pyFOOMB: Python framework for object oriented modeling of bioprocesses

Quantitative characterization of biotechnological production processes requires the determination different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modeling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided flexible implementation models in form ordinary differential equation systems (ODEs). By building on Python powerful multi-purpose programing language, ODEs formulated an object-oriented manner, which facilitates their modular design, reusability, extensibility. Once is implemented, seamless integration analysis experimental data supported various packages that are already available. In particular, iterative workflow generation subsequent estimation we employed concept replicate instances, linked common sets parameters global or local properties. For description multi-stage processes, discontinuities right-hand sides equations via event handling using freely available assimulo package. Optimization problems solved making use parallelized version generalized island approach provided pygmo Furthermore, combination Jupyter notebooks also supports education engineering applied learning scientific language. Finally, applicability strengths will demonstrated comprehensive collection notebook examples.