GlycoPy: A CasADi-based Python Framework for Hierarchical Modeling, Optimization, and Control of Bioprocesses

TL;DR

GlycoPy is a Python framework based on CasADi that facilitates hierarchical modeling, simulation, and control of complex bioprocesses, enabling efficient NMPC implementation.

Contribution

It introduces a unified, object-oriented platform for hierarchical bioprocess modeling, simulation, and optimization with customizable differentiable algorithms.

Findings

Successfully applied to monoclonal antibody glycosylation process

Enabled hierarchical model construction and quasi-steady-state simulation

Supported adaptive NMPC for bioprocess control

Abstract

Efficient implementation of nonlinear model predictive control (NMPC) for bioprocesses remains challenging because large nonlinear models are difficult to organize, simulate, and embed within optimization and control workflows. This difficulty is particularly pronounced for large-scale and multiscale systems that require hierarchical model construction and customized simulation strategies. To address this issue, we present GlycoPy, a CasADi-based Python framework for hierarchical modeling, optimization, and control of bioprocesses. GlycoPy combines an equation-oriented, object-oriented modeling architecture with CasADi's symbolic and differentiable computational capabilities, enabling hierarchical model composition, numerical and symbolic simulation, parameter estimation, dynamic optimization, and NMPC within a unified workflow. A key feature of the framework is its support for customized differentiable simulation algorithms that can be embedded directly in gradient-based optimization and control. GlycoPy is demonstrated on a multiscale monoclonal antibody glycosylation process in Chinese hamster ovary cell culture, where it is used for hierarchical model construction, quasi-steady-state simulation, and adaptive NMPC. The results show that GlycoPy provides a practical and reusable framework for applying advanced optimization and control methods to computationally demanding bioprocesses.