(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems

3. Whole Genome Regulatory Systems: DNA + AND = ANN/DAN

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)

Extance, Andy. Digital DNA. Nature. 537/22, 2016. After a decade and a half of accelerated sequence studies of this archetypal double helix structure, within and beyond genomes, a report on novel realizations about how amazing properties that serve to store larger amounts of data and information for longer than any other method. Of course, Harvard’s George Church is involved, see Nucleic Acid Memory in Nature Materials (15/4, 2016). Another prime paper is A DNA-Based Archival Storage System from the University of Washington and Microsoft Research, on web publications list for Georg Seelig or Karin Strauss, Abstract below.

Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm3, and long-lasting, with observed half-life of over 500 years. This paper presents an architecture for a DNA-based archival storage system. It is structured as a key-value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB random-access subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets. (Seelig/Strauss)

Fierst, Janna and Patrick Phillips. Modeling the Evolution of Complex Genetic Systems. Journal of Experimental Zoology B. 324/1, 2015. We cite this synoptic paper by University of Oregon biologists as a good example of the current cross-integration of several fields and domains which seek to describe life’s emergence. For example, a process of deep neural net learning is present, along with genomes distinguished by this same dynamic phenomena as everywhere else.

In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems. (Abstract)

Finn, Elizabeth and Tom Misteli. Molecular Basis and Biological Function of Variability in Spatial Genome Organization. Science. 365/998, 2919. We note this paper by National Cancer Institute researchers as an example among many efforts of graphic whole system studies as our worldwise intellect proceeds at pace to totally quantify every aspect of whole genomic systems. See also Mapping Human Cell Phenotypes to Genotypes with Single-Cell Genomics in Science (365/1401). In a wider evolutionary view, it could seem that life’s source code, as it rises from universe to us, is meant to reach this sapient moment so as to salve and heal in turn we tinkered beings and to begin a new intentional future procreation.

Fortuna, Miguel and Carlos Melian. Do Scale-Free Regulatory Networks Allow More Expression than Random Ones? Journal of Theoretical Biology. 247/331, 2007. Increasingly, genomic systems of biomolecules in relational interaction are realized to take on non-random network configurations. One could imagine that such dynamic geometries from genes to galaxies might then imply a universal, independent source which is manifestly the same everywhere.

We show that scale-free regulatory networks allow a larger active network size than random ones. This result might have implications for the number of expressed genes at steady state. Small genomes with scale-free regulatory topologies could allow much more expression than large genomes with exponential topologies. This may have implications for the dynamics, robustness and evolution of genomes. (331)

Giuliani, Alessandro, et al. Self-Organization of Genome Expression from Embryo to Terminal Cell-Fate. Entropy. Online December, 2017. System geneticists Giuliani, Istituto Superiore di Sanitá, Rome; Masa Tsuchiya, Keio University, Fujisawa; and Kenichi Yoshikawa, Doshisha University, Kyotanabe; continue their project (search for prior work) to understand and explain genomic phenomena by way of an effective self-organized criticality, so to rightly assimilate with condensed matter physics. We record as one more way that complex dynamic systems theory can give animate life an affinity with universal nature, which then becomes vivified in turn. At the close of 2017, articles as this help qualify a seamless continuity of human beings with the conducive, evolutionary cosmos we find ourselves.

A statistical mechanical mean-field approach to the temporal development of biological regulation provides a phenomenological, but basic description of the dynamical behavior of genome expression in terms of autonomous self-organization with a critical transition. This approach reveals the basis of self-regulation/organization of genome expression, where the extreme complexity of living matter precludes any strict mechanistic approach. The self-organization involves two critical behaviors: scaling-divergent behavior (genome avalanche) and sandpile-type critical behavior. Genome avalanche pattern as competition between order (scaling) and disorder (divergence) reflect the opposite sequence of events characterizing the self-organization process in embryo development. Our results suggest: (i) the existence of coherent waves of condensation/de-condensation in chromatin, which are transmitted across regions of different gene-expression levels along the genome; and (ii) essentially the same critical dynamics we observed for cell-differentiation processes exist in overall RNA expression during embryo development, which is particularly relevant because it gives further proof of SOC control of overall expression as a universal feature. (Abstract)

Gourab, Ghosh Roy, et al. Bow-Tie Architecture of Gene Regulatory Networks in Species of Varying Complexity. Journal of the Royal Society Interface. June, 2021. University of Birmingham, UK and University of Melbourne computational geneticists including Karin Verspoor provide a good 2021 example of the latest abilities to discern a universal invariance across many genomic structures and processes. A further perception involves a deep tendency therein to seek and reside at an optimum critical state. On cue so it seems our 21st century Earthwise erudition has just now reached a robust discovery of a natural uniVerse to humanVerse genesis.

The gene regulatory network (GRN) architecture plays a key role in the biological differences between species. In regard, we review some variations in terms of universal dynamical properties of their gene regulatory systems. A network feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of in and out nodes. Though a bow-tie architecture occurs in many networks, it has not been investigated in complex GRNs from prokaryotes to eukaryotes to multicellular organisms. With respect to dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged with biological complexity, based on the GRN bow-tie architectures. (Abstract excerpt)

Criticality is the property by which a dynamical system tunes to a point or region at the boundary between the ordered and the chaotic phases. Biological regulatory networks are found to be critical or near critical. This property of criticality allows the system to attain an optimal trade-off between the above-mentioned properties of robustness and flexibility. Our hypothesis is that a larger GRN CORE in more complex species moves their gene regulatory systems closer to criticality, and with an increase in flexibility and a decrease in robustness to specific perturbations to the CORE as discussed before, allows a better robustness–flexibility balance. (8-9)

Grimbs, Anne, et al. A System-Wide Network Reconstruction of Gene Regulation and Metabolism in Escherichia coli. arXiv:1803.05429. We cite because Jacobs University and University of Bremen computational biophysicists provide a latest technical exercise of how genetic research has moved to a whole genome phase, which includes nucleotides along with their dynamic interconnections. With this expansion in place, a wider influence upon metabolic processes (aka metabolomics) can be studied in unison.

Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. The first approaches started from metabolic models which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks). (Abstract excerpts)

Hacker, William, et al. Features of Genomic Organization in a Nucleotide-Resolution Molecular Model of Escherichia coli Chromosome. Nucleic Acids Research. Online July, 2017. We note this paper by University of Iowa biochemists because it cites the presence of globular domains with a fractal self-similarity. Here is another way that whole genomes are lately becoming known and treated as integral complex systems, akin to neural, quantum, and linguistic phenomena.

We describe structural models of the Escherichia coli chromosome in which the positions of all 4.6 million nucleotides of each DNA strand are resolved. In both types of model, the chromosome is partitioned into plectoneme-abundant and plectoneme-free regions, with plectoneme lengths and branching patterns matching experimental distributions, and with spatial distributions of highly-transcribed chromosomal regions matching recent experimental measurements of the distribution of RNA polymerases. The models exhibit characteristics similar to those of ‘fractal globules,’ and even the most genomically-distant parts of the chromosome can be physically connected, through paths combining linear diffusion and inter-segmental transfer, by an average of only ∼10 000 bp. We anticipate that the models will prove useful in exploring other static and dynamic features of the bacterial chromosome. (Abstract excerpts)

He, Bing and Kai Tan. Understanding Transcriptional Regulatory Networks Using Computational Models. Current Opinion in Genetics & Development. 37/101, 2016. We enter this paper by a University of Iowa geneticist and an oncologist as an example of whole genome studies by way of Bayesian complex networks and an array of algorithmic methods.

Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. (Abstract)

Huang, Lifang, et al. The Free-Energy Cost of Interaction between DNA Loops. Nature Scientific Reports. 7/12610, 2017. We cite this paper by Guangdong University and Sun Yat-Sen University mathematicians as an example of later 2010s quantifications of genomic functions by way of non-equilibrium thermodynamics, so as to further root and source them in a lively physical ecosmos.

From the viewpoint of thermodynamics, the formation of DNA loops and the interaction between them, which are all non-equilibrium processes, result in the change of free energy, affecting gene expression and further cell-to-cell variability as observed experimentally. However, how these processes dissipate free energy remains largely unclear. Here, by analyzing a mechanic model that maps three fundamental topologies of two interacting DNA loops into a 4-state model of gene transcription, we first show that a longer DNA loop needs more mean free energy consumption. Our studies provide insights into the understanding of gene expression regulation mechanism from the view of energy consumption. (Abstract excerpt)

Previous   1 | 2 | 3 | 4 | 5  Next