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

Abstract

Engineering cellular metabolism has enabled bioprocesses to manufacture sustainable fuels, materials, foods, commodity chemicals, and pharmaceuticals. Yet much of this potential remains unrealized because cellular behavior is complex and decisions must be coordinated across scales from the cell to the plant. Computational modeling therefore becomes essential to navigate this design space and to turn biological capability into reliable, scalable processes. In this review we examine the main cellular modeling approaches, including mechanistic, statistical and hybrid models. We cover classic macroscopic models through genome-scale models incorporating protein synthesis, regulation, and kinetics, together with statistical approaches that learn from databases, high-throughput experimentation, and online sensing. We compare model families by assumptions, data and curation needs, and computational tractability, and map how they guide genetic edits, medium and feeding choices, reactor design and operation, and early linkage to techno-economic and life cycle assessments. Three themes emerge from this synthesis: fitness to purpose matters more than novelty because simple kinetic models remain central for screening and control, while advanced cellular models reveal physiological limits that prioritize what to engineer; value grows when predictions are carried forward to process and plant metrics so economic and environmental trade offs are visible early; and finally, tractability governs multiscale integration which motivates model reduction, precomputation, and fast surrogates. As data pipelines and automation mature, cellular models serve as a shared language across scales, translating biological insight into strain design, feed formulations, control policies, and plant designs that are economically viable and environmentally responsible.