Title: Fooling the classifier: Ligand antagonism and adversarial examples,
Abstract: Machine learning algorithms are sensitive to so-called adversarial
perturbations. This is reminiscent of cellular decision-making where antagonist
ligands may prevent correct signaling, like during the early immune response.
We draw a formal analogy between neural networks used in machine learning and
the general class of adaptive proofreading networks. We then apply simple
adversarial strategies from machine learning to models of ligand
discrimination. We show how kinetic proofreading leads to "boundary tilting"
and identify three types of perturbation (adversarial, non adversarial and
ambiguous). We then use a gradient-descent approach to compare different
adaptive proofreading models, and we reveal the existence of two qualitatively
different regimes characterized by the presence or absence of a critical point.
These regimes are reminiscent of the "feature-to-prototype" transition
identified in machine learning, corresponding to two strategies in ligand
antagonism (broad vs. specialized). Overall, our work connects evolved cellular
decision-making to classification in machine learning, showing that behaviours
close to the decision boundary can be understood through the same mechanisms.