The most significant breakthroughs will no longer come from traditional wet lab experiments alone. Instead, progress will be driven by the smarter application of AI and simulations, with future bioreactors being as much digital as they are physical.
AI models mirror a bioreactor in real time, creating a "digital twin." This allows operators to test process changes and potential failure modes virtually, without touching the actual, expensive physical system, much like having a virtual engineer working alongside them.
Instead of running hundreds of brute-force experiments, machine learning models analyze historical data to predict which parameter combinations will succeed. This allows teams to focus on a few dozen targeted experiments to achieve the same process confidence, compressing months of work into weeks.
By training on multi-scale data from lab, pilot, and production runs, AI can predict how parameters like mixing and oxygen transfer will change at larger volumes. This enables teams to proactively adjust processes, moving from 'hoping' a process scales to 'knowing' it will.
Contrary to the idea that AI will make physical experiments obsolete, its real power is predictive. AI can virtually iterate through many potential experiments to identify which ones are most likely to succeed, thus optimizing resource allocation and drastically reducing failure rates in the lab.