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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
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IV. Ecosmomics: A Survey of Nonlinear Complex Network Sciences

B. Our Own HumanVerse Genome Studies

In Part III, The Information Computation Turn, we saw how space, time, matter and energy is becoming seen as suffused by an informative, program-like quality. Part IV, Cosmic Code, went on to document such an independent procreative system, while Part V, A Quickening Evolution, reported humankind’s nascent integral synthesis of the oriented, gestation of life, mind and self-cognizance. This extensive Part VI, Earth Life Emergence, will attempt to show how these innate, genetic-like, complementary principles are in similar manifestation everywhere. To continue this scenario, its most familiar exemplar, of course, is the genome code that informs the form, function and life span of every organism and person. Since circa 2001, with the sequencing of the human genome, a whole scale revision has been underway as to what constitutes genetic phenomena, broadly considered, which is still being worked out. This section will try to chronicle the many concerted efforts, see also Systems Biology and Genetics above.

While the last half of the 20th century from the 1953 DNA double helix sought to find the nucleotide and associated biomolecules, as readers know, a “systems” turn has begun to study the equally present dynamic regulatory networks that connect all the pieces. In regard, what defines a “gene” is still in abeyance, e. g. strings of codons. With this expansion, a new aspect known as “epigenetics” has opened opens to many external, environmental influences in effect beyond a point molecular locus. So once again the universal complex archetypes of a discrete elemental mode - DNA, along with the interactive relations - AND, with their informative content and communication, are well exemplified.

With these advances, as readers know a multitude of “-omics” have sprung up and taken across every organic scientific field and stage. Proteome, metabolome, cellular interactome, a neural connectome, are among many, each distinguished by an interplay of nodes and links. Thus an emergent genetic phenomena accrues as a fertile cosmos becomes graced with a genome-like essence everywhere. As an example, the popular “major evolutionary transitions” emergence by John Maynard Smith and Eors Szathmary (search & shown next) whence life’s episodic procession from suitable biochemicals to our human phase is at each step facilitated by a novel informative basis. Its linguistic version, one could even say “languagome,” is covered in A Cultural Code.


Barabasi, Daniel and Albert-Laszlo Barabasi. A Genetic Model of the Connectome. Neuron 105/1, 2020.

Cobb, Matthew. Life’s Greatest Secret: The Race to Crack the Genetic Code. New York: Basic Books, 2015.

Cowen, Lenore, et al. Network Propagation: A Universal Amplifier of Genetic Associations. Nature Review Genetics. 18/551, 2020.

Eraslan, Gokcen, et al. Deep Learning: New Computational Modelling Techniques for Genomics. Nature Reviews Genetics. 20/7, 2019.

Field, Dawn and Neil Davies. Biocode: The New Age of Genomics. New York: Oxford University Press, 2015.

Koonin, Eugene and Artem Novozhilov. Origin and Evolution of the Universal Genetic Code. Annual Review of Genetics. 51/45, 2017.

McGillivray, Patrick, et al. Network Analysis as a Grand Unifier in Biomedical Data Science. Annual Review of Biomedical Data Science. Vol. 1, 2018.

Mukherjee, Siddhartha. The Gene: An Intimate History. New York: Scribner, 2016.

Watson, James, et al. DNA: The Story of the Genetic Revolution. New York: Knopf, 2017.

Zimmer, Carl. She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity. New York: Dutton, 2018.

Aguilar-Rodriguez, Jose, et al. The Architecture of an Empirical Genotype-Phenotype Map. Evolution. 72/6, 2018. A bioinformatic team of JA-R, Leto Peel, Massimo Stella, Andreas Wagner and Joshua Payne with postings in Switzerland, Belgium and the UK consider expansions of genomic phenomena by way of network topologies and self-replicating computational programs. See for example Stochastic Turing Patterns in a Synthetic Bacterial Population by David Karig, at al (2018) for a concurrent project.

Recent advances in high‐throughput technologies are bringing the study of empirical genotype‐phenotype (GP) maps to the fore. Here, we use data from protein‐binding microarrays to study an empirical GP map of transcription factor (TF) ‐binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high‐resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are “small‐world” and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF‐binding sites in vivo. We discuss our findings in the context of regulatory evolution. (Abstract)

Allis, C. David, et al, eds. Epigenetics. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 2007. The first comprehensive text (500 pages) on all its aspects from historical to medical implications.

Almirantis, Yannis and Astero Provata. An Evolutionary Model for the Origin of Non-Randomness, Long-Range Order and Fractality in the Genome. BioEssays. 23/7, 2001. Mathematical self-similar, power law distributions characterize genomic systems.

Angier, Natalie. Scientists and Philosophers Find That ‘Gene’ Has a Multitude of Meanings. New York Times. November 11, 2008. A think piece in a Science Times issue dedicated to “Beyond the Gene.” Over the last decade, due to genome sequencings and the systems biology shift, largely unreported until now, an epochal revolution that predates Gregor Mendel has been underway. From beanbag textbook models and discrete, determinant DNA, the genetics has moved toward an increasing sense of equally real, “relational,” network dynamics. Inherited traits are influenced by more than double helix structures, today an “epigenetic” spectrum of topological to environmental effects weigh in. Single-strand RNA also gets into the act of making proteins, as Carl Zimmer explains in Now: The Rest of the Genome. The wider import is to offer a succinct example of a conceptual revolution across many scientific fields. But a deep dissonance persists since the physical ground of being and becoming remains stuck in a mechanical disconnect.

Arneodo, Alain, et al. Multi-scale Coding of Genomic Information: From DNA Sequence to Genome Structure and Function. Physics Reports. 498/2-3, 2010. In a lengthy technical treatise, French systems geneticists contribute to the current integral melding of nested physical and biological realms, each informing and revising the other, which bodes well for a self-animating genesis universe. With Goldenfeld, Shapiro, Kaschube, and others herein, life’s earthly and cosmic revolution closes on this grand, vital discovery.

Understanding how chromatin is spatially and dynamically organized in the nucleus of eukaryotic cells and how this affects genome functions is one of the main challenges of cell biology. Since the different orders of packaging in the hierarchical organization of DNA condition the accessibility of DNA sequence elements to trans-acting factors that control the transcription and replication processes, there is actually a wealth of structural and dynamical information to learn in the primary DNA sequence. In this review, we show that when using concepts, methodologies, numerical and experimental techniques coming from statistical mechanics and nonlinear physics combined with wavelet-based multi-scale signal processing, we are able to decipher the multi-scale sequence encoding of chromatin condensation–decondensation mechanisms that play a fundamental role in regulating many molecular processes involved in nuclear functions. (Abstract)

In a recent past, the DNA double helix was simply considered as a biological macromolecule whose nucleotide sequence codes for our genes. The regulation and control of DNA replication and gene transcription was supposed to be fully delegated to proteins. Nowadays, DNA is more and more recognized as a complex heteropolymer whose structural and mechanical properties play a relevant part in the management of gene information. (115) A consistent framework bridging scales and levels of organization from DNA to chromosome is therefore desirable to get an integrated picture of nuclear functions. Various scientific projects aiming at deciphering the mechanism underlying how information flows across scales during the different stages of the cell cycle are in current process. (115)

Avise, John. Evolving Genomic Networks: A New Look at the Language of DNA. Science. 294/86, 2001. A new metaphor is proposed for the 21st century genome as “a social collective whose DNA sequences display intricate divisions of labor and functional collaborations.” Rather than “beads on a string,” a better image would be a “miniature cellular ecosystem” or “interactive community.”

Barabasi, Daniel and Albert-Laszlo Barabasi. A Genetic Model of the Connectome. Neuron. 105/1, 2020. A son and father team a few miles apart at Harvard University (doctoral candidate) and Northeastern University (professor, group leader, founding theorist, search) apply mathematic scale-free network topologies such as biclique graphs (see below) to better trace and join regulatory genomes with cerebral multiplex intricacies. As A-L S’s 2016 Network Science conveys, from its 1998 advent, an ever wider anatomy/physiology-like webwork has been cast and quantified which now spreads from galactic clusters to literary works, and every node/link entity phase in between.

The connectomes of organisms of the same species show architectural and often local wiring similarities, which raises the question: where and how is neuronal connectivity encoded? Our premise is that the genetic identity of neurons should guide synapse and gap-junction formation and show that genetically driven wiring predicts the existence of specific biclique (see below) motifs in the connectome. We identify significant biclique subgraphs in the connectomes of three species and show that the neurons share expression patterns and morphological characteristics. Our proposed model thus offers a self-consistent framework to link the genetics of an organism to the reproducible architecture of its connectome. (Abstract excerpt)

Biclique: A special kind of bipartite graph where every vertex of the first set is connected to every vertex of the second set.

Beck, Stephan and Alexander Olek, eds. The Epigenome. Weinheim, GDR: Wiley-VCH, 2003. After the location and sequence of human genes by the Genome Project, it is now possible to move on to uncover their temporal and spatial expression, to be known at the Human Epigenome Project.

Beurton, Peter, et al, eds. The Concept of the Gene in Development and Evolution. Cambridge: Cambridge University Press, 2000. A historical review of genetics from an initial molecular emphasis to recent expansions to consider the genome as a integrative system. This new synthesis of particulate and developmental aspects takes on the guise of a complex adaptive system with discrete and epigenetic complements. Raphael Falk’s concluding essay is of special interest.

Bhardwaj, Nitin, et al. Analysis of Diverse Regulatory Networks in a Hierarchical Context Shows Consistent Tendencies for Collaboration in the Middle Levels. Proceedings of the National Academy of Sciences. 107/6841, 2010. A paper from Mark Gerstein’s (co-author) group at Yale that cites a common affinity between genomes and governments. The same, “strikingly similar,” geometries and dynamics, ostensibly springing from a common “nonrandom architecture,” are revealed in both disparate cases. An upshot not noted would then be an intrinsic “genetic” character for our social, communicative abidance. An extended quote is necessary to properly convey.

Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with well-defined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity. (6841)

Biro, Jan. The Proteomic Code. Theoretical Biology and Medical Modelling. 4/1, 2007. In this extensive, much cited, review, the Swedish-American geneticist now at the Homulus Foundation in San Francisco contributes to the 21st century revolution in understanding the expansive information processing dynamics of genetic molecular systems.

The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.

The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.

Boel, Gregory, et al. Omnipresent Maxwell’s Demons Orchestrate Information Management in Living Cells. Microbial Biotechnology. 12/2, 2019. French and Italian biologists including Antoine Danchin seek better explanations for life’s vital interactive conversations as organisms survive and evolve, which are in opposition to entropic losses. By this approach, a biological complementarity between cellular discrimination and recognition modes is glimpsed, along with allusions of a natural self-organization.

Synthetic biology advances require understandings of the critical functions that allow the construction and operation of a living cell. Besides coding for ubiquitous structures, minimal genomes encode a wealth of functions that dissipate energy. Analysis of these functions shows that they manage information under noisy conditions when discrimination of substrates is preferred over a recognition process. We show that such functions, including transporters and the ribosome constructors, behave as an informational agent theorized by (James Clerk) Maxwell, circa 1870, and well known as Maxwell's demon. Altogether these features form the minimal genome required to allow the construction of an autonomous cell and allow them to perform computations in an energy‐efficient way. (Abstract)

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