Abstract
What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of ‘disorder’ (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, ‘losing control’ appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.
Highlights
Karl Friston used insights from Bayesian information theory to show that prediction error is equal to the mean amount of ‘variational free energy’ across time of a living system, such as a cell or a brain [48]. This means that when organisms try to iteratively reduce their prediction errors through active inference, they are trying to reduce their free-energy levels across longer timespans. They are not much different from crystals in which ions arrange themselves into highly ordered patterns, despite the fact that all objects in this universe need to obey the second law of thermodynamics
We showed that living systems can be modeled as hierarchical Bayesian control systems in which central integrative control falls apart in a topdown manner as a result of rising levels of stress, which can be defined as prediction error or variational free energy
Living systems have found a way to temporarily maintain their local form and order, by being able to dissipate energy as efficiently as possible back into the environment. This means that living systems will lose their internal coherence and fall apart when free energy is not dissipated quickly enough into the environment. We argue that this is essentially what happens in any system that is loaded up with free energy beyond its capacity to dissipate it back to the environment: the accumulation of such energy will cause a disintegration of system components and system failure, causing a rise in permutation entropy scores
Summary
Zhu et al showed that bacteria of different species respond in a similar fashion to antibiotic stressors [22]. It is insensitive to signal changes at different temporal scale levels (i.e., high- versus low-frequency components) and highly sensitive to differences in the length of a timeseries and noise artefacts For this reason, several refinements have been proposed of the original PE measure, which involve calculating weighted PE scores that are compared to white noise (pure randomness) across multiple (coarse grained) temporal scale levels. Variance, and temporal autocorrelations into a single value, RMSRW-PE covers all aspects that are considered typical hallmarks of critical slowing down (CSD) This means that living systems become increasingly ‘disordered’ prior to their failure, which we argue results from a loss of integrative regulatory connections that normally synchronize system components to produce order (see text). We conclude by showing how the proposed disorder concept may apply to disease processes in general and to the human situation in particular
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