A5056989853- Gene Regulatory Network Analysis
- COVID-19 epidemiological studies
- Advanced Statistical Methods and Models
- Mathematical and Theoretical Epidemiology and Ecology Models
- Single-cell and spatial transcriptomics
- Protein Structure and Dynamics
- Statistical Methods and Bayesian Inference
- Evolution and Genetic Dynamics
- Metabolomics and Mass Spectrometry Studies
- Viral Infections and Vectors
- Mosquito-borne diseases and control
- Markov Chains and Monte Carlo Methods
- Mathematical Biology Tumor Growth
- Advanced Proteomics Techniques and Applications
- Climate change impacts on agriculture
- Probabilistic and Robust Engineering Design
- Gaussian Processes and Bayesian Inference
- Advanced Multi-Objective Optimization Algorithms
- Cytokine Signaling Pathways and Interactions
- Hydrology and Drought Analysis
- Statistical Methods and Inference
- Simulation Techniques and Applications
- Cancer, Hypoxia, and Metabolism
- Spectroscopy and Quantum Chemical Studies
- Bioinformatics and Genomic Networks
University of Freiburg
2014-2023
Abstract Summary: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field biology. Two most critical steps this are to construct models biochemical reaction networks for large datasets and complex experimental conditions perform efficient reliable parameter estimation model fitting. We present modeling environment MATLAB that pioneers these challenges. The numerically expensive parts calculations such as solving associated sensitivity system...
Data-based mathematical modeling of biochemical reaction networks, e.g., by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality description system model. Recently, broadened eigenvalue spectrum Hessian matrix objective function covering orders magnitudes was observed termed as sloppiness. work, we investigate origin sloppiness from structures...
We discuss issues of structural and practical identifiability partially observed differential equations which are often applied in systems biology. The development mathematical methods to investigate non-identifiability has a long tradition. Computationally efficient detect cure it have been developed recently. Practical on the other hand not investigated at same conceptually clear level. argue that is more challenging than when comes modelling experimental data. classical approach based...
Ordinary differential equation models are frequently applied to describe the temporal evolution of epidemics. However, ordinary also utilized in other scientific fields. We summarize and transfer state-of-the art approaches from fields like Systems Biology infectious disease models. For this purpose, we use a simple SIR model with data an influenza outbreak at English boarding school 1978 more complex vector-borne Zika virus Colombia 2015–2016. Besides parameter estimation using...
Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, graded increase survival of colony-forming unit-erythroid (CFU-E) cells observed upon stimulation with erythropoietin (Epo). To identify components Janus kinase 2/signal transducer and activator transcription 5 (JAK2/STAT5) signal transduction that contribute to population response, we extended cell-population-level model calibrated experimental data study behavior single The single-cell...
Hypoxia as well metabolism are central hallmarks of cancer, and hypoxia-inducible factors (HIFs) metabolic effectors crucial elements in oxygen-compromised tumor environments. Knowledge changes the expression proteins response to HIF function could provide mechanistic insights into adaptation hypoxic stress, tumorigenesis, disease progression. We analyzed time-resolved alterations metabolism-associated protein levels different oxygen potentials across breast cancer cell lines. Effects on...
Likelihood ratios are frequently utilized as basis for statistical tests, model selection criteria and assessing parameter prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood into p-values or confidence intervals requires exact form of test statistic's distribution. The lack knowledge about this distribution nonlinear ordinary differential equation (ODE) models an approximation which assumes so-called asymptotic setting, i.e. a sufficiently...
Ordinary differential equation (ODE) models are frequently used to mathematically represent the dynamic behavior of cellular components, e.g.\ for describing biochemical reaction networks. Solutions these ODE depend non-linearly on parameters, which can be estimated using experimental data by minimizing discrepancy between and model trajectories. In realistic applications, only relative, sparse noisy is available makes fitting a challenging optimization problem. order take account...
Viral outbreaks, such as the current COVID-19 pandemic, are commonly described by compartmental models means of ordinary differential equation (ODE) systems. The parameter values these ODE typically unknown and need to be estimated based on accessible data. In order describe realistic pandemic scenarios with strongly varying situations, model parameters assumed time-dependent. While estimation for typical case time-constant does not pose larger issues, determination time-dependent...
Summary Survival or apoptosis is a binary decision in individual cells. Yet, at the cell population level, graded increase survival of CFU-E cells observed upon stimulation with Erythropoietin (Epo). To identify components JAK2/STAT5 signal transduction that contribute to response, population-level model calibrated experimental data was extended study behavior single The single-cell showed high cell-to-cell variability nuclear phosphorylated STAT5 caused by amount EpoR:JAK2 complexes and...
Abstract Likelihood ratios are frequently utilized as basis for statistical tests, model selection criteria and assessing parameter prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood into p-values or confidence intervals requires exact form of test statistic’s distribution. The lack knowledge about this distribution nonlinear ordinary differential equation (ODE) models an approximation which assumes so-called asymptotic setting, i.e. a...
Describing viral outbreaks, such as the COVID-19 pandemic, often involves employing compartmental models composed of ordinary differential equation (ODE) systems. Estimating parameter values for these ODE is crucial and relies on accessible data. To accurately represent realistic pandemic scenarios with diverse situations, it necessary to consider model parameters time dependent. However, estimating time-dependent parameters, like transition rates in models, notoriously challenging due...
Viral outbreaks, such as the current COVID-19 pandemic, are commonly described by compartmental models means of ordinary differential equation (ODE) systems. The parameter values these ODE typically unknown and need to be estimated based on accessible data. In order describe realistic pandemic scenarios with strongly varying situations, model parameters assumed time-dependent. While estimation for typical case time-constant does not pose larger issues, determination time-dependent...