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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: An Independent Source Script of Generative, Self-Similar, Complex Network Systems

Aschwanden, Markus, et al. Order Out of Randomness: Self-Organization Processes in Astrophysics. arXiv:1708.03394. Reviewed at length in Systems Cosmology, this is an 18 author, 97 page treatise which could be seen as a premier affirmation of an inherently nonlinear, lively, complexifying cosmic genesis.

Ashtiani, Minoo, et al. A System Survey of Centrality Measures for Protein-Protein Interaction Networks. BMC Systems Biology. 12/80, 2018. Our interest in this entry by bioinformatic theorists with postings in Iran and Germany is to record in 2018 how this biochemical domain can be treated by the same multiplex geometries as neural brains. In reflective regard, we peoples may at last be able to confirm the natural presence from quantum and genomic to cerebral and cosmic realms of a node/link, DNA/AND, universe to human image.

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures. (Abstract)

Auffray, Charles, et al. Self-organized Living Systems. Philosophical Transactions of the Royal Society of London A. 361/1125, 2003. After centuries of the reductionist method which identified the components of nature, a new biosystemic paradigm is recommended which can integrate the relational dynamics of living entities and processes. In this view, biology is a science of information in a hierarchical flux, formed by a creative balance between order and chaos.

Azpeitia, Eugenio, et al. Cauliflower Fractal Forms Arise from Perturbations of Floral Gene Networks. Science. 373/192, 2021. A cover image entry by a 14 member biomathematician team with postings in France, Italy, UK, USA, and Spain which further quantifies nature’s innate iterations in kind which serve to form and express such vital geometries. Their novel contribution is to associate their artistry with a genetic source. One is moved again to ask however (whomever) did all this mathematic scriptome get there in the first place. See also Cauliflower and Chaos, Fractals in Every Floret by Sabrina Imbler in the New York Times for July 9, 2021.

Throughout development, plant meristems regularly produce organs in defined spiral, opposite, or whorl patterns. Cauliflowers present an unusual organ arrangement with a multitude of spirals nested over a wide range of scales. How such a fractal, self-similar organization emerges from developmental mechanisms has remained elusive. Combining experimental analyses in an Arabidopsis thaliana cauliflower-like mutant with modeling, we found that curd self-similarity arises because the meristems fail to form flowers but keep the “memory” of their transient passage in a floral state. This study reveals how fractal-like forms may emerge from the combination of key, defined perturbations of floral developmental programs and growth dynamics. (Abstract)

Bak, Per. How Nature Works. New York: Springer, 1996. The late Danish systems scientist provides a succinct account of self-organized criticality poised between order and chaos, a theory which he originated.

Balaban, Valeriu, et al. Quantifying Emergence and Self-Organization of Enterobacter cloacae Microbial Communities. Nature Scientific Reports. 8/12416, 2018. Amongst a flurry of 2018 papers, University of Southern California bioengineers including Paul Bogdan (search) show how nature’s universal complexities are iconically manifest in this prokaryote phase. Once again, a generic process is observed as active, informed agents emerge into complex, modular, nested networks with a collective intelligence. See also Multi-fractal Characterization of Bacterial Swimming Dynamics by this group (Hana Koorehdavoudi, et al) in Proceedings of the Royal Society A (473/2017.0154).

From microbial communities to cancer cells, many such complex collectives embody emergent and self-organising behaviour. Such behaviour drives cells to develop composite features such as formation of aggregates or expression of specific genes as a result of cell-cell interactions within a cell population. Currently, we lack universal mathematical tools for analysing the collective behaviour of biological swarms. To address this, we propose a framework to measure the degree of emergence and self-organisation from scarce spatial data and apply it to investigate the evolution of Enterobacter cloacae aggregates. Multifractal analysis was used to characterise these patterns and calculate dynamics changes in emergence and self-organisation within the bacterial population. (Abstract excerpt)

The multifractal spectrum is the central part of any multifractal analysis and can be used to describe the group properties of interacting agents, such as bacteria aggregates. The multifractal analysis investigates the statistical scaling laws of complex fragmented geometrical objects which cannot be described by classic geometric methods. Considering that microbial communities exhibit complex time-varying aggregation patterns, we employ
the above-mentioned multifractal formalism to characterise the phase-space dimensionality and complexity of the observed dynamics. Consequently, interpreting the microbial community as an intelligent system driven by heterogeneous interactions meant to cooperate for achieving a collective goal allows us to develop two approaches for quantifying the instantaneous degree of emergence and self-organisation in collective system. (5)

Self-organisation, similar to emergence, denotes a collective behaviour and represents the ability of a group to drive the system towards an ordered state. During this transition, all group members, independently and in the absence of a centralised controller, adjust their actions to increase the order of the whole. (6) Collective behaviour refers to complex macroscopic dynamics of microbial communities exhibiting emergence and self-organisation properties without a global controller. Alternatively stated, the cognitive abilities and the adaptation to environmental changes are distributed among individuals forming the group. The emergent behaviour in systems ranging from microbial communities to carcinogenic systems and somatic cellular societies generates complex qualities not present at the individual level such as information generation, collective memory, and efficient cell-to-cell communication. (6)

Bar Yam, Yaneer. Dynamics of Complex Systems. Reading, MA: Addison-Wesley, 1997. Arguably the best introduction to the subject. An 800-page formidable but accessible treatise on complex system dynamics from first principles to protein folding, neural networks, the origin and evolution of life and onto an emerging global civilization.

Baruchi, Itay, et al. Functional Holography of Complex Networks Activity – From Cultures to the Human Brain. Complexity. 10/3, 2005. In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

Baum, Eric. What Is Thought? Cambridge: MIT Press, 2004. An important book because it purports to do for cognitive science what Ervin Schrodinger’s 1944 classic What Is Life? did for biology and genetics. As corporeal life is known to arise from a molecular information, so evolving brains and mental processes can similarly be attributed to a computational DNA. An original contribution to an algorithmic kind of universe which possesses both a genetic-like source and its manifest, animate, cognitive complexity. This programmatic realm is necessarily very compact so our language is deeply metaphorical because the same "story" repeats everywhere. The brain accomplishes this by a diverse array of subroutine modules, each engaged with the semantic meaning of an analogical world. By these theories, a cerebral and cognitive evolution of the ability to remember, think and learn is traced. A rich, dense work which begs for translation. The multifaceted book is cited elsewhere, check Search.

The book explains in some detail why computer scientists are confident that thought, and for that matter life, arises from the execution of a computer program. The execution of a computer program is always equivalent to pure syntax - the juggling of 1s and 0s according to simple rules. The key question, which has been posed primarily by philosophers, is how syntax comes to correspond to semantics, or real meaning in the world.
The answer this book suggests is that semantics arises from the principle, roughly speaking, that a sufficiently compact program explaining and exploiting a complex world essentially captures reality. The point is that the only way one can find an extremely short computer program that makes a huge number of decisions correctly in a vast and complex world is if the world actually has a compact underlying structure and the program essentially captures that structure. (3)

Like the computation of life, the computation of mind is rich, with modules connected to modules flowing in complex flow patterns. Like the computation of life, the computation of mind is the result of evolution. And it is coded by a short program, as the computation of life is, so that there is an underlying order. (65)

Bedau, Mark. Artificial Life: Organization, Adaptation and Complexity from the Bottom Up. Trends in Cognitive Sciences. 7/11, 2003. A good survey not only of ALife but of self-organizing, hierarchical, iterative complex systems.

Bizzarri, Mariano, et al. Complexity in Biological Organization: Key Concepts. Entropy. Online August 12, 2020. In a special Biological Statistical Mechanics issue, systems scientists from Italy, Russia and Cuba, surely a global online faculty, post a 21st century retrospective of advance, emphasis, clarification and convergence in this wide ranging study of nature’s nonlinear essence. The paper first reviews etymology origins of key concepts and terms within this organic revolution – complexity, systems, self-organization, emergence, hierarchy and so on. Renormalization theory, critical transitions and more also receive notice as a revolutionary organic universe to human genesis gains witness, articulation and credence.

The “magic” word complexity evokes a multitude of meanings that obscure its real sense. Here we try and generate a bottom-up reconstruction of the deep sense of complexity by looking at the convergence of different features shared by complex systems. We specifically focus on complexity in biology but stressing the similarities with analogous features encountered in inanimate and artifactual systems in order to track an integrative path toward a new “mainstream” of science overcoming the actual fragmentation of scientific culture. (Abstract)

This statement introduces some very relevant questions given that emergent phenomena share some common—universal traits that are largely insensitive to changes in their microphysical base, as pointed out by studies of the Renormalization Group. “Universality” refers to the fact that phase transitions arising in different systems often possess the same set of critical exponents, while the thermodynamic properties of a system near a phase transition depend only on a small number of features, such as dimensionality and symmetry, and are insensitive to the underlying microphysics. Conclusively, emergence is not an epistemic construct. Instead, it reflects a true ontological reality shared by complex systems of very different nature. (4)

Bonchev, Daniel and Dennis Rouvray, eds. Complexity in Chemistry, Biology, and Ecology. Berlin: Springer, 2005. An increasing number of works are seeking in diverse areas a common denominator and terminology for complex systems behavior. (see Chua below) Earlier on studies focused on a certain aspect such as network geometry or active agents. But all this goes on without examining what kind of universe such phenomena might spring from. So any organic organization remains couched in mechanistic verbiage. This text at once contributes new insights but is caught in this conflation.

The contemporary scientific method is built on reductionism. The surprising finding that this paradigm has limits gave rise to the concept of complexity. This book presents the new science of complexity by presenting diverse concepts from the analyses of a wide range of real world systems (chemical, biochemical, biological, and ecological). Based on a variety of approaches ranging from cellular automata and dynamic evolutionary networks to topology and information theory, the book contains methodologies of practical importance for assessing systems complexity and network analysis in medicine and biology. (Publisher’s Website)

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