IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

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

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)

Verd, Berta, et al. Modularity, Criticality, and Evolvability of a Developmental Gene Regulatory Network. eLife. 8/e43832, 2019. In a highly technical, well referenced, 38 page entry, Barcelona Institute of Science and Technology systems biologists BV, Nick Monk, and Johannes Jaeger (search) identify and describe how these title features are prime functions of dynamic genetic nucleotides and networks. In regard, the presence of genome community modules, along with critically poised responses, offers another instantiation of nature’s archetypal complex cosmome to connectome system.

The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular and are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve independently. Here we partition an experimentally tractable regulatory network—the gap gene system of dipteran insects. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, which explains the differential evolvability of the various features in the system. (Abstract excerpt)

Wall, Brydon, et al. Machine and deep learning methods for predicting 3D genome organization. arXiv:2403.03231. We cite this entry by Virginia Commonwealth University computational physicians as an example of how current neural net Ai methods, which have already taken over protein research, can similarly apply to and enhance complex genetic studies. Altogether life’s whole organismic realm continues to gain a deeply common textual essence.

Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play vital roles in cellular processes by regulating gene expression. However, current catalogs of 3D structures remain incomplete due to low data resolution. Machine learning methods can be an alternative to obtain more interactions and improve resolution. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and suggest future research directions.

Waxman, David and Nina Stoletzki. Scaling and Fractal Behaviour Underlying Meitotic Recombination. BioSystems. Online in Press, 2009. University of Sussex, Center for the Study of Evolution, biologists find inherent mathematical regularities that span in a self-similar way the nested networks of genome systems.

Decreasing the length scale of a geometric object is found to be directly analogous, in a genetics problem, to specifying a multilocus haplotype at a larger number of loci, and it is here that the fractal dimension reveals itself. (1) Overall, the results obtained in this work indicate a general property of the transmission of genes in meiotic recombination, and are independent of essentially all details of: recombination, the distribution of parental genotypes in a population, and the distribution of gamete numbers produced. (7)

Yan, Koon-Kiu, et al. MrTADFinder: A Network Modularity based Approach to Identify Topologically Associating Domains in Multiple Resolutions. PLoS Computational Biology. Online July, 2017. Yan, Shaoke Lou and Mark Gerstein, Program in Bioinformatics, Yale University, advance a better method for parsing genetic topology and content. We note as an example of how whole genomes are being also treated by way of common network properties.

The accommodation of the roughly 2m of DNA in the nuclei of mammalian cells results in an intricate structure, in which the topologically associating domains (TADs) formed by densely interacting genomic regions emerge as a fundamental structural unit. Identification of TADs is essential for understanding the role of 3D genome organization in gene regulation. By viewing the chromosomal contact map as a network, TADs correspond to the densely connected regions in the network. Motivated by this mapping, we propose a novel method, MrTADFinder, to identify TADs based on the concept of modularity in network science. Using MrTADFinder, we identify domains at various resolutions, and further explore the interplay between domains and other chromatin features like transcription factors binding and histone modifications at different resolutions. (Summary)

Zhang, Yang, et al. Computational methods for analysing multiscale 3D genome organization.. Nature Reviews Genetics. 25/3, 2024. We note this report by Carnegie Mellon, NIH, and UCLA geneticists including Tom Misteli at the frontier of this amenable intersection of AI neural net methods with complex genomic forms and functions. Altogether it seems that a common nonlinear narrative, an original literacy from cerebral to ecosmic connectomes, is deftly being deciphered and translated.

Recent progress in whole-genome mapping and imaging technologies has illuminated the spatial organization and folding in of the nucleus. In parallel, advanced computations have revealed multiscale (3D) transcription features. Here, we discuss how machine-learning methods and integrative frameworks, have led to a systematic delineation of genomic and epigenomic features, nuclear components and connective function. However, approaches to scan a wide variety of genomic and imaging datasets are still needed to define cellular phenotypes in health and disease. (Excerpt)

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