The general principle can be referred to as "inductive bias": out of the space of all functions that can be represented by computational means, how does one restrict the search space to only those functions which are relevant for the data/system of study? Developing physical theories is nothing more than the process of whittling down what space of functions can represent the data most accurately, using analytical and scientific techniques to get there.

Bioinformaticists, with sufficiently accurate and detailed models of how genes translate to $BEHAVIOR_OR_OUTCOME, would need far fewer data to extract meaningful insights. So of course throughput hasn't solved the problem. That wasn't the point of the comment above.

I don't think there's any deep relationship between the functions we find that fit observed data well, and those functions being generally useful at making out of class predictions.

Really, I was making a comment about https://en.wikipedia.org/wiki/The_Unreasonable_Effectiveness... which in my mind is still unresolved (that is: why do the math models we use for physical systems generalize so well, if we wouldn't expect them to do so?)