A new mathematical framework for classification in cell state dynamics

Abstract: Our proposal emerges from a crucial challenge in cell biology: how do transcriptionally identical cells make different fate decisions when exposed to therapeutic drugs? We have observed that while cancer cells may appear homogeneous, they can develop remarkably diverse resistance trajectories when treated with drugs. To address this fundamental question, we propose developing new mathematical tools that unite concepts from statistical mechanics of deep neural networks, non-equilibrium statistical mechanics, and information theory to understand how biological networks function as classifiers. Our work extends our recent findings showing how biochemical networks’ classification capacity can be systematically tuned through factors like input promiscuity. We anticipate that this ambitious undertaking will establish a mathematically consistent framework for defining cell states and their transitions, elucidate the minimal requirements for biological networks to classify perturbations, and create predictive tools for cellular responses to therapeutic interventions.