Here is additional context on the features that are in the master table. “Pmix” contains all PURE proteins; `IsNEB` indicates whether the experiment was performed with commercially available NEB PURExpress (if 1), or our in house PURE mixture (if 0); `HasMgAR953` indicates if the experiment was run using a particular stock of Magnesium acetate that we are not confident was at the correct concentration (if 1, otherwise 0); and `isplamGFP` indicates that the plasmid provided coded for either deGFP (if 0) or plamGFP (if 1). The regressors are the parameters of a sigmoidal fit to fluorescence that has been converted to concentration of the relevant GFP reporter molecule.

We want to optimize the production of PURE—maximizing yield as quantified by `sigmoid_steady_state` and rate as quantified by `sigmoid_rate`---determined by using a deGFP reporter, our in house PURE mixture, and assuming we have a correctly produced stock of magensium acetate (`HasMgAR953` = 0). We will use a combination of the GES’s generated here, and we additionally want to improve the model by adjusting four additional parameters: ATP, GTP, Amino Acid Mix, and tRNA Mix. ATP and GTP have been held at 2 mM in all experiments in the dataset so far; Amino Acid Mix contains all amino acids (assume that their relative concentrations will be fixed), and is present at 0.35 mM; tRNA Mix contains all tRNAs (assume their relative concentrations are fixed), and is present at 3.5 ug/uL. We believe that we can feasibly adjust ATP and GTP between 1 and 4 mM, Amino Acid Mix between 0.1 and 0.6 uM, and tRNA Mix between 0 and 5.25 ng/uL. Assume all other parameters can be changed within the `min` and `max` bounds specified in the uploaded csv. 

As additional context, please do not rely on a One-Factor-At-A-Time (OFAT) approach. OFAT is fundamentally flawed for optimizing a highly coupled biochemical network like the PURE system because it fails to capture non-linear dynamics and synergistic cross-correlations between components. The core advantage of the Bayesian Optimization engine we have access to is the ability to efficiently navigate high-dimensional design spaces by varying multiple parameters simultaneously.

What do you think would be the best set of experiments to conduct in order to optimize PURE, given all available information? 