Computational Methodologies for Analyzing, Modeling and Controlling Gene Regulatory Networks

Zamani, Zahra; Hajihosseini, Amirhossein; Masoudi‐Nejad, Ali

doi:10.4137/becb.s5594

Cited by 2 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gene Regulatory Networks (GRNs) govern the levels of these gene products. A GRN is the collection of molecular species and their interactions, which together control gene product abundance [ 1 , 2 ]. Numerous cellular processes are affected by regulatory networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discovering time-lagged rules from microarray data using gene profile classifiers

2011

View full text Add to dashboard Cite

BackgroundGene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes.ResultsThis paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.ConclusionsA novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Over the past few years, several statistical and artificial intelligence techniques have been proposed to carry out the reverse engineering of GRNs from monitoring and analyzing gene expression data [ 1 - 6 ]. These techniques vary from the simplest Boolean models to Continuous and Single Molecule Level models [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Discovering time-lagged rules from microarray data using gene profile classifiers

2011

View full text Add to dashboard Cite

show abstract

“…A gene regulatory network (GRN) is a collection of molecular regulators that interact among them and with other substances in the cell to govern the gene expression levels of mRNA and proteins (Karlebach and Shamir, 2008;Zamani et al, 2010). These networks are crucial in every process of life, and several cellular A c c e p t e d M a n u s c r i p t processes are affected by them.…”

Section: Introductionmentioning

confidence: 99%

“…This technology introduces a large amount of information to analyze, hence several data analysis issues (Alves et al, 2010). There are several techniques that carry out the reverse engineering of GRNs from analyzing gene expression data (Alves et al, 2010;De Jong, 2002;Espanés et al, 2016;Gallo et al, 2011;Karlebach and Shamir, 2008;Lakshmanan et al, 2013;Li et al, 2008;Ponzoni et al, 2007;Pridgeon and Corne, 2004;Zamani et al, 2010). They vary from Boolean models, model-free approaches up to data mining techniques, each of them tries to overcome the disadvantages of the previous one.…”

Section: Introductionmentioning

confidence: 99%

GeRNet: a gene regulatory network tool

et al. 2017

View full text Add to dashboard Cite

Gene regulatory networks (GRNs) are crucial in every process of life since they govern the majority of the molecular processes. Therefore, the task of assembling these networks is highly important. In particular, the so called model-free approaches have an advantage modeling the complexities of dynamic molecular networks, since most of the gene networks are hard to be mapped with accuracy by any other mathematical model. A highly abstract model-free approach, called rule-based approach, offers several advantages performing data-driven analysis; such as the requirement of the least amount of data. They also have an important ability to perform inferences: its simplicity allows the inference of large size models with a higher speed of analysis. However, regarding these techniques, the reconstruction of the relational structure of the network is partial, hence incomplete, for an effective biological analysis. This situation motivated us to explore the possibility of hybridizing with other approaches, such as biclustering techniques. This led to incorporate a biclustering tool that finds new relations between the nodes of the GRN. In this work we present a new software, called GeRNeT that integrates the algorithms of GRNCOP2 and BiHEA along a set of tools for interactive visualization, statistical analysis and ontological enrichment of the resulting GRNs. In this regard, results associated with Alzheimer disease datasets are presented that show the usefulness of integrating both bioinformatics tools.

show abstract

Computational Methodologies for Analyzing, Modeling and Controlling Gene Regulatory Networks

Cited by 2 publications

References 63 publications

Discovering time-lagged rules from microarray data using gene profile classifiers

Discovering time-lagged rules from microarray data using gene profile classifiers

GeRNet: a gene regulatory network tool

Contact Info

Product

Resources

About