u/jackavix

I'm trying to abstract a biological problem into a more general computational problem, as I'm interested in the underlying methodology used in this fields, to ideally translate back to biology.

The core challenge is that I want to modify a system while preserving a desired behaviour in one context, but allowing that behaviour to change in other contexts. The difficulty is that I don't know which internal parts of the system are responsible for preserving the desired behaviour.

A simplified example:

• We have a system (algorithm, function, circuit, program, etc.).
• The system operates within different contexts/environments.
• In Context A, it must produce a desired output.
• In Contexts B, C, D..., it should not produce that output.
• The context may interact with or modify any part of the system.
• We are free to modify the system itself.
• The system can be enormous in complexity, but ideally is optimized for minimimum required complexity that might scale with the number of contexts.
• The contexts can also be enormous in complexity and interact with the system in many ways.
• Some internal components are essential for producing the desired output in Context A.
• Other components are free to change.
• The problem is that we do not know which components are essential and which are not.
• We can only evaluate the system by observing its behaviour in each context.

Are there existing computational methods that tackle this type of problem?