Universität Stuttgart
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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 Indefinite linear quadratic optimal control: periodic dissipativity and turnpike properties(2018) Berberich, JulianItem Open Access Analysis and design of MPC frameworks for dynamic operation of nonlinear constrained systems(2021) Köhler, Johannes; Allgöwer, Frank (Prof. Dr.-Ing.)Item Open Access Understanding the mechanisms of robustness in intracellular protein signalling cascades and gene expression(2018) Paul, Debdas; Radde, Nicole (Prof. Dr. rer. nat.)We seek to understand the structural as well as the mechanistic basis of robustness in intracellular protein signalling cascades and in transcriptional regulation of gene expression. For protein signalling cascades, we employ a comparison based study involving a single, a double and a cascade of two double phosphorylation-dephosphorylation (PD) cycles. Using deterministic modelling approaches based on ordinary differential equations (ODE), we observe that the cascade of two double PD cycles exhibits robust output behaviour compared to that of a single and a double PD cycle upon constant as well as time- varying input perturbations. Furthermore, a system theoretic analysis reveals that the protein phosphorylation cascades act as an efficient low-pass filter that attenuates the noise mimicked as high-frequency input signals. Afterwards, we extend the study for a stochastic environment. Simulation results based on the stochastic simulation algorithm (SSA) reveal a novel phenomenon called dynamic sequestration that plays an ambivalent role as an intrinsic noise filter. Overall, the analysis indicates that complexity can be one of the basic principles of robust biological designs such as intracellular protein signalling cascades. A major function of intracellular signalling cascades is to transmit the extracellular signal to the nucleus to initiate the process of gene expression. Gene expression is an intrinsically stochastic process that results into cell-to-cell variability in protein and messenger RNA (mRNA) levels, often termed as the expression noise. In spite of such noise, how cells achieve robustness is therefore a fundamental biological problem. We conclude the thesis by introducing a rule-based modelling approach based on the Kappa (κ) platform with the goal to understand the underlying mechanisms that ensure robust cellular functioning during gene expression. In particular, we introduce a gene expression model that keeps the process of transcription and excludes the process of translation. Therefore, we quantify the expression noise using mRNA which is the end product of transcription. Besides, the motivation behind adopting a rule-based modelling approach is that unlike the ODE-based approach, the former subsumes the combinatorial complexity arises due to various binding configurations of transcription factors (TF) for regulation of gene expression and offers a compact graphical representation of the same. Afterwards, the representation is transformed into an equivalent set of executable κ rules that are simulated using the SSA to obtain distributions of mRNA copy numbers corresponding to different regulatory mechanisms.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 Identification of models of heterogeneous cell populations from population snapshot data(2011) Hasenauer, Jan; Waldherr, Steffen; Doszczak, Malgorzata; Radde, Nicole; Scheurich, Peter; Allgöwer, FrankBackground: 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.Item Open Access Multivariable controller design for an industrial distillation column(1992) Allgöwer, Frank; Raisch, JörgIn this paper, we report our experience with the design of linear multivariable controllers for a staged binary distillation column with 40 trays. Controller design methods include H∞-minimization, DNA-design, CL-design and H2minimization. Results obtained at the real plant are shown.Item Open Access A systems science view on cell death signalling(2007) Eißing, Thomas; Allgöwer, Frank (Prof. Dr.-Ing.)This thesis provides new insight into cellular signal transduction by integrating biological knowledge into mathematical models, which are subsequently analysed using systems theoretic methods. Signal transduction has been dissected using molecular and genomic approaches providing exciting insight into the biochemistry of life. However, a detailed understanding of its dynamic properties remains elusive. The application of systems science ideas to biology is promising to put the pieces of molecular information back together, as important properties of life arise at the system level only. For example, certain signalling pathways convert graded input signals into all-or-none output signals constituting biological switches. These are implicated in cellular memory and decisions. One such decision is whether or not to undergo programmed cell death (apoptosis). Apoptosis is an important physiological process crucially involved in the development and homoeostasis of multicellular organisms. Switches, such as in apoptosis, can be represented by ordinary differential equation models showing bistable behaviour. Different biochemical mechanisms generating bistability in reaction schemes as encountered in apoptosis are presented and compared in this thesis. Bifurcation studies reveal structural and parametric requirements for bistability. In combination with reported kinetic information, inconsistencies in the literature view of apoptosis signalling in humans are revealed. An additional regulatory mechanism is proposed, which is now supported by experimental evidence. Extended robustness analyses indicate that the cell has achieved a favourable robustness-performance trade-off, imposed by network structure and evolutionary constraints. On the one hand, inhibitors of apoptosis function as noise filters and reduce variability caused by the stochastic nature of reactions. Further, qualitative properties such as bistability are comparably robust to parameter changes supporting proper decisions. On the other hand, quantitative aspects are comparably sensitive. This allows for variability in a population, as observed in experiments, and which is likely important for physiological function as recently indicated in immunological studies. The analyses further indicate that the trade-off leads to fragilities. For example, an up-regulation of inhibitors of apoptosis, as observed in certain cancers, can not only desensitise cells to apoptotic stimuli, as also suggested by experimental studies, but can contribute to cancer aggressiveness and progression through additional mechanisms. Thereby, the analyses provide insight of pharmaceutical relevance. Several results presented in this thesis are not restricted to apoptosis signalling only, but are conceptually relevant to various other signal transduction pathways.Item Open Access The physics behind systems biology(2016) Radde, Nicole; Hütt, Marc-ThorstenSystems 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.Item Open Access 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.