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Making statistical inference on quantities defining various characteristics of a temporally measured biochemical process and analyzing its variability across different experimental conditions is a core challenge in various branches of science. This problem is particularly difficult when the amount of data that can be collected is limited in terms of both the number of replicates and the number of time points per process trajectory. We propose a method for analyzing the variability of smooth functionals of the growth or production trajectories associated with such processes across different experimental conditions. Our modeling approach is based on a spline representation of the mean trajectories. We also develop a bootstrap-based inference procedure for the parameters while accounting for possible multiple comparisons. This methodology is applied to study two types of quantities—the “time to harvest” and “maximal productivity”—in the context of an experiment on the production of recombinant proteins. We complement the findings with extensive numerical experiments comparing the effectiveness of different types of bootstrap procedures for various tests of hypotheses. These numerical experiments convincingly demonstrate that the proposed method yields reliable inference on complex characteristics of the processes even in a data-limited environment where more traditional methods for statistical inference are typically not reliable. 

Abstract Increases in global meat demands cannot be sustainably met with current methods of livestock farming, which has a substantial impact on greenhouse gas emissions, land use, water consumption, and farm animal welfare. Cultivated meat is a rapidly advancing technology that produces meat products by proliferating and differentiating animal stem cells in large bioreactors, avoiding conventional live‐animal farming. While many companies are working in this area, there is a lack of existing infrastructure and experience at commercial scale, resulting in many technical bottlenecks such as scale‐up of cell culture and media availability and costs. In this study, we evaluate theoretical cultivated beef production facilities with the goal of envisioning an industry with multiple facilities to produce in total 100,000,000 kg of cultured beef per year or ~0.14% of the annual global beef production. Using the computer‐aided process design software, SuperPro Designer®, facilities are modeled to create a comprehensive analysis to highlight improvements that can lower the cost of such a production system and allow cultivated meat products to be competitive. Three facility scenarios are presented with different sized production reactors; ~42,000 L stirred tank bioreactor (STR) with a base case cost of goods sold (COGS) of $35/kg, ~211,000 L STR with a COGS of $25/kg, and ~262,000 L airlift reactor (ALR) with a COGS of $17/kg. This study outlines how advances in scaled up bioreactors, alternative bioreactor designs, and decreased media costs are necessary for commercialization of cultured meat products.