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

B. Our Own HumanVerse (Epi) Genomic Heredity

Weiss, Kenneth. The Phenogenetic Logic of Life. Nature Reviews Genetics. 6/1, 2005. In this imaginative, illustrated contribution, the Penn State University geneticist proposes a mostly unnoticed, in-between, domain whereof the genotype emerges into its phenotype. Such ramifying translation is seen to occur by way of modularities, repetitive patterning, sequestration of autonomy, fractal branching, information feedback, altogether a logic of relational principles. Watch for Ken and Anne Buchanan’s new book The Mermaid’s Tale: Four Billion Years of Cooperation in the Making of Living Things in 2009 from Harvard University Press.

However, a more complete evolutionary synthesis, often classified under the catch-phrase the ‘evolution of development’ (EvoDevo), has been emerging, facilitated by advances in molecular genetics that have revealed elements of a unifying phenogenetic logic of life — the phenomena that connect biological phenotypes with their underlying genetic bases. ‘Logic’ is the operative concept, because unlike the stereotype according to which genes are independent, bead-like functional units that are linearly arranged along a chromosome, phenogenetic phenomena are the higher-order, ‘emergent’ results of structure and interaction.

Weiss, Kenneth and Anne Buchanan. Genetics and the Logic of Evolution. Hoboken, NJ: Wiley, 2004. Penn State University biological anthropologists achieve an accessible, cogent review of current novel understandings of genetic systems. Instead of discrete DNA pieces, there is a growing notice of recurrent modular patterning in genomes, a repetitive sequestration, due to a small number of ubiquitous regulatory genes. I have recently heard Ken Weiss speak about the presence of such “invisible general principles” beyond a Darwinian compass, whose algorithmic iteration serves to spawn a “nested serial homology” from DNA to physiology, cells to organs. Together with John Whitfield’s new book on such mathematical recurrences from microbes to ecologies, a once and future natural genesis springing from and exemplifying a common source becomes evident.

Whitfield, John. Across the Curious Parallel of Language and Species Evolution. PLoS Biology. 6/7, 2008. The British science writer reports in this online journal on the dawning realization that the molecular DNA code is strongly isomorphic and isodynamic with linguistic structures. One might add that an implication of this has not yet registered that human knowledge is genetic in kind, and that both codes must spring from the same innate source. By its employ, much as if people are as “genes,” we might intentionally continue creation.

Languages are extraordinarily like genomes…there could be very general laws of lexical evolution to rival those of genetic evolution. (1370)

Wills, Peter. Informed Generation: Physical Origin and Biological Evolution of Genetic Codescript Interpreters. Journal of Theoretical Biology. 257/3, 2009. A significant synthesis noted more in Quickening Evolution.

Witzany, Guenther. Life is Physics and Chemistry and Communication. Annals of the New York Academy of Sciences. Online December, 2014. In a current series of articles, the Austrian philosopher is trying to gather a number of themes into a better appreciation of the nature of genomes, and the universe they arise from and must reflect. After a noting an ancient dichotomy between a holistic oneness or atomist multitudes, starting with a 20th century linguistic basis, increasing recognitions have taken on algorithmic, computational, natural grammar and language aspects. A materialist cast is inadequate, our task is to properly interpret this equally real, more important biosemiotic quality. The preferred approach, due to James Shapiro, John Mattick, many others, is to view an integral genome wherein discrete nucleotides join in networks and communities, see also Witzany 2014 in Cooperative Societies.

Witzany, Gunther, ed. Natural Genetic Engineering and Natural Genome Editing. Annals of the New York Academy of Science. Vol. 1178, 2009. As reported both in this section, and A Cultural Code, a convergence has been going on for some time between the discursive fields of genetics and linguistics, which is reaching a mature affirmation. A July 2008 Salzburg symposium in this regard gathered key players such as James Shapiro, Eugene Koonin, Gertrudis Van de Vijver, Eshel Ben Jacob, Peter Gogarten and others to explore the this cross congruence, the papers of which this volume collects. Genomes, it is agreed, may be best known as a natural language with comparable syntax, grammar, semantics, which is then evident from prokaryotes to animal communities. By this revision, a prior mechanistic, particulate dogma ought to be set aside for a dynamically organic essence that is inherently literal, biosemiotic, informational, graced by signifying communication. A complementary source for this novel synthesis is Witzany’s own volume noted above.

Yan, Koon-Kiu, et al. Comparing Genomes to Computer Operating Systems in Terms of the Topology and Evolution of their Regulatory Control Networks. Proceedings of the National Academy of Sciences. Online Early, May 3, 2010. A proposal from Mark Gerstein’s Computational Biology and Bioinformatics lab at Yale University that views genetic codes as “adaptive complex systems” whose dual components and interconnections are “shaped progressively by a changing environment.” By this cast they become akin to software systems, an analogy developed in the paper, which as noted below, has salient parallels and differences. Consider with Danchin 2009 as examples of how pervasive this latest metaphor has become. Of course cells are not computers, and it begs us to turn the comparison around.

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network.

Zimmer, Carl. She Has Her Mother’s Laugh: The Powers, Perversions, and Potential of Heredity. New York: Dutton, 2018. We record this 650 page volume by the popular science writer and New York Times columnist because it covers every copious aspect of genetic phenomena via personal and social vignettes as this generative source continues to expand its influence.

Zou, James, et al. A Primer on Deep Learning in Genomics. Nature Genetics. 51/1, 2019. Stanford University, Karolinska Institute, Sweden, and Scripps Translational Research Institute, CA genoinformaticians introduce how these neural net methods can apply to and serve genetic studies. See also A Guide to Deep Learning in Healthcare in Nature Medicine by Andre Esteva, et al (25/1, 2019).

Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.

Zweiger, Gary. Transducing the Genome. New York: McGraw-Hill, 2001. The geneticist author orients and explains the paradigm shift in biology from a molecular to an informational basis of discrete genes and biomolecules engaged in dynamic communication. This coded content is now being transduced into an electronic format, which brings novel potentials and responsibility.

The way in which the molecules of life communicate has been likened to the way in which people communicate, so much so that linguistic terminology abounds in molecular biology. Nucleotides are known as letters, triplets that encode amino acids have been called words, collections of genes are known as libraries, proteins translated from nucleotide sequences, and proteins talk to each other. The goal of the molecular biologist in the genomic age has been described as translating the language of the cell. (143)

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