Institut für Angewandte Mathematik und Statistik

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/2

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Gut microbiota patterns predicting long-term weight loss success in individuals with obesity undergoing nonsurgical therapy
(2022) Bischoff, Stephan C.; Nguyen, Nguyen K.; Seethaler, Benjamin; Beisner, Julia; Kügler, Philipp; Stefan, Thorsten
The long-term success of nonsurgical weight reduction programs is variable; thus, predictors of outcome are of major interest. We hypothesized that the intestinal microbiota known to be linked with diet and obesity contain such predictive elements. Methods: Metagenome analysis by shotgun sequencing of stool DNA was performed in a cohort of 15 adults with obesity (mean body mass index 43.1 kg/m2) who underwent a one-year multidisciplinary weight loss program and another year of follow-up. Eight individuals were persistently successful (mean relative weight loss 18.2%), and seven individuals were not successful (0.2%). The relationship between relative abundancies of bacterial genera/species and changes in relative weight loss or body mass index was studied using three different statistical modeling methods. Results: When combining the predictor variables selected by the applied statistical modeling, we identified seven bacterial genera and eight bacterial species as candidates for predicting success of weight loss. By classification of relative weight-loss predictions for each patient using 2–5 term models, 13 or 14 out of 15 individuals were predicted correctly. Conclusions: Our data strongly suggest that gut microbiota patterns allow individual prediction of long-term weight loss success. Prediction accuracy seems to be high but needs confirmation by larger prospective trials.
Modelling and simulation for preclinical cardiac safety assessment of drugs with human iPSC-derived cardiomyocytes
(2020) Kügler, Philipp
As a potentially life threatening side effect, pharmaceutical compounds may trigger cardiac arrhythmias by impeding the heart’s electrical and mechanical function. For this reason, any new compound needs to be tested since 2005 for its proarrhythmic risk both during the preclinical and the clinical phase of the drug development process. While intensive monitoring of cardiac activity during clinical tests with human volunteers constitutes a major cost factor, preclinical in vitro tests with non cardiac cells and in vivo tests with animals are currently under serious debate because of their poor extrapolation to drug cardiotoxicity in humans. For about five years now, regulatory agencies, industry and academia are working on an overhaul of the cardiac drug safety paradigm that is built a) on human heart muscle cells, that can be abundantly bioengineered from donor stem cells without ethical concerns (human induced pluripotent stem cell derived cardiomyocytes, hiPSC-CMs), and b) on computational models of human cardiac electrophysiology both at the cellular and the organ level. The combined use of such human in vitro and human in silico models during the preclinical phase is expected to improve proarrhythmia test specificity (i.e. to lower the false-positive rate), to better inform about the need of thorough heart monitoring in the clinic, and to reduce or even replace animal experiments. This review article starts by concisely informing about the electrical activity of the human heart, about its possible impairment due to drug side effects, and about hiPSC-CM assays for cardiac drug safety testing. It then summarizes the mathematical description of human cardiac electrophysiology in terms of mechanistic ODE and PDE models, and illustrates how their numerical analysis may provide insight into the genesis of drug induced arrhythmias. Finally, this paper surveys proarrhythmic risk estimation methods, that involve the simulation of human heart muscle cells, and addresses opportunities and challenges for future interdisciplinary research.
On the structural analysis and optimal input design for joint state and parameter estimation
(2025) Lepsien, Arthur; Kügler, Philipp; Schaum, Alexander
This paper addresses the problem of joint state and parameter estimation for nonlinear affine-input systems with positive parameters including the design of a closed-loop optimal input adaptation to increase an identifiability measure for the system. The identifiability itself is considered in the context of structural observability of the system dynamics based on structural analysis of the system including the unknown parameters as additional states. In particular, the network graph-based interpretation of structural observability is employed at this point. This analysis motivates to include time derivatives of the measurements as additional system outputs to enhance the structural observability properties. For this purpose robust exact differentiation is considered, relying on the super-twisting algorithm to obtain finite time convergent estimates of these signals. Using the extended measurement signal, a continuous-discrete Extended Kalman filter is proposed that ensures strictly positive estimates for the parameters. Based on the estimates of states and parameters the input signal is determined using a moving horizon optimal predictive control that evaluates the condition number of the Fisher information matrix, thus maximizing the information content of the measurements with respect to the parameters. The proposed scheme extends and combines different previously discussed approaches from the literature and is evaluated by means of a thermal process example in simulation and experiment, showing high potential for similar system identification problems.

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