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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

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

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

Viswanathan, Gandhimohan, et al, eds. The Physics of Foraging: An Introduction to Random Searches and Biological Encounters. Cambridge: Cambridge University Press, 2009. The Federal University of Brazil editors Viswanathan, Marcos Da Luz, and Ernesto Raposo, are joined by Boston University’s Eugene Stanley, a pioneer since the 1970s of this presently extensive synthesis of statistical physics with organic phenomena, animal behaviors, human activities, in their environmental settings. Typical chapters are Random walks and Levy (Paul) flights, Wandering albatross, Human dispersal, and Superdiffusive searches. Once more, a scientific sense of an iterative mathematical domain underlying and guiding the movements of microbes and mice and peoples becomes evident. For later examples, see the work of Frederic Bartumeus (search) and colleagues, such as Experimental Evidence for Inherent Levy Search Behavior for Foraging Animals in Proceedings of the Royal Society B (Vol. 282/No. 1807, 2015).

Do the movements of animals, including humans, follow patterns that can be described quantitatively by simple laws of motion? If so, then why? These questions have attracted the attention of scientists in many disciplines, and stimulated debates ranging from ecological matters to queries such as 'how can there be free will if one follows a law of motion?' This is the first book on this rapidly evolving subject, introducing random searches and foraging in a way that can be understood by readers without a previous background on the subject. It reviews theory as well as experiment, addresses open problems and perspectives, and discusses applications ranging from the colonization of Madagascar by Austronesians to the diffusion of genetically modified crops. The book will interest physicists working in the field of anomalous diffusion and movement ecology as well as ecologists already familiar with the concepts and methods of statistical physics. (Synopsis)

Walker, Sara, et al. The Informational Architecture of the Cell. arXiv:1507.03877. With Hyunju Kim and Paul Davies, in this posting and New Scaling Relation for Information Transfer in Biological Networks, 1508.04174, Arizona State University astrobiologists continue their efforts for a better scientific explanation of living, self-replicating systems from their origins to our human inquiry. With reference to physicist Erwin Schrodinger’s 1940s statement and in line with Nigel Goldenfeld’s current project (search) a novel cross-fertilization, per the quotes, is proposed to inform a new physics by way of biological principles. In this regard, and for this section, the work is a major contribution to this imperative re-unification of cosmos and children.

While we have made significant advances in understanding biology over the last several decades, we have not made comparable advances in physics to unite our new understanding of biology with the foundations of physics. Schrodinger used physical principles to constrain unknown properties of biology associated with genetic heredity. One might argue that this could be done again, but now at the level of the epigenome, or interactome, and so on for any level of biological organization. But this kind of approach only serves to predict structures consistent with the laws of physics; it does not explain why they should exist in the first place. An alternative approach is that we might instead use insights from biology to constrain unknown physics. That is, we suggest a track working in the opposite direction from that proposed by Schrodinger; rather than using physics to inform biology, we propose to start by thinking about biology as a means to inform potentially new physics. (2, 1507.03877)

We avoid introducing new information-theoretic measure, since there already exist a plethora in the literature. Instead we choose a few known measures relevant to biological organization – including those of integrated information theory and information dynamics. Although we focus here on gene regulatory networks, we note that our analysis is not level specific. The formalism introduced in intended to be universal and may apply to any level of biological organization from the first self-organized living chemistries to technological societies. We do not focus on DNA per se, but consider distributed information processing as it operates within the cell, as we believe such analyses have great potential to explain, for example, why physical structures capable of storage and heredity, such as DNA, should exist in the first place. (3, 1507.03877)

Living systems are often described in terms of logic modules, information flows and computation. Such informational language is utilized in fields as diverse as evolutionary biology, neuroscience, pattern formation, colonial decision making in eusocial insects, and protein-protein interaction networks, to name just a few. Although informational analogies are widely applied, an important open question is whether information is intrinsic to the operation of biological systems or merely a useful conceptual metaphor. The debate over the ontological status of information in biology has far-reaching consequences, including implications for our understanding of whether biological organization is fully reducible to the known laws of physics and chemistry or new “informational laws" necessitating a foundational status for information in physical theory are necessary to account for living matter. (1, 1508.04174)

We hypothesize that the features reported herein may be common to biological networks of different function, and in particular, that scaling relations in information transfer may be a hallmark of biological organization. Our results are suggestive of previously unidentified information-based organizational principles that go beyond topological considerations such as scale-free structure, and may be critical to biological function. They thus open a new framework for addressing the debate over the status of information in biology, by demonstrating quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not exhibit the same informational properties as reported here. (13, 1508.04174)

Wang, Zhen, et al. Statistical Physics of Vaccination. arXiv:1608.09010. A global team from Japan, Canada, India, France, Italy, Slovenia, England, Switzerland and China, including Matjaz Perc, post a 150 page study with over 750 references for this nascent 2016 joining and synthesis of universe and human. As the long abstract conveys, complex network systems, as akin to many-body, condensed matter theories, can well quantify infectious disease epidemics as they spread through human populations. From our vantage, these advances imply an independent, universal mathematics which underlies, guides and constrains personal and social lives. By turns, it infers a revolutionary biological cosmos, as if graced by an anatomy, physiology, and indeed a stirring intelligence. The second quote is the closing paragraph – where do you ever find good literature in a technical paper? A further surmise would be a self-healing genesis uniVerse by virtue of an emergent worldwise knowledge which can be fed back to palliate, cure, and prevent the bodily and psychic maladies of a prior stochastic evolution.

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure.

Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. (Abstract)

Shakespeare famously ended his play “A Midsummer Night’s Dream" with an apology from the spirit Puck to the audience: “If we shadows have offended, Think but this, and all is mended — That you have but slumbered here While these visions did appear." We also wish to apologize to the readers in advance, for having inadvertently missed some excellent papers in the literature, having unintentionally misrepresented other papers that we did include, or being completely wrong about where the field should go next. However, there is no need for the reader to pretend our review was something they dreamed while slumbering. Instead, please write us if you feel our review was lacking in some respect, or better yet, publish some research that shows us the value of other perspectives on the field. If nothing else, we hope that our review has convinced the readers that coupled behavior-disease modeling is a rapidly growing area that promises exciting theoretical developments and empirical applications in the coming decade, especially if we draw on the accumulated knowledge of many decades of statistical physics. (125)

Whitelam, Stephen and Robert Jack. The Statistical Mechanics of Dynamic Pathways to Self-Assembly. arXiv:1407.2505. In a paper to appear in the 2015 edition of the Annual Review of Physical Chemistry, LBNL and University of Bath materials scientists explain how a marriage of traditional physics and complex systems theories can well serve and inform an incipient new creation of nature’s living materiality.

We describe some of the important physical characteristics of the `pathways', i.e. dynamical processes, by which molecular, nanoscale and micron-scale self-assembly occurs. We highlight the fact that there exist features of self-assembly pathways that are common to a wide range of physical systems, even though those systems may be different in respect of their microscopic details. We summarize some existing theoretical descriptions of self-assembly pathways, and highlight areas -- notably, the description of self-assembly pathways that occur `far' from equilibrium -- that are likely to become increasingly important. (Abstract)

The term `self-assembly' describes dynamical processes in which components of a system organize themselves, without external direction, into ordered patterns or structures. The range of scales on which self-assembly happens is enormous: we might say that atoms are self-assembled from protons, neutrons and electrons, and that galaxies are self-assembled from their component stars. Here we focus on assembly undergone by components that range in size from a few angstroms (for example, atoms and molecules) to a few microns (for example, colloids). Assembly of such components is important both in the natural world and, increasingly, the laboratory. Structures assembled in the laboratory include three-dimensional crystals, two-dimensional lattices, closed polyhedral shells (10, 11), and other tailored nanoscale shapes. (3)

Wills, Peter. Reflexivity, Coding and Quantum Biology. Biosystems. Online September, 2019. The University of Auckland philosophical biologist continues his frontier studies beyond a constrained Darwinian selection to include self-organizion, epigenetics, autocatalysis, symbolic information with Harold Pattee and Paul Davies, cooperative groupings and more. An active, codified development with a computational guise and a “reflexive” spontaneity thus becomes evident. With this in place, it is mused that evolutionary theories might at last be fulfilling Erwin Schrodinger’s view of an intrinsic physical fertility. Wills is often joined by University of North Carolina biochemist Charles Carter (search both) for papers such as Interdependence, Reflexivity, Fidelity, Impedance Matching, and the Evolution of Genetic Coding in Molecular Biology and Evolution (35/2, 2018).

Biological systems are fundamentally computational in that they process information in a purposeful fashion rather than just transferring bits of it in a syntactical manner. It carries meaning defined by the molecular context of its cellular environment. Information processing in biological systems displays an inherent reflexivity, a tendency for the information-processing to be “about” the behaviour of the molecules that participate in the computational process. This is most evident in the operation of the genetic code, where the specificity of the reactions catalysed by the aminoacyl-tRNA synthetase (aaRS) enzymes is required to be self-sustaining. A cell’s suite of aaRS enzymes completes a reflexively autocatalytic set of molecular components capable of making themselves by way of reflexive information stored in an organism’s genome. The genetic code is a reflexively self-organised, evolved symbolic system of chemical self-description. (Abstract excerpt)

Wolchover, Natalie. A Common Logic to Seeing Cats and Cosmos. Quanta Magazine. Online December, 2014. The science writer reports on another convergence of disparate fields and theories, which are separate mainly because of different definitions. Boston University’s Pankaj Mehta and David Schwab of Northwestern University are joining statistical physics and renormalization theories with deep neural networks to learn how nature’s universal logic can recognize features (cats) among large data displays. An extrapolation might to be imagine the universe to human trajectory as engaged in its own “deep learning” project via our own self-recognition and discovery.

Wright, Katherine. Life is Physics. physics.aps.org/articles/v12/2. Physicists are on the hunt for a “theory of life” that explains why life can exist. A senior editor for the American Physical Society (APS)reviews the many ways this historic re-convergence and theoretical closure is underway in our midst as biology and physics, vibrant life and material animations become one again.

(Ramin) Golestanian and (Nigel) Goldenfeld both believe that the traits of life, such as replication, evolution, and using energy to move, are examples of what condensed-matter physicists call “emergent phenomena”—complex properties that arise from the interactions of a large number of simpler components. For example, superconductivity is a macroscopic property that arises in metals from attractive interactions among its electrons, which lead to a state with zero electrical resistance. In the case of life, the emergent behaviors arise from interactions among molecules and from how the molecules group together to form structures or carry out functions.

Xue, Chi, et al. Scale-invariant Topology and Bursty Branching of Evolutionary Trees Emerge from Niche Construction. Proceedings of the National Academy of Sciences. 117/7679, 2020. University of Illinois genome biologists including Nigel Goldenfeld provide an exercise to show how, by way of statistical physics and network principles, that life’s circuitous, diverse, adaptive course can yet be found to have an intrinsic, self similar topology.

Phylogenetic trees describe both the evolutionary process and community diversity. Recent work has established that they exhibit scale-invariant topology, which quantifies the fact that their branching lies in between balanced binary trees and maximally unbalanced ones. Here, we present a simple, coarse-grained statistical model of niche construction coupled to speciation. Finite-size scaling analysis of the dynamics shows that the resultant phylogenetic tree topology is scale-invariant due to a singularity arising from large niche construction fluctuations that follow extinction events. The same model recapitulates the bursty pattern of diversification in time. (Abstract)

Yukalov, Vyacheslav. Selected Topics of Social Physics: Nonequilibrium Systems. arXiv:2307.10833. The author is a senior complexity expert (search) who was at ETH Zurich in collaboration with Didier Sornette and is now jointly at the Bogolubov Laboratory of Theoretical Physics, Dubna, Russia and the University of Sao Paulo, Brazil. Three main sections are Dynamical Social Systems, Generalized Evolution Equations and Models of Financial Markets. See also Quantum Operation of Affective Artificial Intelligence by YY at 2305.08112. In many formats, we record a growing realization that universe and human from enlivened substance to political populace are deep (wo)manifestations of implicate codified patterns and processes.

This review article is the second part of the project Selected Topics of Social Physics. The first part has been devoted to equilibrium systems. The present part considers nonequilibrium systems. The style of the paper is a tutorial which makes it easy to read for nonspecialists to grasping the basics of social physics and describes recent original models that could be of interest to experienced researchers in the field. The present material is based on the lectures that the author had been giving during several years at the Swiss Federal Institute of Technology in Zurich (Abstract)

Zhou, Cangqi, et al. Cumulative Dynamics of Independent Information Spreading Behavior: A Physical Perspective. Nature Scientific Reports. 7/5530, 2017. Tsinghua University, Beijing, information theorists achieve a novel synthesis across these widely apart realms by which to associate our daily social media with natural complexity dynamics. As the quotes allude, an apparent independent, invariant source is implied, more than analogous, which seems in exemplary effect at each stage and instance.

The popularization of information spreading in online social networks facilitates daily communication among people. Although much work has been done to study the effect of interactions among people on spreading, there is less work that considers the pattern of spreading behaviour when people independently make their decisions. By comparing microblogging, an important medium for information spreading, with the disordered spin glass system, we find that there exist interesting corresponding relationships between them. Based on the analogy with the Trap Model of spin glasses, we derive a model with a unified power-function form for the growth of independent spreading activities. Our model takes several key factors into consideration, including memory effect, the dynamics of human interest, and the fact that older messages are more difficult to discover. Our work indicates that, other than various features, some invariable rules should be considered during spreading prediction. (Abstract)

Our work contributes a useful methodology, the analogy with physical systems, for studying human dynamics. The discovered rule, that applies to the growth of different retweeting activities with a unified form, reveals the nature of complexity in retweeting activities. We hope that our work will shed some light on the study of human dynamics. Our work also indicates that, other than various features adopted in well-tuned machine learning models, some invariable rules, such as the power-law growth of independent retweeting activities, the memory effect in human behaviour, should be taken into consideration during the prediction of information spreading. (2)

Zhou, Shuang. Lyotropic Chromonic Liquid Crystals. International: Springer, 2017. The edition is a Kent State University, Liquid Crystal Institute, award-winning doctoral thesis by a physical chemist now at Harvard. Besides clever understandings of nature’s lively materiality, the first chapter notes how such dynamic and topological propensities can be seen to similarly arise and array across emergent groupings of microbes, fish swarms, avian flocks and ungulate herds. he work thus exemplifies current studies as they find deep connections between biological, animal life and increasingly conducive physical substrates. We cite book summary edits, some chemical definitions, and an Abstract from a talk that Zhou gave at UM Amherst in February, 2018.

This thesis describes lyotropic chromonic liquid crystals (LCLCs) with exotic elastic and viscous properties. The first part presents a thorough analysis of LCLCs as functions of concentration, temperature and ionic contents, while the second part explores an active nematic system: living liquid crystals, which represent a combination of LCLC and living bacteria. LCLCs are an emerging class of liquid crystals that have shown profound connections to biological systems in two aspects. First, the process of chromonic aggregation is similar to DNA oligomers and other super-molecular assemblies of biological origin. Second, LCLCs are biocompatible, serving as a unique anisotropic matrix to interface with living systems. (Abstract excerpts and edits)

In this talk, I will introduce a new active matter system, called living liquid crystals, which combine lyotropic chromonic liquid crystals with living bacteria Bacillus Subtilis. Such system offers independent control of the orientational order – through the nematic liquid crystal, and activity – through the concentration of bacteria and oxygen. The long range nematic order profoundly changes the particle-particle and particle-fluid interactions, and results in a wealth of intriguing phenomena, such as 1) controlling bacteria trajectories through liquid crystal director field, 2) optical visualization of the motion of nanometer-thick bacteria flagella, 3) local melting of the liquid crystal by bacteria flow, 3) cargo particle transportation, 4) bend instability, and 5) low Reynolds number turbulence, among others. Living liquid crystals provide a unique angle to understand active matters physics from particle level to macroscopic level. (Abstract excerpts)

Liquid crystals (LCs) are matter in a state which has properties between those of conventional liquids and those of solid crystals. For instance, a liquid crystal may flow like a liquid, but its molecules may be oriented in a crystal-like way. Lyotropic LCs exhibit phase transitions as a function of both temperature and concentration of the liquid-crystal molecules in a solvent. Nematic denotes a state of a liquid crystal in which the molecules are oriented in parallel but not arranged in well-defined planes.

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