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

2. Computational Systems Physics: Self-Organization, Active Matter

Ross, Tyler, et al. Controlling Organization and Forces in Active Matter through Optically-defined Boundaries. Nature. 572/224, 2019. CalTech bioengineers uncover non-equilibrium phenomena and principles by optically controlling structures and fluid flow in an engineered system of active biomolecules which led to views of an innate tendency to spontaneously organize into animate structures and movements.

Rovelli, Carlo. The Relational Interpretation of Quantum Physics. arXiv:2109.09170. The Aix Marseille University and Perimeter Institute polyphysicist provides a latest finesse of his theoretical perception since the 1990s that interactivities between objects have their own existence which may be more vital that the pieces themselves. As a general validity of this concept has come to most subject fields, this insight also gains credence for this deepest, substantial realm. See also Information is Physical: Cross Perspective Links in Relational Quantum Mechanics by CR and Emily Adlam at arXiv:2203.13342, For a popular article see The Big Idea: Why Relationships are the Key to Existence in the Manchester Guardian for September 5, 2022.

The relational interpretation (or RQM, for Relational Quantum Mechanics) solves the measurement problem by considering an ontology of sparse relative events, or "facts". Facts are realized in interactions between any two physical systems and are relative to these systems. RQM's technical core is the realisation that quantum transition amplitudes determine physical probabilities only when their arguments are facts relative to the same system. The relativity of facts can be neglected in the approximation where decoherence hides interference, thus making facts approximately stable. (Abstract)

Rupe, Adam and James Crutchfield.. On Principles of Emergent Organization. Physics Reports. 1071, 2024. Self-organization is ubiquitous in natural systems at all scales from patterning in quantum wave functions at sub-Plank-lengths to biological morphogenesis to mass distribution at the largest scales of the universe. Herein Pacific Northwest National Laboratory, Richland, WA and Complexity Sciences Center, UC Davis system physicists (search JC) post a 50 page, 228 reference entry as a 21st century survey of better understandings of nature’s energetic creativity. The extensive contents noted below can convey the depth and scope of their theoretical synthesis. In regard, I look back to Erich Jantsch’s 1980 The Self-Organizing Universe whose prescience is at last being fulfilled.

Albeit in technical prose, the key concept is to set aside a prior constructionist method reduced to parts so to allow a new view of an innate, ascendent spontaneity. As 2024 entries (see CommomCode) accomplish, a conducive physical substrate can now provide a supportive explanation for life’s sequential emergence. See also, for example, Synergistic signatures of group mechanisms in higher-order systems by Thomas Robiglio, et al at arXiv:2401.11588 for a similar study.

CONTENTS I. Genesis; II. Narrative and Roadmap; III. Nonlinear Dynamics; IV. Equilibrium Statistical Physics; V. Nonequilibrium Statistical Physics; VI. Intractability and Limits of Constructionism; VII. Organization Beyond Constructionism; VIII. A Statistical Mechanics of Emergence

After more than a century of concerted effort, physics still lacks basic principles of spontaneous self-organization. To appreciate why, we state the problem, outline historical approaches, and survey the present theoretic state. Then, an overview of two modern mathematical formulations of organization - intrinsic computation and evolution operators - lays out a way to overcome past issues. The vantage point they afford can account for the emergence of structures via a statistical mechanics far from equilibrium. The result is a constructive path forward to principles of organization that builds on mathematical identifications. (Abstract)

That said, Section VIII brings together evolution operators and predictive equivalence to provide a statistical mechanics of emergence. While higher-level emergent behaviors cannot be deduced from their lower-level equations, our statistical mechanics provides the physical foundations for a complete and self-contained dynamics governing the emergent behaviors. Thus, evolution operators and intrinsic computation represent complex organization as emergent degrees of freedom while providing the consistent dynamical laws for their evolution. This again is all behavior-driven and so outside the constructionist paradigm. (4)

Saarloos, Win van, et al. Soft Matter: Concepts, Phenomena, and Applications. Princeton: Princeton University Press, 2024. Wim van Saarloos is professor emeritus of theoretical physics at the Lorentz Institute at Leiden University, Vincenzo Vitelli is professor of physics at the University of Chicago and Zorana Zeravcic is professor of physics in the Gulliver Laboratory at ESPCI Paris. In regard they contribute the first book treatment of this animate subject, hardly a decade old. A chapter on Active Matter is included along Non-Equilibrium Pattern Formation, Elasticity, Designing Matter and so on. Altogether one more perspective upon a natural dynamic liveliness due to common codings gains a broad and deep expression.

Soft matter science is an interdisciplinary field at the interface of physics, biology, chemistry, engineering, and materials science. It encompasses colloids, polymers, and liquid crystals as well as rapidly emerging topics such as metamaterials, memory formation and learning in matter, bioactive systems, and artificial life.. The presentation integrates statistical mechanics, dynamical systems, and hydrodynamic approaches with conservation laws and broken symmetries as guiding principles along with computational and machine learning advances.

Sakellariou, Jason, et al. Maximum Entropy Models Capture Melodic Styles. Nature Scientific Reports. 7/9172, 2017. Into the 21st century, Sorbonne Universities and Sapienza University of Rome physicists including Vittorio Loreto can tune into the actual music of the spheres, and its natural harmonies by way of algorithmic, Markov and thermodynamic essences.

Many complex systems exhibit a highly non-trivial structure that is difficult to capture with simple models. Several biological systems form networks of interacting components (neurons, proteins, genes, whole organisms) whose collective behavior is characterized by a complex mosaic of correlations among the different components. Arguably, the ultimate biological origin of purely intellectual constructs such as language or music, should allow us to look at them from a similar point of view, i.e., as complex networks of interacting components. In both cases, one would suspect that essential features of their complexity arise from high-order combinatorial interactions. However, a number of works in recent years have shown that models based on pairwise interactions alone capture most of the correlation structure of some biological systems and even English words. In this paper we extend this idea to the field of music. (1)

Schweitzer, Frank. An Agent-Based Framework of Active Matter with Applications in Biological and Social Systems. arXiv:1806.10829. The ETH Zurich Chair of Systems Design has been a pioneer theorist and practitioner of the complexity revolution since the 1990s. As this paper conveys, a latest phase is an on-going rooting in and synthesis with physical phenomena, along with a strong inclusion of ubiquitous network features. Elemental agents, aka nodes, thus engage in “binary interactions” in the guise of a manifest statistical physics. Their persistent non-equilibrium dynamics can then reveal common, general principles across micro and macro perspectives. In living instantiations, they foster aggregation, cross-communication, self-assemblies, and so on.

Active matter, as other types of self-organizing systems, relies on the take-up of energy that can be used for different actions, such as motion or structure formation. Here we provide a dynamic agent-based approach for these processes at different levels of organization, physical, biological and social. Nonlinear driving variables describe the take-up, storage and conversion of energy, whereas driven variables describe the energy consuming activities. To demonstrate, we recast a number of existing models of Brownian agents and Active Brownian Particles such as clustering and self-wiring of networks based on chemotactic interactions, online communication and polarization of opinions based on emotional influence. The framework obtains critical parameters for active motion and the emergence of collective phenomena and the role of energy take-up and dissipation in dynamic regimes. (Abstract edits)

Scott, Alwyn. The Nonlinear Universe. Berlin: Springer, 2007. The late (1931 – 2007) University of Arizona mathematician was a leading pioneer of this revolution to reconceive an emergent nature in terms of complex dynamical systems. The original director of the Center for Nonlinear Studies at Los Alamos Laboratory, he was a founding editor of Physica D: Nonlinear Phenomena. This present work provides a first hand history from general systems theory to mathematical biology, synergetics, complex adaptive systems, and others, along with their recent application from fractal galaxies to brains and the biosphere. In so doing Scott champions a hierarchical arrangement as nature’s skeletal scale for rising consciousness. A final chapter, Reductionism and Life, contends that this necessary earlier, linear phase quite misses an innate cosmic animation to be newly engaged as synthesis may take over analysis. Please note the quote’s last line.

So what is the secret of Life? Although rooted in nature, living beings are organized as immensely complex dynamic hierarchies, where “immense” is used in the technical sense to denote a finite number of possibilities that is to large to list and “complex” implies a class of natural systems that cannot be reductively modeled. Biological hierarchies achieve their immense complexities through processes of chaotic emergence, a phrase that was coined by philosophers to describe mental self-organization and can be applied to Darwinian evolution, the growth of biological forms, and their daily dynamics….suggesting that there may be something to Henri Bergson’s vitalism after all. (304-305)

Stanley, Eugene, et al. Statistical Physics and Economic Fluctuations. Lawrence Blume and Steven Durlauf, eds. The Economy as an Evolving Complex System III. New York: Oxford University Press, 2005. The authors are involved with a cross-fertilization and synthesis of nonlinear science and commercial business, via a new field named econophysics. Indeed across this wide expanse are found many correspondences which again suggests that the same universal phenomena recurs at every stage and instance.

Statistical physics deals with systems comprising a very large number of interacting subunits, for which predicting the exact behavior of the individual subunit would be impossible. Hence, one is limited to making statistical predictions regarding the collective behavior of the subunits. Recently, it has come to be appreciated that many such systems consisting of a large number of interacting subunits obey universal laws that are independent of the microscopic details. The finding, in physical systems, of universal properties that do not depend on the specific form of the interactions gives rise to the intriguing hypothesis that universal laws or results may also be present in economic and social systems. (70-71) Moreover, the general principles of scale invariance used here have proved useful in interpreting a number of other phenomena, ranging from elementary particle physics and galaxy structure to finance. (71-72)

Thurner, Stefan. A Simple General Model of Evolutionary Dynamics. Meyer-Ortmanns, Hildegard and Stefan Thurner, eds. Principles of Evolution: From the Planck Epoch to Complex Multicellular Life. Berlin: Springer, 2011. As statistical mechanics and complexity science merge, a University of Vienna physicist attempts to express the revolutionary genesis universe which is being increasingly implied. A typical section is named “Evolutionary Dynamics as a Self-Organized Critical System.” But betwixt Ptolemaic and Copernican options, as the volume itself, reduction and mechanism holdovers impede such a vision, still missing a crucial piece of seeing these mathematical propensities as actually genetic in kind.

We show that phase transitions that separate phases of high and low diversity can be approximated surprisingly well by mean-field methods. We demonstrate that the mathematical framework is suited to understand systemic properties of evolutionary systems, such as their proneness to collapse, or their potential for diversification. The framework suggests that evolutionary processes are naturally linked to self-organized criticality and to properties of production matrices, such as their eigenvalue spectra. (119)

Motivated by statistical physics, we believe that it would be possible to formulate evolutionary dynamical systems by focusing on microscopic interactions of agents and then to derive macroscopic – systems – properties and laws. Further there is room for hope that a variety of different specific microscopic interaction mechanism may lead to the same class of macroscopic properties. In physics this led to the concept of universality classes. (122)

Tkacik, Gasper and Aleksandra Walczak. Information Transmission in Genetic Regulatory Networks: A Review. Journal of Physics: Condensed Matter. 23/15, 2011. In regard I heard physicist Nigel Goldenfeld (search) at the University of Massachusetts, Amherst in October 2013 announce that “Biology is the physics of the 21st century.” This report in an Institute of Physics (IOP) journal by Institute of Science and Technology, Austria, and CRNS-Ecole Normale Superieure, Paris theorists could be a good example of this turn, among an increasing number in traditional physics periodicals. It illustrates the realization, and grand promise, that one whole uniVerse must exist and be engaged this way whence physical and living systems can cross inform and fertilize each other. See also, e.g., a paper by Walczak, et al in Cooperative Societies about the nonlinear dynamics of bird flocks.

Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'. (Abstract)

Gašper Tkačik is a theoretical physicist who studies information processing in living systems. He uses tools from statistical physics of disordered systems and from information theory to investigate biological systems such as networks of neurons or genes. The unifying hypothesis driving his research has been that information processing networks have evolved or adapted to maximize the information transmitted from their inputs to the outputs, given the biophysical noise and resource constraints. He works closely with experimentalists and analyzes data sets that record simultaneously the behavior of many network components. Results of his work gave insight into the principles of genetic regulation in early morphogenesis of Drosophila and of information coding in retinal ganglion cells. In the future, he plans to expand his activities to study collective behavior and cellular self-organization. (GT website)

Aleksandra Walczak The precision and reproducibility of cellular processes poses a challenge to many-body physics and our understanding of the physical principles that control the emergent properties of biological systems. In my research I study the behaviour of such strongly coupled nonlinear systems that are not in equilibrium, mostly inspired by existing biological solutions. My main interest lies in the description of systems on the cellular scale - understanding the link between function, development and evolvability of conserved pathways and their elements. My recent focus has been on a number of concrete, not disjoint, topics: gene regulatory networks, the immune system and population genetics. (AW website)

Tkacik, Gasper, et al. Thermodynamics for a Network of Neurons: Signatures of Criticality. arXiv:1407.5946. A team from Austria, France, and the USA including Thierry Mora and William Bialek apply statistical mechanics concepts to an analysis of cerebral function and cogitation. Compare with a concurrent, similar paper by Sequn, Goh, et al about urban people movements.

The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but with more spikes the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N=160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. Networks with more or less correlation among neurons would not reach this critical state. (Abstract)

Trabesinger, Andreas, ed. Complexity. Nature Physics. 8/1, 2012. A general introduction to this special Insight section which focuses on and champions a decade and more of wide and deep progress in “network science.” Along with articles herein by Barabasi, Newman, and Vespignani, are “Between Order and Chaos” by James Crutchfield, and “Networks Formed from Interdependent Networks” by Jianxi Gao, Sergey Buldyrev, Eugene Stanley, and Shlomo Havlin. Within our Natural Genesis 2012 survey, here is a good example of the on-going worldwide discovery of a vital theory of “everywhere” that portends a universe to human genesis. With archetypal network “nodes and links,” or the “agents and interactions” of self-organizing adaptive systems, at what point, by what imagination within a true biological cosmos, can this realization be translated as the semblance and result of its actual parent to child “genetic code?”

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