As far as I know genetic construct design remains a mostly manual process because the decision tree that goes into creating a viable clone is complicated to say

As far as I know genetic construct design remains a mostly manual process because the decision tree that goes into creating a viable clone is complicated to say the least. There are a lot of variables to optimize, from choosing the right expression vector, to cutting and gluing the right genes at the right spot. I think that in many cases, the science behind genetic engineering is mostly empirical. Biologists will often spend a long time to figure out how to express one particular product, and then apply that knowledge to similar products, but if they move to something vastly different, there is no guarantee of success. Automating the process is a valuable effort nevertheless. I think because of it's complexity, it would require a combination ofknowledge-graph based reasoing, with AI. There are databases such as Addgene: https://www.addgene.org/search/catalog/plasmids/?q=Cas9 https://www.addgene.org/search/catalog/plasmids/?q=Cas9, in which you can search existing plasmids created by the scientific community. I think this could be a valuable resource for a machine learning approach.