> We're taking existing scientific workflows and adding structure to them, but there are still plenty of details that are unstructured and performed manually by

> We're taking existing scientific workflows and adding structure to them, but there are still plenty of details that are unstructured and performed manually by scientists, and there's a lot of variability across use cases. That makes sense. It’s difficult to start adding structure to biological sciences. And to the fields credit I don’t think it’s out of lack of ability or effort but just the true complexity of dealing with biological systems. In contrast, even quantum mechanics is seemingly simple! > Possibly we could do a simulation based approach on historical data like that, although modeling it would correctly certainly sounds tricky. True, seems like you will have a very valuable historical dataset though. I’ve been pondering this type of problem for a few years since my (abortive) attempt at a PhD in material sciences. For my project I made some decent progress using various Bayesian and bootstrapping models to work with uncertainty in my models and tissue samples. Bayesian approaches, especially combined with MCMC type analysis can yield a lot of fruit dealing with biological systems with minimal need to understand the underlying models. But as you mention modeling the parameters correctly is challenging and there’s a lacunae in research broaching the topic. But at the least a system like yours could indicate items like conditional probability of failure given certain sequence sets or combinations of experimental procedures (e.g. cell lines A tend to do better when cultured in X media and tested with Y +/- Z hours). The article seems to indicate you’d have most of the data described in a format that’d be amendable to such data mining.