Learning by selective plasmid loss for intracellular synthetic classifiers

We propose a learning mechanism for intracellular synthetic genetic classifiers based on the selective elimination (curing) of plasmids bearing parts of the classifier circuit. Our focus is on a two-input, two-plasmid classifier scheme designed to solve a simple proof-of-concept learning problem. The problem is formulated in terms of Boolean variables, and the learning process boils down to selecting the classification rule from three options, given a set of training examples. We begin with a Boolean description of the classifier circuit, demonstrating how it implements the required learning algorithm. We then transition to a continuous steady-state model and establish conditions on its parameters to ensure that the learning process and the classifier output correspond to the Boolean description, at least approximately. The approach to intracellular classifier learning presented here essentially relies on two key prerequisites: (i) compatibility among the plasmids constituting the classifier, such that they have independent or weakly interacting copy number control systems, and (ii) conditional elimination mechanism in each plasmid triggered by a signal from the gene network. The feasibility of this approach is supported by recent experimental findings on engineering compatible pairs and triplets of plasmids and controlled selective plasmid curing. While learning by plasmid loss has certain limitations in universality, we anticipate that it provides greater persistence of a trained classifier to internal and external fluctuations and to degradation over time, as compared to alternative intracellular learning mechanisms outlined in the literature, such as based on gene network dynamics or on variable copy numbers of plasmids sharing a common copy number control system.