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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

2. Computational Systems Physics: Self-Organization

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?”

Vespignani, Allessandro. Modelling Dynamical Processes in Complex Socio-Technical Systems. Nature Physics. 8/1, 2012. A Northeastern University, Boston, and Institute for Scientific Interchange, Torino, physicist further documents the seamless continuity from cosmos to civilization of the same prototypical, infinitely recurrent, network patterns and processes. What seems to accrue is an infinite nest that can only be explained as the exemplary effect of a natural genetic program.

Questions concerning how pathogens spread in population networks, how blackouts can spread on a nationwide scale, or how efficiently we can search and retrieve data on large information structures are generally related to the dynamics of spreading and diffusion processes. Social behaviour, the spread of cultural norms, or the emergence of consensus may often be modelled as the dynamical interaction of a set of connected agents. Phenomena as diverse as ecosystems or animal and insect behaviour can all be described as the dynamic behaviour of collections of coupled oscillators. Although all these phenomena refer to very different systems, their mathematical description relies on very similar models that depend on the definition and characterization of a large number of individuals and their interactions in spatially extended systems. (32)

The study of dynamical processes and the emergence of macrolevel collective behaviour in complex systems follows a conceptual route essentially similar to the statistical physics approach to non-equilibrium phase transitions. (32-33) One of the most important features affecting dynamical processes in real-world networks is the presence of dynamic self-organization and the lack of characteristic scales – typical hallmarks of complex systems. Although those characteristics have long been acknowledged as a relevant factor in determining the properties of dynamical processes, many real-world networks exhibit levels of heterogeneity that were not anticipated until a few years ago. (34)

Vicsek, Tamas and Anna Zafeiris. Collective Motion. arXiv:1010.5017. Eotvos University, Budapest, biophysicists offer in 47 pages a theoretical and experimental survey of this robust phenomena found across the natural kingdoms. From “non-living systems, to macromolecules, bacteria colonies, cells, insects, fish, birds, mammals, and onto human beings” a recurrent universality of interacting entities for the benefit of both individual and grouping occurs. As a result, a merger between statistical physics and nonlinear science appears increasingly viable.

We review the observations and the basic laws describing the essential aspects of collective motion - being one of the most common and spectacular manifestation of coordinated behavior. As such, these models allow the establishing of a few fundamental principles of flocking. In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units. In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences.

Wall, Michael, ed. Quantitative Biology: From Molecular to Cellular Systems. Boca Raton: CRC Press, 2012. Due by September, a volume in the Chapman & Hall/CRC Mathematical & Computational Biology Series. Typical chapters are Free Energies, Landscapes, and Fitness in Evolution Dynamics BY Robert Austin, System Design Principles, Michael Savage, Chance and Memory by Theodore Perkins, Andrea Weiße, and Peter Swain, and Information Theory and Adaptation, Ilya Nemenman, noted above.

The book is organized into three sections: Fundamental Concepts covers bold ideas that inspire novel approaches in modern quantitative biology. It offers perspectives on evolutionary dynamics, system design principles, chance and memory, and information processing in biology. Methods describes recently developed or improved techniques that are transforming biological research. It covers experimental methods for studying single-molecule biochemistry, small-angle scattering from biomolecules, subcellular localization of proteins, and single-cell behavior. It also describes theoretical methods for synthetic biology and modeling random variations among cells. Molecular and Cellular Systems focuses on specific biological systems where modern quantitative biology methods are making an impact. It incorporates case studies of biological systems for which new concepts or methods are increasing our understanding. Examples include protein kinase at the molecular level, the genetic switch of phage lambda at the regulatory system level, and Escherichia coli chemotaxis at the cellular level. (Publisher)

White, Simon. Fundamentalist Physics: Why Dark Energy is Bad for Astronomy. Reports on Progress in Physics. 70/6, 2007. An astrophysicist at the Max Planck Institute worries that the integral approach of celestial observations will be compromised if it becomes too influenced by an experimental bent to look for a single reductive theory. Only an intentional synthesis of both micro and macro viewpoints can resolve.

Astrophysicists are universalists, democratic in perceiving interest in all aspects of the cosmos, while high-energy physicists are fundamentalists, cleaving to the pursuit of the single Truth. (889)

Wissner-Gross, Alexander and Cameron Freer. Causal Entropic Forces. Physical Review Letters. 110/168702, 2012. For some context, I began my curious readings about a half century ago. In the early 1960s, with the DNA double helix recently found, a big bang origin proven in 1965, we valiant earthlings seemed at odds with, not at home in or lost in, a vast, roiling galactic cosmos. It is a huge achievement to now note studies as this by Harvard University and University of Hawaii theorists, and many others herein, that are able to view and join human and universe in a seamless continuum by way of innate, dynamical, procreative properties. As a nonlinear self-organizing, complexity science explains an increasingly fertile material milieu, might a “systems cosmology” be appropriate for a natural genesis universe.

Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems. (Abstract)

To the best of our knowledge, these tool use puzzle and social cooperation puzzle results represent the first successful completion of such standard animal cognition tests using only a simple physical process. The remarkable spontaneous emergence of these sophisticated behaviors from such a simple physical process suggests that causal entropic forces might be used as the basis for a general—and potentially universal—thermodynamic model for adaptive behavior. Namely, adaptive behavior might emerge more generally in open thermodynamic systems as a result of physical agents acting with some or all of the systems’ degrees of freedom so as to maximize the overall diversity of accessible future paths of their worlds (causal entropic forcing). (168702-4)

These results have broad physical relevance. In condensed matter physics, our results suggest a novel means for driving physical systems toward self-organized criticality. In particle theory, they suggest a natural generalization of entropic gravity. In econophysics, they suggest a novel physical definition for wealth based on causal entropy. In cosmology, they suggest a path entropy-based refinement to current horizon entropy-based anthropic selection principles that might better cope with black hole horizons. Finally, in biophysics, they suggest new physical measures for the behavioral adaptiveness and sophistication of systems ranging from biomolecular configurations to planetary ecosystems. (168702-5)

Yeung, Chi Ho and David Saad. Networking – A Statistical Physics Perspective. Journal of Physics A: Mathematical and Theoretical. 46/10, 2013. Nonlinearity and Complexity Research Group, Aston University, Birmingham, UK researchers offer a Topical Review of the many junctures of this ubiquitous biological propensity with an inherently dynamic physical reality. Once again life’s roots are found to run deeper into an increasingly fertile natural ground. As the second quote avers, while not yet seen akin to a Systems Physics, an epochal revolution is underway.

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. (Abstract)

The Non-linearity and Complexity Research Group has high international visibility in the areas of pattern analysis, probabilistic methods, non-linear dynamics and the application of methods from statistical physics to the analysis of complex systems. The underpinning methodology used includes principled approaches from probabilistic modelling, Bayesian statistics, statistical mechanics and non-linear stochastic and deterministic differential equations. Particularly significant application domains include Biomedical Information Engineering and Signal Processing, Health Informatics, Environmental Modelling and Weather Forecasting, Error-correcting Codes and Multi-user Communication, Complex Systems and Networks, Solitons and Optical Fibers, and Chaos and turbulence. (Aston University)

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