Adaptation through the lense of single-cell multi-omics data Comment on "Dynamic and thermodynamic models of adaptation" by A.N. Gorban et al.

A.N.Gorban and his colleagues in their inspiring review described several theoretical models of adaptation and highlighted multiple examples convincing us in the existence of surprising at first thought phenomenon: the pattern of dynamical changes of basic statistical measures (correlation between features, their variance) can diagnose and prognose crises in the populations of objects exposed to stress (1). The surprise is caused by the universality of this observation. Firstly, it is manifested in many different situations. Secondly and even more surprising, the features used do not have to be specifically designed to measure stress, even though the feature selection is still important. This suggests that the proposed models can serve as an insightful approach for Big Data analysis and interpretation. One can note that all provided examples deal with macroscopic objects (people, patients, mice, plants, companies at the stock market). What if we will change the focus of the middle-out approach to a significantly smaller scale, e.g. from a tumor or a patient to a single cell? Would the suggested principles of adaptation thermodynamics still hold and what specific problems will arise?