Seems pretty straightforward to me: 1) programming objects have properties and methods; 2) within cells it is probably possible to have analagous entities (perhaps various types such as molecules, organelles, etc) which have defined properties and predictable behaviours; 3) could we soon have a computer and a sufficiently comprehensive database of these objects and their behaviours for an AI to start correlating how they are combined and how they would need to act to produce a cellular effect (e.g. regenerate a damaged cell); 4) could this be speeded up with the advent of quantum computing?
Less flippantly: biological processes don't behave similarly to a big network of discrete objects with specific traits (methods and properties in OOP parlance). The domain of biology is composed of lots of molecules that combine to form bigger molecules that in turn get classified into hormones and proteins and amino acids and other organic compounds, and these all interact in super complex ways that are very difficult to model. For example, protein folding is a big area of research that is attempting to model the behaviors of just one set of molecules [1], and it is proving to be a really difficult problem to solve despite throwing enormous amounts of computing power at it [2].
And, we don't even know what we don't know yet in broader biological terms. It's not like we have a pretty good model for biology at macroscopic scales and we're just working out details -- this isn't civil engineering. The details that we're still missing matter a lot in how biological systems behave.
Quantum computing likewise is not a magic pill that will suddenly make all of this easier. Quantum computing is good at solving certain kinds of problems a little bit faster, but expectations for quantum computing have so far greatly outpaced its actual development.
As a side note, "systems thinking" in programmers often leads down dark dead-end alleys full of misunderstandings and wrong questions. Modern science is pretty darn advanced, and today's PhD candidates are introduced to programming as part of their education. It's usually safe to assume that if an advancement in a given field were possible through rudimentary programming, then someone would be working on it; programmers who are curious about specific fields should first start at the basics in those fields and put the time in to become familiar with them. That process will eventually lead to the right questions to ask in those fields.
Thanks for your response - I was curious if AI and tech might be able to bridge from a suitably detailed statistical picture to (at least some) cases of underlying deterministic behaviour, perhaps in a way (or ways) that might surprise us.
That's pretty much what AlphaFold has been doing for protein folding. It has been more successful than any other approach so far, but it hasn't yet "solved" protein folding, despite what some marketing materials and naive reporting has suggested. Last I heard, it was around 60% accurate when compared to experiments.
It does now seem like protein folding is within reach of being solvable, and that will be really cool and likely help advance our understanding of this part of biology, and possibly develop some new treatments for some diseases.
There will still be many more biological processes left to solve, however.
