07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8
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Item Open Access ROSIE : RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data(2022) Jensch, Antje; Lopes, Marta B.; Vinga, Susana; Radde, NicoleThe 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.Item Open Access 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, NicoleWe 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.Item Open Access Methodological concepts for data-integrated modeling of biological systems with applications in cancer biology(2023) Jensch, Antje; Radde, Nicole (Prof. Dr. rer. nat.)Item Open Access 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.Item Open Access 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-MichaelLiver 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.Item Open Access Editorial - computational modeling for liver surgery and interventions(2022) Christ, Bruno; Dahmen, Uta; Radde, Nicole; Ricken, TimItem Open Access Probabilistic modelling of population variability(2025) Wagner, Vincent; Radde, Nicole (Prof. Dr. rer. nat.)Vincent Wagner's dissertation summarises progress in the probabilistic modelling of population variability. It comprises two chapters with complementary approaches to this challenging and broad topic. The first chapter deals with the Method of Moments for the Chemical Master Equation, while the second chapter uses random variable transformations to estimate distributed simulation model parameters.Item Open Access Bayesian estimation reveals that reproducible models in systems biology get more citations(2023) Höpfl, Sebastian; Pleiss, Jürgen; Radde, Nicole E.The Systems Biology community has taken numerous actions to develop data and modeling standards towards FAIR data and model handling. Nevertheless, the debate about incentives and rewards for individual researchers to make their results reproducible is ongoing. Here, we pose the specific question of whether reproducible models have a higher impact in terms of citations. Therefore, we statistically analyze 328 published models recently classified by Tiwari et al. based on their reproducibility. For hypothesis testing, we use a flexible Bayesian approach that provides complete distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e., 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that differences in journal impact factors do not explain this effect and that this effect increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates the long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidence for the increased use of reproducible models in the scientific community.