I read this slowly because it feels like a big part of the methodology required to reach Ray Kurzweil’s idea of running clinical trials in an hour on a super computer. For that understanding biology with enough fidelity to model what a medicine will actually do is the foundation.

From a manufacturing perspective, I would add that we do not have enough data. We need much more, captured with its context at the edge. Cleaning disconnected signals later in the cloud is inefficient and cannot recover context that was never preserved. Which is the problem today you are hitting on. In a biological process, the signal we discard today may explain tomorrow’s failure.

I would love to see bioreactors designed around the approach you describe where sensors are placed very deliberately, instruments calibrated, clocks synchronized, and every signal mapped to the state or transition under study. At the point of collection, we should know the instrument was within tolerance, the process context was intact, and the sample was correctly timed against relevant events.

Trust should be built into the data, not inferred afterward.

We have traditionally done a poor job of this in both cGMP manufacturing and research. It can be very different now. The edge is the right place for this work to happen. A bioreactor should make product and leave a defensible record of what happened to the biology, when it happened, and under what conditions.

Great article.