Digital twins are increasingly positioned as enabling technologies for model-based pharmaceutical biotechnology, yet many proposed architectures treat biological uncertainty as a nuisance to be averaged away rather than as part of the state that must be represented. This distinction matters because a bioprocess may remain numerically predictable while its underlying cell population, model structure, sensor validity, or scale-dependent behavior changes in ways that alter the meaning of that prediction. This Methodological Perspective argues that a useful bioprocess digital twin should therefore represent not only estimated process states but also uncertainty about biological state, parameters, model structure, data provenance, and model validity. Mechanistic and data-driven models are treated as complementary rather than interchangeable components; soft sensors are separated from direct observations; and state updating is distinguished from evidence that the governing model remains adequate. The article develops a proposed probabilistic architecture in which biological variability and competing model explanations remain visible through estimation, updating, and decision support. The objective is not to make digital twins maximally complex, but to prevent apparently precise model outputs from becoming stronger claims than the underlying biological and analytical evidence permits.
IJPRAS
