pyPESTO: A modular and scalable tool for parameter estimation for dynamic models

1 code implementation • 2 May 2023 • Yannik Schälte, Fabian Fröhlich, Paul J. Jost, Jakob Vanhoefer, Dilan Pathirana, Paul Stapor, Polina Lakrisenko, Dantong Wang, Elba Raimúndez, Simon Merkt, Leonard Schmiester, Philipp Städter, Stephan Grein, Erika Dudkin, Domagoj Doresic, Daniel Weindl, Jan Hasenauer

Mechanistic models are important tools to describe and understand biological processes.

Uncertainty Quantification

A Wall-time Minimizing Parallelization Strategy for Approximate Bayesian Computation

1 code implementation • 30 Apr 2023 • Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw, Lutz Brusch, Jan Hasenauer, Yannik Schälte

Evaluation of the strategy on different problems and numbers of parallel cores reveals speed-ups of typically 10-20% and up to 50% compared to the best established approach.

Scheduling

pyABC: Efficient and robust easy-to-use approximate Bayesian computation

1 code implementation • 24 Mar 2022 • Yannik Schälte, Emmanuel Klinger, Emad Alamoudi, Jan Hasenauer

The Python package pyABC provides a framework for approximate Bayesian computation (ABC), a likelihood-free parameter inference method popular in many research areas.

A protocol for dynamic model calibration

2 code implementations • 25 May 2021 • Alejandro F. Villaverde, Dilan Pathirana, Fabian Fröhlich, Jan Hasenauer, Julio R. Banga

Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models.

AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models

2 code implementations • 16 Dec 2020 • Fabian Fröhlich, Daniel Weindl, Yannik Schälte, Dilan Pathirana, Łukasz Paszkowski, Glenn Terje Lines, Paul Stapor, Jan Hasenauer

Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes.

Uncertainty Quantification Vocal Bursts Intensity Prediction