Evaluation of properties over phylogenetic trees using stochastic logics

Background: Model checking has been recently introduced as an integrated framework for extracting information of the phylogenetic trees using temporal logics as a querying language, an extension of modal logics that imposes restrictions of a boolean formula along a path of events. The phylogenetic tree is considered a transition system modeling the evolution as a sequence of genomic mutations (we understand mutation as different ways that DNA can be changed), while this kind of logics are suitable for traversing it in a strict and exhaustive way. Given a biological property that we desire to inspect over the phylogeny, the verifier returns true if the specification is satisfied or a counterexample that falsifies it. However, this approach has been only considered over qualitative aspects of the phylogeny. Results: In this paper, we repair the limitations of the previous framework for including and handling quantitative information such as explicit time or probability. To this end, we apply current probabilistic continuous-time extensions of model checking to phylogenetics. We reinterpret a catalog of qualitative properties in a numerical way, and we also present new properties that couldn't be analyzed before. For instance, we obtain the likelihood of a tree topology according to a mutation model. As case of study, we analyze several phylogenies in order to obtain the maximum likelihood with the model checking tool PRISM. In addition, we have adapted the software for optimizing the computation of maximum likelihoods. Conclusions: We have shown that probabilistic model checking is a competitive framework for describing and analyzing quantitative properties over phylogenetic trees. This formalism adds soundness and readability to the definition of models and specifications. Besides, the existence of model checking tools hides the underlying technology, omitting the extension, upgrade, debugging and maintenance of a software tool to the biologists. A set of benchmarks justify the feasibility of our approach

Formal methods applied to the analysis of phylogenies: Phylogenetic model checking

Los árboles filogenéticos son abstracciones útiles para modelar y caracterizar la evolución de un conjunto de especies o poblaciones respecto del tiempo. La proposición, verificación y generalización de hipótesis sobre un árbol filogenético inferido juegan un papel importante en el estudio y comprensión de las relaciones evolutivas. Actualmente, uno de los principales objetivos científicos es extraer o descubrir los mensajes biológicos implícitos y las propiedades estructurales subyacentes en la filogenia. Por ejemplo, la integración de información genética en una filogenia ayuda al descubrimiento de genes conservados en todo o parte del árbol, la identificación de posiciones covariantes en el ADN o la estimación de las fechas de divergencia entre especies. Consecuentemente, los árboles ayudan a comprender el mecanismo que gobierna la deriva evolutiva. Hoy en día, el amplio espectro de métodos y herramientas heterogéneas para el análisis de filogenias enturbia y dificulta su utilización, además del fuerte acoplamiento entre la especificación de propiedades y los algoritmos utilizados para su evaluación (principalmente scripts ad hoc). Este problema es el punto de arranque de esta tesis, donde se analiza como solución la posibilidad de introducir un entorno formal de verificación de hipótesis que, de manera automática y modular, estudie la veracidad de dichas propiedades definidas en un lenguaje genérico e independiente (en una lógica formal asociada) sobre uno de los múltiples softwares preparados para ello. La contribución principal de la tesis es la propuesta de un marco formal para la descripción, verificación y manipulación de relaciones causales entre especies de forma independiente del código utilizado para su valoración. Para ello, exploramos las características de las técnicas de model checking, un paradigma en el que una especificación expresada en lógica temporal se verifica con respecto a un modelo del sistema que representa una implementación a un cierto nivel de detalle. Se ha aplicado satisfactoriamente en la industria para el modelado de sistemas y su verificación, emergiendo del ámbito de las ciencias de la computación. Las contribuciones concretas de la tesis han sido: A) La identificación e interpretación de los árboles filogeneticos como modelos de la evolución, adaptados al entorno de las técnicas de model checking. B) La definición de una lógica temporal que captura las propiedades filogenéticas habituales junto con un método de construcción de propiedades. C) La clasificación de propiedades filogenéticas, identificando categorías de propiedades según estén centradas en la estructura del árbol, en las secuencias o sean híbridas. D) La extensión de las lógicas y modelos para contemplar propiedades cuantitativas de tiempo, probabilidad y de distancias. E) El desarrollo de un entorno para la verificación de propiedades booleanas, cuantitativas y paramétricas. F) El establecimiento de los principios para la manipulación simbolica de objetos filogenéticos, p. ej., clados. G) La explotación de las herramientas de model checking existentes, detectando sus problemas y carencias en el campo de filogenia y proponiendo mejoras. H) El desarrollo de técnicas "ad hoc" para obtener ganancia de complejidad alrededor de dos frentes: distribución de los cálculos y datos, y el uso de sistemas de información. Los puntos A-F se centran en las aportaciones conceptuales de nuestra aproximación, mientras que los puntos G-H enfatizan la parte de herramientas e implementación. Los contenidos de la tesis están contrastados por la comunidad científica mediante las siguientes publicaciones en conferencias y revistas internacionales. La introducción de model checking como entorno formal para analizar propiedades biológicas (puntos A-C) ha llevado a la publicación de nuestro primer artículo de congreso [1]. En [2], desarrollamos la verificación de hipótesis filogenéticas sobre un árbol de ejemplo construido a partir de las relaciones impuestas por un conjunto de proteínas codificadas por el ADN mitocondrial humano (ADNmt). En ese ejemplo, usamos una herramienta automática y genérica de model checking (punto G). El artículo de revista [7] resume lo básico de los artículos de congreso previos y extiende la aplicación de lógicas temporales a propiedades filogenéticas no consideradas hasta ahora. Los artículos citados aquí engloban los contenidos presentados en las Parte I--II de la tesis. El enorme tamaño de los árboles y la considerable cantidad de información asociada a los estados (p.ej., la cadena de ADN) obligan a la introducción de adaptaciones especiales en las herramientas de model checking para mantener un rendimiento razonable en la verificación de propiedades y aliviar también el problema de la explosión de estados (puntos G-H). El artículo de congreso [3] presenta las ventajas de rebanar el ADN asociado a los estados, la partición de la filogenia en pequeños subárboles y su distribución entre varias máquinas. Además, la idea original del model checking rebanado se complementa con la inclusión de una base de datos externa para el almacenamiento de secuencias. El artículo de revista [4] reúne las nociones introducidas en [3] junto con la implementación y resultados preliminares presentados [5]. Este tema se corresponde con lo presentado en la Parte III de la tesis. Para terminar, la tesis reaprovecha las extensiones de las lógicas temporales con tiempo explícito y probabilidades a fin de manipular e interrogar al árbol sobre información cuantitativa. El artículo de congreso [6] ejemplifica la necesidad de introducir probabilidades y tiempo discreto para el análisis filogenético de un fenotipo real, en este caso, el ratio de distribución de la intolerancia a la lactosa entre diversas poblaciones arraigadas en las hojas de la filogenia. Esto se corresponde con el Capítulo 13, que queda englobado dentro de las Partes IV--V. Las Partes IV--V completan los conceptos presentados en ese artículo de conferencia hacia otros dominios de aplicación, como la puntuación de árboles, y tiempo continuo (puntos E-F). La introducción de parámetros en las hipótesis filogenéticas se plantea como trabajo futuro. Referencias [1] Roberto Blanco, Gregorio de Miguel Casado, José Ignacio Requeno, and José Manuel Colom. Temporal logics for phylogenetic analysis via model checking. In Proceedings IEEE International Workshop on Mining and Management of Biological and Health Data, pages 152-157. IEEE, 2010. [2] José Ignacio Requeno, Roberto Blanco, Gregorio de Miguel Casado, and José Manuel Colom. Phylogenetic analysis using an SMV tool. In Miguel P. Rocha, Juan M. Corchado Rodríguez, Florentino Fdez-Riverola, and Alfonso Valencia, editors, Proceedings 5th International Conference on Practical Applications of Computational Biology and Bioinformatics, volume 93 of Advances in Intelligent and Soft Computing, pages 167-174. Springer, Berlin, 2011. [3] José Ignacio Requeno, Roberto Blanco, Gregorio de Miguel Casado, and José Manuel Colom. Sliced model checking for phylogenetic analysis. In Miguel P. Rocha, Nicholas Luscombe, Florentino Fdez-Riverola, and Juan M. Corchado Rodríguez, editors, Proocedings 6th International Conference on Practical Applications of Computational Biology and Bioinformatics, volume 154 of Advances in Intelligent and Soft Computing, pages 95-103. Springer, Berlin, 2012. [4] José Ignacio Requeno and José Manuel Colom. Model checking software for phylogenetic trees using distribution and database methods. Journal of Integrative Bioinformatics, 10(3):229-233, 2013. [5] José Ignacio Requeno and José Manuel Colom. Speeding up phylogenetic model checking. In Mohd Saberi Mohamad, Loris Nanni, Miguel P. Rocha, and Florentino Fdez-Riverola, editors, Proceedings 7th International Conference on Practical Applications of Computational Biology and Bioinformatics, volume 222 of Advances in Intelligent Systems and Computing, pages 119-126. Springer, Berlin, 2013. [6] José Ignacio Requeno and José Manuel Colom. Timed and probabilistic model checking over phylogenetic trees. In Miguel P. Rocha et al., editors, Proceedings 8th International Conference on Practical Applications of Computational Biology and Bioinformatics, Advances in Intelligent and Soft Computing. Springer, Berlin, 2014. [7] José Ignacio Requeno, Gregorio de Miguel Casado, Roberto Blanco, and José Manuel Colom. Temporal logics for phylogenetic analysis via model checking. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(4):1058-1070, 2013

Application based technical Approaches of data mining in Pharmaceuticals, and Research approaches in biomedical and Bioinformatics

In the past study shows that flow of direction in the field of pharmaceutical was quit slow and simplest and by the time the process of transformation of information was so complex and the it was out of the reach to the technology, new modern technology could not reach to catch the pharmaceutical field. Then the later on technology becomes the compulsorily part of business and its contributed into business progress and developments. But now a days its get technology enabled and smoothly and easily pharma industries managing their billings and inventories and developing new products and services and now its easy to maintain and merging the drugs detail like its cost ,and usage with the patients records prescribe by the doctors in the hospitals .and data collection methods have improved data manipulation techniques are yet to keep pace with them data mining called and refer with the specific term as pattern analysis on large data sets used like clustering, segmentation and classification for helping better manipulation of the data and hence it helps to the pharma firms and industries this paper describes the vital role of data Mining in the pharma industry and thus data mining improves the quality of decision making services in pharmaceutical fields. This paper also describe a brief overviews of tool kits of Data mining and its various Applications in the field of Biomedical research in terms of relational approaches of data minings with the Emphasis on propositionalisation and relational subgroup discovery, and which is quit helpful to prove to be effective for data analysis in biomedical and its applications and in Bioinformatics as well. DOI: 10.17762/ijritcc2321-8169.15038

On FPGA implementations for bioinformatics, neural prosthetics and reinforcement learning problems.

Mak Sui Tung Terrence.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 132-142).Abstracts in English and Chinese.Abstract --- p.iList of Tables --- p.ivList of Figures --- p.vAcknowledgements --- p.ixChapter 1. --- Introduction --- p.1Chapter 1.1 --- Bioinformatics --- p.1Chapter 1.2 --- Neural Prosthetics --- p.4Chapter 1.3 --- Learning in Uncertainty --- p.5Chapter 1.4 --- The Field Programmable Gate Array (FPGAs) --- p.7Chapter 1.5 --- Scope of the Thesis --- p.10Chapter 2. --- A Hybrid GA-DP Approach for Searching Equivalence Sets --- p.14Chapter 2.1 --- Introduction --- p.16Chapter 2.2 --- Equivalence Set Criterion --- p.18Chapter 2.3 --- Genetic Algorithm and Dynamic Programming --- p.19Chapter 2.3.1 --- Genetic Algorithm Formulation --- p.20Chapter 2.3.2 --- Bounded Mutation --- p.21Chapter 2.3.3 --- Conditioned Crossover --- p.22Chapter 2.3.4 --- Implementation --- p.22Chapter 2.4 --- FPGAs Implementation of GA-DP --- p.24Chapter 2.4.1 --- System Overview --- p.25Chapter 2.4.2 --- Parallel Computation for Transitive Closure --- p.26Chapter 2.4.3 --- Genetic Operation Realization --- p.28Chapter 2.5 --- Discussion --- p.30Chapter 2.6 --- Limitation and Future Work --- p.33Chapter 2.7 --- Conclusion --- p.34Chapter 3. --- An FPGA-based Architecture for Maximum-Likelihood Phylogeny Evaluation --- p.35Chapter 3.1 --- Introduction --- p.36Chapter 3.2 --- Maximum-Likelihood Model --- p.39Chapter 3.3 --- Hardware Mapping for Pruning Algorithm --- p.41Chapter 3.3.1 --- Related Works --- p.41Chapter 3.3.2 --- Number Representation --- p.42Chapter 3.3.3 --- Binary Tree Representation --- p.43Chapter 3.3.4 --- Binary Tree Traversal --- p.45Chapter 3.3.5 --- Maximum-Likelihood Evaluation Algorithm --- p.46Chapter 3.4 --- System Architecture --- p.49Chapter 3.4.1 --- Transition Probability Unit --- p.50Chapter 3.4.2 --- State-Parallel Computation Unit --- p.51Chapter 3.4.3 --- Error Computation --- p.54Chapter 3.5 --- Discussion --- p.56Chapter 3.5.1 --- Hardware Resource Consumption --- p.56Chapter 3.5.2 --- Delay Evaluation --- p.57Chapter 3.6 --- Conclusion --- p.59Chapter 4. --- Field Programmable Gate Array Implementation of Neuronal Ion Channel Dynamics --- p.61Chapter 4.1 --- Introduction --- p.62Chapter 4.2 --- Background --- p.63Chapter 4.2.1 --- Analog VLSI Model for Hebbian Synapse --- p.63Chapter 4.2.2 --- A Unifying Model of Bi-directional Synaptic Plasticity --- p.64Chapter 4.2.3 --- Non-NMDA Receptor Channel Regulation --- p.65Chapter 4.3 --- FPGAs Implementation --- p.65Chapter 4.3.1 --- FPGA Design Flow --- p.65Chapter 4.3.2 --- Digital Model of NMD A and AMPA receptors --- p.65Chapter 4.3.3 --- Synapse Modification --- p.67Chapter 4.4 --- Results --- p.68Chapter 4.4.1 --- Simulation Results --- p.68Chapter 4.5 --- Discussion --- p.70Chapter 4.6 --- Conclusion --- p.71Chapter 5. --- Continuous-Time and Discrete-Time Inference Networks for Distributed Dynamic Programming --- p.72Chapter 5.1 --- Introduction --- p.74Chapter 5.2 --- Background --- p.77Chapter 5.2.1 --- Markov decision process (MDPs) --- p.78Chapter 5.2.2 --- Learning in the MDPs --- p.80Chapter 5.2.3 --- Bellman Optimal Criterion --- p.80Chapter 5.2.4 --- Value Iteration --- p.81Chapter 5.3 --- A Computational Framework for Continuous-Time Inference Network --- p.82Chapter 5.3.1 --- Binary Relation Inference Network --- p.83Chapter 5.3.2 --- Binary Relation Inference Network for MDPs --- p.85Chapter 5.3.3 --- Continuous-Time Inference Network for MDPs --- p.87Chapter 5.4 --- Convergence Consideration --- p.88Chapter 5.5 --- Numerical Simulation --- p.90Chapter 5.5.1 --- Example 1: Random Walk --- p.90Chapter 5.5.2 --- Example 2: Random Walk on a Grid --- p.94Chapter 5.5.3 --- Example 3: Stochastic Shortest Path Problem --- p.97Chapter 5.5.4 --- Relationships Between λ and γ --- p.99Chapter 5.6 --- Discrete-Time Inference Network --- p.100Chapter 5.6.1 --- Results --- p.101Chapter 5.7 --- Conclusion --- p.102Chapter 6. --- On Distributed g-Learning Network --- p.104Chapter 6.1 --- Introduction --- p.105Chapter 6.2 --- Distributed Q-Learniing Network --- p.108Chapter 6.2.1 --- Distributed Q-Learning Network --- p.109Chapter 6.2.2 --- Q-Learning Network Architecture --- p.111Chapter 6.3 --- Experimental Results --- p.114Chapter 6.3.1 --- Random Walk --- p.114Chapter 6.3.2 --- The Shortest Path Problem --- p.116Chapter 6.4 --- Discussion --- p.120Chapter 6.4.1 --- Related Work --- p.121Chapter 6.5 --- FPGAs Implementation --- p.122Chapter 6.5.1 --- Distributed Registering Approach --- p.123Chapter 6.5.2 --- Serial BRAM Storing Approach --- p.124Chapter 6.5.3 --- Comparison --- p.125Chapter 6.5.4 --- Discussion --- p.127Chapter 6.6 --- Conclusion --- p.128Chapter 7. --- Summary --- p.129Bibliography --- p.132AppendixChapter A. --- Simplified Floating-Point Arithmetic --- p.143Chapter B. --- "Logarithm, Exponential and Division Implementation" --- p.144Chapter B.1 --- Introduction --- p.144Chapter B.2 --- Approximation Scheme --- p.145Chapter B.2.1 --- Logarithm --- p.145Chapter B.2.2 --- Exponentiation --- p.147Chapter B.2.3 --- Division --- p.148Chapter C. --- Analog VLSI Implementation --- p.150Chapter C.1 --- Site Function --- p.150Chapter C.1.1 --- Multiplication Cell --- p.150Chapter C.2 --- The Unit Function --- p.153Chapter C.3 --- The Inference Network Computation --- p.154Chapter C.4 --- Layout --- p.157Chapter C.5 --- Fabrication --- p.159Chapter C.5.1 --- Testing and Characterization --- p.16

Automatic Music Composition using Answer Set Programming

Music composition used to be a pen and paper activity. These these days music is often composed with the aid of computer software, even to the point where the computer compose parts of the score autonomously. The composition of most styles of music is governed by rules. We show that by approaching the automation, analysis and verification of composition as a knowledge representation task and formalising these rules in a suitable logical language, powerful and expressive intelligent composition tools can be easily built. This application paper describes the use of answer set programming to construct an automated system, named ANTON, that can compose melodic, harmonic and rhythmic music, diagnose errors in human compositions and serve as a computer-aided composition tool. The combination of harmonic, rhythmic and melodic composition in a single framework makes ANTON unique in the growing area of algorithmic composition. With near real-time composition, ANTON reaches the point where it can not only be used as a component in an interactive composition tool but also has the potential for live performances and concerts or automatically generated background music in a variety of applications. With the use of a fully declarative language and an "off-the-shelf" reasoning engine, ANTON provides the human composer a tool which is significantly simpler, more compact and more versatile than other existing systems. This paper has been accepted for publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper. Formatted following TPLP guideline

Rapid assembly lines model building based on template approach and classification of problems using the cladistics technique

Competition in the global economic scenario has led to the use of simulation in many areas such as manufacturing, health systems, military systems and transportation. With the importance of simulation in supporting decision making and operations, model building has been recognised as one of the crucial steps in simulation studies. However, model building is not as easy as it may seem. It can be time-consuming and expensive, and requires special training, skills and experience. This research, therefore, aims to investigate a new method to rapidly build a simulation model based on the classification of problems in assembly lines using a cladistics technique and template approach. Three objectives were established in order to achieve the aim and a four-stage research programme was developed according to these objectives. The first stage starts by developing a thorough understanding of and collecting typical problems in assembly lines. The next stage formulates the classification of problems and the main deliverable is a cladogram, a tree structure that can be used to represent the evolution of problems and their characteristics. The third stage focuses on the development of a proof-of-concept prototype based on an established classification and template approach. The prototype helps users to develop a model by providing the physical elements and specific elements required for the performance measures analysis. The prototype is then tested and validated in the final stage. The results show that the prototype developed can help to rapidly build a simulation model and reduce model development time.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Tree Stochastic Processes

Stochastic processes play a vital role in understanding the development of many natural and computational systems over time. In this thesis, we will study two settings where stochastic processes on trees play a significant role. The first setting is in the reconstruction of evolutionary trees from biological sequence data. Most previous work done in this area has assumed that different positions in a sequence evolve independently. This independence however is a strong assumption that has been shown to possibly cause inaccuracies in the reconstructed trees \cite{schoniger1994stochastic,tillier1995neighbor}. In our work, we provide a first step toward realizing the effects of dependency in such situations by creating a model in which two positions may evolve dependently. For two characters with transition matrices $M_1$ and $M_2$, their joint transition matrix is the tensor product $M_1 \otimes M_2$. Our dependence model modifies the joint transition matrix by adding an `error matrix,\u27 a matrix with rows summing to 0. We show when such dependence can be detected. The second setting concerns computing in the presence of faults. In pushing the limits of computing hardware, there is tradeoff between the reliability of components and their cost (e.g. \cite{kadric2014energy}). We first examine a method of identifying faulty gates in a read-once formula when our access is limited to providing an input and reading its output. We show that determining \emph{whether} a fault exists can always be done, and that locating these faults can be done efficiently as long as the read-once formula satisfies a certain balance condition. Finally for a fixed topology, we provide a dynamic program which allows us to optimize how to allocate resources to individual gates so as to optimize the reliability of the whole system under a known input product distribution