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Introduction
Nature is immensely complex, and many fundamental processes of development and disease are still unrevealed. In my group we develop and apply stochastic and statistical methods for analysing biomedical data, like stochastic differential equations or mixture models. In various contexts we gained knowledge from distributional assumptions and data uncertainty. To better understand causes of diseases may eventually improve human health. If you are interested in my research, please contact me!
Additional affiliations
January 2018 - present
October 2016 - March 2017
January 2016 - present
Education
January 2007 - March 2007
March 2005 - September 2010
September 2002 - September 2003
Publications
Publications (87)
Diffusion processes are a promising instrument for realistically modelling the time-continuous evolution of phenomena not only in the natural sciences but also in finance and economics. Their mathematical theory, however, is challenging, and hence diffusion modelling is often carried out incorrectly, and the according statistical inference is consi...
Significance
Cell-to-cell variations in gene regulation occur in a number of biological contexts, such as development and cancer. Discovering regulatory heterogeneities in an unbiased manner is difficult owing to the population averaging that is required for most global molecular methods. Here, we show that we can infer single-cell regulatory state...
Background:
Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an ope...
DNA methyltransferase 1 (Dnmt1) reestablishes methylation of hemimethylated CpG sites generated during DNA replication in mammalian cells. Two subdomains, the proliferating cell nuclear antigen (PCNA)-binding domain (PBD) and the targeting sequence (TS) domain, target Dnmt1 to the replication sites in S phase. We aimed to dissect the details of the...
In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when time-resolved experiments are performed under possibly several conditions. The time-resolved communication between such species can often be structured by reaction networ...
Introduction
The prognosis of patients with refractory or relapsed AML (R/R‐AML) is very limited. To (re)achieve complete remission, there has recently been increasing evidence that the combination of venetoclax (VEN) with chemotherapy is associated with improved outcomes.
Patients and Methods
Our retrospective, single‐center study of 53 R/R‐AML p...
Background
The shortage of rheumatologists in some areas is a main barrier for timely consultations for patients with a suspected inflammatory rheumatic disease (IRD). Late diagnosis and delayed effective anti-inflammatory therapy increase risk of joint or other organ damage. Chronic musculoskeletal pain is a major driver for rheumatological consul...
Arterial (ATE) and venous (VTE) thromboembolic complications are common causes of morbidity and mortality in BCR-ABL-negative myeloproliferative neoplasms (MPNs). However, there are few studies that include all MPN subtypes and focus on both MPN-associated ATE and VTE. In our single-center retrospective study of 832 MPN patients, a total of 180 fir...
Antibody studies analyze immune responses to SARS-CoV-2 vaccination and infection, which is crucial for selecting vaccination strategies. In the KoCo-Impf study, conducted between 16 June and 16 December 2021, 6088 participants aged 18 and above from Munich were recruited to monitor antibodies, particularly in healthcare workers (HCWs) at higher ri...
Background:
Population-based serological studies allow to estimate prevalence of SARS-CoV-2 infections despite a substantial number of mild or asymptomatic disease courses. This became even more relevant for decision making after vaccination started. The KoCo19 cohort tracks the pandemic progress in the Munich general population for over two years...
Background
Temporal artery biopsy (TAB) was the diagnostic reference standard for decades in giant cell arteritis (GCA). Evidence-based EULAR recommendations regarding the use of imaging techniques in diagnosing GCA have been published in 2018 [1]. Accordingly, the use of colour Doppler ultrasonography (CDUS) should “complement the clinical criteri...
Spatial proximity may facilitate scientific collaboration. We regress its impact within two German research institutions, defining collaboration strength and proximity by the number of joint publications and spatial distance between work places. The methodological focus lies on accounting for (i) the dependency structure in network data and (ii) ex...
Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even resu...
Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter...
Risk factors for disease progression and severity of SARS-CoV-2 infections require an understanding of acute and long-term virological and immunological dynamics. Fifty-one RT-PCR positive COVID-19 outpatients were recruited between May and December 2020 in Munich, Germany, and followed up at multiple defined timepoints for up to one year. RT-PCR a...
In patients with bcr‐abl‐negative myeloproliferative neoplasms (MPN), concerns are often raised about the use of anticoagulants because of an increased bleeding risk. However, there are few MPN studies focusing on bleeding. To investigate bleeding complications in MPN, we report our retrospective, single‐center study of 829 patients with a median f...
Wastewater-based epidemiology (WBE) is a tool now increasingly proposed to monitor the SARS-CoV-2 burden in populations without the need for individual mass testing. It is especially interesting in metropolitan areas where spread can be very fast, and proper sewage systems are available for sampling with short flow times and thus little decay of th...
Recently, there has been increased concern about a risk of secondary malignancies (SM) occurring in myelofibrosis (MF) patients receiving ruxolitinib (RUX). In polycythemia vera (PV), on the other hand, only limited data on the risk of SM under RUX treatment are available. To investigate the association between RUX therapy in PV and SM, we conducte...
Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even resu...
A number of seroassays are available for SARS-CoV-2 testing; yet, head-to-head evaluations of different testing principles are limited, especially using raw values rather than categorical data. In addition, identifying correlates of protection is of utmost importance, and comparisons of available testing systems with functional assays, such as dire...
Background:
In the 2nd year of the COVID-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemi...
Background
Quantitative serological assays detecting response to SARS-CoV-2 are needed to quantify immunity. This study analyzed the performance and correlation of two quantitative anti-S1 assays in oligo-/asymptomatic individuals from a population-based cohort.Methods
In total, 362 plasma samples (108 with reverse transcription-polymerase chain re...
Background
Adaptive immune responses to structural proteins of the virion play a crucial role in protection against coronavirus disease 2019 (COVID-19). We therefore studied T cell responses against multiple SARS-CoV-2 structural proteins in a large cohort using a simple, fast, and high-throughput approach.
Methods
An automated interferon gamma re...
Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter...
Background: In the 2nd year of the Covid-19 pandemic, knowledge about the dynamics of the infection in the general population is still limited. Such information is essential for health planners, as many of those infected show no or only mild symptoms and thus, escape the surveillance system. We therefore aimed to describe the course of the pandemic...
Given the large number of mild or asymptomatic SARS-CoV-2 cases, only population-based studies can provide reliable estimates of the magnitude of the pandemic. We therefore aimed to assess the sero-prevalence of SARS-CoV-2 in the Munich general population after the first wave of the pandemic. For this purpose, we drew a representative sample of 299...
Background
Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue.
Results
We present the R package stochprofML which uses the maximum likelihood principle to parameteriz...
Background. Serosurveys are essential to understand SARS-CoV-2 exposure and enable population-level surveillance, but currently available tests need further in-depth evaluation. We aimed to identify testing-strategies by comparing seven seroassays in a population-based cohort.
Methods. We analysed 6,658 samples consisting of true-positives (n=193),...
The analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote s...
Background
The asthma syndrome is influenced by hereditary and environmental factors. With the example of farm exposure, we study whether genetic and environmental factors interact for asthma.
Methods
Statistical learning approaches based on penalized regression and decision trees were used to predict asthma in the GABRIELA study with 850 cases (9...
A powerful tool in many areas of science, diffusion processes model random dynamical systems in continuous time. Parameters can be estimated from time-discretely observed diffusion processes using Markov chain Monte Carlo (MCMC) methods that introduce auxiliary data. These methods typically approximate the transition densities of the process numeri...
An amendment to this paper has been published and can be accessed via the original article.
Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. We introduce the \proglang{R} package \pkg{stochprofML} which is designed to parameterize heterogeneity from the cu...
This paper deals with the problem of inference associated with linear fractional diffusion process with random effects in the drift. In particular we are concerned with the maximum likelihood estimators (MLE) of the random effect parameters. First of all, we estimate the Hurst parameter H from one single subject. Second, assuming the Hurst index H...
State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switchi...
Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited...
Several tools analyze the outcome of single-cell RNA-seq experiments, and they often assume a probability distribution for the observed sequencing counts. It is an open question of which is the most appropriate discrete distribution, not only in terms of model estimation, but also regarding interpretability, complexity and biological plausibility o...
Background
Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high‐dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data...
We propose a nonparametric estimation for a class of fractional stochastic differential equations (FSDE) with random effects. We precisely consider general linear fractional stochastic differential equations with drift depending on random effects and non-random diffusion. We build ordinary kernel estimators and histogram estimators and study their...
We propose a nonparametric estimation for a class of fractional stochastic differential equations (FSDE) with random effects. We precisely consider general linear fractional stochastic differential equations with drift depending on random effects and non-random diffusion. We build ordinary kernel estimators and histogram estimators and study their...
Venous thromboembolism (VTE) is a major burden in patients with BCR-ABL-negative myeloproliferative neoplasms (MPN). In addition to cytoreductive treatment anticoagulation is mandatory, but optimal duration of anticoagulation is a matter of debate. In our single center study, we retrospectively included 526 MPN patients. In total, 78 of 526 MPN pat...
Aims and Methods Glucose homeostasis and energy balance are under control by peripheral and brain processes. Especially insulin signaling in the brain seems to impact whole body glucose homeostasis and interacts with fatty acid signaling. In humans circulating saturated fatty acids are negatively associated with brain insulin action while animal st...
A powerful tool in many areas of science, diffusion processes model random dynamical systems in continuous time. Parameters can be estimated from time-discretely observed diffusion processes using Markov chain Monte Carlo (MCMC) methods that introduce auxiliary data. These methods typically approximate the transition densities of the process numeri...
3D-culture systems have advanced cancer modeling by reflecting physiological characteristics of in-vivo tissues, but our understanding of functional intratumor heterogeneity including visual phenotypes and underlying gene expression is still limited. Single-cell RNA-sequencing is the method of choice to dissect transcriptional tumor cell heterogene...
Purpose:
Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge.
Patients and methods:
The comparator a...
Modelling biological associations or dependencies using linear regression is often complicated when the analysed data-sets are high-dimensional and less observations than variables are available (n ≪ p). For genomic data-sets penalized regression methods have been applied settling this issue. Recently proposed regression models utilize prior knowle...
Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear...
The file contains results for an additional simulation setting with predictor variables from different distribution families.
In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patie...
Background
The bacterial CRISPR system is fast becoming the most popular genetic and epigenetic engineering tool due to its universal applicability and adaptability. The desire to deploy CRISPR-based methods in a large variety of species and contexts has created an urgent need for the development of easy, time- and cost-effective methods enabling l...
p align="LEFT">In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different timescale, cancer registries typically r...
More than 50% of mammalian genomes consist of retrotransposon sequences. Silencing of retrotransposons by heterochromatin is essential to ensure genomic stability and transcriptional integrity. Here, we identified a short sequence element in intracisternal A particle (IAP) retrotransposons that is sufficient to trigger heterochromatin formation. We...
In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with...
The recent advent of conformation capture techniques has provided unprecedented insights into the spatial organization of chromatin. We present a large-scale investigation of the interchromosomal segment and gene contact networks in embryonic stem cells of two mammalian organisms: humans and mice. Both interaction networks are characterized by a hi...
Signals, recorded over time, are often observed as mixtures of multiple source signals. To extract relevant information from such measurements one needs to determine the mixing coefficients. In case of weakly stationary time series with uncorrelated source signals, this separation can be achieved by jointly diagonalizing sample autocovariances at d...
In biology, more and more information about the interactions in regulatory systems becomes accessible, and this often leads to prior knowledge for recent data interpretations. In this work we focus on multivariate signaling data, where the structure of the data is induced by a known regulatory network. To extract signals of interest we assume a bli...
Bayesian blind source separation applied to the lymphocyte pathway
Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum urate levels highly differ between men and women; however the underlying biological processes in its regulation are still not completely understood and are assumed to re...
Einzelzellanalysen: Biologie in Hochauflösung
Single-cell analysis: biology at high resolution
The amount of weight loss in obese children during lifestyle intervention differs strongly between individuals. The metabolic processes underlying this variability are largely unknown. We hypothesize that metabolomics analyses of serum samples might help to identify metabolic predictors of weight loss. In this study, we investigated 80 obese childr...
In real applications, diffusion models are often known in parametric form for which one wishes to estimate the model parameters. Statistical inference for diffusions is, however, challenging. The difficulty that underlies most approaches is the general intractability of the transition density for discrete-time observations. This chapter reviews fre...
Key mechanisms in life sciences can often be assessed by application of mathematical models. Moreover, real-world phenomena can particularly be captured when such a model allows for random events. This chapter motivates and reviews representative application fields from life sciences and appropriate mathematical models: for the spread of infectious...
Most frequentist techniques for parameter estimation in diffusion processes struggle when inter-observation times are large, which is often the case in life sciences. This chapter introduces Bayesian inference methods which estimate missing data such that the union of missing values and observations forms a high-frequency dataset. This facilitates...
The genetic material of humans and mammals is mainly contained in their cell nuclei, where most genome regulatory processes like DNA replication or transcription take place. These processes are controlled by complex protein networks.
Diffusion processes enable realistic and convenient modelling of dynamic systems. They typically arise as approximations of exact but computationally expensive individual-based stochastic models. However, the correct derivation of an appropriate diffusion approximation is often complicated, and hence their utilisation is not widely spread in the ap...
Stochastic modelling and statistical estimation are important tools for the understanding of complex processes in life sciences. This book motivated the use of diffusion processes for both purposes and contributed to their applicability in practice. This chapter summarises the achievements of this book and points out directions for future work.
As a first application of the methods introduced in the first two parts of this book, this chapter investigates the spread of human influenza. More precisely, it analyses a well-known dataset on an influenza outbreak in a British boarding school and the spatial spread of influenza in Germany during the season 2009/10, in which the swine flu virus w...