Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors

[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order kth (Wk). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the Wk(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated Wk(i) values were used as inputs for different ANNs in order to discriminate correct node connectivity patterns from incorrect random patterns. The MIANN models obtained present good values of Sensitivity/Specificity (%): MRNs (78/78), IWDBNs (90/88), and SFLN (86/84). These preliminary results are very promising from the point of view of a first exploratory study and suggest that the use of these models could be extended to the high-throughput re-evaluation of connectivity in known complex networks (collation)

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

Symmetry in Graph Theory

This book contains the successful invited submissions to a Special Issue of Symmetry on the subject of ""Graph Theory"". Although symmetry has always played an important role in Graph Theory, in recent years, this role has increased significantly in several branches of this field, including but not limited to Gromov hyperbolic graphs, the metric dimension of graphs, domination theory, and topological indices. This Special Issue includes contributions addressing new results on these topics, both from a theoretical and an applied point of view

Computing on evolving social networks

Over the past decade, participation in social networking services has seen an exponential growth, so that nowadays most individuals are “virtually” connected to others anywhere in the world. Consistently, analysis of human social behavior has gained momentum in the computer science research community. Several well-known phenomena in the social sciences have been revisited in a computer science perspective, with a new focus on phenomena of emerging behavior, information diffusion, opinion formation and collective intelligence. Furthermore, the recent past has witnessed a growing interest in the dynamics of these phenomena and that of the underlying social structures. This thesis investigates a number of aspects related to the study of evolving social networks and the collective phenomena they mediate. We have mainly pursued three research directions. The first line of research is in a sense functional to the other two and concerns the collection of data tracking the evolution of human interactions in the physical space and the extraction of (time) evolving networks describing these interactions. A number of available datasets describing different kinds of social networks are available on line, but few involve physical proximity of humans in real life scenarios. During our research activity, we have deployed several social experiments tracking face-to-face human interactions in the physical space. The collected datasets have been used to analyze network properties and to investigate social phenomena, as further described below. A second line of research investigates the impact of dynamics on the analytical tools used to extract knowledge from social networks. This is clearly a vast area in which research in many cases is in its early stages. We have focused on centrality, a fundamental notion in the analysis and characterization of social network structure and key to a number of Web applications and services. While many social networks of interest (resulting from “virtual” or “physical” activity) are highly dynamic, many Web information retrieval algorithms were originally designed with static networks in mind. In this thesis, we design and analyze decentralized algorithms for computing and maintaining centrality scores over time evolving networks. These algorithms refer to notions of centrality which are explicitly conceived for evolving settings and which are consistent with PageRank in important cases. A further line of research investigates the wisdom of crowds effect, an important, yet not completely understood phenomenon of collective intelligence, whereby a group typically exhibits higher predictive accuracy than its single members and often experts. Phenomena of collective intelligence involve exchange and processing of information among individuals sharing some common social structure. In many cases of interest, this structure is suitably described by an evolving social network. Studying the interplay between the evolution of the underlying social structure and the computational properties of the resulting process is an interesting and challenging task. We have focused on the quantitative analysis of this aspect, in particular the effect of the network on the accuracy of prediction. To provide a mathematical characterization, we have revisited and modified a number of models of opinion formation and diffusion originally proposed in the social sciences. Experimental analysis using data collected from some of the social experiments we conducted allowed to test soundness of the proposed models. While many of these models seem to capture important aspects of the process of opinion formation in (physical) social networks, one variant we propose achieves higher predictive accuracy and is also robust to the presence of outliers

Analysis of microRNA role in the development of left ventricular hypertrophy in the stroke-prone spontaneously hypertensive rat

MicroRNAs (miRs) are a group of short non-coding RNAs, on average 22 nucleotides in length, that form an important axis of post-transcriptional regulation of gene expression. They have been identified as major modulators of all biological processes including development, cell differentiation, growth and apoptosis as well as diseases such as cancer, diabetes and cardiovascular disease (CVD). In the developed world CVD remains the leading cause of morbidity and mortality, and a substantial burden on healthcare. Left ventricular hypertrophy (LVH) is defined as an increase in thickness of the myocardium and is an important risk factor in CVD. The stroke-prone spontaneously hypertensive rat (SHRSP) is an animal model of essential hypertension used in research of CVD together with a normotensive reference strain Wistar-Kyoto (WKY). The SHRSP animals exhibit an increase in the size of myocardium prior to the onset of hypertension and have established LVH at 16 weeks of age thus are a good model for investigating the genetics of this condition. The aim of this project was to identify signature expression patterns of novel and previously implicated microRNAs and to investigate their role in the development of LVH in the SHRSP. Furthermore, potential gene targets of candidate selected microRNAs were identified to investigate biological pathways involved in the disease process. MicroRNA microarray profiling was performed by Dr. McBride in the hearts of 5 week old SHRSP and WKY male rats using the LC Sciences (LCS) multispecies chip based on Sanger miRBase 11.0. The data were analysed (Drs. McBride and McClure) using Rank Product (RP) analysis method and evaluated in combination with the statistical analysis provided by LC Sciences (LCS). LCS data indicated 103 microRNAs differentially expressed at 5 weeks of age, 64 at 16 weeks of age, with 9 in common. The RP analysis identified 72 microRNAs differentially expressed between WKY and SHRSP at 5 weeks of age and 51 at 16 weeks of age, and 21 microRNAs were differentially regulated at both time points. Both methods identified a subset of 35 microRNAs in 5 week old hearts and 8 in 16 week old samples. TaqMan® microRNA assays were used to confirm these expression patterns. Based on these data and published literature candidate microRNAs – miR-195, miR-329 and miR-451 were selected for further experimental investigation. Expression of candidate microRNAs (miR-195, miR-329 and miR-451) in neonatal hearts of SHRSP and WKY rats was also investigated. It was found that all three candidate microRNAs were differentially expressed at this time point and there were significantly increased levels in the SHRSP compared to WKY. Cardiac cell line H9c2 AngII model of hypertrophy was used to investigate the effect of AngII on our candidate miRNA expression levels. A 96 hour stimulation of H9c2 cell with AngII resulted in a significant increase in cell size. Levels of miR-195 and miR-329 were not affected by addition of AngII; expression of miR-451 was significantly down-regulated immediately post stimulation, however levels were increased at the final assessment at 96 hours. Adenoviral vectors over-expressing miR-195, miR-329 and miR-451 were designed and generated. These vectors were used to investigate if overexpression of each individual miR could affect cell size in the selected in vitro model of cardiomyocyte hypertrophy. It was found that all candidate microRNAs reduced AngII mediated hypertrophic cell growth at higher doses. Identifying pathways and specific gene targets affected by changes in microRNA levels is of paramount importance. Availability of such data not only provides information about regulation of cardiac homeostasis, but also possible therapeutic approaches for treatment and prevention. Target prediction algorithms (DIANAmT, miRanda, miRDB, miRWalk, PICTAR5, PITA, RNA22, RNAhybrid and Targetscan) were used to identify potential gene targets for candidate microRNAs. To refine these lists to genes relevant to the experimental design Ingenuity Pathway analysis (IPA 9.0) software was used to overlay microRNA microarray data with results of heart mRNA gene expression data (M. McBride, personal communications) from the same cardiac tissue and to relate these to appropriate pathways and cellular functions. A list of 12 genes was generated: similar to CG4768-PA (RGD1309748), KN motif and ankyrin repeat domains 1 (Kank1), sterile alpha motif domain containing 4B (Samd4b), dual specificity phosphatase 10 (Dusp10), follistatin-like 3 (secreted glycoprotein) (Fstl3), jun D proto-oncogene (JunD), forkhead box M1 (Foxm1), SIN3 homolog A transcription regulator (yeast) (Sin3a), cyclin-dependent kinase 1 (Cdk1), kinesin family member 23 (Kif23), bone morphogenetic protein receptor type IA (Bmpr1a) and sestrin 1 (Sesn1). Expression of these candidate targets was assessed in heart tissues from neonates, 5 and 16 week old rats. Six out of ten of these targets were differentially expressed at one or more time points. To further investigate the proposed targeting of these genes by candidate microRNAs, expression levels were measured in each of the predicted targets in H9c2 cell transduced with miR over-expressing viruses. The expression patterns of Cdk1, Kif23, Kank1 and Sin3a were consistent with overexpression of the targeting microRNA, i.e. expression of each gene was down-regulated. In summary, data presented in this thesis elucidate the role of miR-195, miR-329 and miR-451 in the development of LVH in the SHRSP. Understanding the underlying cause for differential expression of these candidate microRNAs, confirming gene targets and identifying relevant pathways will improve the understanding of LVH at the molecular level. It will also help explain the pathophysiology of cardiovascular disease development in this rat model of human hypertension providing a basis for the development of novel therapeutic approaches to treat or prevent LVH

Proceedings of the NASA Conference on Space Telerobotics, volume 3

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc