A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

<p>Abstract</p> <p>Background</p> <p>Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.</p> <p>Results</p> <p>In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of <it>Saccharomyces cerevisiae</it>. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized <it>α</it>-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.</p> <p>Conclusions</p> <p>We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.</p

Selection for improved energy use efficiency and drought tolerance in canola results in distinct transcriptome and epigenome changes

To increase both the yield potential and stability of crops, integrated breeding strategies are used that have mostly a direct genetic basis, but the utility of epigenetics to improve complex traits is unclear. A better understanding of the status of the epigenome and its contribution to agronomic performance would help in developing approaches to incorporate the epigenetic component of complex traits into breeding programs. Starting from isogenic canola (Brassica napus) lines, epilines were generated by selecting, repeatedly for three generations, for increased energy use efficiency and drought tolerance. These epilines had an enhanced energy use efficiency, drought tolerance, and nitrogen use efficiency. Transcriptome analysis of the epilines and a line selected for its energy use efficiency solely revealed common differentially expressed genes related to the onset of stress tolerance-regulating signaling events. Genes related to responses to salt, osmotic, abscisic acid, and drought treatments were specifically differentially expressed in the drought-tolerant epilines. The status of the epigenome, scored as differential trimethylation of lysine-4 of histone 3, further supported the phenotype by targeting drought-responsive genes and facilitating the transcription of the differentially expressed genes. From these results, we conclude that the canola epigenome can be shaped by selection to increase energy use efficiency and stress tolerance. Hence, these findings warrant the further development of strategies to incorporate epigenetics into breeding

Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

Detección de comunidades en redes: Algoritmos y aplicaciones

El presente trabajo de fin de máster tiene como objetivo la realización de un análisis de los métodos de detección de comunidades en redes. Como parte inicial se realizó un estudio de las características principales de la teoría de grafos y las comunidades, así como medidas comunes en este problema. Posteriormente, se realizó una revisión de los principales métodos de detección de comunidades, elaborando una clasificación, teniendo en cuenta sus características y complejidad computacional, para la detección de las fortalezas y debilidades en los métodos, así como también trabajos posteriores. Luego, se estudio el problema de la calificación de un método de agrupamiento, esto con el fin de evaluar la calidad de las comunidades detectadas, analizando diferentes medidas. Por último se elaboraron las conclusiones así como las posibles líneas de trabajo que se pueden derivar.This master's thesis work has the objective of performing an analysis of the methods for detecting communities in networks. As an initial part, I study of the main features of graph theory and communities, as well as common measures in this problem. Subsequently, I was performed a review of the main methods of detecting communities, developing a classification, taking into account its characteristics and computational complexity for the detection of strengths and weaknesses in the methods, as well as later works. Then, study the problem of classification of a clustering method, this in order to evaluate the quality of the communities detected by analyzing different measures. Finally conclusions are elaborated and possible lines of work that can be derived

Computational tools for large-scale biological network analysis

Tese de doutoramento em InformáticaThe surge of the field of Bioinformatics, among other contributions, provided biological researchers with powerful computational methods for processing and analysing the large amount of data coming from recent biological experimental techniques such as genome sequencing and other omics. Naturally, this led to the opening of new avenues of biological research among which is included the analysis of large-scale biological networks. The analysis of biological networks by itself is not new, but until recently researchers were limited to small-scale networks, due to the complexity inherent to biological systems. Recently, Bioinformatics provided researchers with the tools and methodologies needed to create and study large-scale networks. So, progressively larger networks have been built and more biological complex systems have been represented as networks. Since the study of large-scale biological networks is a relatively recent field, there are still few software tools focused in this research area. The main objective of this work was to contribute to this field, through the development of methodologies and computational tools for the creation and analysis of largescale cellular networks. One of the major contributions was the development of InBiNA, an open-source user-friendly application for the analysis of biological networks. InBiNA is a generic tool that can be used with most kinds of cellular networks, being focused in the analysis of integrated networks potentially representing metabolic, regulatory and/or signalling sub-systems. The usefulness of InBiNA has been shown by a case study including some pathways of Escherichia coli’s metabolism, together with different types of regulatory systems controlling these pathways. Also, TNA4OptFlux, a plug-in for the metabolic engineering software platform OptFlux, was created. Using the methodologies developed during this work, this plug-in is capable of combining the model-based phenotype simulation methods of OptFlux with network-based topological analysis methods, giving the user a new way of analysing the metabolism. One of the major applications is the comparison of the networks corresponding to wild-type and mutant strains, designed by strain optimization algorithms to overproduce interesting compounds. This brings interesting tools for the analyses of the strategies followed by mutant strains, as compared to the original ones. A case study, also using E. coli, for the production of succinate shows the usefulness of the tool. In this thesis the capabilities of InBiNA and TNA4OptFlux are presented, confirming their validity and utility as novel tools in the portfolio of Systems Biology research.O aparecimento do campo da Bioinformática trouxe, entre outras contribuições, ferramentas computacionais poderosas para o processamento e a análise das grandes quantidades de dados provenientes das recentes técnicas experimentais de alto débito em Biologia, tais como a sequenciação de genomas e outra ómicas. Naturalmente, isto conduziu à abertura de novas áreas na investigação biológica, entre as quais se inclui a análise de redes biológicas em larga escala. A análise de redes biológicas, por si só, não é uma novidade, mas até muito recentemente os investigadores da área limitavam-se ao estudo de redes em pequena escala, dada a complexidade inerente aos sistemas biológicos. Recentemente, a Bioinformática veio fornecer as ferramentas e as metodologias necessárias para criar e estudar redes em larga escala. Assim, redes progressivamente maiores têm sido construídas e cada vez mais sistemas biológicos complexos têm sido representados como redes. Dado que a análise de redes biológicas em larga-escala é ainda um campo recente, existem ainda poucas ferramentas focadas nesta área. O principal objetivo deste trabalho é o de contribuir para este campo, através do desenvolvimento de metodologias e ferramentas computacionais que permitam a criação e a análise de redes celulares em larga-escala. Uma das principais contribuições deste trabalho foi o desenvolvimento da aplicação InBiNA, uma aplicação aberta com uma interface amigável e que permite a análise de redes biológicas. Trata-se de uma ferramenta genérica que pode ser usada para analisar diversos tipos de redes celulares, sendo focada na análise de redes integradas, potencialmente representando sub-sistemas metabólicos, regulatórios e/ou de transdução de sinal. A utilidade da aplicação foi demonstrada através de um caso de estudo que envolveu a criação de uma rede incluindo algumas vias metabólicas da bactéria Escherichia coli, em conjunto com diferentes tipos de regulação controlando estas vias. Adicionalmente, o plug-in TNA4OptFlux foi desenvolvido, sendo um plug-in para o OptFlux, uma plataforma de software de Engenharia Metabólica. Usando as metodologias desenvolvidas durante este trabalho, este plug-in é capaz de combinar métodos de simulação de fenótipos baseados em modelos metabólicos com métodos de análise topológica de redes biológicas, fornecendo aos utilizadores uma forma distinta de analisar o metabolismo. Uma das principais aplicações passa pela comparação de redes metabólicas correspondentes a estirpes selvagens e mutantes desenhadas por algoritmos de otimização de estirpes que procuram a sobre-produção de compostos com interesse industrial. Assim, conseguem-se produzir ferramentas com interesse para a análise das estratégias seguidas pelas estirpes mutantes, quando comparadas com as originais. Um caso de estudo usando E. coli para a sobre-produção de succinato demonstra a utilidade das ferramentas. Neste trabalho, as capacidade das aplicações InBiNA e TNA4OptFlux são demonstradas, confirmando a sua validade e utilidade como novas ferramentas no portfólio da investigação na Biologia de Sistemas