Deciphering the connectivity structure of biological networks using MixNet

<p>Abstract</p> <p>Background</p> <p>As biological networks often show complex topological features, mathematical methods are required to extract meaningful information. Clustering methods are useful in this setting, as they allow the summary of the network's topology into a small number of relevant classes. Different strategies are possible for clustering, and in this article we focus on a model-based strategy that aims at clustering nodes based on their connectivity profiles.</p> <p>Results</p> <p>We present MixNet, the first publicly available computer software that analyzes biological networks using mixture models. We apply this method to various networks such as the <it>E. coli </it>transcriptional regulatory network, the macaque cortex network, a foodweb network and the <it>Buchnera aphidicola </it>metabolic network. This method is also compared with other approaches such as module identification or hierarchical clustering.</p> <p>Conclusion</p> <p>We show how MixNet can be used to extract meaningful biological information, and to give a summary of the networks topology that highlights important biological features. This approach is powerful as MixNet is adaptive to the network under study, and finds structural information without any a priori on the structure that is investigated. This makes MixNet a very powerful tool to summarize and decipher the connectivity structure of biological networks.</p

Strategies for online inference of model-based clustering in large and growing networks

In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS359 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spatially-constrained clustering of ecological networks

Spatial ecological networks are widely used to model interactions between georeferenced biological entities (e.g., populations or communities). The analysis of such data often leads to a two-step approach where groups containing similar biological entities are firstly identified and the spatial information is used afterwards to improve the ecological interpretation. We develop an integrative approach to retrieve groups of nodes that are geographically close and ecologically similar. Our model-based spatially-constrained method embeds the geographical information within a regularization framework by adding some constraints to the maximum likelihood estimation of parameters. A simulation study and the analysis of real data demonstrate that our approach is able to detect complex spatial patterns that are ecologically meaningful. The model-based framework allows us to consider external information (e.g., geographic proximities, covariates) in the analysis of ecological networks and appears to be an appealing alternative to consider such data

Modeling the structure and dynamics of the auxin signaling network in the shoot apical meristem

International audienceno abstrac

Modeling heterogeneity in random graphs through latent space models: a selective review

We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five years

The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community

The EMBnet Conference 2008, focusing on 'Leading Applications and Technologies in Bioinformatics', was organized by the European Molecular Biology network (EMBnet) to celebrate its 20th anniversary. Since its foundation in 1988, EMBnet has been working to promote collaborative development of bioinformatics services and tools to serve the European community of molecular biology laboratories. This conference was the first meeting organized by the network that was open to the international scientific community outside EMBnet. The conference covered a broad range of research topics in bioinformatics with a main focus on new achievements and trends in emerging technologies supporting genomics, transcriptomics and proteomics analyses such as high-throughput sequencing and data managing, text and data-mining, ontologies and Grid technologies. Papers selected for publication, in this supplement to BMC Bioinformatics, cover a broad range of the topics treated, providing also an overview of the main bioinformatics research fields that the EMBnet community is involved in

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Characterization and analysis of the expression pattern of microRNAs in the grapevine Vitis vinifera

Motivation: MicroRNAs are small (19-24 nt) noncoding RNAs that play an important role in the regulation of multiple cell events, inhibiting gene expression at the posttranscriptional level by binding target mRNAs that are subsequently degraded or sequestered from translation. Plant microRNA genes are typically transcribed by Pol II to yield polyadenylated primary miRNAs (pri-miRNA). These undergo nuclear cleavage to produce to a stem loop intermediate (pre-miRNA) with specific thermodinamic features. Further processing yields a miRNA:miRNA* duplex with 2 nt 3\u2019 overhangs that enters a cytoplasmic ribonucleprotein complex which mediates interaction with target mRNAs. Systematic analyses of micro RNAs and their expression patterns have been performed in only a few plant model species. The availability of the complete genome sequence of the grapevine (Vitis vinifera), has already permitted genome-wide predictions of microRNAs by purely computational methods. Here we present a comprehensive analysis of expression of both mature microRNAs and their primary transcripts in the grapevine using oligonucleotide arrays and next generation sequencing technologies. Methods: We integrate tanscriptome information derived from high-throughput Illumina SOLEXA and ABI SOLiD sequence tags derived from both polyA+ transcripts and isolated small RNAs with oligonucleotide array data. We are thus able to detect both mature microRNAs and to establish whether genomic loci corresponding to the pre-miRNA are expressed in various tissues. Results: Using \u201cnext generation\u201d sequencing technologies and oligonucleotide arrays, we are able to demonstrate tissue specificity of expression of many microRNA genes and their precursor sequences. In many cases, the unambiguous alignment of sequence tags derived from polyA+ RNA to the genomic sequence allow provisional mapping of primary microRNA transcripts. It is hoped that the approach outlined here will ultimately provide insights into the regulation of processing of primary microRNAs and precursor microRNAs as well as facilitating identification of sequence elements involved in the regulation of transcription of microRNA genes