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Institute: search.filters.UBSInstitut.Institut für Systemtheorie und RegelungstechnikPublication Type: search.filters.UBSTyp.Zeitschriftenartikel

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    ROSIE : RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data
    (2022) Jensch, Antje; Lopes, Marta B.; Vinga, Susana; Radde, Nicole
    The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy. Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods. We apply ROSIE to RNA-Seq data from The Cancer Genome Atlas (TCGA) to classify observations into Triple-Negative Breast Cancer (TNBC) and non-TNBC tissue samples. The pre-processed dataset consists of 16,600 genes and more than 1,000 samples. We demonstrate that ROSIE selects important features and outliers in a robust way. Identified outliers are concordant with the distribution of the commonly selected genes by the three methods, and results are in line with other independent studies. Furthermore, we discuss the association of some of the selected genes with the TNBC subtype in other investigations. In summary, ROSIE constitutes a robust and sparse procedure to identify outliers and important genes through binary classification. Our approach is ad hoc applicable to other datasets, fulfilling the overall goal of simultaneously identifying outliers and candidate disease biomarkers to the targeted in therapy research and personalized medicine frameworks.
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    Modeling of biocatalytic reactions: a workflow for model calibration, selection, and validation using Bayesian statistics
    (2019) Eisenkolb, Ina; Jensch, Antje; Eisenkolb, Kerstin; Kramer, Andrei; Buchholz, Patrick C. F.; Pleiss, Jürgen; Spiess, Antje; Radde, Nicole
    We present a workflow for kinetic modeling of biocatalytic reactions which combines methods from Bayesian learning and uncertainty quantification for model calibration, model selection, evaluation, and model reduction in a consistent statistical frame-work. Our workflow is particularly tailored to sparse data settings in which a considerable variability of the parameters remains after the models have been adapted to available data, a ubiquitous problem in many real-world applications. Our workflow is exemplified on an enzyme-catalyzed two-substrate reaction mechanism describing the symmetric carboligation of 3,5-dimethoxy-benzaldehyde to (R)-3,3',5,5'-tetramethoxybenzoin catalyzed by benzaldehyde lyase from Pseudomonas fluorescens. Results indicate a substrate-dependent inactivation of enzyme, which is in accordance with other recent studies.
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    Identification of models of heterogeneous cell populations from population snapshot data
    (2011) Hasenauer, Jan; Waldherr, Steffen; Doszczak, Malgorzata; Radde, Nicole; Scheurich, Peter; Allgöwer, Frank
    Background: Most of the modeling performed in the area of systems biology aims at achieving a quantitative description of the intracellular pathways within a "typical cell". However, in many biologically important situations even clonal cell populations can show a heterogeneous response. These situations require study of cell-to-cell variability and the development of models for heterogeneous cell populations. Results: In this paper we consider cell populations in which the dynamics of every single cell is captured by a parameter dependent differential equation. Differences among cells are modeled by differences in parameters which are subject to a probability density. A novel Bayesian approach is presented to infer this probability density from population snapshot data, such as flow cytometric analysis, which do not provide single cell time series data. The presented approach can deal with sparse and noisy measurement data. Furthermore, it is appealing from an application point of view as in contrast to other methods the uncertainty of the resulting parameter distribution can directly be assessed. Conclusions: The proposed method is evaluated using artificial experimental data from a model of the tumor necrosis factor signaling network. We demonstrate that the methods are computationally efficient and yield good estimation result even for sparse data sets.
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    The physics behind systems biology
    (2016) Radde, Nicole; Hütt, Marc-Thorsten
    Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from ist Theoretical Physics foundation.
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    Mathematical modeling of the pituitary-thyroid feedback loop: role of a TSH-T3-shunt and sensitivity analysis
    (2018) Berberich, Julian; Dietrich, Johannes W.; Hoermann, Rudolf; Müller, Matthias A.
    Despite significant progress in assay technology, diagnosis of functional thyroid disorders may still be a challenge, as illustrated by the vague upper limit of the reference range for serum thyrotropin (TSH). Diagnostical problems also apply to subjects affected by syndrome T, i.e. those 10% of hypothyroid patients who continue to suffer from poor quality of life despite normal TSH concentrations under substitution therapy with levothyroxine (L-T4 ). In this paper, we extend a mathematical model of the pituitary-thyroid feedback loop in order to improve the understanding of thyroid hormone homeostasis. In particular, we incorporate a TSH-T3 –shunt inside the thyroid, whose existence has recently been demonstrated in several clinical studies. The resulting extended model shows good accordance with various clinical observations, such as a circadian rhythm in free peripheral triiodothyronine (FT3). Furthermore, we perform a sensitivity analysis of the derived model, revealing the dependence of TSH and hormone concentrations on different system parameters. The results have implications for clinical interpretation of thyroid tests, e.g. in the differential diagnosis of subclinical hypothyroidism.
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    Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines
    (2017) Jensch, Antje; Thomaseth, Caterina; Radde, Nicole
    Positive and negative feedback loops are ubiquitous motifs in biochemical signaling pathways. The mitogen-activated protein kinase (MAPK) pathway module is part of many distinct signaling networks and comprises several of these motifs, whose functioning depends on the cell line at hand and on the particular context. The maintainance of specificity of the response of the MAPK module to distinct stimuli has become a key paradigm especially in PC-12 cells, where the same module leads to different cell fates, depending on the stimulating growth factor. This cell fate is regulated by differences in the ERK (MAPK) activation profile, which shows a transient response upon stimulation with EGF, while the response is sustained in case of NGF. This behavior was explained by different effective network topologies. It is widely believed that this sustained response requires a bistable system. In this study we present a sampling-based Bayesian model analysis on a dataset, in which PC-12 cells have been stimulated with different growth factors. This is combined with novel analysis methods to investigate the role of feedback interconnections to shape ERK response. Results strongly suggest that, besides bistability, an additional effect called quasi-bistability can contribute to explain the observed responses of the system to different stimuli. Quasi-bistability is the ability of a monostable system to maintain two distinct states over a long time period upon a transient signal, which is also related to positive feedback, but cannot be detected by standard steady state analysis methods.
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    Moment dynamics of Zirconia particle formation for optimizing particle size distribution
    (2019) Halter, Wolfgang; Eisele, Rahel; Rothenstein, Dirk; Bill, Joachim; Allgöwer, Frank
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    SiCaSMA : an alternative stochastic description via concatenation of Markov processes for a class of catalytic systems
    (2021) Wagner, Vincent; Radde, Nicole Erika
    The Chemical Master Equation is a standard approach to model biochemical reaction networks. It consists of a system of linear differential equations, in which each state corresponds to a possible configuration of the reaction system, and the solution describes a time-dependent probability distribution over all configurations. The Stochastic Simulation Algorithm (SSA) is a method to simulate sample paths from this stochastic process. Both approaches are only applicable for small systems, characterized by few reactions and small numbers of molecules. For larger systems, the CME is computationally intractable due to a large number of possible configurations, and the SSA suffers from large reaction propensities. In our study, we focus on catalytic reaction systems, in which substrates are converted by catalytic molecules. We present an alternative description of these systems, called SiCaSMA, in which the full system is subdivided into smaller subsystems with one catalyst molecule each. These single catalyst subsystems can be analyzed individually, and their solutions are concatenated to give the solution of the full system. We show the validity of our approach by applying it to two test-bed reaction systems, a reversible switch of a molecule and methyltransferase-mediated DNA methylation.
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    Schneller sehen durch Regelungstechnik - Moderne Bildgebung in der Nanotechnologie
    (2004) Rieber, Jochen M.; Allgöwer, Frank; Stemmer, Andreas
    Die Nanotechnologie, also die Wissenschaft von Untersuchungen und Manipulationen im Nanometermaßstab, wird als eine der Schlüsseltechnologien des 21. Jahrhunderts angesehen. Wichtige Komponenten dieses aktuellen Forschungszweiges sind das Rastertunnelmikroskop und das Rasterkraftmikroskop, die den Blick in die Nanowelt erlauben. Dieser Artikel gibt einen einführenden Einblick in die Bildgebung der Nanowelt und versucht, den Beitrag von Systemtheorie und Regelungstechnik für die Nanotechnologie anhand der Regelung von Rasterkraftmikroskopen allgemeinverständlich zu beschreiben.
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    Kinetic instabilities in heterogeneously catalyzed reactions. Pt. 1, Rate multiplicity with Langmuir-type kinetics
    (1978) Eigenberger, Gerhart
    This paper is concerned with the occurrence of rate multiplicities (ignition-extinction phenomena). It is shown that rate multiplicity can be caused by the competing chemisorption of A and B upon the same active sites of the catalyst. Rate multiplicity can occur if at least one of the chemisorption or reaction steps is of second order. No rate multiplicity can be expected, if either A or B reacts via an Eley-Rideal type mechanism.