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  • 1
    Book
    Book
    Berlin : Zentralinst. für Kybernetik und Informationsprozesse der Akad. der Wiss. der DDR
    UID:
    gbv_013209590
    Format: 101 S , graph. Darst
    Series Statement: ZKI Informationen 1987,1
    Language: German
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  • 2
    UID:
    b3kat_BV025996316
    Format: 24 S. , graph. Darst.
    Series Statement: Arbeitspapiere der GMD 1100
    Language: Undetermined
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  • 3
    UID:
    gbv_865651302
    Format: 164 Seiten , Illustrationen, Diagramme
    Content: The cytoskeleton is an essential component of living cells. It is composed of different types of protein filaments that form complex, dynamically rearranging, and interconnected networks. The cytoskeleton serves a multitude of cellular functions which further depend on the cell context. In animal cells, the cytoskeleton prominently shapes the cell's mechanical properties and movement. In plant cells, in contrast, the presence of a rigid cell wall as well as their larger sizes highlight the role of the cytoskeleton in long-distance intracellular transport. As it provides the basis for cell growth and biomass production, cytoskeletal transport in plant cells is of direct environmental and economical relevance. However, while knowledge about the molecular details of the cytoskeletal transport is growing rapidly, the organizational principles that shape these processes on a whole-cell level remain elusive.This thesis is devoted to the following question: How does the complex architecture of the plant cytoskeleton relate to its transport functionality? The answer requires a systems level perspective of plant cytoskeletal structure and transport. To this end, I combined state-of-the-art confocal microscopy, quantitative digital image analysis, and mathematically powerful, intuitively accessible graph-theoretical approaches. This thesis summarizes five of my publications that shed light on the plant cytoskeleton as a transportation network: (1) I developed network-based frameworks for accurate, automated quantification of cytoskeletal structures, applicable in, e.g., genetic or chemical screens; (2) I showed that the actin cytoskeleton displays properties of efficient transport networks, hinting at its biological design principles; (3) Using multi-objective optimization, I demonstrated that different plant cell types sustain cytoskeletal networks with cell-type specific and near-optimal organization; (4) By investigating actual transport of organelles through the cell, I showed that properties of the actin cytoskeleton are predictive of organelle flow and provided quantitative evidence for a coordination of transport at a cellular level; (5) I devised a robust, optimization-based method to identify individual cytoskeletal filaments from a given network representation, allowing the investigation of single filament properties in the network context. The developed methods were made publicly available as open-source software tools.Altogether, my findings and proposed frameworks provide quantitative, system-level insights into intracellular transport in living cells. Despite my focus on the plant cytoskeleton, the established combination of experimental and theoretical approaches is readily applicable to different organisms. Despite the necessity of detailed molecular studies, only a complementary, systemic perspective, as presented here, enables both understanding of cytoskeletal function in its evolutionary context as well as its future technological control and utilization.
    Note: Enthält 4 Publikationen , kumulative Dissertation , Dissertation Universität Potsdam 2016
    Additional Edition: Erscheint auch als Online-Ausgabe Breuer, David The plant cytoskeleton as a transportation network Potsdam, 2016
    Language: English
    Keywords: Zellskelett ; Hochschulschrift
    Author information: Selbig, Joachim
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  • 4
    UID:
    gbv_845991132
    Format: xv, 136 Seiten , Illustrationen
    Content: Metabolic systems tend to exhibit steady states that can be measured in terms of their concentrations and fluxes. These measurements can be regarded as a phenotypic representation of all the complex interactions and regulatory mechanisms taking place in the underlying metabolic network. Such interactions determine the system's response to external perturbations and are responsible, for example, for its asymptotic stability or for oscillatory trajectories around the steady state. However, determining these perturbation responses in the absence of fully specified kinetic models remains an important challenge of computational systems biology. Structural kinetic modeling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a parameterised representation of the system's Jacobian matrix in which the model parameters encode information about the enzyme-metabolite interactions. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. The parameter space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Because the sampled parameters are equivalent to the elasticities used in metabolic control analysis (MCA), the results are easy to interpret biologically. In this project, the SKM framework was extended by several novel methodological improvements. These improvements were evaluated in a simulation study using a set of small example pathways with simple Michaelis Menten rate laws. Afterwards, a detailed analysis of the dynamic properties of the neuronal TCA cycle was performed in order to demonstrate how the new insights obtained in this work could be used for the study of complex metabolic systems. The first improvement was achieved by examining the biological feasibility of the elasticity combinations created during Monte Carlo sampling. Using a set of small example systems, the findings showed that the majority of sampled SK-models would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion was formulated that mitigates such infeasible models and the application of this criterion changed the conclusions of the SKM experiment. The second improvement of this work was the application of supervised machine-learning approaches in order to analyse SKM experiments. So far, SKM experiments have focused on the detection of individual enzymes to identify single reactions important for maintaining the stability or oscillatory trajectories. In this work, this approach was extended by demonstrating how SKM enables the detection of ensembles of enzymes or metabolites that act together in an orchestrated manner to coordinate the pathways response to perturbations. In doing so, stable and unstable states served as class labels, and classifiers were trained to detect elasticity regions associated with stability and instability. Classification was performed using decision trees and relevance vector machines (RVMs). The decision trees produced good classification accuracy in terms of model bias and generalizability. RVMs outperformed decision trees when applied to small models, but encountered severe problems when applied to larger systems because of their high runtime requirements. The decision tree rulesets were analysed statistically and individually in order to explore the role of individual enzymes or metabolites in controlling the system's trajectories around steady states. The third improvement of this work was the establishment of a relationship between the SKM framework and the related field of MCA. In particular, it was shown how the sampled elasticities could be converted to flux control coefficients, which were then investigated for their predictive information content in classifier training. After evaluation on the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle with respect to their intrinsic mechanisms responsible for stability or instability. The findings showed that several elasticities were jointly coordinated to control stability and that the main source for potential instabilities were mutations in the enzyme alpha-ketoglutarate dehydrogenase.
    Note: Dissertation Mathematisch-Naturwissenschaftliche Fakultät der Universität Potsdam 2014
    Additional Edition: Online-Ausgabe Erscheint auch als
    Language: English
    Keywords: Hochschulschrift
    Author information: Selbig, Joachim
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  • 5
    UID:
    gbv_1735361011
    Format: vii, 81, xxxii Seiten , Illustrationen, Diagramme
    Content: Since half a century, cytometry has been a major scientific discipline in the field of cytomics - the study of system’s biology at single cell level. It enables the investigation of physiological processes, functional characteristics and rare events with proteins by analysing multiple parameters on an individual cell basis. In the last decade, mass cytometry has been established which increased the parallel measurement to up to 50 proteins. This has shifted the analysis strategy from conventional consecutive manual gates towards multi-dimensional data processing. Novel algorithms have been developed to tackle these high-dimensional protein combinations in the data. They are mainly based on clustering or non-linear dimension reduction techniques, or both, often combined with an upstream downsampling procedure. However, these tools have obstacles either in comprehensible interpretability, reproducibility, computational complexity or in comparability between samples and groups. To address this bottleneck, a reproducible, semi-automated ...
    Note: Dissertation Universität Potsdam 2019
    Additional Edition: Erscheint auch als Online-Ausgabe Hoang, Yen De novo binning strategy to analyze and visualize multi-dimensional cytometric data Potsdam, 2019
    Language: English
    Keywords: Cytometrie ; Hochschulschrift
    Author information: Baumgrass, Ria 1958-
    Author information: Selbig, Joachim
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  • 6
    UID:
    b3kat_BV024925266
    Format: 101 S. , Ill.
    Note: Berlin, Akad. der Wiss., Diss. A, 1988
    Language: German
    Keywords: Maschinelles Lernen ; Hochschulschrift
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  • 7
    UID:
    gbv_019709730
    Format: 62 S. , graph. Darst.
    Series Statement: Arbeitspapiere der GMD 656
    Language: German
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  • 8
    UID:
    gbv_893956007
    Format: 119 Seiten , Illustrationen, Diagramme
    Note: Abweichendes Erscheinungsjahr auf der Titelseite: 2016 , Dissertation Universität Potsdam, Mathematisch-Naturwissenschaftliche Fakultät 2017
    Language: German
    Keywords: Bakteriophagen ; Proteine ; Hochschulschrift
    Author information: Selbig, Joachim
    Author information: Heinle, Karolin
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  • 9
    UID:
    gbv_846060043
    Format: xxvii, 123 Seiten , Illustrationen
    Content: Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.
    Note: En Cotutelle international avec Universität Potsdam, Allemagne (Promotionsverfahren in zwei Ländern) , Dissertation Université de Rennes 1 2014 , Dissertation Universität Potsdam 2014
    Additional Edition: Erscheint auch als Online-Ausgabe Videla, Santiago Reasoning on the response of logical signaling networks with Answer Set Programming Rennes, 2014
    Language: English
    Keywords: Hochschulschrift
    Author information: Selbig, Joachim
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  • 10
    UID:
    gbv_543999718
    Format: 199 S. , Ill., graph. Darst.
    ISBN: 3885792095 , 9783885792093
    Series Statement: GI-Edition 115
    Note: Literaturangaben
    Additional Edition: Online-Ausg. German Conference on Bioinformatics Bonn : Ges. für Informatik, 2007 ISBN 9783885792093
    Language: English
    Subjects: Computer Science , Biology
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Bioinformatik ; Konferenzschrift
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