Are Biological Systems Poised at Criticality?

By Thierry Mora, William Bialek. 2010

776 citations recorded by the source corpus.

Many of life’s most fascinating phenomena emerge from interactions among many elements—many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this “inverse” approach, using examples from families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised near a very special point in their parameter space—a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems.

Subfields: Cognitive Neuroscience, Global and Planetary Change, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/410086da214d32f44d2d2952807b72d87467c828

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Citations in the corpus

Being Critical of Criticality in the Brain (2012, background): “It seems that many biological systems would approach optimality by operating in a regime where they produce power laws (Mora and Bialek, 2011).”
Economic Opportunity and Evolution: Beyond Landscapes and Bounded Rationality (2013, background): “Biological entities can be said to be “poised” for a large set of possible adaptations (cf. Bailly and Longo, 2011; Mora and Bialek, 2011; also see Kauffman, 2012).”
How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins? (2018, background): “This sensitivity (so-called criticality) has also been observed in other models inferred from large-scale biological data, cf. [24].”
Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables (2016, methodology): “To find this exact relationship, we note, using an approach similar to [6], that if we were to plot rank versus energy, we would see a stepwise increase at the energy of each observation, x .”
Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design (2013, neutral): “Secondly, the protective action of B*27 may lie elsewhere in the proteome, as suggested by recent work demonstrating a strong B*27 response to the KY9 Pol epitope (Friedrich et al., 2011; Payne et al., 2010).”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, methodology): “Similar methods have previously been employed to study other complex biological systems, from describing the activity patterns of neuronal networks [16–19] to the prediction of contact residues in protein families [17,20,21].”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…an effective temperature they observed that empirical data are poised near the critical point of the (temperature-dependent) generalized model suggesting that the visual cortex might operate in a close-to-critical regime (Mora and Bialek, 2011; Mora et al., 2015; Tkačik et al., 2014, 2015).”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, background): “biological systems also have optimal computational properties at criticality (Crutchfield and Young, 1988; Kauffman and Johnsen, 1991; Mora and Bialek, 2011; Nykter et al., 2008).”
Statistical mechanics for natural flocks of birds (2011, background): “of these problems, and the mapping from maximum entropy models to statistical mechanics suggests that the observation of scale free correlations in speed fluctuations [17] will locate models of flocking at an especially interesting point in their parameter space [34].”
Griffiths phases and the stretching of criticality in brain networks (2013, methodology): “The criticality hypothesis [8, 10, 14] states that biological systems can perform the complex computations that they require to survive only by operating at the ”edge of chaos” (or critical) i.”