Paul L. Stapor

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

The paper introduces a new adjoint method for computing gradients in ODE models of biochemical reaction networks using steady-state data, achieving up to 4.4x speedup in simulation time for large-scale models.

A more expressive spline representation for SBML models improves code generation performance in AMICI

The paper introduces SBML annotations for compact spline representations, improving model import performance in AMICI and enabling easier model editing.

Mini-batch optimization enables training of ODE models on large-scale datasets

This paper applies mini-batch optimization to ODE models, improving efficiency and results on a cancer signaling dataset.

Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes define survival threshold in erythroid progenitor cells

The paper models single-cell variability in JAK2/STAT5 signaling and cytoplasmic volume to explain how erythroid progenitor cells transition from binary survival/apoptosis decisions to graded population responses upon Epo stimulation.

Benchmarking of numerical integration methods for ODE models of biological systems

This study benchmarks numerical integration methods for ODE models in systems biology, finding that most ODEs are stiff and providing guidelines for algorithm and hyperparameter selection.

Dirac mixture distributions for the approximation of mixed effects models

The paper compares methods for approximating mixed effects models, finding that the modified Cramer-von Mises Distance method provides better accuracy than Monte-Carlo and sigma-point methods.

Challenges in the calibration of large-scale ordinary differential equation models

The paper reviews challenges in calibrating large-scale ODE models and discusses methods for reproducibility, simulation efficiency, and parameter estimation.

Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis

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.