(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

Mozziconacci, Julien, et al. The 3D Genome Shapes the Regulatory Code of Developmental Genes. arXiv:1911.04779. Drawing upon the latest research results, Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensé theoretical geneticists JM, Melody Merle and Annick Lesne contribute a deeper conceptual appreciation of nature’s pervasive, semantic, prescriptive source program.

We revisit the notion of gene regulatory code in embryonic development in the light of new findings about genome spatial organisation. By analogy with the genetic code, we posit that the concept of code can only be used if the corresponding adaptor can clearly be identified. An adaptor is here defined as an intermediary physical entity mediating the correspondence between codewords and objects. In our context, the encoded objects are gene expression levels, while specific transcription factors in the cell nucleus provide the codewords. We propose that an adaptor for this code is the gene domain, that is, the genome segment comprising the gene and its enhancer regulatory sequences. (Abstract excerpt)

Our starting point is the definition of a code that will be used in the present text. Different meanings of this word are encountered in science, from the secret codes in cryptography, the source codes in computer science, to the neural codes and the genetic code. The latter is the emblematic example of a semantic code, in a biological context. The definition of a semantic code relies on three ingredients, namely codewords, objects, and adaptors: codewords are inputs to be interpreted; a single object is associated to each codeword; adaptors are physical entities that implement the association of each codeword with a unique object. (3)

Ochiai, T., et al. Emergence of the Self-Similar Property in Gene Expression Dynamics. Physica A. 382/739, 2007. View along with Fortuna, et al, and many other citations in this section for a rush of perceptions, here globally from Japan to Spain and the US, of the same scale invariance throughout a genesis nature that is equally present in genetic systems.

In this article, we analyze the gene expression dynamics (i.e., how the amount of mRNA molecules in cells fluctuate in time) by using a new constructive approach, which reveals a symmetry embedded in gene expression fluctuations….We call it self-similarity symmetry (i.e., the gene expression short-time fluctuations contain a repeating pattern of smaller and smaller parts that are like the whole, but different in size). Secondly, we reconstruct the global behavior of the observed distribution of gene expression (i.e., scaling-law) and the local behavior of the power-law tail of this distribution. (739)

Ouma, Wilberforce, et al. Topological and Statistical Analyses of Gene Regulatory Networks Reveal Unifying yet Quantitatively Different Emergent Properties. PLoS Computational Biology. April, 2018. Akin to other natural realms, Ohio State University and Michigan State University biologists report the many ways that primary interconnective links between nodal nucleotides altogether form whole system genomes and their active generations of form and function.

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. (Abstract excerpt)

Petkova, Mariela, et al. Optimal Decoding of Information from a Genetic Network.. arXiv:1612.08084. We cite this entry by systems theorists including William Bialek and Gasper Tkacik as an example of how genomes are now being treated by cerebral dynamics, and literary methods, as if a cognitive resource.

Gene expression levels carry information about signals that have functional significance for the organism. Using the gap gene network in the fruit fly embryo as an example, we show how this information can be decoded, building a dictionary that translates expression levels into a map of implied positions. The optimal decoder makes use of graded variations in absolute expression level, resulting in positional estimates that are precise to ~1% of the embryo's length. We test this optimal decoder by analyzing gap gene expression in embryos lacking some of the primary maternal inputs to the network. (Abstract)

Biological networks transform multiple input signals into outputs that have functional significance for the organism. One path to understanding these transformations is to read out, or decode this relevant information directly from the network activity. In neural networks, for example, features of the organism's sensory inputs and motor outputs have been decoded from observed action potential sequences, sometimes with very high accuracy. Decoding provides an explicit test for hypotheses about how biologically meaningful information is represented in the network and which computations are needed to recover it. Here we address these questions in a small genetic network, taking advantage of experimental methods that allow us to measure, quantitatively, the simultaneous expression levels of multiple genes. (1)

Polychronopoulos, Dimitris, et al. Conserved Noncoding Elements Follow Power-Law-Like Distributions in Several Genomes as a Result of Genome Dynamics. PLoS One. 9/5, 2014. National Center for Scientific Research, Greece, and Stanford University bioscientists find pervasive evidence of nested nonlinear patterns and processes across genetic phenomena. Here is another indication of genomes as manifest exemplars of physical principles, which in turn infers as much about nature’s cosmos.

Conserved, ultraconserved and other classes of constrained elements (CNEs), identified by comparative genomics in a wide variety of genomes, are non-randomly distributed across chromosomes. We find widespread power-law-like distributions, i.e. linearity in double logarithmic scale, in the inter-CNE distances, a feature which is connected with fractality and self-similarity. Given that CNEs are often found to be spatially associated with genes, especially with those that regulate developmental processes, we verify by appropriate gene masking that a power-law-like pattern emerges irrespectively of whether elements found close or inside genes are excluded or not. Power-law-like patterns in the genomic distributions of CNEs are in accordance with current knowledge about their evolutionary history in several genomes. (Abstract)

Provata, Astero, et al. DNA Viewed as an Out-of-Equilibrium Structure. Physical Review E. 89/052105, 2014. Provata, National Center for Scientific Research, Athens, with Catherine Nicolis and Gregoire, Free University of Brussels typify the current move to root and explain specific fields, such as genomic or cerebral studies, by way of basic physical, mathematic and thermodynamic phenomena. See also by the authors Complexity Measures for the Evolutionary Categorization of Organisms in Computational Biology and Chemistry (53/1, 2014).

The complexity of the primary structure of human DNA is explored using methods from nonequilibrium statistical mechanics, dynamical systems theory, and information theory. A collection of statistical analyses is performed on the DNA data and the results are compared with sequences derived from different stochastic processes. These results suggest that human DNA can be viewed as a nonequilibrium structure maintained in its state through interactions with a constantly changing environment. Based solely on the exit distance distribution accounting for the nonequilibrium statistics and using the Monte Carlo rejection sampling method, we construct a model DNA sequence. This method allows us to keep both long- and short-range statistical characteristics of the native DNA data. The model sequence presents the same characteristic exponents as the natural DNA but fails to capture spatial correlations and point-to-point details. (Abstract excerpts)

Roman-Vicharra, Cristhian and James Cai. Quantum Gene Regulatory Networks. arXiv:2206.15362. We note this entry by Texas A & M University bioresearchers (see J. Cai website) for its innovative consideration of a deeper physical basis for GRNs, and as an exemplary instance whereby inquisitive human beings, lately members of a global knowsphere, will be moved take up, contribute to and carry forward this historic scientific learning endeavor. The content itself opens to and outlines a fertile quantum ground for to genetic structures to form and process.

In this work, we present a quantum circuit model for inferring gene regulatory networks (GRNs). The model is based on the idea of using qubit-qubit entanglement to simulate interactions between genes. We present results derived from the single-cell transcriptomic data of human cell lines from genes in involved with innate immunity regulation. We demonstrate that our quantum circuit model can predict the presence or absence of regulatory interactions between genes and estimate the strength and direction of the interactions. Based on these results, we suggest that quantum algorithmns can serve the data-driven life sciences. (Excerpt)

Rubenstein, Dustin, et al. Coevolution of Genome Architecture and Social Behavior. Trends in Ecology & Evolution. Online May, 2019. An eleven member international team including Hans Hofmann report the presence of a dynamic reciprocal relation between creaturely social activities and their malleable genetic composition.

Although social behavior can have a strong genetic component, it can also result in selection on genome structure and function, thereby influencing the evolution of the genome itself. Here we explore the bidirectional links between social behavior and genome architecture by considering variation in social and/or mating behavior among populations (social polymorphisms) and across closely related species. We propose that social behavior can influence genome architecture via demographic changes. We establish guidelines to exploit emerging whole-genome sequences using analytical ways to examine genome structure and function at different levels (regulatory vs. structural variation) from an ecological perspective of both molecular biology and population genetics. (Abstract)

Selvarajoo, Kumar and Alessandro Giuliani. Finding Self-Organization from the Dynamic Gene Expressions of Innate Immune Responses. Frontiers in Systems Biology. Online June, 2012. Keio University, Japan, and Istituto Superiore di Sanita, Italy researchers provide another clue to how genomes are distinguished by an intrinsic ability to organize themselves. By such lights, still more evidence accrues of nature’s ubiquitous genetic propensities. See also Tsuchiya, Masa, et al, in Common Code herein for a similar report from this group.

It is breathtaking each time to observe the effects of simple social organization of complex systems. Whether watching the display of patterns formed by shoal of fish in an aquarium, or walking down the tropical jungle to witness the synchronized flashing of fireflies, life surrounding us inspires our thinking on the possible mechanisms required to achieve self assembly. Noticeably, over the years, there have been a large number of works studying the self-organized behavior in biology. The formation of bio-films by bacteria for survival to environmental changes and the synchronization of neural cells for cognition are good macroscopic examples of collective behaviors. How can one witness such coordination in the realm of molecular biology? One essential feature for self-organized system is to display structure emerging from localized interactions. (1)

Overall, viewing the whole genome response in entirety and investigating the response of thousands of gene expressions in correlation matrix offers a simple, yet powerful tool to observe and interpret the complex self-organizing nature of living systems. We believe future studies using non-linear approaches and the concept of chaos may elucidate the presence of self-organized criticality to infer “avalanches” of our immune system. As for now, we stress how the traditional distinction between “house-keeping” and “modulated” genes is untenable when in presence of an integrated whole of relations supporting a self-organized behavior. (3)

Sengupta, Supratim and Paul Higgs. Pathways of Genetic Code Evolution in Ancient and Modern Organisms. Journal of Molecular Evolution. 80/5-6, 2015. Indian Institute of Science Education and Research, Kolkata and McMaster University, Ontario biophysicists retrospectively proceed to trace and describe these dual phases of life’s pervasive genomic basis. Might we then muse that our sapient knowledge itself could be a manifest exemplar of nature’s universal code as it seeks to learn from whence it came, and whomever, going forward, this endowment might procreate?

There have been two distinct phases of evolution of the genetic code: an ancient phase—prior to the divergence of the three domains of life, during which the standard genetic code was established—and a modern phase, in which many alternative codes have arisen in specific groups of genomes that differ only slightly from the standard code. Here we discuss the factors that are most important in these two phases, and we argue that these are substantially different. In the modern phase, changes are driven by chance events such as tRNA gene deletions and codon disappearance events. In contrast, in the ancient phase, selection for increased diversity of amino acids in the code can be a driving force for addition of new amino acids. The pathway of code evolution is constrained by avoiding disruption of genes that are already encoded by earlier versions of the code. The current arrangement of the standard code suggests that it evolved from a four-column code in which Gly, Ala, Asp, and Val were the earliest encoded amino acids. (Abstract)

Shuvaev, Sergey, et al. Network Cloning Using DNA Barcodes. arXiv:1611.00834. Cold Spring Harbor Laboratory researchers Shuvaev, Alexei Koulakov, Anthony Zador, and Batuhan Baserdem, with expertise from physics to neuroscience, advance their endeavor to cross-integrate neural network dynamics with genome sequencing techniques. Search the coauthors, and this section for similar syntheses due to nature’s universal source program, which is encountered in these formats and more. See also High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA by Justus Kebschull, et al in Neuron (91/975, 2016), and Sequencing the Connectome by Anthony Zador, et al in PLoS Biology (10/10, 2012).

Cold Spring Harbor Laboratory researchers Shuvaev, Alexei Koulakov, Anthony Zador, and Batuhan Baserdem, with expertise from physics to neuroscience, advance their endeavor to cross-integrate neural network dynamics with genome sequencing techniques. Search the coauthors, and this section for similar syntheses due to nature’s universal source program, which is encountered in these formats and more. See also High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA by Justus Kebschull, et al in Neuron (91/975, 2016), and Sequencing the Connectome by Anthony Zador, et al in PLoS Biology (10/10, 2012).

Szedlak, Anthony, et al. Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks. PLoS Computational Biology. Online June, 2016. As the Abstract relates, here is another example of genomes being described in terms of complex systems theory. As a consequence, genetic phenomena can be appreciated as an illustrative manifestation of independent, physical (organic) principles.

The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. (Abstract)

Previous   1 | 2 | 3 | 4 | 5  Next