  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 and complex systems. We present pyPESTO,
a modular framework for systematic parameter estimation, with scalable
algorithms for optimization and uncertainty quantification. While tailored to
ordinary differential equation problems, pyPESTO is broadly applicable to
black-box parameter estimation problems. Besides own implementations, it
provides a unified interface to various popular simulation and inference
methods. pyPESTO is implemented in Python, open-source under a 3-Clause BSD
license. Code and documentation are available on GitHub
(https://github.com/icb-dcm/pypesto).
