A5082656548- Markov Chains and Monte Carlo Methods
- Gaussian Processes and Bayesian Inference
- Gene Regulatory Network Analysis
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
- Microbial Metabolic Engineering and Bioproduction
- COVID-19 epidemiological studies
- Bayesian Methods and Mixture Models
- Target Tracking and Data Fusion in Sensor Networks
- SARS-CoV-2 detection and testing
- Control Systems and Identification
- Statistical Methods and Bayesian Inference
- Mass Spectrometry Techniques and Applications
- Advanced Control Systems Optimization
- Neural Networks and Applications
- Mathematical Biology Tumor Growth
- Complex Network Analysis Techniques
- Statistical Methods and Inference
- Stochastic processes and statistical mechanics
- Probabilistic and Robust Engineering Design
- Protein Structure and Dynamics
- Numerical methods for differential equations
- Simulation Techniques and Applications
- Evolution and Genetic Dynamics
- Liver Disease Diagnosis and Treatment
Helmholtz Zentrum München
2018-2024
Technical University of Munich
2019-2024
University of Bonn
2022-2024
Ludwig-Maximilians-Universität München
2021
Roche Pharma AG (Germany)
2021
Center for Environmental Health
2019-2021
German Center for Infection Research
2021
Universität der Bundeswehr München
2021
Institut für Mikrobiologie der Bundeswehr
2021
Reproducibility and reusability of the results data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for specification parameter estimation problems in systems biology. Here, we introduce PEtab, a which facilitates using Systems Biology Markup Language (SBML) models set tab-separated value files describing observation model experimental data as well parameters to be estimated. We already implemented PEtab support into eight well-established...
Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large comprehensive models, computational cost simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation sensitivity analysis routines tailored scalable, gradient-based parameter estimation uncertainty quantification.AMICI published under permissive BSD-3-Clause license...
Given the large number of mild or asymptomatic SARS-CoV-2 cases, only population-based studies can provide reliable estimates magnitude pandemic. We therefore aimed to assess sero-prevalence in Munich general population after first wave For this purpose, we drew a representative sample 2994 private households and invited household members 14 years older complete questionnaires blood samples. seropositivity was defined as Roche N pan-Ig ≥ 0.4218. adjusted prevalence for sampling design,...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. pyPESTO is a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, broadly applicable black-box problems. Besides own implementations, it provides unified interface...
Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need be inferred. This renders efficient reliable integration methods essential. However, these depend on hyperparameters, which strongly impact the ODE solution. Despite...
Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding biological processes and integration heterogeneous datasets. However, some require consideration comprehensive therefore large-scale models. Parameter estimation for such poses great challenges, in particular when data are on a relative scale. Results Here, we propose novel hierarchical approach combining (i) efficient analytic evaluation optimal scaling, offset error model...
A number of seroassays are available for SARS-CoV-2 testing; yet, head-to-head evaluations different testing principles limited, especially using raw values rather than categorical data. In addition, identifying correlates protection is utmost importance, and comparisons systems with functional assays, such as direct viral neutralisation, needed.We analysed 6658 samples consisting true-positives ( n =193), true-negatives =1091), specimens unknown status =5374). For primary testing, we used...
Abstract Background In the 2nd year of COVID-19 pandemic, knowledge about dynamics infection in general population is still limited. Such information essential for health planners, as many those infected show no or only mild symptoms and thus, escape surveillance system. We therefore aimed to describe course pandemic Munich living private households from April 2020 January 2021. Methods The KoCo19 baseline study took place June including 5313 participants (age 14 years above). From November...
The Python package pyABC provides a framework for approximate Bayesian computation (ABC), likelihood-free parameter inference method popular in many research areas.At its core, it implements sequential Monte-Carlo (SMC) scheme, with various algorithms to adapt the problem structure and automatically tune hyperparameters.To scale computationally expensive problems, efficient parallelization strategies multi-core distributed systems.The is highly modular designed be easily usable.In this major...
drawing conclusions from probabilistic models as part of principled workflows for data analysis (Bürkner et al., 2022;Gelman 2020;Schad 2021).Typical problems in Bayesian are the approximation intractable posterior distributions diverse model types and comparison competing same process terms their complexity predictive performance.However, despite theoretical appeal utility, practical execution is often limited by computational bottlenecks: Obtaining even a single may already take long time,...
Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error often not clear. It has been shown that ABC actually gives exact under implicit assumption a measurement noise model. Noise being common biological systems, intriguing to exploit this insight. But difficult practice, general highly...
Risk factors for disease progression and severity of SARS-CoV-2 infections require an understanding acute long-term virological immunological dynamics. Fifty-one RT-PCR positive COVID-19 outpatients were recruited between May December 2020 in Munich, Germany, followed up at multiple defined timepoints to one year. viral culture performed seroresponses measured. Participants classified applying the WHO clinical scale. Short symptom test time (median 5.0 days; p = 0.0016) high loads (VL;...
Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most are based on officially reported case numbers, which depend test availability strategies. The time dependence of these factors renders interpretation difficult might even result in estimation biases. Here, we present a computational modelling framework that allows integration numbers with seroprevalence estimates obtained from representative...
The hepatitis C virus (HCV) is capable of spreading within a host by two different transmission modes: cell-free and cell-to-cell. However, the contribution each these mechanisms to HCV spread unknown. To dissect modes spread, we measured lifecycle kinetics used an in vitro assay monitor after low multiplicity infection absence presence neutralizing antibody that blocks spread. By analyzing data with spatially explicit mathematical model describes viral on single-cell level, quantified...
Derivative-free optimization can be used to estimate parameters without computing derivatives. As there exist many methods, it is unclear which use in practice. Here, we present two comparative studies of 18 state-of-the-art methods: Firstly, evaluate them on a set 466 classic test problems dimension 2 300. Secondly, study their performance parameter estimation 8 ODE models biological processes, comparing derivative-based optimization. We observe that different problem features necessitate...
Abstract 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 identify testing-strategies by comparing seven seroassays in a population-based cohort. Methods analysed 6,658 samples consisting of true-positives (n=193), true-negatives (n=1,091), specimens unknown status (n=5,374). For primary testing, we used Euroimmun-Anti-SARS-CoV-2-ELISA-IgA/IgG...
Population-based serological studies allow to estimate prevalence of SARS-CoV-2 infections despite a substantial number mild or asymptomatic disease courses. This became even more relevant for decision making after vaccination started. The KoCo19 cohort tracks the pandemic progress in Munich general population over two years, setting it apart Europe.Recruitment occurred during initial wave, including 5313 participants above 13 years from private households Munich. Four follow-ups were held...
Biological tissues are dynamic and highly organized. Multi-scale models helpful tools to analyse understand the processes determining tissue dynamics. These usually depend on parameters that need be inferred from experimental data achieve a quantitative understanding, predict response perturbations, evaluate competing hypotheses. However, even advanced inference approaches such as approximate Bayesian computation (ABC) difficult apply due computational complexity of simulation multi-scale...
Abstract Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate spread of COVID-19. Design efficient NPIs requires identification structure disease transmission network. We here identify key parameters COVID-19 network for time periods before, during, and after application strict first wave infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. assume a Watts–Strogatz small-world which allows distinguish...
Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding biological processes and integration heterogeneous datasets. However, some require consideration comprehensive therefore large-scale models. Parameter estimation for such poses great challenges, in particular when data are on a relative scale. Results Here, we propose novel hierarchical approach combining (i) efficient analytic evaluation optimal scaling, offset, error model...
Abstract Approximate Bayesian Computation (ABC) is a likelihood-free parameter inference method for complex stochastic models in systems biology and other research areas. While conceptually simple, its practical performance relies on the ability to efficiently compare relevant features simulated observed data via distance functions. Complications can arise particularly from presence of outliers data, which severely impair inference. Thus, robust methods are required that provide reliable...
Abstract Motivation Biological tissues are dynamic and highly organized. Multi-scale models helpful tools to analyze understand the processes determining tissue dynamics. These usually depend on parameters that need be inferred from experimental data achieve a quantitative understanding, predict response perturbations, evaluate competing hypotheses. However, even advanced inference approaches such as Approximate Bayesian Computation (ABC) difficult apply due computational complexity of...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. We present pyPESTO, a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, pyPESTO is broadly applicable black-box problems. Besides own implementations, it provides...
Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely the comparison of relevant features in simulated observed are popular otherwise intractable problems. To address this problem, have been developed to scale-normalize data, derive informative low-dimensional summary statistics using inverse regression models data. However, while approaches only...
Abstract Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need be inferred. This renders efficient reliable integration methods essential. However, these depend on hyperparameters, which strongly impact the ODE solution....