Growing functional modules from a seed protein via integration of protein interaction and gene expression data

<p>Abstract</p> <p>Background</p> <p>Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI) networks. A key concept in the organization of PPI networks is the existence of dense subnetworks (functional modules) in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules.</p> <p>Results</p> <p>In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point.</p> <p>Conclusion</p> <p>The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency.</p

Gene regulatory networks modelling using a dynamic evolutionary hybrid

<p>Abstract</p> <p>Background</p> <p>Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.</p> <p>Results</p> <p>The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes.</p> <p>Conclusions</p> <p>The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks.</p> <p>The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: <url>http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/</url>.</p

A Clustering based Method Accelerating Gene Regulatory Network Reconstruction

AbstractOne important direction of Systems Biology is to infer Gene Regulatory Networks and many methods have been developed recently, but they cannot be applied effectively in full scale data. In this work we propose a framework based on clustering to handle the large dimensionality of the data, aiming to improve accuracy of inferred network while reducing time complexity. We explored the efficiency of this framework employing the newly proposed metric Maximal Information Coefficient (MIC), which showed superior performance in comparison to other well established methods. Utilizing both benchmark and real life datasets, we showed that our method is able to deliver accurate results in fractions of time required by other state of the art methods. Our method provides as output interactions among groups of highly correlated genes, which in an application on an aging experiment were able to reveal aging related pathways

Scatter plots of statistical metrics for the derived and artificial functional modules

<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> Each data point represents statistical value for a certain functional module (x-axis) and its artificially created corresponding module (y-axis). The red dashed line corresponds to the line y = x. When a data point is below the line then the artificial module has a lower statistical value than the derived one, while the opposite stands for the case a data point is above the line. When the data point is on the line it means that the derived and its corresponding artificial module have the same value. The metric used in this plot is connectivity density, which is a measure of how densely connected is a specific module. Representation of R measuring the coverage in protein complexes of a detected functional module. It is evident from both diagrams that in all cases the derived from DMSP functional modules have better statistical values than the artificial ones

In this diagram we present the functional enrichment of modules in biological process GO terms

<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> It is evident that the majority (75%) of the modules extracted by DMSP has p-value bins larger than 9, whereas 80% of the modules resulting from the PPI method (W&H) and 83% of the modules determined by clustering the co-expression network (CENC) have p-value bins ranging between 0 to 6

In we present the number of modules determined by DMSP on various values of connectivity density

<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p> In blue dotted line we depict the number of functional modules, by keeping looser criteria for the determination of the modules. When we apply stricter criteria (as in red dotted line) there is a slight decrease in the number of the modules but at the same time the connectivity density values are better. In figure we display the initial size of the kernel, as well as the final size of the module, for modules with various sizes, and densities above 0.5. As we can see, DMSP manages to expand the initial size of the kernel in various degrees in order to determine the most coherent module each time

Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset

10.1109/EMBC.2014.69435932014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014318-32

Growing functional modules from a seed protein via integration of protein interaction and gene expression data-0

<p><b>Copyright information:</b></p><p>Taken from "Growing functional modules from a seed protein via integration of protein interaction and gene expression data"</p><p>http://www.biomedcentral.com/1471-2105/8/408</p><p>BMC Bioinformatics 2007;8():408-408.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2233647.</p><p></p>present interactions that have been experimentally determined. In this figure, we give some examples of modules with less than 20 members (A, B, C) and modules with more than 20 members (D, E). In each one of these modules protein complexes were identified. This module contains the Arp2p/Arp3p complex the Replication Factor C complex the 20S Proteasome that was discovered in its entirety, the SRB-Srb10p complexes and the ADA-SAGA-TFIID complexes

Surfactant Protein A and B Gene Polymorphisms and Risk of Respiratory Distress Syndrome in Late-Preterm Neonates

<div><p>Background and Objectives</p><p>Newborns delivered late-preterm (between 34^0/7 and 36^6/7 weeks of gestation) are at increased risk of respiratory distress syndrome (RDS). Polymorphisms within the surfactant protein (SP) A and B gene have been shown to predispose to RDS in preterm neonates. The aim of this study was to investigate whether specific SP-A and/or SP-B genetic variants are also associated with RDS in infants born late-preterm.</p><p>Methods</p><p>This prospective cross-sectional study included 56 late-preterm infants with and 60 without RDS. Specific SP-A1/SP-A2 haplotypes and SP-B Ile131Thr polymorphic alleles were determined in blood specimens using polymerase-chain-reaction and DNA sequencing.</p><p>Results</p><p>The SP-A1 6A⁴ and the SP-A2 1A⁵ haplotypes were significantly overrepresented in newborns with RDS compared to controls (OR 2.86, 95%CI 1.20–6.83 and OR 4.68, 95%CI 1.28–17.1, respectively). The distribution of the SP-B Ile131Thr genotypes was similar between the two late-preterm groups. Overall, the SP-A1 6A⁴ or/and SP-A2 1A⁵ haplotype was present in 20 newborns with RDS (35.7%), resulting in a 4.2-fold (1.60–11.0) higher probability of RDS in carriers. Multivariable regression analysis revealed that the effect of SP-A1 6A⁴ and SP-A2 1A⁵ haplotypes was preserved when adjusting for known risk or protective factors, such as male gender, smaller gestational age, smaller weight, complications of pregnancy, and administration of antenatal corticosteroids.</p><p>Conclusions</p><p>Specific SP-A genetic variants may influence the susceptibility to RDS in late-preterm infants, independently of the effect of other perinatal factors.</p></div