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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VI. Earth Life Emergence: A Development of Body, Brain, Selves and Societies

3. Genome Complex Systems

    This image of a complex genome system is from a Stanford Medicine Genome Technology Center page Integrated Knowledge Bases for Complex Biological Systems (Google). Its caption is: The need to integrate complex data from diverse sources can be addressed through advances in network theory, computation and ontologies. Integrating genome sequence, expression and polymorphism data, and clinical phenotypes, we are designing and building software that lets researchers explore, extend and analyze complex interacting networks of biological information.


As our worldwise sapiensphere proceeds to collectively learn on her/his own, we add this new section, akin to Quantum CS, Systems Neuroscience and elsewhere, because genetic studies, broadly conceived, are also coming to view and treat vital genome-wide associations by the same informational, self-organized, complex modular network criticalities as everywhere else. In this nascent Omics era, akin to quantome, neurome, languagome, and cosmome, whole genomes are seen to exemplify this universal, independent mathematical source. Nodal DNA nucleotides + link-like AND regulatory networks across dynamic scales (Dan + Ann) serve to form and guide each myriad creature, life’s evolutionary gestation, ourselves, and possibly even a celestial ovoGenesis. Gene regulatory networks (GRNs) are receiving concerted notice in 2018, which we have tried to gather. Precursor work is included along with innovative contributions as this significant domain becomes ever rooted in a conducive physical cosmos.

Aguirre, Jacobo, et al. On the Networked Architecture of Genotype Spaces and Its Critical Effects on Molecular Evolution. arXiv:1804.06835. In April 2018, Charles III University of Madrid, Interdisciplinary Group of Complex Systems theorists Aguirre, Pablo Catalan, Jose Cuesta, and Susanna Manrubia post a 48 page, 205 reference inclusive synthesis that ranges across statistical physics, multiplex networks, nonlinear complexities, which further takes on a computational guise. In this contribution, a whole dynamic genome serves as an exemplar, but as these integrations proceed they are found to apply in kind from quantum and cerebral realms to ecosystems and cultural societies (as this whole site seeks to document).

Evolutionary dynamics is often viewed as a subtle process of change accumulation that causes a divergence among organisms and their genomes. However, this interpretation is an inheritance of a gradualistic view that has been challenged at the macroevolutionary, ecological, and molecular level. Actually, when the complex architecture of genotype spaces is taken into account, the evolutionary dynamics of molecular populations becomes intrinsically non-uniform, sharing deep qualitative and quantitative similarities with slowly driven physical systems: non-linear responses analogous to critical transitions, sudden state changes, or hysteresis, among others. Furthermore, the phenotypic plasticity inherent to genotypes transforms classical fitness landscapes into multiscapes where adaptation in response to an environmental change may be very fast. The quantitative nature of adaptive molecular processes is deeply dependent on a networks-of-networks multilayered structure of the map from genotype to function that we begin to unveil. (Abstract)

Angelin-Bonnet, Olivia, et al. Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling. arXiv:1805.01098. Massey University, New Zealand mathematical geneticists present a tutorial chapter for a forthcoming Springer volume with the first title. As the Abstract notes, in contrast to the 20th century central dogma of only genes, into the 2010s, an equal presence of these pervasive linkages play a prime role in genomic contributions.

Modelling gene regulatory networks not only requires a thorough understanding of the biological system depicted but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarise the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Amongst other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks and the quantitative modelling of regulation. (Abstract)

Bouyloukos, Costas, et al. GREAT: A Web Portal for Genome Regulatory Architecture Tools. Nucleic Acid Research. 44/Web Server Issue, 2016. We choose this sample entry by CNRS, University of Paris, geneticists including Francois Kepes from this 2016 update of worldwide endeavors to develop software to treat, sequence, and catalog whole genomes. A companion 2016 Database issue, each accessible in full, contains more reports from the USA, Europe, and Asia. Please note the second quote for its observation of an innately ordered genomic organization.

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of a genome layout - defined as the respective positioning of co-functional genes - and its relation with chromosome architecture and gene expression. (Abstract excerpt)

The arrangement of genomic features along chromosomes does not appear to be random. The relative linear order of features -such as genes- which constitutes the genome layout, has been shaped by evolutionary adaptations to accommodate multiple constraints. Transcription regulation, at the genome scale, is among the most crucial of these constraints for cell success. Two main insights indicate nonrandom genome layout. First, the analysis of contiguous genomic segments between related genomes has highlighted synteny, that is the conservation of short-range gene order. Second, the detection of long-range regularities in the positioning of genes which are co-regulated, co-evolved or co-expressed along the genomes of all prokaryotic phyla, one Archae and one Eukaryote. (W77)

Capozziello, Salvatore, et al. The Chern-Simons Current in Systems of DNA-RNA Transcriptions. Annalen der Physik. 530/4, 2018. In this European physics journal since 1799 (search Physik), a global research group from the University of Naples, Slovak Academy of Sciences, Chulalongkorn University, Bangkok, and National Technical University of Athens scope out this recognition of mathematical principles being found in active effect for living phenomena. An especial case is their formative presence in genomic activities. Some 319 years later, such worldwide collaborations seem at last to be quantifying an integral synthesis of emergent life and persons with a truly conducive ecosmos uniVerse.

A Chern‐Simons current, coming from ghost and anti‐ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases A, T, C, G, U. We give a general discussion on the use of supersymmetry in biological systems, discuss the codon and anti‐codon ghost fields and develop an algebraic construction for the DNA area which does not seem active in biological systems. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T‐cell receptor gene by using a gene tensor correlation network related to the Chern‐Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern‐Simons current. (Abstract edits)

The Chern–Simons theory, named after Shiing-Shen Chern and James Harris Simons, is a 3-dimensional topological quantum field theory of Schwarz type, developed by Edward Witten. It is so named because its action is proportional to the integral of the Chern–Simons 3-form. In condensed-matter physics, Chern–Simons theory describes the topological order in fractional quantum Hall effect states. In mathematics, it has been used to calculate knot invariants and three-manifold invariants such as the Jones polynomial. (Wikipedia)

Cattani, Carlo and Gaetano Pierro. On the Fractal Geometry of DNA by the Binary Image Analysis. Bulletin of Mathematical Biology. Online June, 2013. We cite this technical paper by University of Salerno geneticists as an example of the extent that genomes are now found to be equally defined by such dynamic, self-similar regulatory networks. One might well imagine DNA/AND. Moreover these are the same archetypal topologies that distinguish every other strata and instance in nature.

In some recent papers, the fractal nature of nucleotide distribution in DNA has been investigated in order to classify/compare DNA sequences and to single out some singularities in the nucleotide distribution. Almost all these papers are motivated by the hypothesis that although the nucleotide distribution looks like a random distribution, some parameters and typical patterns suggest to us that there might exist some fractal rules behind, which enable the chaotic organization of nucleotides. Furthermore, this structural organization seems to obey recursive laws, which are fractal-like. At the moment, we can assume that the following two axioms hold. (1) Nucleotides are distributed according to some hidden rules. (2) These rules are based on some recursive fractal law so that the nucleotide distribution obeys some fractal-like organization. (2)

Chapman, Robert, et al. EcoGenomics: An Analysis of Complex Systems via Fractal Geometry. Integrative and Comparative Biology. 46/6, 2006. A salient research contribution to the total rethinking of genetic informational systems. In this regard, microarray transcription profiles are more than a collection of DNA strands, rather they are characterized by a dynamic regularity which can be explained by artificial neural networks and fractal analysis. Just as scalar ecological patterns such as species abundance are discernible through nonlinear methods, so do genomes express the same nested forms and processes. In perspective, a new nature is being untangled as no longer composed of “postage stamp” fragments but graced by a universal creative geometry. And this achievement need be recognized as an awesome discovery by cerebral humankind of a natural genesis.

Ecogenomics is a convenient descriptor for the application of advanced molecular technologies to studies of organismal responses to environmental challenges in their natural settings. (902) Fractals are also self-similar, meaning that they contain copies of themselves within themselves, and, while they may not exhibit the same details at all scales, they exhibit the same type of structure. Hence, they are considered to be scale invariant. (903)

Chia, Nicholas and Nigel Goldenfeld. Statistical Mechanics of Horizontal Gene Transfer in Evolutionary Ecology. Journal of Statistical Physics. 142/1287, 2011. As per the quotes, University of Illinois, Institute for Genomic Biology, physicists scope out commonalities between disparate biological phenomena, lately seen as many element, interactive complex systems, with seemingly inorganic many-body, condensed matter. The article concludes that “very generic processes” appear to repeat at every scalar instance, which would be much indicative of a genetic essence at work.

The biological world, especially its majority microbial component, is strongly interacting and may be dominated by collective effects. In this review, we provide a brief introduction for statistical physicists of the way in which living cells communicate genetically through transferred genes, as well as the ways in which they can reorganize their genomes in response to environmental pressure. We discuss how genome evolution can be thought of as related to the physical phenomenon of annealing, and describe the sense in which genomes can be said to exhibit an analogue of information entropy. As a direct application of these ideas, we analyze the variation with ocean depth of transposons in marine microbial genomes, predicting trends that are consistent with recent observations using metagenomic surveys. (Abstract, 1287)

The possibility that some notion of `adaptability' underlies transposon dynamics points toward two very interesting ideas. The first is that transposons, as well as other mobile genetic elements such as viruses and plasmids, could be more than just parasitic gene sequences feeding off host genomes. Instead, they may be required for the long term survival, adaptability, and diversification of an organismal lineage. The second is that evolutionary dynamics such as transposon proliferation may be driven by generic processes rather than governed by the specific histories of individual populations. This means that the properties of biological organism can and should be understood from the viewpoint of the statistics of a physical process rather than the particulars of a historical accident. (1297)

Cortini, Ruggero, et al. The Physics of Epigenetics. arXiv:1509.04145. In a paper to appear in the Review of Modern Physics, French scientists at Sorbonne and CNRS laboratories, including Annick Lesne and Jean-Marc Victor, contribute to the vital 21st century synthesis of life with its contextual milieu by showing how physical phenomena such as complex systems are a basis for extra-genomic transcriptions. A working title definition is given in the second quote. Whatever to then make of such a fecund cosmic ground, which seems to be graced by a primal genetic essence?

In higher organisms, all cells share the same genome, but every cell expresses only a limited and specific set of genes that defines the cell type. During cell division, not only the genome, but also the cell type is inherited by the daughter cells. This intriguing phenomenon is achieved by a variety of processes that have been collectively termed epigenetics: the stable and inheritable changes in gene expression patterns. This article reviews the extremely rich and exquisitely multi-scale physical mechanisms that govern the biological processes behind the initiation, spreading and inheritance of epigenetic states. Strikingly, to achieve stability and heritability of epigenetic states, cells take advantage of many different physical principles, such as the universal behavior of polymers and copolymers, the general features of non-equilibrium dynamical systems, and the electrostatic and mechanical properties related to chemical modifications of DNA and histones. By putting the complex biological literature under this new light, the emerging picture is that a limited set of general physical rules play a key role in initiating, shaping and transmitting this crucial "epigenetic landscape". (Abstract excerpts)

Recently, some authors recently proposed an operational definition of epigenetics: “An epigenetic trait is a stably heritable phenotype resulting from changes in a chromosome without alterations in the DNA sequence". This will be the definition we use in this review. To be even more specific, we note that: Epigenetics is the modification of the function(s) of a gene, that is stable and heritable during mitosis, possibly during meiosis. Epigenetics is not the reversible regulation of transcription in response to metabolic cues, because this is not stable nor heritable. (2)

Cowen, Lenore, et al. Network Propagation: A Universal Amplifier of Genetic Associations. Nature Reviews Genetics. 18/9, 2017. We note this paper by geneticists Cowen, Tufts University, with Trey Ideker, UC San Diego, Ben Raphael, Princeton, and Roded Sharan, Tel Aviv University, for its emphasis on the active presence in genomic systems of network multiplexities. In regard, as everywhere else, nature’s independent, tendency to form node and link structures are similarly manifest in complex genomes.

Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes. Recently, numerous bioinformatic techniques have been proposed for genetic analysis using networks, based on random walks, information diffusion and electrical resistance. These approaches have been applied successfully to identify disease genes, genetic modules and drug targets. In fact, all these approaches are variations of a unifying mathematical machinery — network propagation — suggesting that it is a powerful data transformation method of broad utility in genetic research. (Abstract)

Daniels, Bryan, et al. Logic and Connectivity Jointly Determine Criticality in Biological Gene Regulatory Networks. arXiv:1805.01447. Eight senior theorists mostly from Arizona State University including Sara Walker, Hyunju Kim and Stuart Kauffman advance current realizations of how ubiquitous interconnective webs play a major part in the active conveyance of prescriptive information between nucleotide genes. A further insight is that genome complex systems seem to be situated and poised in a critical balance. This is an increasingly common finding we note, e.g. brain neural nets. So here is another iconic paper in the later 2010s which quantifies and qualifies that a natural evolutionary genesis tends to reside in an optimum state of dynamic creative complementarity.

The complex dynamics of gene expression in living cells can be well-approximated using Boolean networks. The average sensitivity is a natural measure of stability in these systems: values below one indicate typically stable dynamics associated with an ordered phase, whereas values above one indicate chaotic dynamics. This yields a theoretically motivated adaptive advantage to being near the critical value of one, at the boundary between order and chaos. Here, we measure average sensitivity for 66 publicly available Boolean network models describing the function of gene regulatory circuits across diverse living processes. We find the average sensitivity values for these networks are clustered around unity, indicating they are near critical. This suggests the local properties of genes interacting within regulatory networks are selected to collectively be near-critical, and this non-local property of gene regulatory network dynamics cannot be predicted using the density of interactions alone. (Abstract excerpt)

De Lazzari, Eleonora, et al. Family-Specific Scaling Laws in Bacterial Genomes. Nucleic Acids Research. 45/13, 2017. Sorbonne University, University of Chicago, and University of Illinois (Sergei Maslov) computational geneticists offer a good example of how whole genomes are lately being treated as generic complex systems that exhibit the same super-linear scaling and modular complexity as any other exemplary phase such as brains or cities.

Among several quantitative invariants found in evolutionary genomics, one of the most striking is the scaling of the overall abundance of proteins, or protein domains, sharing a specific functional annotation across genomes of given size. The size of these functional categories change, on average, as power-laws in the total number of protein-coding genes. Here, we show that such regularities are not restricted to the overall behavior of high-level functional categories, but also exist systematically at the level of single evolutionary families of protein domains. This analysis provides a deeper view on the links between evolutionary expansion of protein families and the functional constraints shaping the gene repertoire of bacterial genomes. (Abstract excerpt)

Dios, Francisco, et al. DNA Clustering and Genomic Complexity. Computational Biology and Chemistry. 53/A, 2014. In a topical issue on Complexity in Genomes, Spanish bioinformatic researchers propose methods by which to quantify the nested topological intricacy of genomes. See also Self-organizing Approach for Meta-Genomes, and Menzerath-Altmann Law in Mammalian Exons Reflects the Dynamics of Gene Structure Evolution, and others in this issue. And as one peruses the paper, it occurs that “galactic clustering” might also be similarly arranged, each arising from the same natural source.

Early global measures of genome complexity (power spectra, the analysis of fluctuations in DNA walks or compositional segmentation) uncovered a high degree of complexity in eukaryotic genome sequences. The main evolutionary mechanisms leading to increases in genome complexity (i.e. gene duplication and transposon proliferation) can all potentially produce increases in DNA clustering. To quantify such clustering and provide a genome-wide description of the formed clusters, we developed GenomeCluster, an algorithm able to detect clusters of whatever genome element identified by chromosome coordinates. We obtained a detailed description of clusters for ten categories of human genome elements, including functional (genes, exons, introns), regulatory (CpG islands, TFBSs, enhancers), variant (SNPs) and repeat (Alus, LINE1) elements, as well as DNase hypersensitivity sites. The observation of ‘clusters-within-clusters’ parallels the ‘domains within domains’ phenomenon previously detected through global statistical methods in eukaryotic sequences, and reveals a complex human genome landscape dominated by hierarchical clustering. (Abstract excerpt)

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