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IV. Ecosmomics: A Survey of Animate Complex Network Systems

B. Our Own HumanVerse Genome Studies

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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