IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

Lin, Yi, et al. Systems Science. Boca Raton: CRC Press, 2012. Yi Lin is a mathematican with academic appointments across China and the USA, and several texts on nonlinear theories to his credit. Coauthor Xiaojun Duan, with a Chinese PhD in systems engineering, professes at the National University of Defense Technology, Changsha. She drew upon her course material for the book’s topical range from historical backgrounds and nonlinear dynamics to self-organization, complex adaptive systems, synergetics, nonequilibrium thermodynamics, fractals, chaotic behavior, nested networks, emergence, and onto “open complex giant systems” of global and cosmic scale. The extraordinary volume is of such merit we offer an exemplary array of quotes.

But this edition is unique among its genre by achieving from an Eastern hemisphere an innovative appreciation of such ubiquitous natural generative phenomena. As also noted in Yi Lin and Shoucheng OuYang 2010 (search), Western science, as is well known, tends to particulate material reduction and a mechanist cosmos, life, and human devoid of source or destiny. From a holistic Asian perspective, a once and future accord can be realized, per the quotes, whence this 21st century systems vision is seen as confirmation of ancient organic procreative essences. Indeed, the arcane terms above are interpreted as new encounters with and versions of an eternal, independent Tao, with its “dialectics” of agent and relation, node and link, entity and empathy, surely masculine Yang and feminine Yin, as they reciprocally spiral into manifest development. (While the word dialectics is used often, no mention of Marxism is ever made.)

In short, the characteristics of systems science require scholars of different backgrounds to talk and to conduct research together so that they can potentially discover implicit connections underlying the artificially separated disciplines. Through integrations of multiple fields, commonalities of systems can be discovered so that practical problems can be resolved. (16)

One of the main theoretical contributions of (Ludwig von) Bertalanffy is the introduction of emergence into systems science. This concept has created an overall and long-lasting influence to the development of systems science. Its philosophical foundation is dialectics. Reductionism and holism, and analytical thinking and synthetic thinking, have always been pairs of opposites in the development of science. In the past several hundred years, science has advocated reductionism and denounced holism and emphasized on analytic thinking and despised synthetic thinking. To establish systems and to open up the research of complexity, there is a need to revisit these two pairs of opposites and to go beyond reductionism and analytic thinking in order to reestablish the dominance of holism and synthetic thinking on the basis of modern science. (19)

In Chinese society since antiquity, the philosophical thought that all worldly things give birth while in opposition to each other and mutually support and react to each other has been well accepted and widely recognized. However, this philosophical thought has not been rigorously described using scientific language. Also, it could not explain the ultimate whys and hows of things in terms of analytically describing interactions. (107) Synergetics is an interdisciplinary science that studies the formation and self-organization of patterns and structures appearing in open systems that are far from thermodynamic equilibrium. (107) In short, in the theory of synergetic, cooperation, orderliness, order parameters, and slow-change and fast-change variables are all products of competition. Through conflicts, opposites are unified, with the unification, there are still opposites. This fundamental law of dialectics plays out vividly in synergetics. (114)

When one looks at the mechanism for chaotic and random phenomena to appear and to evolve from the angle of behavioral forms, he or she can define these phenomena as due to self-organization. It is the foundation for all evolutionary processes to take place, be they natural or historical, physical or chemical, biological or human or societal. No matter what properties the systems’ components possess, and no matter which level they are situated at, these processes are similar, and their attributes are all self-organization. (115) From molecules to the universe, from cells to societies, all the objects that are so seemingly different possess one common attribute without a single exception: they evolve in the direction of an increasing degree of orderliness. Such evolutions are spontaneous. (115)

Universality of Fractal Structures Fractal is a concept with a wide-ranging significance. There are fractal phenomena in nature, societies, and human thoughts. It can be categorized into four main classes: natural fractals, time fractals, social fractals, and thought fractals. Natural fractals include geometric fractals, functional fractals, information fractals, energy fractals, etc. Time fractals mean such systems that have the property of self-similarity along the time axis. Social fractals include those self-similar phenomena seen in human activities and societal phenomena. Thought fractals stand for those self-similarities existing in human knowledge and consciousness. (157)

Philosophical Significance of Fractal Theory The fractal theory points out the dialectical relationship between wholes and parts of the objective world. It destroys the membrane between wholes and parts and locates the media and bridges that connect parts and wholes. That is there are self-similarities between wholes and parts. The Fractal theory provides a new methodology for people to comprehend the whole from parts and a different basis for people to fathom infiniteness from finiteness. Additionally, the fractal theory further deepens and enriches the universal connectedness of the world and the unitarity principle of the cosmos. (158)

Science and Theories of Complexity After the 1970s, nonlinear science, as represented by dissipative structures, synergetics, catastrophe theory, chaos theory, fractal theory, hypercycle theory, etc., made great advances and has shaken the determinism that has ruled physics, geometry, and mathematics for over 300 years. Determinism enabled physics to express various behaviors in predictable forms. However, natural, biological, ecological, social, and economic systems are fully filled with nonlinearity and complexity. Thus, investigations of such systems cannot be placed on reductionism. Instead, the thought of wholeness has to be employed. Complex adaptive systems theory represents an important theoretical achievement. Since entering the 1990s, complex systems and complexity have been a hot topic of scientific activities. (199)

Three Begets All Things of the World The origin of systems thinking in China can be traced back to Lao Tzu of the time of spring and Autumn over more than 2000 years ago. Lao Tzu is one of the earliest philosophers, who considered such questions as: Where and how is the world from? Where is it going to? In chapter 42 of Tao De Ching, Lao Tzu says that “Tao breeds one, one breeds two, two breeds three, and three begets all things of the world.” Why did Lao Tzu jump directly from “three” to “all things.”? It is because throughout Chinese history, yin and yang have represented complementary opposites; through these opposites, the colorful world has been formed. By using the point of view of the present systems science, the significance of “three” can be quite clearly illustrated. (202)

Basics of Complex Adaptive Systems Complex systems represent one of the current main research directions in systems science, while complex adaptive systems (CASs) stand for a class of very representative complex systems. (221) This point of view of interactions is very instructive. When agents are said to be the basis of the whole, it does not mean that the isolated and separated agents form the foundation of the whole. If it were so, one would have returned to the point of view of reductionism. It is the interactions between the agents and between the agents and their environments that contribute the foundation of the whole. When one says that “the whole is greater than the sum of its parts,” he or she talks about the “added value” created by these interactions. The rich and colorful behavioral patterns of CASs come from these increased values. (225) Third, the thought of adaptability organically links together the macrocosm and microcosm. Through the interactions between agents and between agents and their respective environments, changes in individual agents become the foundations of changes in the whole system so that these changes of different levels are considered at the same time. (225)

In principle, the fundamental properties of open complex giant systems are the same as those of general systems, including wholeness, correlation, systemhood, orderliness and dynamics, (along with) hierarchy, evolution, and emergence. (246) The treatment of such open complex giant systems as the human brain, human body, and geological system have gone beyond the total scientific achievement of the past 200 or more years on the basis of reductionism. It can be concluded that there is a need to organically combine the wholeness theory of the Chinese culture with reductionism in order to guide the investigations of open complex giant systems. (246)

Livio, Mario. The Golden Ratio. New York: Broadway Books, 2002. Which is mathematical Fibonacci series found in evidence throughout nature. In so doing, it takes on fractal qualities as motifs repeat themselves with a nested self-similarity, much like Russian dolls.

Loose, Martin, et al. Protein Self-Organization: Lessons from the Min System. Annual Review of Biophysics. 40/315, 2011. This chapter by Dresden University (Loose), Max Planck Institute (Karsten Kruse), and Saarland University (Petra Schwille) scientists is a good example of the shift in biological research to admit and study the deep, creative, presence and play of “Collective dynamic behavior: a system that emerges from the interactions of a large number of components” at each and every phase from biomolecules to cells, organisms, and onto fish schools and bird flocks.

One of the most fundamental features of biological systems is probably their ability to self-organize in space and time on different scales. Despite many elaborate theoretical models of how molecular self-organization can come about, only a few experimental systems of biological origin have so far been rigorously described, due mostly to their inherent complexity. The most promising strategy of modern biophysics is thus to identify minimal biological systems showing self-organized emergent behavior. One of the best-understood examples of protein self-organization, which has recently been successfully reconstituted in vitro, is represented by the oscillations of the Min proteins in Escherichia coli. (Abstract, 315)

The ability to self-organize and spontaneously form dynamic and spatially variable structures is among the most intriguing features of living systems. The ability to form temporal and spatial architectures and patterns, as cells and particularly organisms continuously do with high fidelity, also has its origin in the specific properties of the basic elements, i.e., proteins. This means that self-organization not only plays an important role for pattern formation on the level of whole organisms and tissues, but it is also important for the spatial and temporal organization of molecules inside cells. (316)

Lorenz, Dirk, et al. The Emergence of Modularity in Biological Systems. Physics of Life Reviews. 8/2, 2011. With coauthors Alice Jeng and Michael Deem, Rice University biophysicists cite Herbert Simon’s 1962 classic image, which a half century later can be verified as a natural, dynamic persistence for life to form into distinct, viable modules or communities at every nested phase and moment. Section 2.2.4 is “Spontaneous Emergence of Modularity as a Phase Transition,” while 3.2 is “Modularity in Metabolic Networks, Gene Networks, and Protein-Protein Interactions Networks,” and 3.6 “Social Networks,” indeed an iterative, developmental universality. See also “Modularity, Comparative Embryology and Evo-Devo” by Shigeru Kuratani in this journal (332/1, 2009), and “Hierarchical Evolution of Animal Body Plans” in Developmental Biology (337/1, 2010) by Jiankui He and Michael Deem.

In our review of the empirical evidence, we will show that natural and man-made systems employ modularity to a non-zero extent. That is, we will show that the polynomial approximation achieved by modularity and hierarchy has evolved in real networks. Modularity has been observed in all parts of biology on scales from proteins and genes to cells, to organs, to ecosystems. Proteins are often made up of almost independent modules, which may be exchanged through evolution. Topological Analysis of networks of genes or proteins has revealed modularity as well. Motifs and modules have been found in transcriptional regulation networks, and modules have been found across all scales in metabolic networks. Animal body plans can also be decomposed into clear structural or functional units. Food webs also show compartmentalization. Thus, a hierarchy of modules can be observed that spans many scale of biology. (130)

Ma’ayan, Avi. Complex Systems Biology. Journal of the Royal Society Interface. Vol. 14/Iss. 134, 2017. The director of the Mount Sinai Center for Bioinformatics, New York City, provides a succinct survey of this thirty year scientific endeavor to perceive and quantify a natural and social anatomy and physiology. We note for 1987 James Gleick’s Chaos book and the Santa Fe Institute (which I visited that summer). Into the later 2010s, as noted, e.g., in Figure 1, an expansive, self-similar synthesis from iconic cellular form and function to a dynamic cities can be achieved whence many almost exact copies of agents populate new complex environments and complex environments gradually congeal into complex agents. See also Lean Big Data Interpretation in Systems Biology and Systems Pharmacology by Ma’ayan, et al in Trends in Pharmacological Sciences (35/9, 2014).

Complex systems theory is concerned with identifying and characterizing common design elements that are observed across diverse natural, technological and social complex systems. Systems biology, a more holistic approach to study molecules and cells in biology, has advanced rapidly in the past two decades. However, not much appreciation has been granted to the realization that the human cell is an exemplary complex system. Here, I outline general design principles identified in many complex systems, and then describe the human cell as a prototypical complex system. Considering concepts of complex systems theory in systems biology can illuminate our overall understanding of normal cell physiology and the alterations that lead to human disease. (Abstract)

Mount Sinai Center for Bioinformatics. In this era of Big Data biomedicine, the Center for Bioinformatics develops analytic methods to help experimental biologists to study the increasingly diverse and complex data that are collected from human cells, tissues, and patients. The Medical School has strong departments in basic science research and clinical care, and we aim to strengthen between these departments. We focus on analyzing, visualizing, and mining data from experiments that profile the molecular state of human cells and tissues by transcriptomics, epigenomics, proteomics, and metabolomics for drug discovery. We also explore how to integrate these datasets with genomics and electronic medical health records data to advance precision medicine.

The Ma’ayan Laboratory develops computational and mathematical methods to study the complexity of regulatory networks in mammalian cells. We apply machine learning and other statistical mining techniques to study how intracellular regulatory systems function as networks to control cellular processes such as differentiation, dedifferentiation, apoptosis and proliferation. We develop software systems to help experimental biologists form novel hypotheses from high-throughput data, while aiming to better understand the structure and function of regulatory networks in mammalian cellular and multi-cellular systems.

Mainzer, Klaus. Challenges of Complexity in the 21st Century. European Review. 17/2, 2009. An “Interdisciplinary Introduction” to a special issue on the topic, whose articles by Jean-Marie Lehn, Peter Schuster, Wolf Singer, and Gunter Schiepek and others range from systems chemistry to self-organizing brains and psychotherapies. But however aptly dynamic self-organization can bring a novel theoretical explanation to every such realm, it has yet to dawn that a radically kind of genesis universe is thus implied and revealed.

Structures in nature can be explained by the dynamics and attractors of complex systems. They result from collective patterns of interacting elements that cannot be reduced to the features of single elements in a complex system. Nonlinear interactions in multi-component systems often have synergetic effects that can neither be traced back to single causes nor be forecast in the long run. The mathematical formalism of complex dynamical systems is taken from statistical physics. (223)

Mainzer, Klaus. Symmetry and Complexity: The Spirit and Beauty of Nonlinear Science. Singapore: World Scientific, 2005. A new book by the chair of philosophy of science at the University of Augsburg and director of its Institute of Interdisciplinary Informatics. Not seen yet, we quote from the publisher’s website.

Cosmic evolution leads from symmetry to complexity by symmetry breaking and phase transitions. The emergence of new order and structure in nature and society is explained by physical, chemical, biological, social and economic self-organization, according to the laws of nonlinear dynamics. All these dynamical systems are considered computational systems processing information and entropy….In the complex world of globalization, it strongly argues for unity in diversity.

Mainzer, Klaus. The Concept of Law in Natural, Technical and Social Systems. European Review. 22/S1, 2014. In a special issue on Basic Ideas in Science: The Concept of Law, the Technical University of Munchen philosopher, who has been writing about complexity since the 1990s, contrasts a prior phase of Newtonian mechanism with a Dynamic Concept of Laws that has arisen over this period. Rather than linear fixations, an actual nature of malleable, evolving intricacies and activities across scales of life and mind is being found. It is now known that genomes, brains, economies, and every milieu dynamically organize themselves in a similar way. As the quote notes, by 2014 their universal manifestion is proven from quanta to media, which then reveals a persistent, scale-free invariance. For a companion paper herein, see General Laws and Centripetal Science by Gerard Jagers.

Natural Laws of Self-organization: Laws of nonlinear dynamics do not only exhibit instability and chaos, but also self-organization of structure and order. The intuitive idea is that global patterns and structures emerge from locally interacting elements such as atoms in laser beams, molecules in chemical reactions, proteins in cells, cells in organs, neurons in brains, agents in markets, and so on. Complexity phenomena have been reported from many disciplines (e.g. biology, chemistry, ecology, physics, sociology, economics, and so on) and analysed from various perspectives such as Schrodinger’s order from disorder, Prigogine’s dissipative structure, Haken’s synergetics, Langton’s edge of chaos, etc. (S8)

Manukyan, Liana, et al. A Living Mesoscopic Cellular Automaton Made of Skin Scales. Nature. 544/173, 2017. University of Geneva and the Swiss Institutes of Bioinformatics researchers seek a better translation from natural mathematics into manifest biological form by way of this generative method. See also How the Lizard Gets Its Speckled Scales in the same issue by Leah Edelstein-Keshet, a University of British Columbia mathematician.

In vertebrates, skin colour patterns emerge from nonlinear dynamical microscopic systems of cell interactions. Here we show that in ocellated lizards a quasi-hexagonal lattice of skin scales, rather than individual chromatophore cells, establishes a green and black labyrinthine pattern of skin colour. We analysed time series of lizard scale colour dynamics over four years of their development and demonstrate that this pattern is produced by a cellular automaton (a grid of elements whose states are iterated according to a set of rules based on the states of neighbouring elements) that dynamically computes the colour states of individual mesoscopic skin scales to produce the corresponding macroscopic colour pattern. Our study indicates that cellular automata are not merely abstract computational systems, but can directly correspond to processes generated by biological evolution. (Abstract)

Mazzolini, Andrea, et al. Statistics of Shared Components in Complex Component Systems. arXiv:1707.08356. When this chapter about a independent, recurrent, genetic-like code was first posted in 2004, it was mainly a report of sporadic efforts by disparate researchers and schools, couched in abstract terms. Some 13 years on, University of Turin and Sorbonne University, Paris, biophysicists here describe a common complexity in exemplary evidence across a wide natural and social range from microbes to literature. As intimated and sought through history, in 1960s general systems theory, a 1980s goal for the Santa Fe Institute, at long last, with many similar entries by way of novel worldwide collaborations, are such inklings of its historic, revolutionary articulation.

Many complex systems are modular. Such systems can be represented as "component systems", such as LEGO bricks in LEGO sets. In other component systems, instead, the underlying functional design and constraints are not obvious a priori, and their detection is often a challenge, requiring a clear understanding of component statistics. Importantly, some quantitative invariants appear to be common to many systems, most notably a broad distribution of component abundances, which often resembles the well-known Zipf's law. Here, we specifically focus on the statistics of shared components, i.e., the distribution of the number of components shared by different system-realizations. To account for the effects of component heterogeneity, we consider a simple null model, which builds system-realizations by random draws from a universe of possible components. Surprisingly, this model can positively explain important features of empirical component-occurrence distributions obtained from data on bacterial genomes, LEGO sets, and book chapters. (Abstract excerpts)

A large number of complex systems in very different contexts - ranging from biology to linguistics, social sciences and technology - can be broken down to clearly defined basic building blocks or components. For example, books are composed of words, genomes of genes, and many technological systems are assemblies of simple modules. Once components are identified, a specific realization of a system (e.g., a specific book, a LEGO set, a genome) can be represented by its parts list, which is the subset of the possible elementary components (e.g. words, bricks, genes),with their abundances, present in the realization. (1)

The striking similarities of laws governing both component abundance and occurrence found in empirical systems of very different origins (LEGO sets, genomes, book chapters) support the idea that the concept of “component system” defined in this work can capture in a unified framework a large class of complex systems with some common global properties. Such “universal” phenomena may be regarded as emergent properties due to system heterogeneity, which transcend the specific design, generative process or selection criteria at the origin of a system. Analogous phenomena occur, for example, in ecosystems, where emergent species-abundance distributions appear for forests, birds or insects. (9)

McDonough, John and Andrzej Herczyhski. Fractal Patterns in Music. arXiv:2212.12497. grace and move our human scores, an actual music and songs of the spheres. The opus chosen are Handel’s The Harmonious Blacksmith, Haydn’s Piano Sonata No. 53, The Planets: Uranus by Holst, onto Sonata in A Major by Scarlatti and others. By one more melodious composition our 21st century and 2020s complexity sciences continue to perceive and listen to a common veracity and universal reprise. See also Kulkarni, Suman, et al. Information Content of Note Transitions in the Music of J. S. Bach by Suman Kulkarni, et al at 2301.00783.

If our aesthetic preferences are affected by fractal geometry of nature, scaling regularities would be expected to appear in all art forms. While a variety of statistical tools have been proposed to analyze time series in sound, no consensus has as yet exists as a good measure of complexity in music. Here we offer a new approach based on the self-similarity of the melodic lines at various temporal scales. Our definition of the fractal dimension is based on a temporal scaling hierarchy and the tonal contours of its musical motifs. These concepts are tested on “musical” Cantor Sets and Koch Curves and then applied to selected masterful compositions spanning five centuries. (Excerpt)

McLeish, Tom. Are There Ergodic Limits to Evolution? Interface Focus. 5/6, 2015. n this Are There Limits to Evolution? issue, a Durham University biophysicist tries to apply a physical theory about relevant landscape searches whereof “random” micro phases are averaged out to a predictable “fitness optima” result. But a Google of “ergodic” brings a variety of definitions, so an effort to clarify its usage would serve its usage. In any event, an affinity of “statistical mechanics and evolutionary dynamics” is seen to support innate tendencies for evolution to converge on similar forms and ways.

We examine the analogy between evolutionary dynamics and statistical mechanics to include the fundamental question of ergodicity — the representative exploration of the space of possible states (in the case of evolution this is genome space). Several properties of evolutionary dynamics are identified that allow a generalization of the ergodic dynamics, familiar in dynamical systems theory, to evolution. (Abstract)

For several generations of thinkers in the field of evolutionary dynamics, there has been a fruitful conversation with the concepts and methodologies of statistical mechanics [1]. The analogy arises, because random mutation between alleles at the genotype level induces a coarse-grained diffusion within the space of coded structures at the phenotype level, in a similar way that intermicrostate dynamics generates the sampling of macrostates in statistical mechanics. So divergence among genotypes (e.g. in bacteria) may nonetheless map onto a convergence in phenotype, in a manner isomorphic to the mapping of large numbers of configurational microstates into the same macrostate in statistical mechanics. There are three principal common ingredients that make the analogy between statistical mechanics and evolution fruitful: (i) a very large space of states; (ii) a coarse-grained set of properties that emerge from the microscopic states; and (iii) a stochastic dynamical process that moves the system from one state, or set of states, to another. (1)

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