Dynamic Changes in Subgraph Preference Profiles of Crucial Transcription Factors

Transcription factors with a large number of target genes—transcription hub(s), or THub(s)—are usually crucial components of the regulatory system of a cell, and the different patterns through which they transfer the transcriptional signal to downstream cascades are of great interest. By profiling normalized abundances (A(N)) of basic regulatory patterns of individual THubs in the yeast Saccharomyces cerevisiae transcriptional regulation network under five different cellular states and environmental conditions, we have investigated their preferences for different basic regulatory patterns. Subgraph-normalized abundances downstream of individual THubs often differ significantly from that of the network as a whole, and conversely, certain over-represented subgraphs are not preferred by any THub. The THub preferences changed substantially when the cellular or environmental conditions changed. This switching of regulatory pattern preferences suggests that a change in conditions does not only elicit a change in response by the regulatory network, but also a change in the mechanisms by which the response is mediated. The THub subgraph preference profile thus provides a novel tool for description of the structure and organization between the large-scale exponents and local regulatory patterns

Backup machinery of yeast transcriptional regulatory network

Several studies have suggested the existence of backup machinery of transcriptional regulatory networks (TRNs). Here, we have quantified the backup machinery of yeast gene's TRNs under five different conditions in terms of alternate paths and have revealed that a statistically significant (p<0.0001) stronger backup is maintained for endogenous processes (ENPs) than exogenous processes (EXPs). A number of biologically important genes (SUC2, MF(ALPHA)2, CLN2 etc) are observed that maintain a higher backup. Hub and random transcription factor (TF) knockouts in TRNs have showed ENPs are more robust to deletion than EXPs. While higher average connectivity of TFs in EXPs than ENPs can't explain the higher robustness in ENPs, we have found that the later have a densely interconnectedness explaining their specialized architecture that may have evolved due to evolutionary pressure. Some non-hub TFs identified here are more likely to be essential, and if not essential, have a larger impact on fitness

The Transcriptional Regulatory Network of Mycobacterium tuberculosis

Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb), which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known

Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking

Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs. With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii

Bioinformatics approaches for cancer research

Cancer is the consequence of genetic alterations that influence the behavior of affected cells. While the phenotypic effects of cancer like infinite proliferation are common hallmarks of this complex class of diseases, the connections between the genetic alterations and these effects are not always evident. The growth of information generated by experimental high-throughput techniques makes it possible to combine heterogeneous data from different sources to gain new insights into these complex molecular processes. The demand on computational biology to develop tools and methods to facilitate the evaluation of such data has increased accordingly. To this end, we developed new approaches and bioinformatics tools for the analysis of high-throughput data. Additionally, we integrated these new approaches into our comprehensive C++ framework GeneTrail. GeneTrail presents a powerful package that combines information retrieval, statistical evaluation of gene sets, result presentation, and data exchange. To make GeneTrail';s capabilities available to the research community, we implemented a graphical user interface in PHP and set up a webserver that is world-wide accessible. In this thesis, we discuss newly integrated algorithms and extensions of GeneTrail, as well as some comprehensive studies that have been performed with GeneTrail in the context of cancer research. We applied GeneTrail to analyze properties of tumor-associated antigens to elucidate the mechanisms of antigen candidate selection. Furthermore, we performed an extensive analysis of miRNAs and their putative target pathways and networks in cancer. In the field of differential network analysis, we employed a combination of expression values and topological data to identify patterns of deregulated subnetworks and putative key players for the deregulation. Signatures of deregulated subnetworks may help to predict the sensitivity of tumor subtypes to therapeutic agents and, hence, may be used in the future to guide the selection of optimal agents. Furthermore, the identified putative key players may represent oncogenes, tumor suppressor genes, or other genes that contribute to crucial changes of regulatory and signaling processes in cancer cells and may serve as potential targets for an individualized tumor therapy. With these applications, we demonstrate the usefulness of our GeneTrail package and hope that our work will contribute to a better understanding of cancer.Krebs ist eine Folge von tiefgreifenden genetischen Veränderungen, die das Verhalten der betroffenen Zellen beeinflussen. Während phänotypische Effekte wie unaufhörliches Wachstum augenscheinliche Merkmale dieser komplexen Klasse von Krankheiten sind, sind die Zusammenhänge zwischen genetischen Veränderungen und diesen Effekten oftmals weit weniger offensichtlich. Mit der stetigen Zunahme an Daten, die aus Hochdurchsatz-Verfahren stammen, ist es möglich geworden, heterogene Daten aus verschiedenen Quellen zu kombinieren und neue Erkenntnisse über diese Zusammenhänge zu gewinnen. Dementsprechend sind auch die Anforderungen an die Bioinformatik gewachsen, geeignete Applikationen und Verfahren zu entwickeln, um die Auswertung solcher Daten zu vereinfachen. Zu diesem Zweck haben wir neue Ansätze und bioinformatische Werkzeuge für die Analyse von entsprechenden Daten für die Krebsforschung entwickelt, welche wir in unser umfangreiches C++ System GeneTrail integriert haben. GeneTrail stellt ein mächtiges Softwarepaket dar, das Informationsgewinnung, statistische Auswertung von Gen Mengen, visuelle Darstellung der Resultate und Datenaustausch kombiniert. Um GeneTrail';s Fähigkeiten der Forschungsgemeinschaft zugänglich zu machen, haben wir eine graphische Benutzerschnittstelle in PHP implementiert und einen Webserver aufgesetzt, auf den weltweit zugegriffen werden kann. In der vorliegenden Arbeit diskutieren wir neu integrierte Algorithmen und Erweiterungen von GeneTrail, sowie umfangreiche Untersuchungen im Bereich Krebsforschung, die mit GeneTrail durchgeführt wurden. Wir haben GeneTrail angewendet, um Eigenschaften von Tumorantigenen zu untersuchen, um aufzuklären, welche dieser Eigenschaften zur Selektion dieser Proteine als Antigene beitragen. Des Weiteren haben wir eine umfangreiche Analyse von miRNAs und deren potentiellen Zielpfaden und -netzen in verschiedenen Krebsarten durchgeführt. Im Bereich differentieller Netzwerkanalyse kombinierten wir Expressionswerte und topologische Netzwerkdaten, um Muster deregulierter Teilnetzwerke und mögliche Schlüsselgene für die Deregulation zu identifizieren. Signaturen deregulierter Teilnetzwerke können helfen die Sensitivität verschiedener Tumorarten gegenüber Therapeutika vorherzusagen und damit zukünftig eine optimal angepasste Therapie zu ermöglichen. Außerdem können die identifizierten potentiellen Schlüsselgene Oncogene, Tumorsuppressorgene, oder andere Gene darstellen, die zu wichtigen Änderungen von regulatorischen Prozessen in Krebszellen beitragen, und damit auch als potentielle Ziele für eine individuelle Tumortherapie in Frage kommen. Mit diesen Anwendungen untermauern wir den Nutzen von GeneTrail und hoffen, dass unsere Arbeit in Zukunft zu einem besseren Verständnis von Krebs beiträgt

Methods for inference and analysis of gene networks from RNA sequencing data

RNA (Ribonuceic Acid) sequencing technology is a powerful technology used to give re- searchers essential information about the functionality of genes. The transcriptomic study and downstream analysis highlight the functioning of the genes associated with a specific biological process/treatment. In practice, differentially expressed genes associated with a particular treatment or genotype are subjected to downstream analysis to find some critical set of genes. This critical set of genes/ genes pathways infers the effect of the treatment in a cell or tissue. This disserta- tion describes the multiple stages framework of finding these critical sets of genes using different analysis methodologies and inference algorithms. RNA sequencing technology helps to find the differentially expressed genes associated with the treatments and genotypes. The preliminary step of RNA-seq analysis consists of extracting the mRNA(messenger RNA) followed by mRNA libraries’ preparation and sequencing using the Illumina HiSeq 2000 platform. The later stage analysis starts with mapping the RNA sequencing data (obtained from the previous step) to the genome annotations and counting each annotated gene’s reads to produce the gene expression data. The second step involves using the statistical method such as linear model fit, clustering, and probabilistic graphical modeling to analyze genes and gene networks’ role in treatment responses. In this dissertation, an R software package is developed that compiles all the RNA sequencing steps and the downstream analysis using the R software and Linux environment. Inference methodology based on loopy belief propagation is conducted on the gene networks to infer the differential expression of the gene in the further step. The loopy belief propagation algorithm uses a computational modeling framework that takes the gene expression data and the transcriptional Factor interacting with the genes. The inference method starts with constructing a gene-Transcriptional Factor network. The construction of the network uses an undirected proba- bilistic graphical modeling approach. Later the belief message is propagated across all the nodes of the graphs. The analysis and inference methods explained in the dissertation were applied to the Arabidopsis plant with two different genotypes subjected to two different stress treatments. The results for the analysis and inference methods are reported in the dissertation

Genome-wide analysis of transcriptional dependence and probable target sites for Abf1 and Rap1 in Saccharomyces cerevisiae

Abf1 and Rap1 are general regulatory factors (GRFs) that contribute to transcriptional activation of a large number of genes, as well as to replication, silencing and telomere structure in yeast. In spite of their widespread roles in transcription, the scope of their functional targets genome-wide has not been previously determined. Here, we use microarrays to examine the contribution of these essential GRFs to transcription genome-wide, by using ts mutants that dissociate from their binding sites at 37°C. We then combine this data with published ChIP-chip studies and motif analysis to identify probable direct targets for Abf1 and Rap1. We also identify a substantial number of genes likely to bind Rap1 or Abf1, but not affected by loss of GRF binding. Interestingly, the results strongly suggest that Rap1 can contribute to gene activation from farther upstream than can Abf1. Also, consistent with previous work, more genes that bind Abf1 are unaffected by loss of binding than those that bind Rap1. Finally, we show for several such genes that the Abf1 C-terminal region, which contains the putative activation domain, is not needed to confer this peculiar ‘memory effect’ that allows continued transcription after loss of Abf1 binding

Combined network analysis and interpretable machine learning reveals the environmental adaptations of more than 10,000 ruminant microbial genomes

BackgroundThe ruminant gastrointestinal contains numerous microbiomes that serve a crucial role in sustaining the host’s productivity and health. In recent times, numerous studies have revealed that variations in influencing factors, including the environment, diet, and host, contribute to the shaping of gastrointestinal microbial adaptation to specific states. Therefore, understanding how host and environmental factors affect gastrointestinal microbes will help to improve the sustainability of ruminant production systems.ResultsBased on a graphical analysis perspective, this study elucidates the microbial topology and robustness of the gastrointestinal of different ruminant species, showing that the microbial network is more resistant to random attacks. The risk of transmission of high-risk metagenome-assembled genome (MAG) was also demonstrated based on a large-scale survey of the distribution of antibiotic resistance genes (ARG) in the microbiota of most types of ecosystems. In addition, an interpretable machine learning framework was developed to study the complex, high-dimensional data of the gastrointestinal microbial genome. The evolution of gastrointestinal microbial adaptations to the environment in ruminants were analyzed and the adaptability changes of microorganisms to different altitudes were identified, including microbial transcriptional repair.ConclusionOur findings indicate that the environment has an impact on the functional features of microbiomes in ruminant. The findings provide a new insight for the future development of microbial resources for the sustainable development in agriculture

Internationalization strategies of the companies via e-commerce

E-commerce eliminated all national borders between countries and provided a new dimension to the internationalization process. Technological developments have made the study of electronic commerce in the international environment inevitable. This study highlights the possibilities and challenges of e-commerce and, consequently, the impact of this form of internationalization on company strategy. For the development of an empirical study, five interviews were analyzed with the primary objective of exploring the motivations that led companies to expand online. As the consumer is the leading player in this process, its perspective concerning ecommerce was studied. For this, an online survey was created to analyze the impact of social networks on the consumer's relationship. The results suggest an apparent connection between internationalization strategies and the adaptation of e-commerce. In this study, we conclude that e-commerce is one of the main internationalization strategies.O e-commerce eliminou todas as fronteiras nacionais entre os países e proporcionou uma nova dimensão ao processo de internacionalização. A evolução tecnológica tornou inevitável o estudo do comércio eletrónico no ambiente internacional. Este estudo visa evidenciar as possibilidades e desafios do e-commerce e, consequentemente, o impacto dessa forma de internacionalização na estratégia das empresas. Para o desenvolvimento de um estudo empírico, foram analisadas cinco entrevistas com o objetivo principal de analisar as motivações que levaram as empresas a expandirem online. Como o consumidor é o principal player neste processo, foi estudada a perspetiva do mesmo em relação ao e-commerce. Para isso, foi criado um inquérito online para analisar o impacto das redes sociais na relação com o consumidor. Os resultados sugerem uma conexão aparente entre as estratégias de internacionalização e a adaptação do e-commerce. Neste estudo, podemos verificar que o e-commerce é uma das principais estratégias de internacionalização