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

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

Rossi, Paolo. Surname Distribution in Population Genetics and in Statistical Physics. Physics of Life Reviews. Online June, 2013. As the Abstract notes, a University of Pisa physicist finds parallels between a person’s family name, genomic sources, and onto condensed material phenomena. We enter as an example of a growing incidence of such studies that draw common correspondences from disparate physical realms to personal lives. A further reason, as many entries attest, is a recognition of a mathematical domain that, unbeknownst, underlies, guides, channels, our individual and collective days and destinies, see herein Callegari about migrations, and Bohorquez about insurgencies.

Surnames tend to behave like neutral genes, and their distribution has attracted a growing attention from geneticists and physicists. We review the century-long history of surname studies and discuss the most recent developments. Isonymy has been regarded as a tool for the measurement of consanguinity of individuals and populations and has been applied to the analysis of migrations. The analogy between patrilineal surname transmission and the propagation of Y chromosomes has been exploited for the genetic characterization of families, communities and control groups. Surname distribution is the result of a stochastic dynamics, which has been studied either as a Yule process or as a branching phenomenon: both approaches predict the asymptotic power-law behavior which has been observed in many empirical researches. Models of neutral evolution based on the theory of disordered systems have suggested the application of field-theoretical techniques, and in particular the Renormalization Group, to describe the dynamics leading to scale-invariant distributions and to compute the related (critical) exponents. (Abstract)

Rosso, Osvaldo, et al. Topics on Non-Equilibrium Statistical Mechanics and Nonlinear Physics II. Philosophical Transactions of the Royal Society A. 373/Iss. 2056, 2015. An introduction to papers from a 2014 conference in Brazil on these concerns, Google “Medyfinol” for info. Among the contributions is Causal Information Quantification of Prominent Dynamical Features of Biological Neurons by Fernando Montani, et al, which can represent this union and cross-invigoration of emergent persons able to learn this with a conducive physical materiality.

The research in non-equilibrium statistical mechanics and nonlinear physics is a scientific approach to the investigation of how relationships between parts give rise to the collective behaviour of a system, and how the system interacts and forms relationships with its environment. Such problems are tackled, mostly with new concepts and tools related to information theory, statistical mechanics and nonlinear dynamics. They aim at representing and understanding the organized albeit unpredictable behaviour of natural systems that are considered intrinsically complex. In fact, the exciting fields of complexity, chaos and nonlinear science have experienced impressive growth in recent decades. (Abstract)

Rotrattanadumrong, Rachapun and Robert Endres. Emergence of Cooperativity in a Model Bioflim. Journal of Physics D: Applied Physics. 50/234006, 2017. As the quotes say, Imperial College, London system biophysicists trace an insistent tendency even at this bacterial stage to get along with each other as a way to improve group survival benefits. See also a note added below about the Special Issue on Collective Behavior of Living Matter of which it is part edited by Ben Fabry, Daniel Zitterbart and R. Endres. And it well serves this section when a paper that joins microbes and physical phenomena can appear in a Physics journal.

Evolution to multicellularity from an aggregate of cells involves altruistic cooperation between individual cells, which is in conflict with Darwinian evolution. How cooperation arises and how a cell community resolves such conflicts remains unclear. In this study, we investigated the spontaneous emergence of cell differentiation and the subsequent division of labour in evolving cellular metabolic networks. In spatially extended cell aggregates, our findings reveal that resource limitation can lead to the formation of subpopulations and cooperation of cells, and hence multicellular communities. A specific example of our model can explain the recently observed oscillatory growth in Bacillus subtilis biofilms. (Abstract)

Biological systems are usually conceptualized as networks of interacting genes and proteins. Yet an analysis of genetic programs fails to explain higher-level functions such as multi-cellular aggregation, tissue organization, embryonic development, and collective behaviour of individuals. Such collective processes are often insensitive to microscopic details of the underlying system and are emergent properties that arise from the interactions between cells or individuals. In recent years, novel theoretical and experimental approaches have spurred the development of statistical models of complex biological systems and generated progress in our understanding of emergent collective processes in biology. (Special Issue)

Saclioglu, Cihan, et al. Group Behavior in Physical, Chemical and Biological Systems. Journal of Biosciences. 39/2, 2014. In an issue on Individuals and Groups (search S. Newman), biophysicists Saclioglu, Sabanci Universitesi, Istanbul with Onder Pekcan, Kadir Has Universitesi, Istanbul and Vidyanand Nanjundiah, Indian Institute of Science, Bangalore scope out how to situate and root in substantial nature, as must be the case, life’s persistent evolutionary formation of social assembles at each and every stage and instance. Section 2, for example, is Physical principles underlying collective behavior: Elementary particles and emergent macroscopic manifestations. Maybe in this Indian journal and milieu, an Eastern mind can better perceive how obvious this holistic unity must be. It goes on to a similar Group Behavior in Chemistry, Groups in Biology segment and more, altogether akin to 2017 papers by van Gestel/Tarnita and Sebe-Pedros, et al of a universal, independent recurrence from universe to us.

Groups exhibit properties that either are not perceived to exist, or perhaps cannot exist, at the individual level. Such ‘emergent’ properties depend on how individuals interact, both among themselves and with their surroundings. The world of everyday objects consists of material entities. These are, ultimately, groups of elementary particles that organize themselves into atoms and molecules, occupy space, and so on. It turns out that an explanation of even the most commonplace features of this world requires relativistic quantum field theory and the fact that Planck’s constant is discrete, not zero. Groups of molecules in solution, in particular polymers (‘sols’), can form viscous clusters that behave like elastic solids (‘gels’). Group behaviour among cells or organisms is often heritable and therefore can evolve. This permits an additional, typically biological, explanation for it in terms of reproductive advantage, whether of the individual or of the group. (Abstract excerpt)

Selesnick, Stephen, et al. Quantum-like Behavior without Quantum Physics. Journal of Biological Physics. Online July, 2017. As the Abstract notes, mathematician Selesnick, and physicist Gualtiero Piccinini, University of Missouri, with philosopher J. P. Rawling, Florida State University, consider ways that quantum effects, as they become better understood into the 21st century, can be noticed and quantified across classical phenomena such as neural activities.

Recently there has been much interest in the possible quantum-like behavior of the human brain in such functions as cognition, the mental lexicon, memory, etc., producing a vast literature. These studies are both empirical and theoretical, the tenets of the theory in question being mainly, and apparently inevitably, those of quantum physics itself, for lack of other arenas in which quantum-like properties are presumed to obtain. However, attempts to explain this behavior on the basis of actual quantum physics going on at the atomic or molecular level within some element of brain or neuronal anatomy do not seem to survive much scrutiny. In this paper we lay the groundwork for a theory that might explain the provenance of quantum-like behavior in complex systems whose internal structure is essentially hidden or inaccessible. The results reveal certain effects in such systems which, though quantum-like, are not identical to the kinds of quantum effects found in physics. (Abstract)

Seoane, Luis. Fate of Duplicated Neural Structures. arXiv:2008.00531. The Barcelona systems theorist is presently a postdoc at MIT’s Center for Brains Minds + Machines (search LS and visit the CBMM site). After prior collaborations with Ricard Sole (see arXiv site) and others, he continues his project to give life’s evolutionary biology and cognition a deeper statistical and computational physics basis, as it necessarily has to have. His innovative 2020 entry is a good instance of this scientific frontier as it seeks to quantify and join human and universe into a unified organic genesis. Its sections course through bilateral hemispheres, reactive and/or predictive brains, cortical columns, active algorithms, linguistics and more as each form and move by an energetic flow. The 193 references over the 2000s and 2010s document a 21st century worldwise revolution from everything in pieces to altogether now. So this panicky year seems also a time of historic local, global and ecosmic synthesis which, if we could witness, read and avail might help mitigate and guide.

Statistical mechanics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism percolates throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored which yields life, cognition, and Darwinian evolution. Neurons and neural circuits then reside between statistical mechanics, computation, and cognitively in natural selection. A statistical physics theory of neural circuits would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions.

With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at central nervous systems with a stress on computational thresholds that might prompt this redundancy and at duplicated circuits for complex phenotypes. From this we derive phase diagrams and transitions between single and duplicated circuits, which constrain evolutionary paths that lead to complex cognition. Similar phase diagrams and transitions might constrain internal connectivity patterns of neural circuits at large. Thus the formalism of statistical mechanics seems a natural framework for this promising line of research. (Abstract)

Simpson, Kevin, et al. Spatial biology of Ising-like synthetic genetic networks. BMC Biology. 21/185, 2003. This contribution by Pontificia Universidad Católica de Chile, Santiago geneticists could exemplify the 2023 cross-discipline integrations as it at once cites theoretic reasons for complex genome systems which can now be seen to be deeply grounded in active physical principles. By so doing the whole dynamic operation is realized to embody a Ising model (see below) as it forms self-organizing fractal patterns.

Understandings of how spatial patterns of gene expression emerge from the interaction of individual gene networks remains a challenge in biology. We propose an experimental system with a theoretical framework that captures the emergence of short- and long-range correlation from interacting gene networks. Our method combines synthetic biology, statistical mechanics, and computational simulations to study the spatial behavior of synthetic gene networks. By this approach, we describe the spatial behavior of bi-stable and chemically coupled synthetic gene networks that self-organize into long-range correlations with power-law scalings. The resultant patterns are then found to resemble ferromagnetic and anti-ferromagnetic configurations of the Ising model near critical points. (Excerpt)

Here, we apply a theoretical framework based on the Ising model to study how spatial correlations emerge from chemically coupled, bistable SGNs in Escherichia coli colonies. Ww construct synthetic toggle switches whose states are based on quorum sensing signaling. These SGNs self-organize in long-range spatial correlations and fractal patterns reminiscent of ferromagnetic systems of the Ising model. (2)

Our findings shed light on the spatial biology of coupled and bistable gene networks in growing cell populations. This emergent spatial behavior could provide insights into the study and engineering of self-organizing gene patterns in eukaryotic tissues and bacterial consortia. (12)

The Ising model, after the physicist Ernst Ising, is a mathematical model of ferromagnetism in statistical mechanics. It consists of discrete variables that represent magnetic dipole moments of atomic "spins" that can be in one of two states.

Siva, Karthnik, et al. Spin Glass Models of Syntax and Language Evolution. arXiv:1508.00504. We note this paper by Caltech mathematicians as a good example of the cosmic synthesis of physics and people, condensed matter and cultural discourse, a rooting of life and us in a fertile uniVerse. The Principles and Parameters model of Generative Linguistics due to Noam Chomsky is applied from Albanian to Zulu which leads to subject-verb language networks. If linguistics can be described by way of particles in relative motions, then this affinity would imply in turn that physical substance is literally textual in kind. The senior coauthor is Professor Matilde Marcolli. If there is any doubt that women can do STEM studies, check her website publications page where you will find work on Multifractals, Mumford Curves, Eternal Inflation and much more. For an April 2016 edition see her Syntactic Parameters and a Coding theory Perspective on Entropy and Complexity of Language Families paper in Entropy. And for even more see Semantic Spaces at 1605.0504238 and Syntactic Phylogenetic Trees at 1607.02791..

Using the SSWL database of syntactic parameters of world languages, and the MIT Media Lab data on language interactions, we construct a spin glass model of language evolution. We treat binary syntactic parameters as spin states, with languages as vertices of a graph, and assigned interaction energies along the edges. We study a rough model of syntax evolution, under the assumption that a strong interaction energy tends to cause parameters to align, as in the case of ferromagnetic materials. We also study how the spin glass model needs to be modified to account for entailment relations between syntactic parameters. This modification leads naturally to a generalization of Potts models with external magnetic field, which consists of a coupling at the vertices of an Ising model and a Potts model with q=3, that have the same edge interactions. We describe the results of simulations of the dynamics of these models, in different temperature and energy regimes. We discuss the linguistic interpretation of the parameters of the physical model. (Abstract)

Skjeltorp, Arne and Geir Helgesen. Editorial. European Physical Journal Special Topics. 225/4, 2016. An introduction to an issue on Cooperative Particles with papers such as Entangled Active Matter: From Cells to Ants and Patchy Colloidosomes – An Emerging Class of Structures, see abstract below. For another case, see Emergent Behavior in Active Colloids by Andreas Zotti and Holger Stark in the Journal of Physics: Condensed Matter (28/253001, 2016).

Both cells and ants belong to the broad field of active matter, a novel class of non-equilibrium materials composed of many interacting units that individually consume energy and collectively generate motion or mechanical stresses. However cells and ants differ from fish and birds in that they can support static loads. This is because cells and ants can be entangled, so that individual units are bound by transient links. Entanglement gives cells and ants a set of remarkable properties usually not found together, such as the ability to flow like a fluid, spring back like an elastic solid, and self-heal. In this review, we present the biology, mechanics and dynamics of both entangled cells and ants. We apply concepts from soft matter physics and wetting to characterize these systems as well as to point out their differences, which arise from their differences in size.

Smerlak, Matteo. Natural Selection as Coarsening. arXiv:1707.05317. A Perimeter Institute for Theoretical Physics senior postdoctoral researcher deftly considers how life’s seemingly vicarious evolution might actually be rooted in and exemplify fundamental physical phenomena. A concern is a somewhat arcane terminology, better definitions and translations would help, as they might apply to biological systems. But in the later 2010s, evinced by entries here and throughout, a salutary synthesis, long foreseen, is at last coming together. See also Limiting Fitness Distributions to Evolutionary Dynamics by Smerlak and Ahmed Youssef at 1511.00296.

Analogies between evolutionary dynamics and statistical mechanics, such as Fisher's second-law-like "fundamental theorem of natural selection" and Wright's "fitness landscapes", have had a deep and fruitful influence on the development of evolutionary theory. Here I discuss a new conceptual link between evolution and statistical physics. I argue that natural selection can be viewed as a coarsening phenomenon, similar to the growth of domain size in quenched magnets or to Ostwald ripening in alloys and emulsions. In particular, I show that the most remarkable features of coarsening---scaling and self-similarity---have strict equivalents in evolutionary dynamics. This analogy has three main virtues: it brings a set of well-developed mathematical tools to bear on evolutionary dynamics; it suggests new problems in theoretical evolution; and it provides coarsening physics with a new exactly soluble model. (Abstract)

Takatori, S. C. and J. F. Brady. Towards a Thermodynamics of Active Matter. Physical Review E. 91/032117, 2015. Caltech chemical engineers proceed to quantify the unique behaviors of cosmic nature’s animate media by way of nonequilibrium theories.

Self-propulsion allows living systems to display self-organization and unusual phase behavior. Unlike passive systems in thermal equilibrium, active matter systems are not constrained by conventional thermodynamic laws. A question arises, however, as to what extent, if any, can concepts from classical thermodynamics be applied to nonequilibrium systems like active matter. Here we use the new swim pressure perspective to develop a simple theory for predicting phase separation in active matter. Using purely mechanical arguments we generate a phase diagram with a spinodal and critical point, and define a nonequilibrium chemical potential to interpret the “binodal.” We provide a generalization of thermodynamic concepts like the free energy and temperature for nonequilibrium active systems. (Abstract)

Tsimring, Lev, et al. Focus on Swarming in Biological and Related Systems. New Journal of Physics. Circa 2013 -, 2014. Tsimring, Biocircuits Institute, University of California, San Diego, with Hugues Chate, Service Physics Solid State, CEA Saclay, France and Igor Aronson, Argonne National Laboratory, introduce an open collection on statistical physics as applied to organism assemblies for a broad range of articles. For example, “From Organized Internal Traffic to Collective Navigation of Bacterial Swarms” by Gil Ariel, et al, “Collective Motion Dynamics of Active Solids and Active Crystals,” Eliseo Ferrante, et al, and “Swarming, Schooling, Milling: A Data-Driven Fish School Model” by Daniel Calovi, et al. Search, for example, the February 2014 issue to locate.

In the last 15 years, the collective motion of large numbers of self-propelled objects has become an increasingly active area of research. The examples of such collective motion abound: flocks of birds, schools of fish, swarms of insects, herds of animals etc. Swarming of living creatures is believed to be critical for the population survival under harsh conditions. The ability of motile microorganisms to communicate and coordinate their motion leads to the remarkably complex self-organized structures found in bacterial biofilms. Active intracellular transport of biological molecules within the cytoskeleton has a profound effect on the cell cycle, signaling and motility. Collective motion and self-propulsion leads to new and non-trivial material properties of the 'active' medium.

There has been a rapidly growing number of computational and theoretical works on the generic dynamic and statistical properties of collective behavior exhibited by self-propelled particles with simplified interactions, from point-like particles to rigid self-propelled rods. A variety of dynamic phases were predicted, from moving clusters, bands, to swarming states. However, to date, the connection between these simulations and experimentally observed dynamics of self-propelled particles remains unsatisfactory. (Excerpts)

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