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

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

Letsou, William and Long Cai. Noncommutative Biology: Sequential Regulation of Complex Networks. PLoS Computational Biology. Online August, 2016. CalTech biochemists first describe an apparently constant recurrence in genomes of a common network dynamics and geometry. With this iconic system in place, its similar presence can be noted in many other, far removed natural and social realms.

DNA is the blueprint of life. Yet the order in which a cell follows these instructions makes it capable of generating thousands of different fates. How this information is extracted from underlying gene regulatory networks is unclear, especially given that biological networks are highly interconnected, and that the number of signaling pathways is relatively small. The conventional approach for increasing the information capacity of a limited set of regulators is to use them in combination. Surprisingly, combinatorial logic does not increase the diversity of target configurations or cell fates, but instead causes information bottlenecks. A different approach, called sequential logic, uses noncommutative sequences of a small set of regulators to drive networks to a large number of novel configurations. In this paper we show how sequential logic outperforms combinatorial logic, and argue that noncommutative sequences underlie a number of cases of biological regulation, e.g. how a small number of signaling pathways generates a large diversity of cell types in development. (Summary)

A fundamental question in systems biology is how a small number of signaling inputs specifies a large number of cell fates through the coordinated expression of thousands of genes. This problem is especially challenging given that gene regulatory and other types of networks in biology tend to be highly interconnected and their regulators promiscuous, with regulators affecting multiple targets and targets being affected by multiple regulators. Examples of this architecture include: transcription factor binding networks in bacteria, yeast, plants, and animals; cellular signalling pathways involved in growth and differentiation; the interactome of protein kinases and phosphatases; and synaptic connections between different layers of the brain. (2)

Finally, our results connect outside of biology to strategic planning in social, political, and economic arenas. Anyone familiar with negotiating knows about the limitations inherent in trying to make interconnected groups of people move in specific directions, especially when the actions affect all participants at once. Multiparty negotiations and tournaments may benefit from time-ordered strategies in which enemies temporarily team up, or fringe interest groups are transiently pacified. Determining whether this prediction is borne out in congressional and international negotiations, for example, is an interesting question for political science. In conclusion, the direct path to an outcome in a networks with many interacting parts may have many unintended and prohibitively expensive consequences. A multi-step strategy may achieve the same outcome with minimal cost and side effects. (18-19)

Levin, Simon. Complex Adaptive Systems. Bulletin of the American Mathematical Society. 40/1, 2003. The article lays out guidelines by which to study the evolving biosphere in terms of its nonlinear properties of many autonomous agents, diversity, resiliency, localized interactions, cooperation, pattern emergence and so on.

The notion of complex adaptive systems has found expression in every from cells to societies, in general with reference to the self-organization of complex entities, across scales of space, time and organizational complexity. (3)

Levin, Simon. The Evolution of Ecology. The Chronicle Review. August 13, 2010. The Princeton ecologist notes how this environmental science, since a systems view was necessary from its inception, has grown to be an exemplary model for studies across the ranges of nature and society.

Ecology views biological systems as wholes, not as independent parts, while seeking to elucidate how the wholes emerge from and affect the parts. Increasingly, such a holistic perspective, rechristened at places like the Santa Fe Institute as "the theory of complex adaptive systems," has informed understanding and improved management of economic and financial systems, social systems, complex materials, and even physiology and medicine. Essentially, that means little more than taking an ecological approach to such systems. (13)

Locey, Ken and Jay Lennon. Scaling Laws Predict Global Microbial Diversity. Proceedings of the National Academy of Sciences. 113/5970, 2016. Indiana University biologists attest to the natural presence of a consistent mathematical organization across a widest range of bacterial entities and colonies.

Ecological scaling laws are intensively studied for their predictive power and universal nature but often fail to unify biodiversity across domains of life. Using a global-scale compilation of microbial and macrobial data, we uncover relationships of commonness and rarity that scale with abundance at similar rates for microorganisms and macroscopic plants and animals. We then show a unified scaling law that predicts the abundance of dominant species across 30 orders of magnitude to the scale of all microorganisms on Earth. Using this scaling law combined with the lognormal model of biodiversity, we predict that Earth is home to as many as 1 trillion (1012) microbial species. (Significance)

Luque, Jordi, et al. Speech Earthquakes: Scaling and Universality in Human Voice. arXiv:1408.0985. We cite this report by Spanish and British researchers as a good example of current discoveries of how the same generic patterns and processes recur across the widest domains from peoples to the planet. As the quote describes, disparate phenomena such as conversation and seismic events are yet found to exemplify and repeat in kind everywhere, which quite implies a universal, independent, mathematical source.

Speech is a distinctive complex feature of human capabilities. In order to understand the physics underlying speech production, in this work we empirically analyse the statistics of large human speech datasets ranging several languages. We first show that during speech the energy is unevenly released and power-law distributed, reporting a universal robust Gutenberg-Richter-like law in speech. We further show that such earthquakes in speech show temporal correlations, as the interevent statistics are again power-law distributed. Since this feature takes place in the intra-phoneme range, we conjecture that the responsible for this complex phenomenon is not cognitive, but it resides on the physiological speech production mechanism.

Moreover, we show that these waiting time distributions are scale invariant under a renormalisation group transformation, suggesting that the process of speech generation is indeed operating close to a critical point. These results are put in contrast with current paradigms in speech processing, which point towards low dimensional deterministic chaos as the origin of nonlinear traits in speech fluctuations. As these latter fluctuations are indeed the aspects that humanize synthetic speech, these findings may have an impact in future speech synthesis technologies. Results are robust and independent of the communication language or the number of speakers, pointing towards an universal pattern and yet another hint of complexity in human speech. (Abstract)

Lynn, Christopher, et al. Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Nature Physics. January 2024, . Into this year, CCNY, Princeton and University of Chicago neuroscientists are able to report a constant cerebral topology which appears constant across mammalian species. As a result their next insight is to attribute this genomic commonality to the presence of universal, independent self-organizing forces.

The connections in networks of neurons are heavy-tailed, with a small number of connected more strongly. However, it remains unclear whether these patterns emerge from underlying mechanisms. Here we propose a minimal model of synaptic self-organization wherein links are pruned and synaptic strength rearranges by a mixture of preferential and random dynamics. Under these generic rules, networks evolve to produce connectivities that are scale-free with a power-law exponent. We confirm these predictions in the connectomes of several animals, suggesting that this cerebral phenomena may arise from general principles of network self-organization rather than mechanisms specific to individual species or systems.
Together, the results and models developed here demonstrate how two fundamental features of connectomes—heavy–tailed connectivity and clustering—can arise from simple network dynamics, providing a framework for future investigations into the self–organization of neuronal connectivity. (13)

Magliocca, Nicholas, et al. Modeling Cocaine Traffickers and Counterdrug Interdiction Forces as a Complex Adaptive System. Proceedings of the National Academy of Sciences. Early online April 1, 2019. Eight systems geographers posted in Alabama, Arizona, Wyoming, Texas, Oregon, and Ohio identify a common mathematical patterning that even criminal chaos seems to hold to and be constrained by. Our interest extends to a concurrent paper, Structure, Spatial Dynamics of Novel Seed Dispersal Mutualistic Networks in Hawaii (Visentin herein), which notes similar structuring dynamics across ecosystems. Within our 21st century scan, it is increasingly evident to a point of proof and discovery that an independent generative source is in exemplary presence everywhere.

The US government’s cocaine interdiction mission in the transit zone of Central America is now in its fifth decade despite its long-demonstrated ineffectiveness, both in cost and results. We developed a model that builds an interdisciplinary understanding of the structure and function of narco-trafficking networks and their coevolution with interdiction efforts as a complex adaptive system. The model produced realistic predictions of where and when narco-traffickers move in and around Central America in response to interdiction. The model demonstrated that narco-trafficking is as widespread and difficult to eradicate as it is because of interdiction, and increased interdiction will continue to spread traffickers into new areas, allowing them to continue to move drugs north. (Significance)

Makarieva, Anastassia, et al. Mean Mass-Specific Metabolic Rates are Strikingly Similar Across Life's Major Domains: Evidence for Life's Metabolic Optimum. Proceedings of the National Academy of Sciences. 105/16004, 2008. An international research team finds, as the quotes aver, a consistent, universal repetition across all spatial and evolutionary natural taxa. From our late global vantage might it seem in retrospect the entirety of earth life appears as a single developing organism?

A fundamental but unanswered biological question asks how much energy, on average, Earth's different life forms spend per unit mass per unit time to remain alive. Here, using the largest database to date, for 3,006 species that includes most of the range of biological diversity on the planet—from bacteria to elephants, and algae to sapling trees—we show that metabolism displays a striking degree of homeostasis across all of life. We demonstrate that, despite the enormous biochemical, physiological, and ecological differences between the surveyed species that vary over 1020-fold in body mass, mean metabolic rates of major taxonomic groups displayed at physiological rest converge on a narrow range from 0.3 to 9 W kg−1. (16994)

We have demonstrated that across dramatically different life forms, mean mass-specific metabolic rates converge on a relatively narrow range that is striking in contrast to the 20 orders of magnitude difference in the body mass of the studied species. This remarkable and previously unappreciated phenomenon is likely associated with the pervasive biochemical universality of living matter. (16999)

Marcus, Gary. Startling Starlings. Nature. 440/1117, 2006. A review of a research article in the same issue by Timothy Gentner, et al: Recursive Syntactic Pattern Learning by Songbirds. Recursion, or hierarchical self-embedding, was long thought to distinguish human language. In this study, the nested building-up of intricate communication is found to also occur in avian species. And the universally recurrent, code-like, pattern of emergence appears again in regnant speech.

Marquet, Pablo, et al. Scaling and Power-laws in Ecological Systems. Journal of Experimental Biology. 208/9, 2005. Seven authors from the Pontificia Universidad Catolica de Chile, Universidad de Concepcion, Chile, and the Santa Fe Institute, describes how the same dynamic phenomena are found to repeat across a wide range of hierarchical levels. Universally recurrent patterns and processes are now seen to characterize an increasingly intelligible Nature because of this attribute. The entire journal issue is devoted to scaling in biology and ecology.

During the past decade or so, several empirical and theoretical investigations have suggested that biological systems in general, and ecological systems in particular, seem to operate near a critical state, which results in the ubiquity of power-law behavior in several descriptors of their dynamics and might even belong to the same universality class as other complex systems such as economic systems. Thus the analysis of power-law and scaling relationships can help us to identify general principles that apply across a wide range of scales and levels of organizations, revealing the existence of universal principles within the seeming idiosyncratic nature of ecological systems. (1750)

This non-trivial rescaling suggests that in spite of differences in body size, life history and ecology, all the species under study fall along a single power-law relationship, which suggests that they share a common universal probability density distribution of growth rates. This powerful statement is further strengthened by the fact that this universal distribution is a power-law. (1759-1760) This strongly suggests that there may indeed exist universal principles that underlie the growth dynamics of complex adaptive systems that are involved in the acquisition, transformation and storage of information, materials and/or energy. (1760)

Mondal, Shrabani, et al. Universal Dynamic Scaling in Chemical Reactions At and Away from Equilibrium. arXiv:2101.01613. UM Boston, Center for Quantum and Nonequilibrium Systems contribute to on-going endeavors which are finding the expansive presence of an invariant, active self-similarity beyond the usual realms of living phenomena. See also On the Conditions for Mimicking Natural Selection in Chemical Systems by Gregoire Danger, et al in Nature Reviews Chemistry (4/102, 2020) for another example. As these efforts grow an spread in the 2020s, they achieve still deeper evidence for an organic fertility with an independent generative source.

Physical kinetic processes are known to exhibit universal scaling of observables that fluctuate in space and time. Are there analogous dynamic scaling laws that are unique to the chemical reaction mechanisms available to and natural and synthetic conditions? Here, we formulate two complementary approaches to the dynamic scaling of stochastic fluctuations in thermodynamic phenomena at and away from an equilibrium state. A survey of common chemical mechanisms reveals classes that organize according to the molecularity of the reactions, (non) equilibrium phases, and the extent of autocatalysis in the reaction network. Altogether, these results establish dynamic universality for the thermodynamic fluctuations well-mixed chemical reactions. (Abstract excerpt)

Universal scaling behavior has been found in biochemical networks [8], the stochastic exponential growth and division of bacterial cells [9, 10], the growth of human cancers [11], and dissipative self-assembly [12]. Formal analogies have expanded the scope of kinetic roughening theory [13, 14] even further by treating the fluctuations of mathematical functions as surrogates for the physical interface [1]. Examples include biological systems such as DNA [15], complex networks [16], crudeoil prices [17], heartbeat signals [18], strongly interacting gases [19], and material fracture [20). (1)


Universal behaviors have been extensively explored for physical phenomena, and here we have shown that universal dynamical scaling extends to the thermodynamic observables of chemical phenomena at and away from equilibrium. These observables satisfy three interconnected dynamic scaling classes we have tested of chemistry from simple, elementary reactions to complex, coupled autocatalytic reactions. Dynamical universality classes are typically determined by the dimensionality, conservation laws, symmetry of the order parameter, range of the interactions, and the coupling of the order parameter to conserved quantities. (16)

Moret, Marcelo. Self-Organized Critical Model for Protein Folding. Physica A. In Press, April, 2011. In accord with contemporary papers that report upon quantum to neural and astral dynamic complexities, a Brazilian biophysicist finds the realm of protein topologies to similarly exhibit a fractally scaled self-organized criticality.

We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. (Abstract)

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