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

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

Naldi, Giovanni, et al, eds. Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences. Boston: Birkhauser. 2010. University of Milan (Naldi), Ferrera (Lorenzo Pareschi), and Pavia (Guiseppe Toscani) systems physicists provide, per the Preface quotes next, a cogent update on the worldwide discovery of a universality of complex dynamical systems of agents and affairs. As an emergent convergence, statistical mechanics and complexity science now flow together to reveal a new genesis nature from molecule to metropolis.

The description of emerging collective behaviors and self–organization in a group of interacting individuals has gained increasing interest from various research communities in biology, engineering, physics, as well as sociology and economics. In the biological context, swarming behavior of bird flocks, fish schools, insects, bacteria, and people is a major research topic in behavioral ecology with applications to artificial intelligence. Likewise, emergent economic behaviors, such as distribution of wealth in a modern society and price formation dynamics, or challenging social phenomena such as the formation of choices and opinions are also problems in which the emergence of collective behaviors and universal equilibria has been shown. (v)

The novelty here is that important phenomena in seemingly different areas such as sociology, economy and biology can be described by closely related mathematical models. In this book we present selected research topics that can be regarded as new and challenging frontiers of applied mathematics. These topics have been chosen to elucidate the common methodological background underlining the main idea of this book: to identify similar modeling approaches, similar analytical and numerical techniques, for systems made out of a large number of “individuals” that show a “collective behavior,” and obtain from them “average” information. The expertise obtained from dealing with physical situations is considered as the basis for the modeling and simulation of problems for applications in the socio-economic and life sciences, as a newly emerging research field. In most of the selected contributions, the main idea is that the collective behaviors of a group composed of a sufficiently large number of individuals (agents) could be described using the laws of statistical mechanics as it happens in a physical system composed of many interacting particles. This opens a bridge between classical statistical physics and the socio-economic and life sciences. (v-vi)

Nardini, Cesare, et al. Entropy Production in Field Theories without Time Reversal Symmetry: Quantifying the Non-Equilibrium Character of Active Matter. Physical Review X. 7/021007, 2017. A six member team with postings in the UK, Scotland, and France press this theoretical frontier researching these lively propensities of material nature as they engender self-emergent animate behaviors.

Needleman, Daniel and Zvonimir Dogic. Active Matter at the Interface between Materials Science and Cell Biology. Nature Reviews Materials. 2/17048, 2017. We cite this entry by Harvard and Brandeis University physicists as a latest iconic case of human endeavors to learn and express what innate essence that extant nature might actually have. In regard, the paper opens with a quote by Gottfried Leibniz from Jessica Riskin’s fine history The Restless Clock (2016 herein) that intertwines machine and organism aspects. We add three quotes which reflect the conflation. The consideration is that this novel 2010s perception of intrinsic, non-equilibrium, self-organized vitalities may finally achieve a quantified meld of physical matter and animate biology.

The remarkable processes that characterize living organisms, such as motility, self-healing and reproduction, are fuelled by a continuous injection of energy at the microscale. The field of active matter focuses on understanding how the collective behaviours of internally driven components can give rise to these biological phenomena, while also striving to produce synthetic materials composed of active energy-consuming components. The synergistic approach of studying active matter in both living cells and reconstituted systems assembled from biochemical building blocks has the potential to transform our understanding of both cell biology and materials science. This methodology can provide insight into the fundamental principles that govern the dynamical behaviours of self-organizing subcellular structures, and can lead to the design of artificial materials and machines that operate away from equilibrium and can thus attain life-like properties. In this Review, we focus on active materials made of cytoskeletal components, highlighting the role of active stresses and how they drive self-organization of both cellular structures and macroscale materials, which are machines powered by nanomachines. (Abstract)

Figure 1: Organisms are machines made from machines. Organisms are composed of tissues, which are non-equilibrium assemblages of cells. Cells are built from non-equilibrium self-organized structures, and subcellular structures are composed of energy-transducing molecular motors and filaments. In the schematic, one cell is undergoing cell division and contains a spindle (a structure that segregates chromosomes during cell division), which is made of microtubules (filament structures in the cytoskeleton) and molecular motors. The close-up views show a molecular motor that is crosslinking and sliding between two microtubules and the end of a microtubule that is dynamically shrinking. (2)

Here we review recent advances that have transformed active matter into a mature and rapidly expanding research field that spans diverse disciplines, ranging from soft matter physics to cell biology, to materials science, and to engineering. We focus on experimental
work at the interface between cell biology and materials science, as well as on the potential for each of these lines of research to influence and benefit the others. We first provide a brief historical perspective on the importance of active processes in the biological organization of cells. Next, we discuss active materials assembled from purified cytoskeletal components, which are classified according to the symmetries of their structures and stresses, and we review advances that demonstrate the essential role of active stresses and out-of-equilibrium self-organization in cytoskeletal systems in cells. We conclude by placing these topics in the broader context of other realizations of active matter. We also note that active matter is a much broader field that is being investigated using a wide array of synthetic model systems that are either externally or internally driven. (2)

Nemenman, Ilya. Information Theory and Adaptation. Wall, Michael, ed. Quantitative Biology: From Molecular to Cellular Systems. Boca Raton: CRC Press, 2012. In this chapter, the Emory University, Departments of Physics, Biology, Computational and Life Sciences Strategic Initiative, theorist embarks on this mission to understand the many ways that communicated content plays a major role in biological viability.

In this Chapter, we ask questions (1) What is the right way to measure the quality of information processing in a biological system? and (2) What can real-life organisms do in order to improve their performance in information-processing tasks? We then review the body of work that investigates these questions experimentally, computationally, and theoretically in biological domains as diverse as cell biology, population biology, and computational neuroscience. (Abstract, arXiv:1011.5466)

“What is physics? ... -- The idea ... that the world is understandable.” John J. Hopfield. I am a physicist working to understand how biological systems, such as cells, organisms, and populations, learn from their surrounding environment and respond to it (we call this "biological information processing"). I am interested in physical problems in this biological domain. That is “Are there phenomenological, coarse-grained, and yet functionally accurate representations of biological processes, or are we forever doomed to every detail mattering?” I hope to achieve some quantitative understanding of such complex phenomena as evolution, sensory processes, animal behavior, human cognition, and, who knows, maybe one day even human consciousness. (Ilya Nemenman website)

Newman, Mark. Communities, Modules and Large-scale Structure in Networks. Nature Physics. 8/1, 2012. The University of Michigan, Center for the Study of Complex Systems, mathematician is a leading theorist, advocate and explainer of this grand perception of a whole integral dimension of cosmos, life, persons, and culture beyond reduced, isolate things alone. In an Insight – Complexity section, a recent emphasis arising from these studies is the common tendency to form semi-autonomous modular communities, indeed an array of nested networks that much increases robust survival. His latest book Networks: An Introduction (Oxford, 2010) is a standard source.

Nieves, Veronica, et al. Maximum Entropy Distributions of Scale-Invariant Processes. Physical Review Letters. 105/118701, 2010. Postdoc physicist Nieves, grad student Jingfeng Wang and Rafael Bras, Dean of Engineering (newly Georgia Tech Provost), University of California, Irvine, and grad student Elizabeth Wood, MIT, find that circa 2010 nature’s grand self-similarity, as many other recent papers attest, is robustly evident from cosmos to civilization, and can indeed be modeled in its universal mode by such physical principles.

Organizations of many variables in nature such as soil moisture and topography exhibit patterns with no dominant scales. The maximum entropy (ME) principle is proposed to show how these variables can be statistically described using their scale-invariant properties and geometric mean. The ME principle predicts with great simplicity the probability distribution of a scale-invariant process in terms of macroscopic observables. The ME principle offers a universal and unified framework for characterizing such multiscaling processes. (Abstract, 118701)

Such scale-invariant behavior resulting from self-organization emerges as the most probable and macroscopically reproducible state. It turns out that the geometric mean provides essential information for shaping river networks. (118701-4) The geometric mean is also identified as an important parameter, in addition to the moments, in characterizing multiscaling incremental processes of soil moisture and topography. ….this analysis supports the assertion that the ME theory is a universal and unified framework to characterize those processes governed by scale-invariant laws. (118701-4)

Pan, Raj Kumar and Sitabhra Sinha. Modularity Produces Small-World Networks with Dynamical Time-Scale Separation. EPL (Europhysics Letters). 85/68006, 2009. Institute of Mathematical Sciences, C. I. T. Campus, Chennai, India systems scientists contend that including the ubiquitous presence of modular network substructures in SWNs can reveal a universality that holds across natural phenomena from physical Ising spin-orderings to metabolisms to societies. Now this paper, any many others like it, can illustrate the two extant ways of perceiving such phenomena. As this site records, such nonlinear dynamics can either be noted and studied as instantiated from galaxies to Gaia, or as this distillation unto an independent propensity. The authors go on to say that such commonalities can be applied to contain disease epidemics, as they similarly express this geometry.

Peat, David. Trapped in a World View. New Scientist. January 5, 2008. The physicist and author proposes that cultural forms of language can, unbeknownst, determine what kind of reality is seen. In Europe, where modern physics began and proceeded, a ‘noun’ or object emphasis holds whereof electrons, e.g., are akin to billiard balls with a distinct position and velocity. But this vernacular is unable to express or include equally relevant dynamical processes between what are in actuality condensations. As a contrast, the Algonquian Indian language is ‘verb’ based which can readily convey an ‘indigenous’ nature as an integral, living, spiritual presence. Now this is not trivial. Much of our scientific impasse today, with physics and cosmology languishing back in time, out in space, and down into matter, could be traced to a linguistic inability to fathom and describe an organic genesis. Instead a sterile mechanical materialism reigns. Or one might say trying to explain a maternal gestation unto nativity by way of or to a paternal mindset.

Peng, Ji, et al. Signal Propagation in Complex Networks. Physics Reports. May, 2023. Ten global theorists including Matjaz Perc and Jurgen Kurths provide an extensive Volume 1017 on this propensity of natural, personal and societal interconnectivities, each in some cerebral way, to take on dynamic, oscillatory, multiplex communications. So into these 2020s, the more we can altogether learn, understand, avail, guide, manage about these agencies and realities the better.

Collective behavior is the hallmark of complex systems, and as such, it has attracted much attention during the past two decades. It is an emergent phenomenon that is due to the interactions between many units that make up complex systems, be it neurons in the brain or ants in an anthill, as well as due to external disturbances that often act upon them. Most importantly, collective behavior is often universal in nature, such that models describing. (Introduce)

Perc, Matjaz. Beauty in Artistic Expressions through the Eyes of Networks and Physics. Journal of the Royal Society Interface. March 11, 2020. The University of Maribor, Slovenia complexity theorist has become a leading expositor in Europe and beyond through a steady flow of papers (search) about physical, biologic to social areas. Here he applies the latest findings, as the abstract notes, to cultural phases to show how each in turn can be modeled by the same dynamic mathematics. So to say in 2020, a grand implication presents itself via an integral survey from statistical physics to literary corpora. As illustrated by flavour tastes, artistic styles, musical modes, how children learn, and more, it is evident that an iconic universality of particle/wave, node/link, DNA/AND, me/We, yin/yang complements in a whole system triality has been verified. This entry well conveys a revolutionary discovery in our midst of a participatory universe to wuman epitome genesis co-creation.

Beauty is subjective, it cannot be defined in absolute terms. But we all know or feel when something is beautiful to us. And in such instances, methods of statistical physics and network science can be used to quantify and better understand what evokes that pleasant experience. From the complexity and entropy of art paintings to an array of food flavors, research at the interface of art, physics and network science abounds. We review the existing literature, focusing on culinary, visual, musical and literary arts. We also touch upon cultural history and culturomics, as well as connections between physics and social sciences in general. We find that synergies between these fields yield entertaining results that can often be enjoyed by layman and experts alike. (Abstract excerpt)

Perotti, Juan, et al. Emergent Self-Organized Complex Network Topology out of Stability Constraints. Physics Review Letters. 103/108701, 2009. With nature becoming untangled by way of such dynamic geometries, researchers at the Universidad Nacional de Cordoba, Argentina, and Northwestern University, USA, are able to distill and discern universal, independent characteristics that repeat everywhere from genes to websites.

Although most networks in nature exhibit complex topologies, the origins of such complexity remain unclear. We propose a general evolutionary mechanism based on global stability. This mechanism is incorporated into a model of a growing network of interacting agents in which each new agent's membership in the network is determined by the agent's effect on the network's global stability. It is shown that out of this stability constraint complex topological properties emerge in a self-organized manner, offering an explanation for their observed ubiquity in biological networks. (Abstract, 108701)

Pietronero, Luciano. Complexity Ideas from Condensed Matter and Statistical Physics. Europhysics News. 39/6, 2008. The STATPHYS 23 (Google) conference held in Rome, July 2007 is seen as initiating a fertile merger of this older field with the new sciences of nonlinear dynamics, since it became evident they studied the same phenomena from different approaches. In this note, a senior University of Rome physicist provides a succinct introduction to a combined “Physics of Complex Systems.” But a further scope is added by seeing a turn from a long reduction phase to particles or “bricks” only to getting on with their integral (re)assembly or “architecture.” Pietronero points out that these advances are uncovering grand affinities whence the same “self-organized fractal growth dynamics” from material aggregates to galactic clusters. A need going forward is the attainment of agreed, clear terminologies.

The basic idea is that nature is organized in a hierarchical way and that there are individual elements and collective emergent properties every time one moves from a level of the hierarchy to the next one. The later development of the renormalization group has provided a formalism which permits to interpret these intuitions within a rigorous framework. Examples of these various levels can be quarks and nuclear physics, atoms, molecules, proteins, the emergence of life and on up to the macroscopic scales and the entire universe. The idea is that each discipline refers to the step between one level and the next one. In this process the essential concepts are the basic elements and their interactions. These lead to emergent properties and collective behaviours which cannot be identified from the original elements. (26)

The study of complex systems refers to the emergence of collective properties in systems with a large number of parts in interaction among them. These elements can be atoms or macromolecules in a physical or biological context, but also people, machines or companies in a socio-economic context. The science of complexity tries to discover the nature of the emerging behaviour of complex systems, often invisible to the traditional approach, by focusing on the structure of the interconnections and the general architecture of systems, rather than on the individual components. It is a change of perspective in the forma mentis of scientists rather than a new scientific discipline. Traditional science is based on a reductionistic reasoning for which, if one knows the basic elements of a system, it is possible to predict its behaviour and properties. It is easy to realize, however, that for a cell or for the socio-economic dynamics one faces a new situation in which the knowledge of the individual parts is not sufficient to describe the global behaviour of the structure. Starting from the simplest physical systems, like critical phenomena in which order and disorder compete, these emergent behaviours can be identified in many other systems, from ecology to the immunitary system, to the social behaviour and economics. The science of complexity has the objective of understanding the properties of these systems. (28)

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