Jan Hasenauer

This paper integrates single-cell perturbation experiments using nonlinear mixed effect modeling to robustly reconstruct cell-to-cell variability and improve parameter estimates.

This study identifies an increase in asymmetric stem cell divisions with age as a key factor in the decline of neurogenesis in adult mice.

The paper introduces an integrated modelling approach to analyze spatial imaging data, showing it can reliably estimate parameters even with noise and provide biological insights.

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

The paper benchmarks optimization methods for parameter estimation in large kinetic models and finds that a hybrid metaheuristic outperforms multi-start local searches.

This paper introduces a hierarchical optimization method for parameter estimation in ODE models using relative data, showing improved convergence and computational efficiency.

pyABC provides a distributed and scalable framework for likelihood-free inference using ABC-SMC, enabling efficient computation and customization for complex models.

This study introduces a hierarchical population model to identify latent causes of cell-to-cell variability in heterogeneous populations using single-cell data.

The paper introduces second order adjoint sensitivity analysis and a hybrid optimization-integration method to improve computational efficiency and robustness of parameter estimation for ODE models.

This paper introduces a scalable method for parameter estimation in genome-scale biochemical reaction networks using adjoint sensitivity analysis, showing improved efficiency and scalability compared to existing methods.