Ensuring the evolutionary stability of genetically engineered microorganisms

In the past few years synthetic biology has made promising advancements in many fields. However, one of the most important challenges of synthetic biology has been relatively neglected, and that is the evolutionary stability of synthetic constructs. The expression of synthetic constructs in microorganisms creates a metabolic load from the manufacturing of the unneeded protein (from the organism’s point of view); this metabolic load leads to a decrease in fitness. Unavoidably, a mutation in the population will arise that either greatly decreases or erase all together the expression of the synthetic construct. This now mutated induvial poses a fitness greater than the rest of the population, and he will take over the population, leading to the loss of the construct. Our team is trying to address this issue by physically attaching the target gene (that you wish to express) to the N’ terminal side of an essential gene of that organism, under the same promotor. This way, any mutation to the target gene or its promotor will also harm the essential gene, and the mutated individual will die, instead of taking over the population. Our team created a sophisticated AI algorithm that matches a genetic construct to the best essential gene while also optimizing both of their DNA for stable and strong expression levels. This software won the best software tool in IGEM 2020, and since then was further validated empirically in the lab on insulin production in yeast.