IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

4. Universality Affirmations: A Critical Complementarity

Nagata, Shintaro and Macoto Kikuchi. Emergence of Cooperative Bistability and Robustness of Gene Regulatory Networks. . An Osaka University biochemist and a biophysicist report that the common bistability state (Wikipedia) of dynamical systems can likewise be recognized in this genomic mode, whence GRNs reside in two coordinated, genes on and off, positions at once. See also a slide presentation Simultaneous emergence of Cooperative Response and Mutational Robustness in Gene Regulatory Networks by the authors at www.cp.cmc.osaka-u.ac.jp/~kikuchi/presentation/CCS2018.

Gene regulatory networks (GRNs) are complex systems in which many genes mutually regulate their expressions for changing the cell state adaptively to environmental conditions. The GRNs utilized by living systems possess several kinds of robustness which here means that they do not lose their functions when exposed to mutation or noises. In this study, we explore the fitness landscape of GRNs and investigate how the robust feature emerges in the "well-fitted" GRNs. Thus the more sensitively a GRN responds to the input, the fitter it is. To do this, they exhibit bistability, which necessarily emerges as the fitness becomes high. These properties are universal irrespective of the evolutionary pathway, because we did not perform evolutionary simulations. (Abstract excerpt)

The emergence of the new fixed points can be considered as an innovation or a big evolutionary jump. Then, what can we infer about the evolution based on them? The cooperative bistability and the robustness against noises are the consequence of the high fitness. Thus, we can say that this evolutional jump occurs inevitably as the fitness increases irrespective of the evolutionary pathway. We may identify this as the universality of evolution. (9)

Nicolaides, Christos, et al. Self-Organization of Network Dynamics into Local Quantized States. Nature Scientific Reports. 6/21360, 2016. In a contribution that typifies how such papers can be written nowadays, MIT engineers distill and describe a general, mechanism by which an interaction of many nodes, entities, or components in heterogeneous networks results in a spontaneous, emergent self-organization. We cite the Abstract, and the first two paragraphs where many references are listed that describe this same, archetypal phenomena from biology and brains to ecologies and societies.

Self-organization and pattern formation in network-organized systems emerges from the collective activation and interaction of many interconnected units. A striking feature of these non-equilibrium structures is that they are often localized and robust: only a small subset of the nodes, or cell assembly, is activated. Understanding the role of cell assemblies as basic functional units in neural networks and socio-technical systems emerges as a fundamental challenge in network theory. A key open question is how these elementary building blocks emerge, and how they operate, linking structure and function in complex networks. Here we show that a network analogue of the Swift-Hohenberg continuum model—a minimal-ingredients model of nodal activation and interaction within a complex network—is able to produce a complex suite of localized patterns. Hence, the spontaneous formation of robust operational cell assemblies in complex networks can be explained as the result of self-organization, even in the absence of synaptic reinforcements. (Abstract)

Pattern formation in reaction-diffusion systems has emerged as a mathematical paradigm to understand the connection between pattern and process in natural and sociotechnical systems. The basic mechanisms of pattern formation by local self-activation and lateral inhibition, or short-range positive feedback and long-range negative feedback are ubiquitous in ecological and biological spatial systems, from morphogenesis and developmental biology to adaptive strategies in living organisms and spatial heterogeneity in predator-prey systems. Heterogeneity and patchiness associated with Turing patterns in vegetation dynamics have been proposed as a connection between pattern and process in ecosystems, suggesting a link between spatial vegetation patterns and vulnerability to catastrophic shifts in water-stressed ecosystems. The theory of non-equilibrium self-organization and Turing patterns has been recently extended to network-organized natural and socio-technical systems, including complex topological structures such as multiplex, directed and Cartesian product networks. Self-organization is rapidly emerging as a central paradigm to understand neural computation. (1)

Self-organized activation has been shown to emerge spontaneously from the heterogenous interaction among neurons, and is often described as pattern formation in two-population networks. Localization of neural activation patterns is a conceptually challenging feature in neuroscience. Cell assemblies, or small subsets of neurons that fire synchronously, are the functional unit of the cerebral cortex in the Hebbian theory of mental representation and learning. Associative learning forms the basis of our current understanding of the structure and function of neural systems. It is also the modeling paradigm for information-processing artificial neural networks. The emergence of cell assemblies in complex neural networks is a fascinating example of pattern formation arising from the collective dynamics of interconnected units. Understanding the mechanisms leading to pattern localization remains a long-standing problem in neuroscience. Here we show that simple mechanisms of nodal interaction in heterogeneous networks allow for the emergence of robust local activation patterns through self-organization. (1)

Nicolas-Carlock, J., et al. Universal Fractality of Morphological Transitions in Stochastic Growth Processes. Nature Scientific Reports. 7/3523, 2017. Benemérita Universidad Autónoma de Puebla, Mexico theorists cleverly quantify the presence of common, ever repetitive dynamic forms across organismic nature. We note that over the course of this website chronicle since 2000 and before, it was not possible until just now to assert and mathematically prove such a whole scale recurrence. See also the cited paper Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks in Physical Review Letters (117/138301, 2016) and Angular and Radial Correlation Scaling in Stochastic Growth Morphodynamics at arXiv:1803.03715.

Stochastic growth processes give rise to diverse and intricate structures everywhere in nature, often referred to as fractals. In general, these complex structures reflect the non-trivial competition among the interactions that generate them. In particular, the paradigmatic Laplacian-growth model exhibits a characteristic fractal to non-fractal morphological transition as the non-linear effects of its growth dynamics increase. So far, a complete scaling theory for this type of transitions, as well as a general analytical description for their fractal dimensions has been lacking. In this work, we show that despite the enormous variety of shapes, these morphological transitions have clear universal scaling characteristics. Using a statistical approach to fundamental particle-cluster aggregation, we introduce two non-trivial fractal to non-fractal transitions that capture all the main features of fractal growth. (Abstract excerpt)

Found everywhere in nature, the intricate structures generated by fractal growth usually emerge from non-trivial self-organizing and self-assembling pattern formation. One striking feature of these systems is the morphological transition they undergo as a result of the interplay between entropic and energetic processes in their growth dynamics, that ultimately manifest themselves in the geometry of their structure. It is here where, despite their complexity, great insight can be obtained into the fundamental elements of their dynamics from the powerful concepts of fractal geometry. (1)

Norrman, Andreas and Lukasz Rudnicki. Quantum Correlations and Complementarity of Vectorial Light Fields. arXiv:1904.07533. We review this entry by MPI Science of Light researchers much more in Quantum Organics, especially for its introduction of a “triality” concept to join and unite complements.

Nosonovsky, Michael and Prosun Roy. Scaling in Collodial and Biological Networks. Entropy. 22/6, 2020. We cite this contribution by University of Wisconsin bioengineers as another good example of how worldwide collaborations are finding a consistency of active topologies which form into similar nested recurrences across material, biochemical, cellular, metabolic to neural and communicative domains. By a philoSophia 2020 vision, a revolutionary organic genesis ecosmos seems well underway to being quantified.

Scaling and dimensional analysis is applied to networks that describe various physical systems. Some of these networks possess fractal, scale-free, and small-world properties. First, we consider networks arising from granular and colloidal systems due to pairwise interaction between the particles. Many networks found in colloidal science possess self-organizing properties and/or self-organized criticality. Then, we discuss the allometric laws in branching vascular networks, artificial neural networks, cortical neural networks, as well as immune networks. Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. The information content, scaling, dimensional, and topological properties of these networks are discussed. (Abstract excerpt)

The brain networks possess many characteristics typical to other networks, including over‐frequency and power‐law activities, avalanches, small‐world, scale‐free, and fractal topography. It is particularly interesting to look for the correlation between the spatial distribution (for example, hubs) and temporal organization (frequency spectrum) of human brain cognitive activities. Such research is being conducted by many groups, for example, the study of the DMN during such activities as the comprehension of a text in a natural language versus contemplating it (the “language of thought”). The information content of the neural networks can be studied using the standard characteristics of the information theory, such as the Shannon entropy. It may provide ways to distinguish between DNA‐encoded information and information generated during the embryonal and post‐embryonal development, which may be driven by the self‐organizing process. (22)

Ossandon, Sebastian, et al. Neural Network Approach for the Calculation of Potential Coefficients in Quantum Mechanics. Computer Physics Communications. 214/31, 2017. We note this paper by Chilean scientists in Valparaiso, Santiago, and Chillan as an instance of 2017 integrative frontiers as cerebral dynamics gain an iconic utility and application from human to universe. In this instance, they serve to quantify quantum phenomena, as also for chemical, genetic, behavioral, and evolutionary realms. What might all this infer and portend, we wonder in April 2017, with everything so amenable to a neural, brainy essence? Who are we phenomenal interlocutors as if a procreative cosmos trying to accomplish its own self-cognizance?

A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss. (Abstract)

Parastesh, Fatemeh, et al. Chimeras. Physics Reports. October, 2020. Amirkabir University of Technology, University of Western Australia, Northwestern Polytechnical University, Xi’an, China. CNR Institute of Complex Systems, Fiorentino, Italy (Stefano Boccaletti), and University of Maribor, Slovenia (Matjaz Perc) system theorists post a major 80 page, 324 reference review about this recently recognized condition. While its name is taken from organisms with a double genome, here it stands for a complex dynamism which actively resides in more or less orderly or coherent modes at the same time. The paper covers various occasions such as chemical or neural, their network topologies, and more. The import in later 2020 is to add a major explication that natural phenomena everywhere seeks and prefers a “middle way golden balance” optimum poise.

Chimeras are this year coming of age since they were first observed by Kuramoto and Battogtokh in 2002. What started as an observation of a coexistence of synchronized and desynchronized states turned out to be an important new paradigm of nonlinear dynamics at the interface of physical and life sciences. Here we present a major review of chimeras, dedicated to all aspects of their theoretical and practical existence. We cover different dynamical systems in which they have been observed along with network structure for the emergence of chimeras. (Abstract)

Pavithran, Induja, et al. Universality in Spectral Condensation. Nature Scientific Reports. 10/17405, 2020. As the Abstract says, by an advanced technical finesse nine scientists from the Indian Institute of Technology, Madras, UC San Diego, and the Potsdam Institute for Climate Impact Research including Jurgen Kurths uncover a constant presence of this manifest physical phenomena. The article number means that it is amongst thousands each year, millions more if eprint sites are added. Whenever might we be able to perceive our worldwise endeavor as a vital work of ecosmic self-quantification and ultimate discovery?

Self-organization is the spontaneous formation of spatial, temporal, or spatiotemporal patterns in complex systems far from equilibrium. During such self-organization, energy distributed in a broadband of frequencies gets condensed into a dominant mode, analogous to a condensation phenomenon. We call this phenomenon spectral condensation and study its occurrence in fluid mechanical, optical and electronic systems. We define a set of spectral measures to quantify this condensation spanning several dynamical systems. Further, we uncover an inverse power law behaviour of spectral measures with the power corresponding to the dominant peak in the power spectrum in all the aforementioned systems. (Abstract)

Persi, Erez, et al. Criticality in Tumor Evolution and Clinical Outcome. Proceedings of the National Academy of Sciences. 115/E11101, 2018. University of Maryland and National Center for Biotechnology Information researchers including Yuri Wolf and Eugene Koonin report findings across a wide range of cancer cases that a complex generative dynamics is in effect which arrays as a critically poised state. It is said that appreciations of this common tendency could well aid diagnostics and treatment.

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. (Abstract excerpt)

Peruzzo, Fabio, et al. Spatial Patterns Emerging from a Stochastic Process near Criticality. arXiv:1907.08852. Into the year 2019, University of Leeds mathematicians including Sandro Azaele (search), draw upon a wealth of 21st century science so as to assert that living systems across every natural and social phase can be seen to seek and reach a preferred state of critical balance. As many other entries prove, this finding bodes well for a discovery of the universal complex recurrence of a dynamic complementarity. This constant phenomena arises from “nonlinearities of interacting agents,” that is nodal, particulate entities and relational, wave-like links, which are rooted in the physical cosmos, as it come to life again.

There is mounting empirical evidence that many communities of living organisms display key features which closely resemble those of physical systems at criticality. We here introduce a model framework for the dynamics of a community of individuals which undergoes local birth-death, immigration and local jumps on a regular lattice. We study these properties when the system is close to its critical point. Within a physically relevant regime dominated by fluctuations, it is possible to calculate analytically the probability density function of the number of individuals living in a given volume, which captures the close-to-critical behavior of the community across spatial scales. We discuss how this model in the critical-like regime is in agreement with several biodiversity patterns observed in tropical rain forests. (Abstract)

Plenz, Dietmar, et al. Self-Organized Criticality in the Brain. . . Into the 2020s, National Institute of Mental Health, Critical Brain Dynamics Section (D. Plenz director, search) neuroscientists report on a decade and more of convergent research findings which now have reached a proven validity that human cerebral activity does indeed seek and reside at an optimum dynamic poise. In respect, one more robust exemplar, on the way to an invariant universality, is achieved by virtue of our own microcosmic cognizance. The paper also appears in Frontiers in Physics for July 2021.

Self-organized criticality (SOC) refers to the ability of complex systems to evolve towards a phase transition at which interactions between system components lead to scale-invariant events beneficial for overall performance. For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here we review experimental findings of isolated, layered cortex preparations to self-organize towards the four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials. The precise interaction between up-states, nested oscillations and avalanches in layered cortex provides compelling evidence for SOC in the brain. (Abstract excerpt)

Poirot, Olivier and Youri Timsit. Neuron-Like Networks between Ribsomal Proteins within the Ribosome. Nature Scientific Reports. 6/26485, 2016. We report this entry by Information Génomique et Structurale CNRS, Aix-Marseille Université researchers as an example of the 2016 historic synthesis in our global collaborative midst. At once, it shows how the archetypal scale-free networks found from cosmic webs to literary classics also appears in protein phenomena. This iconic universality is then seen as akin to neural net node and link informational activities.

The Ribosome is a minute particle consisting of RNA and associated proteins, found in large numbers in the cytoplasm of living cells. They bind messenger RNA and transfer RNA to synthesize polypeptides and proteins.

From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins.

Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing. (Abstract)

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