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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: A Survey of Animate Complex Network Systems

B. Our Own HumanVerse Genome Studies

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)

Gorlich, Dennis. The Relational Basis of Molecular Codes. Biosemiotics. Online December, 2013. A University of Muenster and Jena School for Microbial Communication researcher proposes that illuminating insights for genomic and cellular studies could be gained by way of their semantic aspects. See also by Gorlich and Peter Dittrich “Molecular Codes in Biological and Chemical Reaction Networks” in PLoS One (8/1, 2013).

In this paper I reviewed the network based definition of molecular codes. While code nest can be used to define new measures of semantic capacity, i.e., a system’s capacity to realize semantic relationships between molecular species, code linkage can be used to formalize the notion of systems of codes. This, in general, can lead to a system based understanding of cells, especially in genome scale models. Both concepts, since they are applicable algorithmically, allow to base research in biosemiotics on data (and) network models. (Conclusion)

Greenbury, Sam, et al. The Effect of Scale-free Topology on the Robustness and Evolvability of Genetic Regulatory Networks. Journal of Theoretical Biology. 267/48, 2010. As the wholescale revision of genomes by way of nonlinear complex dynamics proceeds apace, Oxford and CalTech biophysicists and biochemists further illume and confirm how these native geometries indeed grace genomes.

We investigate how scale-free (SF) and Erdős–Rényi (ER) topologies affect the interplay between evolvability and robustness of model gene regulatory networks with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006) we find that networks with SFin topologies, that is SF topology for incoming nodes and ER topology for outgoing nodes, are significantly more evolvable towards specific oscillatory targets than networks with ER topology for both incoming and outgoing nodes. Similar results are found for networks with SFboth and SFout topologies. The functionality of the SFout topology, which most closely resembles the structure of biological gene networks is compared to the ER topology in further detail through an extension to multiple target outputs, with either an oscillatory or a non-oscillatory nature. (Abstract, 48)

Greenspan, Ralph. Selection, Gene Interaction, and Flexible Gene Networks. Stillman, Bruce, et al, eds. Evolution: The Molecular Landscape. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 2009. In a review for this Darwin festschrift, a Kavli Institute for Brain and Mind (San Diego) geneticist advises that the “single-gene mutation” paradigm from the 1950s to c. 2000, is set aside for a 21st century view of arrays of dynamical interrelationships in such local and genomic topologies. But one may add this revolution goes beyond a theoretical revision. Rather it implies that these qualities spring from and exemplify an independent creative source. (See also papers by Dundr, and Del Bianco, in Cold Spring Harbor Perspectives in Biology (herein) which consider self-organizing agencies.)

Griffiths, Paul and Karola Stotz. Genetics and Philosophy: An Introduction. Cambridge: Cambridge University Press, 2013. University of Sydney philosophers of biology who have long been immersed in this discourse and debate provide a thorough coverage of the field and issues. As a general chronology, chapters flow from Mendel’s Gene and The Material Gene to The Reactive Genome, Outside the Genome, Gene as Information, Behavioral Gene, and The Evolving Gene. The last post-sequence decade is a necessary shift from reduction methods that found all the pieces to their subsequent whole systems integration. In regard, the newly dynamic genome now extends and opens beyond nucleotides to external environmental influences. But the work remains couched in “neo-mechanist” terms, while definitions vie as to what a gene might or might not be, again in abstractions. So in fairness, we quote from an abstract to Karola Stotz’s 2008 online paper “How (not) to be a Reductionist in a Complex Universe” (search Google).

This paper understands reductionism as a relation between explanations, not theories. It argues that knowledge of the micro-level behavior of the components of systems is necessary, but only combined with a full specification of the contingent context sufficient for a full explanation of systems phenomena. The paper takes seriously fundamental principles independent and transcendent of the laws of quantum mechanics that govern most of real-world phenomena. It will conclude in showing how the recent postgenomic srevolution, taking seriously the physical principle of organization and collective behavior, can be understood as attempting to complement a reductionist investigative strategy with an antireductionist explanatory strategy. (Stotz Paper)

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