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).

翻译：机理模型是描述和理解生物过程的重要工具。然而，这类模型通常依赖于未知参数，而针对大规模复杂系统的参数估计极具挑战性。我们提出pyPESTO——一个面向系统化参数估计的模块化框架，配备可扩展的优化与不确定性量化算法。该工具虽专为常微分方程问题设计，但广泛适用于黑箱参数估计任务。除自行实现的算法外，pyPESTO还为多种主流仿真与推断方法提供统一接口。本工具采用Python实现，基于三条款BSD许可证开源发布，代码与文档可通过GitHub获取（https://github.com/icb-dcm/pypesto）。