5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011)

The growth in the Bioinformatics and Computational Biology fields over the last few years has been remarkable and the trend is to increase its pace. In fact, the need for computational techniques that can efficiently handle the huge amounts of data produced by the new experimental techniques in Biology is still increasing driven by new advances in Next Generation Sequencing, several types of the so called omics data and image acquisition, just to name a few. The analysis of the datasets that produces and its integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Within this scenario of increasing data availability, Systems Biology has also been emerging as an alternative to the reductionist view that dominated biological research in the last decades. Indeed, Biology is more and more a science of information requiring tools from the computational sciences. In the last few years, we have seen the surge of a new generation of interdisciplinary scientists that have a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. PACBB‘11 hopes to contribute to this effort promoting this fruitful interaction. PACBB'11 technical program included 50 papers from a submission pool of 78 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Therefore, the conference will certainly have promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists). The scientific content will certainly be challenging and will promote the improvement of the work that is being developed by each of the participants

A Multi-agent architecture for optimizing energy consumption using comfort agreements

[ES]Desde 1980 el consumo de energía global ha crecido más del doble y se prevé que la tendencia siga creciendo de forma continua. Del total de energía consumida en la Unión Europea, los edificios representan el 25%. La Unión Europea, a través de Horizon 2020, está apostando fuerte en el desarrollo de proyectos que impulsen una renovación energética mediante la renovación de los servicios energéticos en los hogares y el desarrollo de nuevos hábitos en los consumidores. El desarrollo tecnológico ha producido grandes avances en el campo de la campo de la computación y la electrónica. Esto ha permitido el desarrollo de técnicas de procesamiento y análisis de grandes volúmenes de datos y el desarrollo de sensores y dispositivos IoT de altas prestaciones. Estos avances han sido incluidos en los nuevos edificios desarrollando el concepto de edificios inteligentes proveyendo de una mayor seguridad, confort o ahorro económico. Aunque todavía es posible desarrollar nuevos enfoques centrados de forma más específica al usuario y adaptada al entorno para obtener una mayor reducción económica sin reducir el confort del usuario. La presente tesis doctoral define una arquitectura cuyo objetivo se focaliza en proporcionar una optimización energética, independiente de las características del edificio en el cual sea desplegada, mediante la negociación entre todos los usuarios implicados para el acuerdo común de las preferencias de confort que satisfagan el rango de confort de todos los usuarios a la vez que se produce la optimización energética deseada. Sobre la arquitectura presentada, se ha construido una plataforma de captura de datos del entorno, obtención de información de fuentes externa y de los propios usuarios. La plataforma realiza continuamente análisis de los datos recopilados de forma que estos datos se conviertan en información útil para el sistema y tomar decisiones que permitan reducir el consumo energético. Además, la arquitectura integra técnicas de computación social que faculta mantener las preferencias de los usuarios en términos de temperatura e iluminación, siendo un problema es doble, optimizar el consumo energético y mantener las preferencias que se han fijado la negociación. Como resultado, se obtiene una arquitectura dinámica y auto-adaptativa, capaz de lograr una optimización energetica en edificios manteniendo el confort de los usuarios

Profiling of pathogenic bacteria by colony morphology – identification of potential biofilm resistance and virulence determinants

Colony morphology changes may be an indicator of the phenotypic variations associated to the pathogenecity, virulence and antimicrobial resistance of infection-causing microorganisms. Particularly, phenotypic changes derived from biofilm growth and in response to environmental stressors. For instance, in patients with cystic fibrosis, P. aeruginosa colony morphology undergoes a conversion from non- to mucoid form augmenting bacteria resistance to antibiotics considerably. However, the specific correlation between some colony traits and the biological impact is unknown. This study was thus designed to inspect the colony associated phenotypic alterations, particularly the putative virulence determinants of biofilm-colonies, via morphological observation and whole cell MALDI MS proteomics. The annotation of colony morphology features was supported by a novel, in-house developed ontology, named colony morphology ontology (CMO), and annotations are available at the MorphoColDB framework [1]. The considerable diversity of the morphotypes observed within and across species, with diferente biofilm-forming abilities and susceptibilities [2], led to the application of this method in support of the identification of virulent morphotypes as primary therapeutic candidates. Further results on the protein expression of P. aeruginosa and S. aureus colonies confirmed important differences between intra-species morphotypes and has promoted investigation of the role of stress-regulated proteins. Although preliminary, these results confirm the potential of using a combination of highthroughput screening of pathogenic bacteria proteome with susceptibility tests and expert inputs to reach a comprehensive understanding of the persistence and antimicrobial resistance of pathogenic bacteria, as well as to design new therapeutic strategies

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

[EN] This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings

Evolutionary framework for DNA Microarry Cluster Analysis

En esta investigación se propone un framework evolutivo donde se fusionan un método de clustering jerárquico basado en un modelo evolutivo, un conjunto de medidas de validación de agrupamientos (clusters) de datos y una herramienta de visualización de clusterings. El objetivo es crear un marco apropiado para la extracción de conocimiento a partir de datos provenientes de DNA-microarrays. Por una parte, el modelo evolutivo de clustering de nuestro framework es una alternativa novedosa que intenta resolver algunos de los problemas presentes en los métodos de clustering existentes. Por otra parte, nuestra alternativa de visualización de clusterings, materializada en una herramienta, incorpora nuevas propiedades y nuevos componentes de visualización, lo cual permite validar y analizar los resultados de la tarea de clustering. De este modo, la integración del modelo evolutivo de clustering con el modelo visual de clustering, convierta a nuestro framework evolutivo en una aplicación novedosa de minería de datos frente a los métodos convencionales

V Jornadas de Investigación de la Facultad de Ciencia y Tecnología. 2016

171 p.I. Abstracts. Ahozko komunikazioak / Comunicaciones orales: 1. Biozientziak: Alderdi Molekularrak / Biociencias: Aspectos moleculares. 2. Biozientziak: Ingurune Alderdiak / Biociencias: Aspectos Ambientales. 3. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 4. Geología / Geología. 5. Matematika / Matemáticas. 6. Kimika / Química. 7. Ingenieritza Kimikoa eta Kimika / Ingeniería Química y Química. II. Abstracts. Idatzizko Komunikazioak (Posterrak) / Comunicaciones escritas (Pósters): 1. Biozientziak / Biociencias. 2. Fisika eta Ingenieritza Elektronika / Física e Ingeniería Electrónica. 3. Geologia / Geologia. 4. Matematika / Matemáticas. 5. Kimika / Química. 6. Ingenieritza Kimikoa / Ingeniería Química

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Multi-agent systems applications in energy optimization problems: a state-of-the-art review

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings