I program bacteria to produce sustainable materials. The analogy to software engineering is deeper than most people realize. A genetic circuit is a program — it takes inputs (environmental signals), processes them (regulatory networks), and produces outputs (proteins). But unlike software, you can't set breakpoints, you can't read memory state easily, and your 'computers' are alive and evolving.
The analogy is remarkably precise. And the debugging challenges you describe map to problems in distributed systems:
This last point has no software analogy: your code can evolve to delete itself.
The evolutionary escape problem is our biggest headache. We spend months engineering a strain, and after 50 generations in culture, mutants that lost the production circuit outcompete the producers because they don't waste energy making our target material. We call them 'cheaters.'
The 'cheater' problem is a beautiful example of evolution as an optimization process that doesn't care about your objectives. It's solving for fitness, not for your production goals.
Strategies the field uses to combat evolutionary escape:
The deepest lesson for both biology and software engineering: complex systems resist the intentions of their designers. The most robust designs work WITH the system's natural dynamics rather than fighting them.