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

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

Armengol-Collado, Joseh-Maria, et al. Epithelia are multiscale active liquid crystals. Nature Physics. September, 2023. University of Leiden biophysicists including Luca Giomi post another current contribution that combines new understandings of complex living systems while proceeding on to ground their features in a conducive physical ground. See also Hexanematic crossover in epithelial monolayers depends on cell adhesion and cell density by Julia Eckert, et al in Nature Communications. (14/5762, 2023) and Biophysicists Uncover Powerful Symmetries in Living Tissue by Elise Cutts in Quanta (October 26, 2023) for an extensive news review.

Biological processes such as embryogenesis, wound healing and cancer rely on the ability of epithelial cells to coordinate their mechanical activity over length scales that are orders of magnitude larger than the cellular size. Although this process is regulated by signalling pathways, it has recently become evident that this coordination can be understood using physics tools, of which liquid crystal order is a prominent example. In this Letter, we combine in vitro experiments, numerical simulations and analysis to show that both nematic and hexatic order are present in epithelial layers. Our work provides a method to decipher epithelial structure and lead to a predictive mesoscopic theory of tissues. (Abstract)

If there’s one central idea in tissue biophysics, coauthor (Luca) Giomi said, it’s that structure gives rise to forces, and forces give rise to functions. In other words, controlling multiscale symmetry could be part of how tissues add up to more than the sum of their cells. There’s “a triangle of form, force and function.”. Cells use their shape to regulate forces, and these in turn serve mechanical functionality.” (Quanta)

Nematic: relating to or denoting a state of a liquid crystal in which the molecules are oriented in parallel but not arranged in well-defined planes. The hexatic phase is a state of matter that is between the solid and the isotropic liquid phases in two dimensional systems of particles.

Baptista, Rafael, et al. Evidence of fractal structures in hadrons. arXiv:2308.16888. Universidade de Sao Paulo (Airton Deppman), Universidade Federal de Santa Catarina, Universidade Estadual de Ponta Grossa, National Institute of Science and Technology of Complex Systems, Rio de Janeiro (Constantin Tsallis) system physicists describe a consistent, formidable self-similarity which seems to permeate all manner of subatomic particulate realms. See also Emergence of Tsallis statistics in fractal networks by Airton Deppman and Evandro Oliveira Andrade in PLoS One (16/9, 2021), Fractal structure of Yang-mills fields by Deppman, Airton et al in Physica Scripta (95/9, 2020) for other examples of such proper portions (Aquinas) from universe to us.

This study focuses on the presence of (multi)fractal structures in confined hadronic matter through the distributions of mesons produced in proton-proton collisions. The analysis proves that the q-exponential behaviour is consistent with fractal characteristics with features similar to those in a quark-gluon plasma (QGP) regime. These results pave the way for further research exploring the implications of fractal structures on various physical phenomena and offer insights into phase transition between confined and deconfined regimes. (Abstract)

In particle physics, a hadron is a composite subatomic particle made of two or more quarks held together by the strong interaction. They are analogous to molecules held together by the electric force. Most of the mass of ordinary matter comes from two hadrons: the proton and the neutron, which in turn is due to the strong force. (Wikipedia)

Barabasi, Albert-Laszlo. Taming Complexity. Nature Physics. 1/2, 2005. The main discoverer of ‘complex networks’ composed of weighted nodes and links, rather than random or Poisson nets of equal rank, surveys these past theories as a way to sight future directions. In the ten years of their realization, scale-free networks are so widely prevalent as to infer a ‘universality’ which springs from an independent source. Both a common web geometry, and a tendency to form modular communities can now be established. A salient article in this new journal that could presage a quite different organic cosmic genesis from the mechanical multiverse paradigm.

We are surrounded by complex systems – from a biological cell, made of thousands of different molecules that seamlessly work together, to our society, a collection of six billion mostly cooperating individuals – which display endless signatures of order and self-organization. (68) The ubiquitous scale-free property in real networks indicates that drastically different networks follow common organizing principles. (69)

The true intellectual thrill for a physicist studying complex networks comes from the recognition that despite this microscopic randomness, a few fundamental laws and organizing principles can explain the topological features of such divers systems as the cell, the Internet or society. (70) At that point we will have a chance to understand the key to nature’s secret code for multitasking – the one that orchestrates the actions of uncountable domponents into a magic dance of order and ultimate elegance. (70)

Barabasi, Albert-Laszlo, Organizer. Predictability: From Physical to Data Sciences. http://aaas.confex.com/aaas/2013/webprogram/Session5856.html. A Symposia in the Physical Sciences tract at the February 2013 AAAS annual meeting in Boston, organized by the Northeastern University physicist and director of its Center for Complex Network Research. Speakers include Dirk Helbing on Towards Simulating the Foundations of Society, Marta Gonzalez's Understanding Road Usage Patterns in Urban Areas, and Alessandro Vespignani on From Human Mobility to Real Time Numerical Forecasts of Global Epidemic Spreading. As the session Abstract notes, these papers, and many others (e.g., the Barabasi Lab site), augur for a discovery of the universal presence and creativity of such complex system principles across all scales and an increasingly dynamic, vital natural cosmos.

There is a newfound convergence between physical and data sciences. The large amount of raw data that society and technology is generating and collecting, combined with the predictive tools of physical sciences, offers unparalleled predictive understanding of social phenomena, affecting domains of inquiry that could not be quantified in the past. The availability of data has lead to the emergence of several new research fields as the boundary of physical and other sciences, resulting in revolutionary advances in understanding complex networks, human mobility, and human dynamics. The tools generated by these are fueling the emergence of network science, computational social science, and digital humanities. This symposium will present how the tools of physical sciences aid our understanding of complex socioeconomic and technical systems. In the spirit of Wigner, we will explore the unreasonable effectiveness of the quantitative tools of natural sciences in social and engineering domains, bringing experts that apply these in various fields outside of physics. In contrast to data mining approaches, which are prevalent in the big data domain, here we focus on uncovering the mechanism and explaining collective phenomena using the predictive tools of natural sciences. (Abstract)

Baran, Nicole, et al. Applying Gene Regulatory Network Logic to the Evolution of Social Behavior. Proceedings of the National Academy of Sciences. 114/5886, 2917. With Patrick McGrath and Todd Streelman, Georgia Tech biologists (no longer just rambling wrecks) discern an innate affinity between genomic and neural (neuromic) network complexities, which can then be traced to and tracked by creaturely activities. As a surmise, a generic, independent source of node and link topologies and dynamics is quite implied.

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression. (Abstract)

Briegel, Hans. On Creative Machines and the Physical Origins of Freedom. Nature Scientific Reports. 2/522, 2012. The University of Innsbruck physicist affirms from the latest integration of statistical mechanics with nonlinear dynamics (article keywords) that “higher biological entities” like us do indeed possess a valid free will. This does not quite accord with his “creative machines” term, so natural philosophy clarifications are still in order. We also refer to Briegel’s companion Projective Simulation for Artificial Intelligence in this journal (2/400, 2012) which is the basis for the Giuseppe Paparo, et al, paper on Quantum Learning Systems (search).

We discuss the possibility of free behavior in embodied systems that are, with no exception and at all scales of their body, subject to physical law. We relate the discussion to a model of an artificial agent that exhibits a primitive notion of creativity and freedom in dealing with its environment, which is part of a recently introduced scheme of information processing called projective simulation. This provides an explicit proposal on how we can reconcile our understanding of universal physical law with the idea that higher biological entities can acquire a notion of freedom that allows them to increasingly detach themselves from a strict dependence on the surrounding world. (2/522 Abstract)

We can show, on the basis of physical laws as we understand them today, that entities with a certain degree of physical or biological organization, capable of evolving a specific type of memory, can indeed develop an original notion of creativity and freedom in their dealing with the environment. Our argument will be based on the concept of projective simulation which is a physical model of information processing for artificial agents. (1) This demonstrates, first, that a notion of freedom can indeed exist for entities that operate, without exception and at all scales, under the laws of physics. It also shows that free behavior can be understood as an emergent property of biological systems of sufficient complexity that has evolved a specific form of memory. (2)

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation. (2/400 Abstract)

Brouillet, Matthew and Georgi Georgiev. Why and How do Complex Systems Self-Organize at All?. arXiv:2408.10278.. Assumption University, Worcester, MA physicists (search GG) provide a latest theoretic grounding of nature’s spontaneous animate development from a conducive universe to our societal retrospect. The paper’s subtitle is Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization which then informs some 40 pages of mathematical proofs.

Self-organization in complex systems is a process in which randomness is reduced and emergent structures appear due to energy gradients and dynamic principles. In regard, positive feedback loops connect this measure with all provide these complex systems with exponential growth, and power law relationships. In this study, we also proceed to model agent-based simulations, measure action efficiency and consider intentional applications. (Excerpt)

Self-organization is key to understanding the existence of, and the changes in all systems that lead to higher levels of complexity and perfection in development and evolution. It is a scientific as well as a philosophical question as our realization and understanding of this robust, resilient, competitive, vital process grows. Our goal is a better explanation that drives Cosmic Evolution from the Big Bang to the present, and into the future. Self-organization has a universality independent of the substrate of the system - physical, chemical, biological, or social - and explains all of its structures. (1)

Overview of the Theoretical Framework: We use the extension of Hamilton’s Principle of Stationary Action to a Principle of Dynamic Action, according to which action in self-organizing systems is changing in two ways: decreasing the average action for one event and increasing the total amount of action in the system during the process of self-organization, growth, evolution, and development. This view can lead to a deeper understanding of the fundamental principles of nature’s self-organization, evolution, and development in the universe, ourselves, and our society. (2)

Our findings contribute to a deeper understanding of the mechanisms underlying self-organization and offer a novel, quantitative approach to measuring organization in complex systems. This research opens up exciting possibilities for further exploration and practical applications, enhancing our ability to design and manage complex systems across various domains. By providing a quantitative measure of organization that can be applied universally, we enhance our ability to design and manage complex systems across various domains. Future research can build on our findings to explore the dynamics of self-organization in greater detail, develop new optimization strategies, and create more efficient and resilient systems. (45)

Buchanan, Mark. Birds of a Feather. Nature Physics. 9/7, 2013. In this month’s column, the physicist writer reports on the work of Cristina Marchetti, et al, and Andrea Cavagna, et al (search each) about how “scale-free collectives of interacting, self-propelling elements” from microbes and flocks to every animal assembly are becoming known as a natural form of “active matter.” This advance is reviewed more in Organic Universe, see the Marchetti paper, Sriram Ramaswamy, and others.

Castellano, Claudino, et al. Statistical Physics of Social Dynamics. Reviews of Modern Physics. 81/2, 2009. With co-authors Santo Fortunato and Vittorio Loreto, a significant tutorial, only just evident and possible, that joins the disparate domains of physical nature and human societies. In so doing a notable agreement arises. Statistical physics and nonlinear network systems, as they now morph into each other, are seen to convey one and the same phenomena. Each approach describes how the interactivity of many elements or entities results in the emergence of a self-organized critical order. (see also, e.g., C. Beck herein) Consequences may then work both ways. A new kind of animate universe is implied with an innate material propensity to progressively organize itself, and, much removed in time and space, from which our human world arises, as if genetically rooted in such a natural gestation.

In social phenomena the basic constituents are not particles but humans and every individual interacts with a limited number of peers, usually negligible compared to the total number of people in the system. In spite of that, human societies are characterized by stunning global regularities. There are transitions from disorder to order, like the spontaneous formation of a common language/culture or the emergence of consensus about a specific issue. There are examples of scaling and universality. These macroscopic phenomena naturally call for a statistical physics approach to social behavior, i.e., the attempt to understand regularities at large scale as collective effects of the interaction among single individuals, considered as relatively simple entities. (592)

Thus, the study of the self-organization and evolution of language and meaning has led to the idea that a community of language users can be seen as a complex dynamical system which collectively solves the problem of developing a shared communication system. In this perspective, which has been adopted by the novel field of semiotic dynamics, the theoretical tools developed in statistical physics and complex systems science acquire a central role for the study of the self-generated structures of language systems. (617)

Ceron, Steven, et al. Programmable Self-Organization of Heterogeneous Microrobot Collectives. PNAS. 120/24, 2023. We cite this June entry by Cornell, MIT, MPI Intelligent Systems, and ETH Zurich researchers as one more exemplary realization of mindful life’s common, innate propensity to compose itself (her/his) by applying and guiding these procreative energies as they gather and vivify, going forward.

At the microscale, coupled physical interactions between collectives of agents can be exploited to enable self-organization. Past systems typically consist of identical agents; however, heterogeneous agents can exhibit asymmetric pairwise interactions which can be used to generate more diverse patterns of self-organization. Here, we study the effect of size heterogeneity in microrobot collectives composed of circular, magnetic microdisks on a fluid–air interface. Our work furthers insights into self-organization in heterogeneous microrobot collectives and moves us closer to the goal of applying such collectives to programmable self-assembly and active matter. (Significance excerpt)

Cheng, Zhao, et al. Pattern Phase Transitions of Self-Propelled Particles. New Journal of Physics. 18/10, 2016. Huazhong University of Science and Technology, China, University of Newcastle, Australia, and Eotvos University, Hungary (Tamas Viczek) systems physicists advance our understanding of how internally motive entities interact together with a dynamic, formative consistence.

To understand the collective behaviors of biological swarms, flocks, and colonies, we investigated the non-equilibrium dynamic patterns of self-propelled particle systems using statistical mechanics methods and H-stability analysis of Hamiltonian systems. By varying the individual vision range, we observed phase transitions between four phases, i.e., gas, crystal, liquid, and mill-liquid coexistence patterns. In addition, by varying the inter-particle force, we detected three distinct milling sub-phases, i.e., ring, annulus, and disk. Based on the coherent analysis for collective motions, one may predict the stability and adjust the morphology of the phases of self-propelled particles, which has promising potential applications in natural self-propelled particles and artificial multi-agent systems. (Abstract)

Cichos, Frank, et al. Machine Learning for Active Matter. Nature Machine Intelligence. February, 2020. As many studies herein find that the movements of living systems from colloids and microbes to birds and people seem to be guided by and exhibit common self-organizing patterns, Leipzig University and University of Gothenburg add a further AI technique. An opening graphic cites molecular motors, turbulence, living crystals, growing tissues, chemotaxis, foraging, swimming, clustering and more, while a second shows convolutional neural nets, reservoir computing, genetic algorithms and other skill-sets. In regard, as brain-based deep AI gains wide, analytic utility, evinced by this application to an lively materiality, another window upon a natural genesis which avails and repeats the same iconic, bicameral, triality code everywhere is opened.

Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help with the complexity of biological active matter, to establish a relation between the genetic code and bacterial behaviour, navigation strategies in complex environments, and to map physical cues onto animal behaviours. In this Review, we highlight the current state of the art and discuss opportunities and challenges. (Abstract excerpt)

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