07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Institute: search.filters.UBSInstitut.Institut für Systemtheorie und RegelungstechnikStart date: 2021

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Now showing 1 - 10 of 16
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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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    Analysis and design of MPC frameworks for dynamic operation of nonlinear constrained systems
    (2021) Köhler, Johannes; Allgöwer, Frank (Prof. Dr.-Ing.)
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    Physics-informed regression of implicitly-constrained robot dynamics
    (2022) Geist, Andreas René; Allgöwer, Frank (Prof. Dr.-Ing.)
    The ability to predict a robot’s motion through a dynamics model is critical for the development of fast, safe, and efficient control algorithms. Yet, obtaining an accurate robot dynamics model is challenging as robot dynamics are typically nonlinear and subject to environment-dependent physical phenomena such as friction and material elasticities. The respective functions often cause analytical dynamics models to have large prediction errors. An alternative approach to analytical modeling forms the identification of a robot’s dynamics through data-driven modeling techniques such as Gaussian processes or neural networks. However, solely data-driven algorithms require considerable amounts of data, which on a robotic system must be collected in real-time. Moreover, the information stored in the data as well as the coverage of the system’s state space by the data is limited by the controller that is used to obtain the data. To tackle the shortcomings of analytical dynamics and data-driven modeling, this dissertation investigates and develops models in which analytical dynamics is being combined with data-driven regression techniques. By combining prior structural knowledge from analytical dynamics with data-driven regression, physics-informed models show improved data-efficiency and prediction accuracy compared to using the aforementioned modeling techniques in an isolated manner.
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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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    Dynamics of systems with a discontinuous hysteresis operator and interval translation maps
    (2021) Kryzhevich, Sergey; Avrutin, Viktor; Begun, Nikita; Rachinskii, Dmitrii; Tajbakhsh, Khosro
    We studied topological and metric properties of the so-called interval translation maps (ITMs). For these maps, we introduced the maximal invariant measure and study its properties. Further, we study how the invariant measures depend on the parameters of the system. These results were illustrated by a simple example or a risk management model where interval translation maps appear naturally.
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    Bifurcations of hidden orbits in discontinuous maps
    (2021) Avrutin, Viktor; Jeffrey, Mike R.
    One-dimensional maps with discontinuities are known to exhibit bifurcations somewhat different to those of continuous maps. Freed from the constraints of continuity, and hence from the balance of stability that is maintained through fold, flip, and other standard bifurcations, the attractors of discontinuous maps can appear as if from nowhere, and change period or stability almost arbitrarily. But in fact this is misleading, and if one includes states inside the discontinuity in the map, highly unstable ‘hidden orbits’ are created that have iterates on the discontinuity. These populate the bifurcation diagrams of discontinuous maps with just the necessary unstable branches to make them resemble those of continuous maps, namely fold, flip, and other familiar bifurcations. Here we analyse such bifurcations in detail, focussing first on folds and flips, then on bifurcations characterised by creating infinities of orbits, chaotic repellers, and infinite accumulations of sub-bifurcations. We show the role that hidden orbits play, and how they capture the topological structures of continuous maps with steep branches. This suggests both that a more universal dynamical systems theory marrying continuous and discontinuous systems is possible, and shows how discontinuities can be used to approximate steep jumps in continuous systems without losing any of their topological structure.
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    A statistical framework to optimize experimental design for inference problems in systems biology based on normalized data
    (2022) Thomaseth, Caterina; Radde, Nicole (Prof. Dr. rer. nat.)
    Inference problems in Systems Biology are primarily based on the theoretical assumption that a measured dataset comprises noisy realizations following some underlying stochastic distribution, having well-defined statistical properties. This uncertainty in the input quantities propagates through the inference process, influences the uncertainty of the estimated model parameters and subsequently affects the quality and reliability of model predictions. Understanding the mechanisms of noise propagation over an inference problem will therefore be instrumental in designing an optimal and robust experimental protocol to reduce the uncertainty of the estimated quantities of interest. This thesis investigates the underlying mechanisms of noise propagation from measured experimental data to estimated parameters by developing a statistical framework to characterize and analyse non-linear transformations of stochastic distributions. Among such non-linear transformations, data normalization, a required step for some common experimental techniques, requires specific attention, representing an additional modification of noise properties. Mathematically, the normalization step translates into ratios of two distributions. We consider standard assumptions on the distributions associated with biological raw data. In this thesis we explore three specific classes of inference problems relevant for Systems Biology applications. At first we consider the problem of statistical inference of different parametrized error models for normalized data. Subsequently, we investigate the effect of such error models when coupled with different normalization strategies on results of parameter estimation for dynamic models of biochemical reaction networks. We conclude this thesis by analysing the effects of noise propagation on Modular Response Analysis based network reconstruction. From our simulation results, we observe that non-linear noise transformations may lead to very uncertain and/or erroneous inference results. Additionally, based on the quantification of statistical measures for accuracy and precision of the inference results, we derive practical advice for an optimized and robust experimental design in order to reduce the uncertainty of the estimated quantities.
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    Hepatectomy-induced alterations in hepatic perfusion and function : toward multi-scale computational modeling for a better prediction of post-hepatectomy liver function
    (2021) Christ, Bruno; Collatz, Maximilian; Dahmen, Uta; Herrmann, Karl-Heinz; Höpfl, Sebastian; König, Matthias; Lambers, Lena; Marz, Manja; Meyer, Daria; Radde, Nicole; Reichenbach, Jürgen R.; Ricken, Tim; Tautenhahn, Hans-Michael
    Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.