Rule responder HCLS eScience infrastructure

paschke at inf.fu-berlin.de The emerging field of integrative bioinformatics provides en-abling methods and technologies for transparent information integration across distributed heterogenous data sources, tools and services. The aim of this article is to evolve a flex-ible and expandable distributed Pragmatic Web eScience infrastructure in the domain of Health Care and Life Sci-ence (HCLS), called Rule Responder HCLS. Rule Respon-der HCLS is about providing information consumers with rule-based agents to transform existing information into rel-evant information of practical consequences, hence provid-ing control to the end-users by enabling them to express in a declarative rule-based way how to turn existing informa-tion into personally relevant information and how to react or make automated decisions on top of it

Genetic algorithm in ab initio protein structure prediction using low resolution model : a review

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution

On Enumerating Minimal Siphons in Petri nets using CLP and SAT solvers: Theoretical and Practical Complexity

International audienceno abstrac

Constrained activity sequencing

Mnoho rozvrhovacích problémů lze považovat za problémy s hledáním posloupností aktivit splňujících určité podmínky a omezení. Typickým příkladem je rozvrhování letů v leteckém průmyslu, kde úlohou je přiřazení segmentů letů a servisních aktivit k jednotlivým letadlům. Tato diplomová práce se zaměřuje na hledání takovýchto omezených posloupností aktivit. Cílem práce je navrhnout formální model problému, včetně přesné specifikace podmínek a účelové funkce, schopný najít (sub)optimální posloupnosti aktivit. Navrhovaný model je založen na technikách Programování s omezujícími podmínkami.Many scheduling problems can be seen as activity sequencing problems, where the activity sequence in demand satisfies certain constraints. A typical example is scheduling in the airline industry where the task is to assign to each aircraft a segment of flight and nonflight activities while guaranteeing certain required properties. This diploma thesis deals with such type of constrained activity sequencing. The aim is to propose a formal model of the problem, including specification of all constraints and objectives, capable of finding (near)optimal sequences of activities. The proposed model is based on Constraint programming techniques.Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Exact, constraint-based structure prediction in simple protein models

Die Arbeit untersucht die exakte Vorhersage der Struktur von Proteinen in dreidimensionalen, abstrakten Proteinmodellen; insbesondere wird ein exakter Ansatz zur Strukturvorhersage in den HP-Modellen (Lau und Dill, ACS, 1989) des kubischen und kubisch-flächenzentrierten Gitters entwickelt und diskutiert. Im Gegensatz zu heuristischen Methoden liefert das vorgestellte exakte Verfahren beweisbar korrekte Strukturen. HP-Modelle (Hydrophob, Polar) repräsentieren die Rückgratkonformation eines Proteins durch Gitterpunkte und berücksichti\-gen ausschließlich die hydrophobe Wechselwirkung als treibende Kraft bei der Ausbildung der Proteinstruktur. Wesentlich für die erfolgreiche Umsetzung des vorgestellten Verfahrens ist die Verwendung von constraint-basierten Techniken. Im Zentrum steht die Berechnung und Anwendung hydrophober Kerne für die Strukturvorhersage

Integrating protein structural information

Dissertação apresentada para obtenção de Grau de Doutor em Bioquímica,Bioquímica Estrutural, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThe central theme of this work is the application of constraint programming and other artificial intelligence techniques to protein structure problems, with the goal of better combining experimental data with structure prediction methods. Part one of the dissertation introduces the main subjects of protein structure and constraint programming, summarises the state of the art in the modelling of protein structures and complexes, sets the context for the techniques described later on, and outlines the main points of the thesis: the integration of experimental data in modelling. The first chapter, Protein Structure, introduces the reader to the basic notions of amino acid structure, protein chains, and protein folding and interaction. These are important concepts to understand the work described in parts two and three. Chapter two, Protein Modelling, gives a brief overview of experimental and theoretical techniques to model protein structures. The information in this chapter provides the context of the investigations described in parts two and three, but is not essential to understanding the methods developed. Chapter three, Constraint Programming, outlines the main concepts of this programming technique. Understanding variable modelling, the notions of consistency and propagation, and search methods should greatly help the reader interested in the details of the algorithms, as described in part two of this book. The fourth chapter, Integrating Structural Information, is a summary of the thesis proposed here. This chapter is an overview of the objectives of this work, and gives an idea of how the algorithms developed here could help in modelling protein structures. The main goal is to provide a flexible and continuously evolving framework for the integration of structural information from a diversity of experimental techniques and theoretical predictions. Part two describes the algorithms developed, which make up the main original contribution of this work. This part is aimed especially at developers interested in the details of the algorithms, in replicating the results, in improving the method or in integrating them in other applications. Biochemical aspects are dealt with briefly and as necessary, and the emphasis is on the algorithms and the code