The role of computational cellular models in industrial bioprocesses: From genetic engineering to plant design.

Computational cellular models integrate mechanistic, statistical, and hybrid approaches to coordinate decisions from cell to plant, enabling earlier, more reliable bioprocess design across scales.

• Why it matters: Much of the potential of engineered metabolism remains unrealized because cellular behavior is complex and design choices must be coordinated across scales. Modeling is needed to navigate this design space and translate biological capability into scalable, reliable processes.
• What they did: The review surveys mechanistic, statistical, and hybrid modeling, spanning macroscopic models to genome-scale models with protein synthesis, regulation, and kinetics, plus data-driven methods using databases, high-throughput experiments, and online sensing. It compares model families by assumptions, data and curation needs, and computational tractability, and links them to strain edits, medium and feeding, reactor design, and early techno-economic and life cycle assessment.
• The result: The synthesis identifies three themes: fitness to purpose over novelty, value when predictions extend to process and plant metrics, and tractability as the driver for multiscale integration via reduction, precomputation, and fast surrogates. As data pipelines and automation mature, models act as a shared language across scales for economically viable and environmentally responsible plant designs.

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