Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms

BACKGROUND: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli. RESULTS: We sought to discover central components of the metabolic network, regulated by the expression of associated genes under changing conditions. We mapped gene expression data from E. coli under aerobic and anaerobic conditions onto the enzymatic reaction nodes of its metabolic network. An adjacency matrix of the metabolites was created from this graph. A consecutive ones clustering method was used to obtain network clusters in the matrix. The wavelet method was applied on the adjacency matrices of these clusters to collect features for the classifier. With a feature extraction method the most discriminating features were selected. We yielded network sub-graphs from these top ranking features representing formate fermentation, in good agreement with the anaerobic response of hetero-fermentative bacteria. Furthermore, we found a switch in the starting point for NAD biosynthesis, and an adaptation of the l-aspartate metabolism, in accordance with its higher abundance under anaerobic conditions. CONCLUSION: We developed and tested a novel method, based on a combination of rationally chosen machine learning methods, to analyse gene expression data on the basis of interaction data, using a metabolic network of enzymes. As a case study, we applied our method to E. coli under oxygen deprived conditions and extracted physiologically relevant patterns that represent an adaptation of the cells to changing environmental conditions. In general, our concept may be transferred to network analyses on biological interaction data, when data for two comparable states of the associated nodes are made available

PQ TREES, CONSECUTIVE ONES PROBLEM AND APPLICATIONS

A PQ tree is an advanced tree–based data structure, which represents a family of permutations on a set of elements. In this research article, we considered the significance of PQ trees and the Consecutive ones Problem to Computer Science and bioinformatics and their various applications. We also went further to demonstrate the operations of the characteristics of the Consecutive ones property by simulation, using high level programming languages. Attempt was also made at developing a PQ tree–Consecutive Ones analyzer, which could be instrumental not only as an educative tool to inquisitive students, but also serve as an important tool in developing clustering software in the field of bioinformatics and other application domains, with respect to solving real life problems

Using gene expression data and network topology to detect substantial pathways, clusters and switches during oxygen deprivation of Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.</p> <p>Results</p> <p>Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium <it>E. coli </it>to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of <it>E. coli </it>against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.</p> <p>Conclusion</p> <p>Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.</p

Machine learning based analyses on metabolic networks supports high-throughput knockout screens

Background: Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics. Results: This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium. Conclusion: Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets

A Functional Workbench for Anopheles gambiae Micro Array Analysis

Insecticide resistance, a character inherited that encompasses alteration in one or more of insect’s genes is now a major public health challenge combating world efforts on malaria control strategies. Anopheles has developed heavy resistance to pyrethroids, the only World Health Organization (WHO) recommended class for Indoor Residual Spray (IRS) and Long-Lasting Insecticide Treated Nets (LLITNs) through P450 pathways. We used the biochemical network of Anopheles gambiae (henceforth Ag) to deduce its resistance mechanism(s) using two expression data (when Ag is treated with pyrethroid and when controlled). The employed computational techniques are accessible by a robust, multi-faceted and friendly automated graphic user interface (GUI) tagged ‘workbench’ with JavaFX Scenebuilder. In this work, we introduced a computational platform to determine and also elucidate for the first time resistance mechanism to a commonly used class of insecticide, Pyrethroid. Significantly, our work is the first computational work to identify genes associated or involved in the efflux system in Ag and as a resistance mechanism in the Anopheles

MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks

[Abstract] Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) in living beings or Atmospheric reaction networks in planets and moons. Network of neurons appear in the worm C. elegans, in Human brain connectome, or in Artificial Neural Networks (ANNs). Infection spreading networks exist for contagious outbreaks networks in humans and in malware epidemiology for infection with viral software in internet or wireless networks. Social-legal networks with different rules evolved from swarm intelligence, to hunter-gathered societies, or citation networks of U.S. Supreme Court. In all these cases, we can see the same question. Can we predict the links based on structural information? We propose to solve the problem using Quantitative Structure-Property Relationship (QSPR) techniques commonly used in chemo-informatics. In so doing, we need software able to transform all types of networks/graphs like drug structure, drug-target interactions, protein structure, protein interactions, metabolic reactions, brain connectome, or social networks into numerical parameters. Consequently, we need to process in alignment-free mode multitarget, multiscale, and multiplexing, information. Later, we have to seek the QSPR model with Machine Learning techniques. MI-NODES is this type of software. Here we review the evolution of the software from chemoinformatics to bioinformatics and systems biology. This is an effort to develop a universal tool to study structure-property relationships in complex systems

Pattern recognition of gene expression data on signalling networks of cancer

Krebs ist ein Ergebnis abweichender zellulärer Signalübertragungen. Das Verständnis der Eigenschaften dieser komplexen Netzwerke wird es ermöglichen, effiziente therapeutische Strategien zu entwickeln. Oft werden bei der Analyse von Tumoren nur einzelne Signalpfade berücksichtigt. Diese Art Analyse vernachlässigt das Prinzip zusammenhängender Signalproteine in einem Netzwerk. Die Analyse, die in dieser Dissertation beschrieben wird, verwendet einen auf Netzwerken basierenden Ansatz, um ein Verständnis der komplexen zellulären Signaltransduktionspfade (sog. Signalwege) zu ermöglichen. In dieser Dissertation wurden menschliche Tumor-Genexpressionsdaten in das menschliche Protein-Protein-Interaktionsnetzwerk eingebettet und Signalwege mittels eines auf der Graphentheorie basierenden Ansatzes vorausberechnet. Mehrere Eigenschaften von normalen und Tumorsignalnetzwerken wurden aus diesen berechneten Signalwege unter Verwendung von 10 Tumordatensätzen abgeleitet. Es wird gezeigt, dass die Signalwege der betrachteten Tumore verglichen mit denen in normalen Gewebe kürzere Kaskaden und stärker differenzierte Signalwege verwenden. Das Signalnetzwerk im Tumor ist allgemein differenzierter und stärker vernetzt als in normalen Zellen. Eine netzwerkbasierende Analyse wurde ausgeführt, um die verschiedenen Netzwerkeigenschaften zwischen normalen und Tumorzellen mittels mehrerer Tumorgenexpressions-Datensätzen zu vergleichen. Die Ergebnisse bestätigen ein Model weniger geordneter Signalwege in Tumoren, was in einer größeren Robustheit der Signalwege des Tumors resultiert. Mit den Erkenntnissen dieser Studie wird ein neues Signalübertragungsmotiv vorgeschlagen, das sich in hoher Anzahl in den analysierten Datensätzen findet

Herramientas informáticas y de inteligencia artificial para el meta-análisis en la frontera entre la bioinformática y las ciencias jurídicas

[Resumen] Los modelos computacionales, conocidos por su acrónimo en idioma Inglés como QSPR (Quantitative Structure-Property Relationships) pueden usarse para predecir propiedades de sistemas complejos. Estas predicciones representan una aplicación importante de las Tecnologías de la Información y la Comunicación (TICs). La mayor relevancia es debido a la reducción de costes de medición experimental en términos de tiempo, recursos humanos, recursos materiales, y/o el uso de animales de laboratorio en ciencias biomoleculares, técnicas, sociales y/o jurídicas. Las Redes Neuronales Artificiales (ANNs) son una de las herramientas informáticas más poderosas para buscar modelos QSPR. Para ello, las ANNs pueden usar como variables de entrada (input) parámetros numéricos que cuantifiquen información sobre la estructura del sistema. Los parámetros conocidos como Índices Topológicos (TIs) se encuentran entre los más versátiles. Los TIs se calculan en Teoría de Grafos a partir de la representación de cualquier sistema como una red de nodos interconectados; desde moléculas a redes biológicas, tecnológicas, y sociales. Esta tesis tiene como primer objetivo realizar una revisión y/o introducir nuevos TIs y software de cálculo de TIs útiles como inputs de ANNs para el desarrollo de modelos QSPR de redes bio-moleculares, biológicas, tecnológico-económicas y socio-jurídicas. En ellas, por una parte, los nodos representan biomoléculas, organismos, poblaciones, leyes tributarias o concausas de delitos. Por otra parte, en la interacción TICs-Ciencias Biomoleculares- Derecho se hace necesario un marco de seguridad jurídica que permita el adecuado desarrollo de las TICs y sus aplicaciones en Ciencias Biomoleculares. Por eso, el segundo objetivo de esta tesis es revisar el marco jurídico-legal de protección de los modelos QSAR/QSPR de sistemas moleculares. El presente trabajo de investigación pretende demostrar la utilidad de estos modelos para predecir características y propiedades de estos sistemas complejos.[Resumo] Os modelos de ordenador coñecidos pola súas iniciais en inglés QSPR (Quantitative Structure-Property Relationships) poden prever as propiedades de sistemas complexos e reducir os custos experimentais en termos de tempo, recursos humanos, materiais e/ou o uso de animais de laboratorio nas ciencias biomoleculares, técnicas, e sociais. As Redes Neurais Artificiais (ANNs) son unha das ferramentas máis poderosas para buscar modelos QSPR. Para iso, as ANNs poden facer uso, coma variables de entrada (input), dos parámetros numéricos da estrutura do sistema chamados Índices Topolóxicos (TIs). Os TI calcúlanse na teoría dos grafos a partir da representación do sistema coma unha rede de nós conectados, incluíndo tanto moléculas coma redes sociais e tecnolóxicas. Esta tese ten como obxectivo principal revisar e/ou desenvolver novos TIs, programas de cálculo de TIs, e/ou modelos QSPR facendo uso de ANNs para predicir redes bio-moleculares, biolóxicas, económicas, e sociais ou xurídicas onde os nós representan moléculas biolóxicas, organismos, poboacións, ou as leis fiscais ou as concausas dun delito. Ademais, a interacción das TIC con as ciencias biolóxicas e xurídicas necesita dun marco de seguridade xurídica que permita o bo desenvolvemento das TIC e as súas aplicacións en Ciencias Biomoleculares. Polo tanto, o segundo obxectivo desta tese é analizar o marco xurídico e legal de protección dos modelos QSPR. O presente traballo de investigación pretende demostrar a utilidade destes modelos para predicir características e propiedades destes sistemas complexos.[Abstract] QSPR (Quantitative Structure-Property Relationships) computer models can predict properties of complex systems reducing experimental costs in terms of time, human resources, material resources, and/or the use of laboratory animals in bio-molecular, technical, and/or social sciences. Artificial Neural Networks (ANNs) are one of the most powerful tools to search QSPR models. For this, the ANNs may use as input variables numerical parameters of the system structure called Topological Indices (TIs). The TIs are calculated in Graph Theory from a representation of any system as a network of interconnected nodes, including molecules or social and technological networks. The first aim of this thesis is to review and/or develop new TIs, TIs calculation software, and QSPR models using ANNs to predict bio-molecular, biological, commercial, social, and legal networks where nodes represent bio-molecules, organisms, populations, products, tax laws, or criminal causes. Moreover, the interaction of ICTs with Biomolecular and law Sciences needs a legal security framework that allows the proper development of ICTs and their applications in Biomolecular Sciences. Therefore, the second objective of this thesis is to review the legal framework and legal protection of QSPR techniques. The present work of investigation tries to demonstrate the usefulness of these models to predict characteristics and properties of these complex systems