in the context of reverse engineering of genetic networks. The second part of this review discusses a number of variations upon the basic methodology, including analysis of discrete vs. continuous data or static vs. dynamic Bayesian networks, different methods of exploring the potentially huge search space of network structures, and the use of priors to improve the prediction performance. This review concludes with a discussion of methods for evaluating the performance of network structure inference algorithms.

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Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Processes ◽

10.3390/pr9101758 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1758

Author(s):

Seong Beom Cho

Keyword(s):

Gene Regulatory Networks ◽

Cancer Biology ◽

Regulatory Networks ◽

Expression Profiles ◽

Classification Model ◽

Biological Knowledge ◽

Analysis Model ◽

Regulatory Relationship ◽

Transcriptomics Data ◽

Gene Regulatory

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

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