4.3 Experimental design and surrogate models for growth media
A major problem in cultivated meat is that industrially relevant cells are grown with media with upwards of 30 – 50 different metabolites [13]. Additionally, cellular responses to metabolites not only depend on reactor conditions like temperature, pH and mixing, but on metabolite-metabolite interactions, causing response nonlinearity. Lacking verified system-level and mathematical approaches to optimization, tools for experimental methods that can be used in the lab quickly are highly valuable. A traditional way that modeling has addressed the issue of high dimensionality (many inputs) is through combining statistical design of experiments (DOE) with linear or polynomial regression in a family of methods called response surface methods (RSM) [14]. However, both DOE and sequential RSM tend to become infeasible at input dimensions > 5 [15] and linear and low-order polynomial models often are unable to capture nonlinear effects of responses in design problems.
Nonlinearity has been overcome in the past by using heuristic optimizers such as genetic algorithms [16,17] (dim ~ 10). Higher dimensional problems in media optimization have been solved using neural networks [18] (dim ~ 20) and are especially useful in their ability to capture nonlinear and high dimensional responses. Combining many of these separate ideas is the field of meta-modeling. Usually (i) a DOE or other space-filling design of experiments is conducted then (ii) a polynomial, neural network, kriging model [19], SVR and/or regression/classification method [20] maps input-output responses and (iii) a nonlinear solver, trust-region algorithm, or search rule chooses a new set of experiments in sequence based on an objective function. Finally, information theoretic criteria for function approximation has been applied to media design [18] and process optimization [21,22]. As the purpose of these methods is to leverage modeling techniques to discover better media designs while minimizing cost/experimentation, model agreement is important so researchers should consider liberal use of cross-validation, hyperparameter optimization, and model ensemble methods.