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IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Generative Code-Script Source

5. Common Code: A Further Report of Reliable, Invariant Principles

Scheffer, Marten. Critical Transitions in Nature and Society. Princeton: Princeton University Press, 2009. Because research results are now sufficiently established and wide ranging, a Wageningen University (Netherlands) theoretical ecologist can accomplish a book length compilation of how life’s evolution, natural ecosystems, and human civilizations have each and all come to be poised in a dynamical self-organized state. An initial Part explains such Emergent Patterns in Complex Systems, which are then shown to universally apply, via Case Studies, from lakes, climate, the Cambrian burst of species, oceans to cities in unsustainable crisis. A running implication is that the whole biotic Earth ought to be seen as residing in a critical state between order and chaos, which now quite demands our mitigating analysis and attention. See also an article from a team lead by Scheffer "Early-Warning Signals for Critical Transitions," Nature (461/53, 2009).

Scheffer, Marten, et al. Early-Warning Signals for Critical Transitions. Nature. 461/53, 2009. A ten man team across the EU and USA find that when complex dynamical systems from climates and fisheries to asthma attacks and financial failures approach a “tipping point” bifurcation, certain constant signs become evident. These are: slower recovery from perturbations, increased autocorrelation (repeated patterns), and increased variance magnitude. Such phenomena are then seen to appear everywhere “…regardless of differences in the details of each system,” which would suggest an independent mathematical existence. See also Scheffer’s new book Critical Transitions in Nature and Society.

Schlosser, Gerhard and Gunter Wagner, eds. Modularity in Development and Evolution. Chicago: University of Chicago Press, 2003. This comprehensive work on the constant employ of modular components and processes is reviewed more in Part V: A Quickening Evolution.

Modularity pervades every level of biological organization. From proteins to populations, larger biological units are built of smaller, quasi-autonomous parts. (Craig Nelson 17) The modular architecture of metazoan body plans is generated by a similarly modular genetic regulatory hierarchy. (CN 30)

Schuster, Peter. Nonlinear Dynamics from Physics to Biology. Complexity. 12/4, 2007. The Austrian editor-in-chief of this journal perceives the self-organization paradigm to now reach a broad acceptance across the scientific disciplines and rightly apply to human intention. This article was prompted by the 2006 conference of the European Complex Systems Society, check their website for more such info.

Schwab, Julian, et al. Concepts in Boolean Network Modeling. Computational and Structural Biotechnology Journal. March, 2020. Ulm University system biochemists contribute some latest verifications of Stuart Kauffman’s first 1969 notice that living nature can be described by these mathematical topologies. The paper reviews their technical features and a few biological applications. In closing it notes that in contrast to a reductive focus, if in addition the presence of these real interconnective dynamics is allowed, then an integral model of life’s evolutionary animation can be achieved. The 140 references from this composite 21st century endeavor (search Villani, et al) well augurs for a 2020 discovery.

Boolean network models are one of the simplest models to study complex dynamic behavior in biological systems. They can be applied to unravel the mechanisms regulating the properties of the system or to identify promising intervention targets. Since its introduction by Stuart Kauffman in 1969 for describing gene regulatory networks, various biologically based networks and tools for their analysis were developed. Here, we summarize and explain the concepts for Boolean network modeling. We also present application examples and guidelines to work with and analyze Boolean network models. (Abstract)

Boolean networks are well-studied discrete models of biological networks such as gene regulatory networks where DNA segments in a cell interact with each other indirectly through their RNA and protein expression products or with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA. (Google BN)

Sigaki, Higor, et al. History of Art Paintings through the Lens of Entropy and Complexity. Proceedings of the National Academy of Sciences. 115/E8585, 2018. Akin to how Nakamura and Kaneko (above) find nonlinear patterns amongst musical compositions, systems physicists Sigaki and Haroldo Ribeiro, Univerisidade Estadual de Maringa, Brazil and Matjaz Perc, University of Maribor, Slovenia discern the presence of intrinsic recurrent forms across a wide array (over fifty) of artistic schools from Romanticism to Art-Deco.

Art is the ultimate expression of human creativity that is deeply influenced by the philosophy and culture of the corresponding historical epoch. Here, we present a large-scale quantitative analysis of almost 140,000 paintings, spanning nearly a millennium. Based on local spatial patterns in the images of these paintings, we estimate the permutation entropy and the statistical complexity. These measures map the degree of visual order of artworks into a scale of order–disorder and simplicity–complexity. The dynamical behavior of these measures reveals a clear temporal evolution of art, marked by transitions that agree with the main historical periods of art. Our research shows that different artistic styles have a distinct average degree of entropy and complexity, thus allowing a hierarchical organization and clustering of styles. (Abstract excerpt)

Singh Sandhu, Kuljeet, et al. Large-Scale Functional Organization of Long-Range Chromatin Interaction Networks. Cell Reports. Vol. 2/Pg. 1207, 2012. “Chromatin is the combined DNA and proteins that make up the nucleus of a cell.” Various headings are Chromatin Communities Organize Functional Compartmentalization, Transcription-Associated Chromatin Interactions Form a Complex Hierarchical Network, Chromatin Communities are Evolutionarily Constrained. In this new online journal from Cell Press, 17 co-authors from Singapore, India, Australia, Croatia, USA, and Hungary, perceive in self-organizing genomes an innate propensity to form into invariant, communally nested networks. This paper could be paired with “Community Landscapes,” Istvan Kovacs, et al, herein, with Peter Csermely listed on both, because they realize that this lively phenomena is an iconic exemplar of nature’s universal sustainable reciprocity of agency and communion.

Chromatin interactions play important roles in transcription regulation. To better understand the underlying evolutionary and functional constraints of these interactions, we implemented a systems approach to examine RNA polymerase-II-associated chromatin interactions in human cells. We found that 40% of the total genomic elements involved in chromatin interactions converged to a giant, scale-free-like, hierarchical network organized into chromatin communities. The communities were enriched in specific functions and were syntenic (see next) through evolution. Altogether, our analyses reveal a systems-level evolutionary framework that shapes functionally compartmentalized and error-tolerant transcriptional regulation of human genome in three dimensions. (Summary excerpts)

In classical genetics, synteny describes the physical co-localization of genetic loci on the same chromosome within an individual or species. Today, however, biologists usually refer to synteny as the conservation of blocks of order within two sets of chromosomes that are being compared with each other. This concept can also be referred to as shared synteny. (Wikipedia)

Sinha, Saurabh, et al. Behavior-related Gene Regulatory Networks: A New Level of Organization in the Brain. Proceedings of the National Academy of Sciences. 117/23270, 2020. In this significant contribution, fifteen systems biologists from the University of Illinois, SUNY Buffalo, UT Austin (Hans Hofmann), UM Amherst, University of Toronto, and Cornell University trace these basic genetic functions all the way to their cerebral presence and effect. By so doing, a novel dimension can be added to neural operations and cognitive behaviors. In a further take, here is another example of a common interchange of this archetypal formative system.

Neuronal networks are the standard model today to describe brain activity associated with animal behavior. Recent studies now reveal an extensive role for a completely distinct layer of networked activities in the brain — the gene regulatory network (GRN) — that expresses thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial and temporal dimensions across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing upon the rich literature on GRNs related to animal development. (Abstract excerpt)

A rich body of genetic and, more recently, genomic studies have revealed that behavior is also associated with the coordinated activities of genes that operate in brain cells. Many studies have found significant, predictable, and specific changes in brain gene expression profiles associated with behavioral responses to particular environmental stimuli. These findings suggest that a second layer of network biology — that of gene regulatory networks — also underlies behavior. (23270)

Smith, Eric and Harold Morowitz. Universality in Intermediary Metabolism. Proceedings of the National Academy of Sciences. 101/13168, 2004. The stoichiometry, energetics, and reaction concentration dependence of the reductive tricarboxylic acid cycle, via its network and autocatalytic properties, is proposed as a primordial metabolic core.

Widespread or universal structures and processes in cellular biochemistry are central to a coherent understanding of life, much as universality in physics has become central to understanding order in condensed-matter systems. (13168)

Sole, Ricard and Sergi Valverde. Macroevolution in Silico: Scales, Constraints and Universals. Santa Fe Institute Working Papers. 12-11-019, November, 2012. Barcelona systems biologists continue their project to gain quantified insights into life’s iterative, sequential advance into somatic and behavioral complexities.

Large-scale evolution involves several layers of complexity spanning multiple scales, from genes and organisms to whole ecosystems. In this paper we review several models involving the macroevolution of artificial organisms, communities or ecosystems, highlighting their importance and potential role in expanding the modern synthesis. Afterwards, we summarize the key results obtained from our model of artificially evolved ecosystems where individuals are defined as embodied entities within a physical, simulated world where they can evolve different traits and exploit multiple resources. Starting from an initial state where single cells with identical genotypes are present, the system evolves towards complex communities where the feedbacks between population expansion, evolved cell adhesion and the structure of the environment leads to a major innovation resulting from the emergence of ecosystem engineering. The tempo and mode of this process illustrates the relevance in considering a physical embedding as part of the model description, and the feedbacks between different scales within the evolutionary hierarchy. (Abstract)

The existence of universal trends in large scale evolution might seem a rather bold idea. In the end, the paths followed by evolutionary trajectories are tangled and seem unique. Even so, convergent dynamics might be widespread. Such convergence is in itself a major component of evolution. Convergence is also a mark of universality and the common laws pervading the physics of adhesion or diffusion are likely to constrain potential pattern forming mechanisms. Disparate systems often display very common traits (particularly in their large scale patterns) associated with universal properties of the underlying dynamics.

Song, Chaoming, et al. Self-Similarity of Complex Networks. Nature. 433/392, 2005. The power-law scaling which distinguishes small world networks is found to exhibit a self-repeating fractal geometry. This is achieved by a renormalization procedure that ‘coarse-grains’ the system into a nest of nodes and modules. This novel work is introduced by Steven Strogatz in the same issue (365) with the title Romanesque Networks since this property is ideally displayed by broccoli of this name.

Here we show that real complex networks, such as the world-wide web, social, protein-protein interaction networks and cellular networks are invariant or self-similar under a length-scale transformation. (392) These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics. (392)

St-Onge, Jonathan, et al. Socio-Semantic Networks as Mutualistic Networks. Nature Scientific Reports. 12/1889, 2022. We cite this entry by University of Quebec scholars as an instance of how these independent topologies are exemplified even by textual and behavioral stages. These common manifestations are then seen as similar to a far-removed symbiotic occasion of insect pollination patterns.

Several studies have shown that discourse and social relationships are intertwined and co-evolve. However, we lack theoretical models to explain this phenomenon. Here, we propose to model socio-semantic networks as an interaction between an ecological community process and a document-based process. We consider the link between semantic and social ties as analogous to interactions found in pollination networks whereby agents visit hidden topics in a similar way to how insects visit specific plants. Our results show that a socio-semantic interaction matrix respects a mutualism so to better understand the dynamic of human socio-semantic interactions. (Abstract excerpt)

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