IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

C. Our Own HumanVerse (Epi) Genomic Heredity

Eraslan, Gokcen, et al. Deep Learning: New Computational Modelling Techniques for Genomics. Nature Reviews Genetics. 20/7, 2019. We review this paper by Technical University of Munich researchers along with Deep Neural Networks for Interpreting RNA-binding Protein Target Preferences by Mahsa Ghanbari and Uwe Ohler in Genome Research (January 2020) as an example of how frontier AI neural net techniques derived from our own cerebral cognition are being readily applied to model and analyze genetic phenomena. By this wide utility, they serve as an archetypal exemplar of self-organizing complexities which are similarly invariant from quantum to social systems. OK

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the increasing volume of genomics data requires more expressive machine learning models. By leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks such as the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing. (Erasian Abstract excerpt)

Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from multiple sources of raw data. However, the interpretability of these models has not yet been investigated in detail. We have designed a multitask and multimodal deep neural network to characterize in vivo RBP targets. Learning across multiple RBPs at once, we are can avoid experimental biases and identify RNA sequence motifs and transcript context patterns the most important for each individual RBP. (Ghanbari Abstract excerpt)

Fariselli, Piero and Amos Maritan. Thermodynamic perspectives into DNA stability and information encoding in the human genome. Communications Physics.. 8/102, 2025. University of Torino and University of Padova system theorists (search AM) offer a deeper energetic explanation for the steady presence of nucleotide descriptive contents.

The perpetuation of species depends on two vital factors at the DNA level: the encoding of information essential for survival and adaptation, and the stability of DNA to preserve this content. Our study focusses on the latter aspect and confirms that local interactions within DNA are sufficient to provide a thermodynamic foundation for effective genome reliability. By evaluating the effective energy of DNA sequences, this framework offers insights into physical principles, information encoding, and mutation dynamics. (Excerpt)

Field, Dawn and Neil Davies. Biocode: The New Age of Genomics. New York: Oxford University Press, 2015. As our 2010s worldwide sciencesphere proceeds to learn on its own, an especial advance is underway in this expansive field of genetic phenomena. Among a burst of books, an Oxford University senior research geneticist and the director of the UC Berkeley South Pacific Research Station here survey its frontiers. With an acknowledgement of its 1980s originator Barbara McClintock, a dynamic 3-dimensional genome of nucleotides and networks is the current version. A constant theme is the generative, systemic significance of this informational domain across life’s fauna and flora of evolving species. Since the 2000 human genome project, the sequencing of every creature from primates to invertebrates, along with extinct predecessors, continues apace. As a consequence, a novel project is proposed to achieve a composite global genome as the sum DNA total of biospheric beings, which would be a boon to an ecological sustainability. See also herein The Founding Charter of the Genomic Observatories Network by the authors, who lead a large international team.

Flint, Jonathan. The Meaning of Life. Current Biology. 28/R761, 2018. The entry is a review of Carl Zimmer’s 2018 book She Has Her Mother’s Laugh: The Powers, Perversions and Potential of Heredity. We want to record this luminous volume, but also a deep quandary at the essence of natural science and philosophy. The reviewer is a renowned professor at the UCLA Brain Research Institute, who explains the above title early on. The “meaning” topic came up at a dinner party, to which his answer was: as a geneticist, I could confidently assure everyone that life has no meaning, all it does is transmit DNA. The expert essay closes by saying again while life may have heritable standards, it still has no meaning.

In regard, Dr. Flint is also a founding member of the UCLA Grand Challenge Depression Project, which has become a popular, 500 million dollar effort to cure and banish this common affliction. Why can’t anyone ever see, address and corrent this glaring contradiction, which is really at the root of our existential despair. Some 25 years ago I had a paper in Environmental Ethics (16/3) which worried that we will not be able to achieve a living, sustainable planet in a dead, pointless universe. By 2018 our biospheric dilemma has become critical to terminal, while denunciations of any greater (genesis) reality of which human beings are a central phenomenon grow in vehemence. No wonder people are deeply depressed.

Galperin, Michael, et al. 2015 Nucleic Acids Research Database Issue. Nucleic Acids Research. 43/D1, 2015. An introduction to an annual survey by this biweekly international journal of intensifying progress in sequencing and curating genomes and epigenomes from human beings through vertebrate and invertebrate species and onto ancient genetic codes across life’s developmental evolution. As its long list of projects and software programs attests, our humankind phenomenon appears as the way a genesis uniVerse converts itself into sentiently perceived written description and by virtue its further creative continuance. See also among numerous papers The Global Genome Biodiversity Network by Gabriele Droege, et al in this issue.

The 2015 Nucleic Acids Research Database Issue contains 172 papers that include descriptions of 56 new molecular biology databases, and updates on 115 databases whose descriptions have been previously published in NAR or other journals. Following the classification that has been introduced last year in order to simplify navigation of the entire issue, these articles are divided into eight subject categories. This year's highlights include RNAcentral, an international community portal to various databases on noncoding RNA; ValidatorDB, a validation database for protein structures and their ligands; SASBDB, a primary repository for small-angle scattering data of various macromolecular complexes; MoonProt, a database of ‘moonlighting’ proteins, and two new databases of protein–protein and other macromolecular complexes, ComPPI and the Complex Portal. (Abstract excerpt)

Garcia, Juan, et al. Ecophysiological Significance of Scale-dependent Patterns in Prokaryotic Genomes Unveiled by a Combination of Statistic and Genometric Analyses. Genomics. 91/538, 2009. A team which includes Eugene Stanley finds, as the quote avers, that genetic systems are imbued with the same repetitive discrete dialogue as all the rest of nature and humanity.

One of the main features of a DNA sequence related to the whole genome structural composition is the long-range correlation, a scale invariant property of DNA. In a correlated sequence, occurrence of a nucleotide in a specific position depends on the previous nucleotides (memory). The long-range correlation is related directly to the fractal structure of the DNA sequence or self-similarity. A sequence is defined as self-similar if its fragments can be rescaled to resemble the original sequence itself. Thus, a long-range correlated sequence suggests the existence of repetitive patterns inside it. (538)

Garcia Coll, Cynthia, et al, eds. Nature and Nurture: The Complex Interplay of Genetic and Environmental Influences on Human Behavior and Development. Mahwah, NJ: Erlbaum, 2004. A survey of current efforts to move beyond a genetic determinism and nature vs. nurture dichotomy by recognizing the active presence of “complex, relational, and dynamic developmental systems.” Another example of the conceptual shift from a particulate mechanism to recognize a creative environmental context.

Burgeoning biological, development, and behavioral evidence suggests that human behavior is the result of complex dynamic interactions between genes and the physical-experiential environment, operating at many dimensions from the molecular to the cultural, social, and historical. (225) This new paradigm implies: Adopting a theory that defines a human being as an embodied person functioning as a self-organizing system of cognitive, emotional, and motivational meanings, thereby moving conceptually away from trying to explain behavior as a product of two distinct processes – nurture or nature. (227)

Garfield, David and Gregory Wray. The Evolution of Gene Regulatory Interactions. BioScience. January 26, 2010. In a section for “21st Century Directions in Biology,” Duke University geneticists survey the epochal shift in thinking past a long molecular phase from Gregor Mendel’s day to the double helix and lately sequencing projects. For an entirely new realm of complementary interrelations between what discrete genes may be, still under review, that altogether carries prescriptive information which then distinguishes a dynamic genome. Along with other recent postings, an implication presses upon us – as universal self-organizing complex adaptive systems are found to grace genotype activity, they may be seen to take on a “genetic” character, suggestive of an independent, original cosmic parental code.

Until quite recently, evolutionary biologists, like most biologists, tended to study single genes as isolated entities. These studies have added enormously to our understanding of biological evolution. But because gene regulation by its very nature involves interactions between two (or more) genes, researchers have missed a range of evolutionary phenomena that can be observed only at the level of networks of interacting genes. (15)

For many years, a major obstacle prevented progress in studying the evolution of gene networks: Its easier to identify a gene than a gene interaction, and much easier to identify changes in gene regulation. As a result, we know a lot more about how individual genes and proteins evolve than we do about how the interactions between genes evolve, …. (16)

Garte, Seymour. Fractal Properties of the Human Genome. Journal of Theoretical Biology. 230/2, 2004. The availability of completely sequenced genomes now allows the detection of their fractal-like geometry.

This self-similarity with respect to scale renders the genome similar to other natural self-similar structures such as coast lines, clouds and mountain ranges. (252)

Gershenson, Carlos. Guiding the Self-organization of Random Boolean Networks. arXiv:1005.5733v1. In a mid 2010 paper to appear in Theory in Biosciences, Mexican systems scientist and Complexity Digest editor-in-chief adds and articulates one more take on and aspect of genetic regulatory networks as dynamical systems then akin to everywhere else in a genesis nature.

The concept of self-organization originated within cybernetics and has propagated into almost all disciplines. Given the broad domains where self-organization can be described, its formal definition is problematic. Nevertheless, we can use the concept to study a wide variety of phenomena. To better understand self-organization, the following notion can be used: A system described as self-organizing is one in which elements interact in order to achieve dynamically a global function or behavior. In other words, a global pattern is produced from local interactions. (1)

Examples of self-organizing systems include a cell (molecules interact to produce life), a brain (neurons interact to produce cognition), an insect colony (insects interact to perform collective tasks), flocks, schools, herds (animals interact to coordinate collective behavior), a market (agents interact to define prices), traffic (vehicles interact to determine flow patterns), an ecosystem (species interact to achieve ecological homeostasis), a society (members interact to define social properties such as language, culture, fashion, esthetics, ethics, and politics). (1-2)

Wikipedia on RBN: The first Boolean networks were proposed by Stuart A. Kauffman in 1969, as random models of genetic regulatory networks. A Random Boolean network (RBN) is a system of N binary-state nodes (representing genes) with K inputs to each node representing regulatory mechanisms. The two states (on/off) represent respectively, the status of a gene being active or inactive. The variable K is typically held constant, but it can also be varied across all genes, making it a set of integers instead of a single integer. In the simplest case each gene is assigned, at random, K regulatory inputs from among the N genes, and one of the possible Boolean functions of K inputs.

Gerstein, Mark, et al. Comparative Analysis of the Transcriptome across Distant Species. Nature. 512/445, 2014. “modENCODE” from the Abstract means Model Organism, in this case fly, worm, and human. See also in the same issue (449) Comparative Analysis of Metazoan Chromatin. In each instance, an international ENCODE team of some 100 scientists, which by name seem about 50/50 from eastern and western hemispheres, scope out these frontiers of our human uniVerse genome project to retro-sequence the breadth of species and depths of evolutionary time. The main ENCODE website at www.encodeproject.org/publications, with some negotiation, contains an extensive, on-going bibliography.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters. (Abstract)

Goldberg, Aaron, et al. Epigenetics: A Landscape Takes Shape. Cell. 128/4, 2007. An introductory survey to the nascent realization that much more is genetically going on than textbook bean bag model. A prime case is to appreciate chromatin beyond just packaged DNA so as to situate its protein components in complex networks which serve to regulate many genome functions. As a consequence, one might add, a new window is opened to the presence and activity of universal dynamic principles in this vital informative realm. But a pinball model of ‘molecular machinery’ is inadvertently employed, conceptual benefit could accrue via an inherently organic milieu from which such novel properties naturally spring.

Since the genomes of over 180 organisms have been sequenced, it is becoming increasingly clear that the complex biology of an organism arises from more information than is contained in its DNA sequence. (627 Issue Summary)

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