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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Evaluation and Development of Methods for Identification of Biochemical Networks / Evaluering och utveckling av metoder för identifiering av biokemiska nätverk

Jauhiainen, Alexandra January 2005 (has links)
<p>Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications. </p><p>System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear systems exists. </p><p>This is a master thesis in systems biology treating identification of biochemical systems. Methods based on both local parameter perturbation data and time series data have been tested and evaluated in silico. </p><p>The advantage of local parameter perturbation data methods proved to be that they demand less complex data, but the drawbacks are the reduced information content of this data and sensitivity to noise. Methods employing time series data are generally more robust to noise but the lack of available data limits the use of these methods. </p><p>The work has been conducted at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics in Göteborg, and at the division of Computational Biology at the Department of Physics and Measurement Technology, Biology, and Chemistry at Linköping University during the autumn of 2004.</p>
2

Structure Based Study of CA SPASE-3 and D-Arginine Dehydrogenase

Fu, Guoxing 07 December 2012 (has links)
Caspases are important players in programmed cell death. Normal activities of caspases are critical for the cell life cycle and dysfunction of caspases may lead to the development of cancer and neurodegenerative diseases. They have become a popular target for drug design against abnormal cell death. In this study, the recognition of P5 position in substrates by caspase-3, -6 and -7 has been investigated by kinetics, modeling and crystallography. Crystal structures of caspase-3 and -7 in complexes with substrate analog inhibitor Ac-LDESD-CHO have been determined at resolutions of 1.61 and 2.45 Å, respectively, while a model of caspase-6/LDESD is constructed. Enzymatic study and structural analysis have revealed that Caspase-3 and -6 recognize P5 in pentapeptides, while caspase-7 lacks P5-binding residues. D-arginine dehydrogenase catalyzes the flavin-dependent oxidative deamination of D-amino acids to the corresponding imino acids and ammonia. The X-ray crystal structures of DADH and its complexes with several imino acids were determined at 1.03-1.30 Å resolution. The DADH crystal structure comprises a product-free conformation and a product-bound conformation. A flexible loop near the active site forms an “active site lid” and may play an essential role in substrate recognition. The DADH Glu87 forms an ionic interaction with the side chain of iminoarginine, suggesting its importance for DADH preference of positively charged D-amino acids. Comparison of the kinetic data of DADH activity on different D-amino acids and the crystal structures demonstrated that this enzyme is characterized by relatively broad substrate specificity, being able to oxidize positively charged and large hydrophobic D-amino acids bound within a flask-like cavity. Understanding biology at the system level has gained much more attention recently due to the rapid development in genome sequencing and high-throughput measurements. Current simulation methods include deterministic method and stochastic method. Both have their own advantages and disadvantages. Our group has developed a deterministic-stochastic crossover algorithm for simulating biochemical networks. Simulation studies have been performed on biological systems like auto-regulatory gene network and glycolysis system. The new system retains the high efficiency of deterministic method while still reflects the random fluctuations at lower concentration.
3

Evaluation and Development of Methods for Identification of Biochemical Networks / Evaluering och utveckling av metoder för identifiering av biokemiska nätverk

Jauhiainen, Alexandra January 2005 (has links)
Systems biology is an area concerned with understanding biology on a systems level, where structure and dynamics of the system is in focus. Knowledge about structure and dynamics of biological systems is fundamental information about cells and interactions within cells and also play an increasingly important role in medical applications. System identification deals with the problem of constructing a model of a system from data and an extensive theory of particularly identification of linear systems exists. This is a master thesis in systems biology treating identification of biochemical systems. Methods based on both local parameter perturbation data and time series data have been tested and evaluated in silico. The advantage of local parameter perturbation data methods proved to be that they demand less complex data, but the drawbacks are the reduced information content of this data and sensitivity to noise. Methods employing time series data are generally more robust to noise but the lack of available data limits the use of these methods. The work has been conducted at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics in Göteborg, and at the division of Computational Biology at the Department of Physics and Measurement Technology, Biology, and Chemistry at Linköping University during the autumn of 2004.
4

Systematic approximation methods for stochastic biochemical kinetics

Thomas, Philipp January 2015 (has links)
Experimental studies have shown that the protein abundance in living cells varies from few tens to several thousands molecules per species. Molecular fluctuations roughly scale as the inverse square root of the number of molecules due to the random timing of reactions. It is hence expected that intrinsic noise plays an important role in the dynamics of biochemical networks. The Chemical Master Equation is the accepted description of these systems under well-mixed conditions. Because analytical solutions to this equation are available only for simple systems, one often has to resort to approximation methods. A popular technique is an expansion in the inverse volume to which the reactants are confined, called van Kampen's system size expansion. Its leading order terms are given by the phenomenological rate equations and the linear noise approximation that quantify the mean concentrations and the Gaussian fluctuations about them, respectively. While these approximations are valid in the limit of large molecule numbers, it is known that physiological conditions often imply low molecule numbers. We here develop systematic approximation methods based on higher terms in the system size expansion for general biochemical networks. We present an asymptotic series for the moments of the Chemical Master Equation that can be computed to arbitrary precision in the system size expansion. We then derive an analytical approximation of the corresponding time-dependent probability distribution. Finally, we devise a diagrammatic technique based on the path-integral method that allows to compute time-correlation functions. We show through the use of biological examples that the first few terms of the expansion yield accurate approximations even for low number of molecules. The theory is hence expected to closely resemble the outcomes of single cell experiments.
5

A review of modelling and verification approaches for computational biology

Konur, Savas January 2020 (has links)
This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties.
6

Un calcul de réécriture de graphes : applications à la biologie et aux systèmes autonomes / A rewriting calculus for graphs : applications to biology and autonomous systems

Andrei, Oana-Maria 05 November 2008 (has links)
L'objectif de cette thèse est d'explorer des descriptions formelles pour la structure et le fonctionnement des systèmes biologiques, ainsi que des outils formels pour raisonner au sujet de leur comportement. Cette thèse s'inscrit dans les travaux étudiant les modèles informatiques sûrs où les calculs sont exprimés par l'intermédiaire de la réécriture, et où nous pouvons compter sur la vérification formelle pour exprimer et valider les propriétés des modèles. Dans cette thèse nous développons un calcul de réécriture d'ordre supérieur pour décrire des molécules, des règles de réaction, et la génération des réseaux biochimiques. Le calcul est basé sur la métaphore chimique en décrivant les calculs en termes de solutions chimiques dans lesquelles les molécules représentant des données agissent l'une sur l'autre librement selon des règles de réaction. Ainsi nous avons obtenu un Calcul Biochimique Abstrait étendant le modèle chimique d'ordre supérieur en considérant des molécules structurées. Le calcul est équipé d'une spécification naturelle de la concurrence et des mécanismes de contrôle grâce à l'expression des stratégies de réécriture sous forme de molécules. La description des complexes moléculaires ou des réactifs chimiques appartient à une classe spécifique de graphes. Nous définissons la structure des graphes avec ports et nous montrons que les principes du calcul biochimique instanciés pour les graphes avec ports sont assez expressifs pour modéliser des systèmes autonomes et des réseaux biochimiques. En plus, les techniques de la réécriture stratégique ouvrent la voie au raisonnement basé sur les calculs et à la vérification des propriétés des systèmes modélisés / The objective of this thesis is to explore formal descriptions for the structure and functioning of biological systems, as well as formal tools for reasoning about their behavior. This work takes place in the overall prospective to study safe computational models where computations are expressed via rewriting, and where we can rely on formal verification to express and validate suitable properties. In this thesis we develop a higher-order calculus rewriting for describing molecules, reaction patterns, and biochemical network generation. The calculus is based on the chemical metaphor by describing the computations in terms of chemical solutions in which molecules representing data freely interact according to reaction rules. This way we obtained an Abstract Biochemical Calculus as an extension of the higher-order chemical model by considering structured molecules. The calculus is provided with a natural specification of concurrency and of controlling mechanisms by expressing rewrite strategies as molecules. The description of molecular complexes or chemical reactants belong to specific classes of graphs. We define the structure of port graphs and we show how the principles of the biochemical calculus instantiated for port graphs are expressive enough for modeling autonomous systems and biochemical networks. In addition, strategic rewriting techniques open the way to reason about the computations and to verify properties of the modeled systems
7

Polynomial Models for Systems Biology: Data Discretization and Term Order Effect on Dynamics

Dimitrova, Elena Stanimirova 12 September 2006 (has links)
Systems biology aims at system-level understanding of biological systems, in particular cellular networks. The milestones of this understanding are knowledge of the structure of the system, understanding of its dynamics, effective control methods, and powerful prediction capability. The complexity of biological systems makes it inevitable to consider mathematical modeling in order to achieve these goals. The enormous accumulation of experimental data representing the activities of the living cell has triggered an increasing interest in the reverse engineering of biological networks from data. In particular, construction of discrete models for reverse engineering of biological networks is receiving attention, with the goal of providing a coarse-grained description of such networks. In this dissertation we consider the modeling framework of polynomial dynamical systems over finite fields constructed from experimental data. We present and propose solutions to two problems inherent in this modeling method: the necessity of appropriate discretization of the data and the selection of a particular polynomial model from the set of all models that fit the data. Data discretization, also known as binning, is a crucial issue for the construction of discrete models of biological networks. Experimental data are however usually continuous, or, at least, represented by computer floating point numbers. A major challenge in discretizing biological data, such as those collected through microarray experiments, is the typically small samples size. Many methods for discretization are not applicable due to the insufficient amount of data. The method proposed in this work is a first attempt to develop a discretization tool that takes into consideration the issues and limitations that are inherent in short data time courses. Our focus is on the two characteristics that any discretization method should possess in order to be used for dynamic modeling: preservation of dynamics and information content and inhibition of noise. Given a set of data points, of particular importance in the construction of polynomial models for the reverse engineering of biological networks is the collection of all polynomials that vanish on this set of points, the so-called ideal of points. Polynomial ideals can be represented through a special finite generating set, known as Gr&ouml;bner basis, that possesses some desirable properties. For a given ideal, however, the Gr&ouml;bner basis may not be unique since its computation depends on the choice of leading terms for the multivariate polynomials in the ideal. The correspondence between data points and uniqueness of Gr&ouml;bner bases is studied in this dissertation. More specifically, an algorithm is developed for finding all minimal sets of points that, added to the given set, have a corresponding ideal of points with a unique Gr&ouml;bner basis. This question is of interest in itself but the main motivation for studying it was its relevance to the construction of polynomial dynamical systems. This research has been partially supported by NIH Grant Nr. RO1GM068947-01. / Ph. D.
8

Stochastic simulation and analysis of biochemical networks

Pahle, Jürgen 27 June 2008 (has links)
Stochastische Effekte können einen großen Einfluss auf die Funktionsweise von biochemischen Netzwerken haben. Vor allem Signalwege, z.B. Calciumsignaltransduktion, sind anfällig gegenüber zufälligen Schwankungen. Daher stellt sich die wichtige Frage, wie dadurch der Informationstransfer in diesen Systemen beeinträchtigt wird. Zunächst werden eine Reihe von stochastischen Simulationsmethoden diskutiert und systematisch klassifiziert. Dies dient als methodische Grundlage der ganzen Dissertation. Der Schwerpunkt liegt hier auf approximativen und hybriden Ansätzen, einschließlich der Hybridmethode des Softwaresystems Copasi, deren Implementierung Teil dieser Arbeit war. Die Dynamik biochemischer Systeme zeigt in den meisten Fällen einen Übergang von stochastischem zu deterministischem Verhalten mit steigender Partikelzahl. Dieser Übergang wird für Calciumsignaltransduktion und andere Systeme untersucht. Es zeigt sich, dass das Auftreten stochastischer Effekte stark von der Sensitivität des Systems abhängt. Ein Maß dafür ist die Divergenz. Systeme mit hoher Divergenz zeigen noch mit hohen Teilchenzahlen stochastische Effekte und umgekehrt. Schließlich wird der Einfluss von zufälligen Fluktuationen auf die Leistungsfähigkeit von Signalpfaden erforscht. Dazu werden simulierte sowie experimentell gemessene Calcium-Zeitreihen stochastisch an die Aktivierung eines Zielenzyms gekoppelt. Das Schätzen des informationstheoretischen Maßes Transferentropie unter unterschiedlichen zellulären Bedingungen dient zur Abschätzung des Informationstransfers. Dieser nimmt mit steigender Partikelzahl zu, ist jedoch sehr abhängig von der momentanen Dynamik (z.B. spikende, burstende oder irreguläre Oszillationen). Die hier entwickelten Methoden, wie der Gebrauch der Divergenz als Indikator für den stoch./det. Übergang oder die stochastische Kopplung und informationstheoretische Analyse mittels Transferentropie, sind wertvolle Werkzeuge für die Analyse von biochemischen Systemen. / Stochastic effects in biochemical networks can affect the functioning of these systems significantly. Signaling pathways, such as calcium signal transduction, are particularly prone to random fluctuations. Thus, an important question is how this influences the information transfer in these pathways. First, a comprehensive overview and systematic classification of stochastic simulation methods is given as methodical basis for the thesis. Here, the focus is on approximate and hybrid approaches. Also, the hybrid solver in the software system Copasi is described whose implementation was part of this PhD work. Then, in most cases, the dynamic behavior of biochemical systems shows a transition from stochastic to deterministic behavior with increasing particle numbers. This transition is studied in calcium signaling as well as other test systems. It turns out that the onset of stochastic effects is very dependent on the sensitivity of the specific system quantified by its divergence. Systems with high divergence show stochastic effects even with high particle numbers and vice versa. Finally, the influence of noise on the performance of signaling pathways is investigated. Simulated and experimentally measured calcium time series are stochastically coupled to an intracellular target enzyme activation process. Then, the information transfer under different cellular conditions is estimated with the information-theoretic quantity transfer entropy. The amount of information that can be transferred increases with rising particle numbers. However, this increase is very dependent on the current dynamical mode of the system, such as spiking, bursting or irregular oscillations. The methods developed in this thesis, such as the use of the divergence as an indicator for the transition from stochastic to deterministic behavior or the stochastic coupling and information-theoretic analysis using transfer entropy, are valuable tools for the analysis of biochemical systems.
9

The functionality of spatial and time domain artificial neural models

Capanni, Niccolo Francesco January 2006 (has links)
This thesis investigates the functionality of the units used in connectionist Artificial Intelligence systems. Artificial Neural Networks form the foundation of the research and their units, Artificial Neurons, are first compared with alternative models. This initial work is mainly in the spatial-domain and introduces a new neural model, termed a Taylor Series neuron. This is designed to be flexible enough to assume most mathematical functions. The unit is based on Power Series theory and a specifically implemented Taylor Series neuron is demonstrated. These neurons are of particular usefulness in evolutionary networks as they allow the complexity to increase without adding units. Training is achieved via various traditiona and derived methods based on the Delta Rule, Backpropagation, Genetic Algorithms and associated evolutionary techniques. This new neural unit has been presented as a controllable and more highly functional alternative to previous models. The work on the Taylor Series neuron moved into time-domain behaviour and through the investigation of neural oscillators led to an examination of single-celled intelligence from which the later work developed. Connectionist approaches to Artificial Intelligence are almost always based on Artificial Neural Networks. However, another route towards Parallel Distributed Processing was introduced. This was inspired by the intelligence displayed by single-celled creatures called Protoctists (Protists). A new system based on networks of interacting proteins was introduced. These networks were tested in pattern-recognition and control tasks in the time-domain and proved more flexible than most neuron models. They were trained using a Genetic Algorithm and a derived Backpropagation Algorithm. Termed "Artificial BioChemical Networks" (ABN) they have been presented as an alternative approach to connectionist systems.
10

Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks

Vera-Licona, Martha Paola 27 June 2007 (has links)
Systems biology is an emergent field focused on developing a system-level understanding of biological systems. In the last decade advances in genomics, transcriptomics and proteomics have gathered a remarkable amount data enabling the possibility of a system-level analysis to be grounded at a molecular level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the system's structure and/or dynamics. The objective of this dissertation is to introduce and propose solutions to some of the challenges inherent in reverse-engineering of biological systems. First, previously developed reverse engineering algorithms are studied and compared using data from a simulated network. This study draws attention to the necessity for a uniform benchmark that enables an ob jective comparison and performance evaluation of reverse engineering methods. Since several reverse-engineering algorithms require discrete data as input (e.g. dynamic Bayesian network methods, Boolean networks), discretization methods are being used for this purpose. Through a comparison of the performance of two network inference algorithms that use discrete data (from several different discretization methods) in this work, it has been shown that data discretization is an important step in applying network inference methods to experimental data. Next, a reverse-engineering algorithm is proposed within the framework of polynomial dynamical systems over finite fields. This algorithm is built for the identification of the underlying network structure and dynamics; it uses as input gene expression data and, when available, a priori knowledge of the system. An evolutionary algorithm is used as the heuristic search method for an exploration of the solution space. Computational algebra tools delimit the search space, enabling also a description of model complexity. The performance and robustness of the algorithm are explored via an artificial network of the segment polarity genes in the D. melanogaster. Once a mathematical model has been built, it can be used to run simulations of the biological system under study. Comparison of simulated dynamics with experimental measurements can help refine the model or provide insight into qualitative properties of the systems dynamical behavior. Within this work, we propose an efficient algorithm to describe the phase space, in particular to compute the number and length of all limit cycles of linear systems over a general finite field. This research has been partially supported by NIH Grant Nr. RO1GM068947-01. / Ph. D.

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