A heuristic gene regulatory networks model for cardiac function and pathology

Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an increase in information about the human genome and cardiovascular disease. Understanding the role of genes in cardiac function and pathology requires modeling gene interactions and identification of regulatory genes as part of a gene regulatory network (GRN). Feature selection and data reduction not sufficient and require domain knowledge to deal with large data. We propose three novel innovations in constructing a GRN based on heuristics. A 2D Visualised Co-regulation function. Post-processing to identify gene-gene interactions. Finally a threshold algorithm is applied to identify the hub genes that provide the backbone of the GRN. The 2D Visualized Co-regulation function performed significantly better compared to the Pearson's correlation for measuring pairwise associations (t=3.46, df=5, p=0.018). The F-measure, improved from 0.11 to 0.12. The hub network provided a 60% improvement to that reported in the literature. The performance of the hub network was then also compared against ARACNe and performed significantly better (p=0.024). We conclude that a heuristics approach in developing GRNs has potential to improve our understanding of gene regulation and interaction in diverse biological function and disease.

[1]  Tongbin Li,et al.  Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data , 2015, BMC Bioinformatics.

[2]  Aibin He,et al.  Co-occupancy by multiple cardiac transcription factors identifies transcriptional enhancers active in heart , 2011, Proceedings of the National Academy of Sciences.

[3]  Adrian E. Raftery,et al.  Integrating external biological knowledge in the construction of regulatory networks from time-series expression data , 2012, BMC Systems Biology.

[4]  G. Palm,et al.  Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty , 2015, PloS one.

[5]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[6]  Fabio Fioravanti,et al.  Modeling gene regulatory network motifs using statecharts , 2012, BMC Bioinformatics.

[7]  Satoru Miyano,et al.  A Structure Learning Algorithm for Inference of Gene Networks from Microarray Gene Expression Data Using Bayesian Networks , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[8]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[9]  Eric T. Wang,et al.  Alternative splicing regulates vesicular trafficking genes in cardiomyocytes during postnatal heart development , 2014, Nature Communications.

[10]  Wei-Po Lee,et al.  Computational methods for discovering gene networks from expression data , 2009, Briefings Bioinform..

[11]  E. Raineri,et al.  Evolvability and hierarchy in rewired bacterial gene networks , 2008, Nature.

[12]  Kathleen Marchal,et al.  Exploring the Operational Characteristics of Inference Algorithms for Transcriptional Networks by Means of Synthetic Data , 2008, Artificial Life.

[13]  D. Spray,et al.  Sex-dependent gene regulatory networks of the heart rhythm , 2010, Functional & Integrative Genomics.

[14]  Shu-Lin Wang,et al.  Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification , 2012, BMC Bioinformatics.

[15]  G. Kundu,et al.  Comparative Characterization of Cardiac Development Specific microRNAs: Fetal Regulators for Future , 2015, PloS one.

[16]  Peter Langfelder,et al.  When Is Hub Gene Selection Better than Standard Meta-Analysis? , 2013, PloS one.

[17]  A. Martínez-Antonio.  Operation of the Gene Regulatory Network in Escherichia coli , 2012 .

[18]  P. Finn,et al.  Hubs in biological interaction networks exhibit low changes in expression in experimental asthma , 2007, Molecular systems biology.

[19]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[20]  A. Zeng,et al.  An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. , 2004, Nucleic acids research.