Bhanu K. Kamapantula

Performance of wireless sensor topologies inspired by E. coli genetic networks

Wireless Sensor Networks (WSNs) form a critical component in modern computing applications; given their size, ability to process and communicate information, and to sense stimuli, they are a promising part of The Internet of Things. However, they are also plagued by reliability and node failure problems. Here we address these problems by using E. coli Gene Regulatory Networks (GRNs) - believed to be robust against signaling disruptions, such as gene failures - to study the transmission properties of randomly-generated WSNs and transmission structures derived from these genetic networks. Selection of sink nodes is crucial to the performance of these networks; here we introduce two sink-node selection techniques: a Motif-based, and a Highest Degree-based approach. Using NS-2 simulations, the performance of both networks is evaluated under varying channel loss models. Successful packet receipts are compared among these networks, which are shown to be higher using GRNs for the communication structure, rather than randomly generated WSNs. This work paves the way for future development of fault-tolerant and robust WSN deployment and routing algorithms.

Leveraging the robustness of genetic networks: a case study on bio-inspired wireless sensor network topologies

Wireless sensor networks (WSNs) form a critical component in modern computing applications; given their size, ability to process and communicate information, and to sense stimuli, they are a promising part of the Internet of Things. However, they are also plagued by reliability and node failure problems. Here we address these problems by using the Gene Regulatory Networks (GRNs) of the organism Escherichia coli—believed to be robust against signaling disruptions, such as gene failures—to study the transmission properties of randomly-generated WSNs and transmission structures derived from these genetic networks. Selection of sink nodes is crucial to the performance of these networks; here we introduce four sink-node selection techniques: two motif-based, an attractor based and a highest degree-based approach and perform comprehensive simulations to assess their performance. Specifically, we use NS-2 simulations to evaluate the packet transmission robustness properties of such GRN-derived communication structures as against typical randomly deployed sensor network topologies under varying channel loss models. Packet receipt rates are compared among these networks, which are shown to be higher using GRNs for the communication structure, rather than randomly generated WSNs. We also evaluate the performance of communication structures derived from existing biological network generation models to assess their applicability in providing robust communication. This work paves the way for future development of fault-tolerant and robust WSN deployment and routing algorithms based on the inherent signal transmission robustness properties of the gene regulatory network topologies.

PANNOTATOR: an automated tool for annotation of pan-genomes.

Due to next-generation sequence technologies, sequencing of bacterial genomes is no longer one of the main bottlenecks in bacterial research and the number of new genomes deposited in public databases continues to increase at an accelerating rate. Among these new genomes, several belong to the same species and were generated for pan-genomic studies. A pan-genomic study allows investigation of strain phenotypic differences based on genotypic differences. Along with a need for good assembly quality, it is also fundamental to guarantee good functional genome annotation of the different strains. In order to ensure quality and standards for functional genome annotation among different strains, we developed and made available PANNOTATOR (http://bnet.egr.vcu.edu/iioab/agenote.php), a web-based automated pipeline for the annotation of closely related and well-suited genomes for pan-genome studies, aiming at reducing the manual work to generate reports and corrections of various genome strains. PANNOTATOR achieved 98 and 76% of correctness for gene name and function, respectively, as result of an annotation transfer, with a similarity cut-off of 70%, compared with a gold standard annotation for the same species. These results surpassed the RAST and BASys softwares by 41 and 21% and 66 and 17% for gene name and function annotation, respectively, when there were reliable genome annotations of closely related species. PANNOTATOR provides fast and reliable pan-genome annotation; thereby allowing us to maintain the research focus on the main genotype differences between strains.

DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis

BackgroundMicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.MethodsIn this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network.Results and ConclusionOur tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.

Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks

We examine and compare transcriptional networks extracted from the bacterium Escherichia coli and the bakers yeast Saccharomyces cerevisiae using discrete event simulation based in silico experiments. The packet receipt rate is used as a dynamical metric to understand information flow, while machine learning techniques are used to examine underlying relationships inherent to the network topology. To this effect, we defined sixteen features based on structural/topological significance, such as transcriptional motifs, and other traditional metrics, such as network density and average shortest path, among others. Support vector classification is carried out using these features after parameters were identified using a cross-validation grid-search method. Feature ranking is performed using analysis of variance F-value metric. We found that feed-forward loop based features rank consistently high in both the bacterial and yeast networks, even at different perturbation levels. This work paves the way to design specialized engineered systems, such as wireless sensor networks, that exploit topological properties of natural networks to attain maximum efficiency.

miRegulome: a knowledge-base of miRNA regulomics and analysis.

miRNAs regulate post transcriptional gene expression by targeting multiple mRNAs and hence can modulate multiple signalling pathways, biological processes, and patho-physiologies. Therefore, understanding of miRNA regulatory networks is essential in order to modulate the functions of a miRNA. The focus of several existing databases is to provide information on specific aspects of miRNA regulation. However, an integrated resource on the miRNA regulome is currently not available to facilitate the exploration and understanding of miRNA regulomics. miRegulome attempts to bridge this gap. The current version of miRegulome v1.0 provides details on the entire regulatory modules of miRNAs altered in response to chemical treatments and transcription factors, based on validated data manually curated from published literature. Modules of miRegulome (upstream regulators, downstream targets, miRNA regulated pathways, functions, diseases, etc) are hyperlinked to an appropriate external resource and are displayed visually to provide a comprehensive understanding. Four analysis tools are incorporated to identify relationships among different modules based on user specified datasets. miRegulome and its tools are helpful in understanding the biology of miRNAs and will also facilitate the discovery of biomarkers and therapeutics. With added features in upcoming releases, miRegulome will be an essential resource to the scientific community. Availability: http://bnet.egr.vcu.edu/miRegulome.

Feature ranking in transcriptional networks: packet receipt as a dynamical metric

Machine learning techniques may be useful in determining the features contributing to some biological properties, such as robustness, which is the tendency for biological systems to resist a change of state. In this work, we compare transcriptional subnetworks extracted from the bacterium Escherichia coli and the bakers yeast Saccharomyces cerevisiae using in silico experiments. We use the packet receipt rate as a metric to quantify biological robustness, which is different from the usual structural metrics since it captures the dynamic behavior of the network. We define seventeen features based on structural significance, such as transcriptional motifs, and conventional metrics, such as average shortest path and network density, among others. Feature ranking is performed, based on a grid-search method to identify Support Vector Machine classifier parameters using cross validation. Our results indicate that feed-forward loop based features are important for bacterial transcriptional networks, whereas network density, degree-centrality based and bifan-based features are found to be significant for yeast-derived transcriptional networks. Interestingly, results suggest that feature significance varies with network size (number of nodes). As a first, this study quantifies the impact of the feed-forward loop and bifan transcriptional motif abundance observed in natural networks.

Determining miRNA-disease associations using bipartite graph modelling

Exploring miRNA-disease interactions is critical to identify the impact of a disease on other diseases. Mapping this problem to a graph theoretical concept offers a unique perspective to study unseen relationships among diseases. In our work, maximum weighted matching has been used after mapping the miRNA-disease associations as a bipartite graph. We also address the limitation of this approach using disease ranking scheme and the results are presented.

Quantifying robustness in biological networks using NS-2

Biological networks are known to be robust despite signal disruptions such as gene failures and perturbations. Extensive research is currently under way to explore biological networks and identify the underlying principles of their robustness. Structural properties such as power-law degree distribution and motif abundance have been attributed for robust performance of biological networks. Yet, little has been done so far to quantify such biological robustness. We propose a platform to quantify biological robustness using network simulator (NS-2) by careful mapping of biological properties at the gene level to that of wireless sensor networks derived using the topology of gene regulatory networks found in different organisms. A Support Vector Machine (SVM) learning model is used to measure the correlation of packet transmission rates in such sensor networks. These sensor networks contain important topological features of the underlying biological network, such as motif abundance, node/gene coverage, and transcription-factor network density, which we use to map the SVM features. Finally, a case study is presented to evaluate the NS-2 performance of two gene regulatory networks, obtained from the bacterium Escherichia coli and the baker’s yeast Sachharomyces cerevisiae.

The Structural Role of Feed-Forward Loop Motif in Transcriptional Regulatory Networks

We present multiple approaches to identify the significance of topological metrics that contribute to biological network robustness. We examine and compare the communication efficiency of transcriptional networks extracted from the bacterium Escherichia coli and the baker’s yeast Saccharomyces cerevisiae using discrete event simulation based in silico experiments. The packet receipt rate is used as a dynamical metric to understand information flow, while unsupervised machine learning techniques are used to examine underlying relationships inherent to the network topology. To this effect, we defined sixteen features based on structural/topological significance, such as transcriptional motifs, and other traditional metrics, such as network density and average shortest path, among others. Support vector classification is used with these features after parameters were identified using a cross-validation grid-search method. Feature ranking is performed using analysis of variance F-value metric. We found that feed-forward loop (FFL) based features consistently show up as significant in both the bacterial and yeast networks, even at different noise levels. We then use a supervised machine learning technique (random forests) to investigate the structural prominence of the FFL motif in information transmission using subnetworks (larger sample size compared to the unsupervised approach) extracted from Escherichia coli transcriptional regulatory network. Further, we study the role of FFLs in signal transduction within the complete Escherichia coli regulatory network. Although our work reveals a minimal role of FFLs in signal transduction, it highlights the structural role of FFLs in information transmission captured by random forest regression. This work paves the way to design specialized engineered systems, such as wireless sensor networks, that exploit topological properties of natural networks to attain maximum efficiency.