July 20, 2021 Journal article Open Access

Andrei Zinovyev

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