III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

2. A Consilience Unity as Physics, Biology and People Become One

Mohseni, Masoud, et al. Quantum Effects in Biology. Cambridge: Cambridge University Press,, 2014. Among the editors and authors are Martin Plenio, Seth Lloyd, Graham Fleming, and Elisabet Romero (See Nature Physics 10/9, 2014). One of the first book-length collections which gathers years of research and realizations that, if properly understood, “quantum” phenomena are not arcane and off-putting. Instead, as the quote notes, their creative presence can then be found across all realms of living, quickening nature.

Quantum biology, as introduced in the previous chapter, mainly studies the dynamical influence of quantum effects in biological systems. In processes such as exciton transport in photosynthetic complexes, radical pair spin dynamics in magnetoreception, and photo-induced retinal isomerization in the rhodopsin protein, a quantum description is a necessity rather than an option. The quantum modelling of biological processes is not limited to solving the Schrödinger equation for an isolated molecular structure. Natural systems are open to the exchange of particles, energy or information with their surrounding environments that often have complex structures. Therefore the theory of open quantum systems plays a key role in dynamical modelling of quantum-biological systems. Research in quantum biology and open quantum system theory have found a bilateral relationship. Quantum biology employs open quantum system methods to a great extent while serving as a new paradigm for development of advanced formalisms for non-equilibrium biological processes. (Chapter 2, Open Quantum System Approaches to Biological Systems)

Mora, Thierry, et al. Questioning the Activity of Active Matter in Natural Flocks of Birds. arXiv:1511.01958. A team of nine physicists across Europe including Irene Giardina, Leonardo Parisi, Aleksandra Walczak, and Andrea Cavagna continue to expand and finesse a viewing animal groupings as exemplars of complex adaptive self-organizing systems. For a philosophical surmise, one might imagine a universally iterative nature not as only a book, an encyclopedia testament, but as a three dimensional, graphic revelation which our phenomenal human phase is meant to read, and to enhance anew.

The correlated motion of large bird flocks is an instance of self-organization where global order emerges from local interactions. Despite the analogy with ferromagnetic systems, a major difference is that flocks are active -- animals move relative to each other, thereby dynamically rearranging their interaction network. Although the theoretical importance of this off-equilibrium ingredient has long been appreciated, its relevance to actual biological flocks remains unexplored. Here we introduce a novel dynamical inference technique based on the principle of maximum entropy, which takes into account network reshuffling and overcomes the limitations of slow experimental sampling rates. We apply this method to three-dimensional data of large natural flocks of starlings, inferring independently the strength of the social alignment forces, the range of these forces, and the noise.

Moretti, Paolo and Miguel Munoz. Griffiths Phases and the Stretching of Criticality in Brain Networks. Nature Communications. Online October, 2013. We cite this paper as a good example of a growing sense, often by translation of terms, of the deepest affinities between all manner of biological and cerebral form and behavior with such basic physical phenomena. In this case University of Granada, Spain, computational neuroscientists draw parallels between the definitive self-organized critical poise of neural activity and mechanisms of statistical physics. The import of this study was noted by Claus Hilegetag and Marc-Thorsten Hutt in “Hierarchical Modular Brain Connectivity is a Stretch for Criticality” in Trends in Cognitive Sciences (Online November 2013).

Hallmarks of criticality, such as power-laws and scale invariance, have been empirically found in cortical-network dynamics and it has been conjectured that operating at criticality entails functional advantages, such as optimal computational capabilities, memory and large dynamical ranges. As critical behaviour requires a high degree of fine tuning to emerge, some type of self-tuning mechanism needs to be invoked. Here we show that, taking into account the complex hierarchical-modular architecture of cortical networks, the singular critical point is replaced by an extended critical-like region that corresponds—in the jargon of statistical mechanics — to a Griffiths phase. Using computational and analytical approaches, we find Griffiths phases in synthetic hierarchical networks and also in empirical brain networks such as the human connectome and that of Caenorhabditis elegans. Stretched critical regions, stemming from structural disorder, yield enhanced functionality in a generic way, facilitating the task of self-organizing, adaptive and evolutionary mechanisms selecting for criticality. (Abstract)

A new paper shows that a characteristic feature of the arrangement of brain networks, their modular organization across several scales, is responsible for an expanded range of critical neural dynamics. This finding solves several puzzles in computational neuroscience and links fundamental aspects of neural network organization and brain dynamics. (Hilgetag, Hutt Abstract)

Morone, Flaviano, et al. Fibration Symmetries Uncover the Building Blocks of Biological Networks. Proceedings of the National Academy of Sciences. 117/8306, 2020. CCNY systems physicists including Hernan Makse describe a novel geometric intersect between living systems and their physical substrate by way of these webwork intricacies. Notably then the contribution serve belies its own inorganic building block metaphor.

The success of symmetries in explaining the physical world, from general relativity to particle physics and all phases of matter, raises the question of whether the same concept could explain emergent properties of biological systems. In other words, if life is an emergent property of physics then the symmetry principles that inform physics should also apply to the organizing principles of life. Here we show that a form of symmetry called fibration can describe the nodes and links of biological networks and other social and infrastructure networks. This result broadly opens the way to understand how information-processing networks are assembled from the bottom up. (Significance)

Mugler, Andrew and Bo Sun. Special Issue on Emergent Collective Behavior form Groups of Cells. Physical Biology. 115/6363, 2018. Purdue University and Oregon State University biophysicists introduce a collection of new findings about how physical phenomena are intimately engaged in biological development and activities. See for example Biophysical Constraints Determine the Selection of Pheneotypic Fluctuations During Directed Evolution by Hong-Yah Shih, et al in this issue.

Single cells perform extraordinary tasks: they follow chemical cues, process environmental information, make life-or-death decisions, and replicate themselves. Yet, few cells are truly 'single'. Even single-celled organisms exist and interact within complex communities. In recent years, it has become particularly evident that when groups of cells act collectively, their performance improves or they perform new tasks altogether. This special issue collects papers focusing on the new and improved behaviors that emerge when cells interact. In these reports cells interact in three major ways: competition, cooperation, and communication, and the interactions may be mechanical or biochemical in nature. A wide range of systems are discussed, from virus-host pathogenesis, growth and evolution of bacteria, to the motility, mechanosensing, and force generation of mammalian cells. (Summary)

Nagel, Sidney. Experimental Soft-Matter Science. Reviews of Modern Physics. 89/025002, 2017. A summary of a January 2016 workshop as admissions and insights grow about these non-equilibrium lively material forms, of which the article is a good tutorial. This heretofore unnoticed realm is cited as disordered, nonlinear, thermal and entropic, observable, gravity-affected, nonlocal, patterned, interfacial elastic, memory retaining, to wit active matter. That is to say it expresses an organic essence.

Soft materials consist of basic units that are significantly larger than an atom but much smaller than the overall dimensions of the sample. The label “soft condensed matter” emphasizes that the large basic building blocks of these materials produce low elastic moduli that govern a material’s ability to withstand deformations. Aside from softness, there are many other properties that are also caused by the large size of the constituent building blocks. Soft matter is dissipative, disordered, far from equilibrium, nonlinear, thermal and entropic, slow, observable, gravity affected, patterned, nonlocal, interfacially elastic, memory forming, and active. This is only a partial list of how matter created from large component particles is distinct from “hard matter” composed of constituents at an atomic scale. (Abstract)

Nastasiuk, Vadim. Emergent Quantum Mechanics of Finances. Physica A. Online February, 2014. On the shores of the Black Sea, a South Ukrainian National Pedagogical University, Odessa, researcher proposes ways that quantum phenomena might be in dynamic effect even for widely removed economic transactions. If a cross-correspondence can be drawn, it would then aid studies of market volatilities. In regard, might a wider palliative discovery at last accrue by which to realize a single, infinitely iterative, genesis, a natural guidance for peoples to move beyond the guns of 2014 and become planetary patriots?

This paper is an attempt at understanding the quantum-like dynamics of financial markets in terms of non-differentiable price–time continuum having fractal properties. The main steps of this development are the statistical scaling, the non-differentiability hypothesis, and the equations of motion entailed by this hypothesis. From perspective of the proposed theory the dynamics of S&P500 index are analyzed. (Abstract)

Newbolt, Joel and Nickolas Lewis. Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves. Nature Communications. 15/ 3462, 2024. We cite this entry by NYU Courant Institute and Institut Polytechnique de Paris mathematicians for new findings about entities in motion and also for its deeper exemplary attribution to generic active physical sources.

Collectively locomoting animals are often seen akin to states of matter whereby group phenomena emerge from individuals. Motivated by linear formations, we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements. Force measurements and perturbations inform a wake model that views self-ordering as mediated by the self-amplification of disturbances as a resonance cascade. These results derive from generic features, and hence may arise more generally in macroscale, flow-mediated collectives. (Excerpt)

Nourmohammad, Armita, et al. Universality and Predictability in Molecular Quantitative Genetics. arXiv:1309.3312. Posted November 2013, Nourmohammad, and Torsten Held, Princeton University integrative geneticists, and Michael Lassig, University of Colonge biophysicist, convey the present merger of statistical, condensed matter physics with all aspects of life’s evolution, along with the popular trend to report and affirm a universal recurrence of the same phenomena across every domain and instance. The paper is forthcoming in Current Opinion in Genetics and Development. See also among Lassig’s prior papers “From Fitness Landscapes to Seascapes: Non-Equilibrium Dynamics of Selection and Adaptation” with Ville Mustonen in Trends in Genetics (25/3, 2009).

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology. (Abstract)

This article is on universality in molecular evolution. We introduce universality as an emerging statistical property of complex traits, which are encoded by multiple genomic loci. We give examples of experimentally observable universal trait characteristics, and we argue that universality is a key concept for a new quantitative genetics of molecular traits. Three aspects of this concept are discussed in detail. First, universal statistics governs evolutionary modes of conservation and adaptation for quantitative traits, which can be used to infer natural selection that determines these modes. Furthermore, there is a close link between universality and predictability of evolutionary processes. Finally, universality extends to the evolution of higher-level units such as metabolic and regulatory networks, which provides a link between quantitative genetics and systems biology. (1)

In a broad sense, universality means that properties of a large system can become independent of details of its constituent parts. This term has been coined in statistical physics, where it refers to macroscopic properties of large systems that are independent of details at the molecular scale. Universality also arises in evolutionary biology. As in physics, it is a property of systems with a large number of components, and it has strong consequences for experiment and data analysis. (1)

Again, the reason for universality is that changes in one pathway component tend to be buffered by compensatory changes in other components. These compensations can be statistical or systematic, that is, generated by feedback loops in the pathway organization. Universality and predictability of pathway output emerge primarily in complex, higher-level pathways, which have multiple compensatory channels. This suggests the hierarchy of molecular functions is reflected by an evolutionary hierarchy: universality and predictability increase, while stochasticity decreases with increasing level of complexity. (10)

Nurisso, Marco, et al. Higher-order Laplacian Renormalization. arXiv:2401.11298. Turin Polytechnic, Aix-Marseille University and Northeastern University London physicists have come up with a novel approach, as the quotes say, by which this primary theoretical explanation can be applied to better reveal multiplex network forms and functions, along with providing a physical origin.

We propose a cross-order Laplacian renormalization group (X-LRG) scheme for diverse higher-order networks. This formula is a pillar of the theory of scaling, scale-invariance, and universality in physics. An RG scheme based on diffusion dynamics was recently introduced for complex networks with dyadic interactions. Yet despite many polyadic links, we still lack a general RG model for multiplex systems. Our approach uses a diffusion process to group nodes or simplices, which allows us to probe higher-order structures. We apply to synthetic linkages and detect scale-invariant profiles of real-world examples. (Excerpt)

The renormalization group (RG) [1] is a cornerstone of modern theoretical physics because it allows us to study how a physical system depends on the scale of observation, defining universality classes and formalizing the concept of scale-invariance. While the RG has provided valuable insights, extending its framework to complex networks has posed a problem mainly due to the correlations between scales caused by small-world effects. (1)

Finally, our results provide a new lens to address questions on the origin of different high er-order invariant structures in various domains, and on their effects on dynamical processes taking place on them, as well as to the limits they might pose on the predictability and reconstruction of complex systems. (8)

Nussinov, Zohar, et al. Inference of Hidden Structures in Complex Physical Systems by Multi-Scale Clustering. arXiv:1503.01626. American and Indian physicists contend that condensed matter/statistical mechanic studies, which are lately coming to assume an intricately networked nature, have found a persistent tendency to form modular communities. Such whole units, with their own integrity while immersed in multiple layers, are a prime, natural feature. And if we might avail, a much better society could be conceived as many, interlinked communal villages in a local and global organic, physiological milieu.

We survey the application of a relatively new branch of statistical physics--"community detection"-- to data mining. In particular, we focus on the diagnosis of materials and automated image segmentation. Community detection describes the quest of partitioning a complex system involving many elements into optimally decoupled subsets or communities of such elements. We review a multiresolution variant which is used to ascertain structures at different spatial and temporal scales. Significant patterns are obtained by examining the correlations between different independent solvers. Similar to other combinatorial optimization problems in the NP complexity class, community detection exhibits several phases. (Abstract)

Pachter, Jonathon, et al.. Entropy, irreversibility and inference at the foundations of statistical physics. Nature Reviews Physics.. 6/382, 2024. Laufer Center for Physical and Quantitative Biology, Stony Brook University theorists including Ken Dill propose a timely contrast of prior many-body theories with a revised 2020s frontier by way of non-equilibrium, maximum entropy agencies. By so doing they achieve a more suitable conceptual basis for a deep natural vitality from which life’s occasion and evolutionary development can go forth to its crucial Earthuman comprehension.

Statistical physics relates the properties of macroscale systems to the distributions of their microscale agents. A main factor has been the maximization of entropy, an equilibrium variational principle. Recent work has sought extensions to non-equilibrium fast and slow processes in the fluctuation relations of stochastic thermodynamics via large deviation theory. When recognized as an inference principle, an entropy maximum can be generalized for non-equilibria and applied to other entropic phases. Our goal is to enhance crosstalk among disparate researchers working to compare and contrast different approaches while pointing to common roots. (Abstract)

We first note that two disparate perspectives are used in statistical physics. From its earliest days, it was framed in terms of the positions and momenta of collisional particles, their conservation of energy, and puzzles of irreversibility and disorder. The second perspective is the language of probabilities, which extends the reach of statistical physics to problems and processes well beyond functions of temperature and pressure. Newtonian mechanics is a limited starting point for general principles of model-making, especially for myriad living systems whose constituents are themselves high-dimensional. (1)

The early conceptions of statistical physics saw systems as large ensembles of replicates, with probabilities as frequencies, and assumptions of chaotic collisions. A newer view holds that maximizing entropy is a workable way of drawing inferences about probabilities where the user is responsible for a proper model of physics, the equivalencies among states, and choice of constraints. The same maximization of entropy procedure applies to non-equilibrium predictions of forces and flows. This opens up a broad area of dynamical modeling applicable to situations far-from-equilibrium and with non-linearities and even few-particle distributions. (15)

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