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

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

Lin, Chieh, et al. Using Neural Networks for Reducing the Dimensions of Single-Cell RNA-Seq Data. Nucleic Acids Research. Online July, 2017. This entry by Carnegie Mellon computer scientists is an example of the ready application of these technical methods to genetic phenomena. A luminous implication is then a deep similarity between cerebral and genomic processes.

While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some of which incorporate prior biological knowledge, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. (Abstract excerpts)

Mandal, Saurav, et al. Complex Multifractal Nature in Mycobacterium tuberculosis Genome. Nature Scientific Reports. 7/46395, 2017. Jawaharial Nehru University and Mayo Clinic systems scientists employ sophisticated computational procedures to discern how genomes are suffused by the same scalar, universal self-similarities as brains, microbial communities, languages, and galactic webworks. Our task on this site, into the later 2010s, is how to document and convey this uniVerse to human sapiensphere discovery into a salutary revolution. Its essence resides in a geno/phenotype doubleness of this common code as it spawns an exemplary, nested gestation.

The mutifractal and long range correlation (C(r)) properties of strings, such as nucleotide sequence can be a useful parameter for identification of underlying patterns and variations. In this study C(r) and multifractal singularity function f(α) have been used to study variations in the genomes of a pathogenic bacteria Mycobacterium tuberculosis. Genomic sequences of M. tuberculosis isolates displayed significant variations in C(r) and f(α) reflecting inherent differences in sequences among isolates. M. tuberculosis isolates can be categorised into different subgroups based on sensitivity to drugs, these are DS (drug sensitive isolates), MDR (multi-drug resistant isolates) and XDR (extremely drug resistant isolates). C(r) follows significantly different scaling rules in different subgroups of isolates, but all the isolates follow one parameter scaling law. The richness in complexity of each subgroup can be quantified by the measures of multifractal parameters displaying a pattern in which XDR isolates have highest value and lowest for drug sensitive isolates. Therefore C(r) and multifractal functions can be useful parameters for analysis of genomic sequences. (Abstract)

Massip, Florian, et al. How Evolution of Genomes is Reflected in Exact DNA Sequence Match Statistics. Molecular Biology and Evolution. 32/2, 2015. We cite this entry by MPI Molecular Genetics and INRA Unite Mathematiques Informatique et Genome researchers as a current example of how whole genomes are being treated by principles from condensed matter physics. See also Sheinman in the prior section for more work by this group.

Genome evolution is shaped by a multitude of mutational processes, including point mutations, insertions, and deletions of DNA sequences, as well as segmental duplications. These mutational processes can leave distinctive qualitative marks in the statistical features of genomic DNA sequences. One such feature is the match length distribution (MLD) of exactly matching sequence segments within an individual genome or between the genomes of related species. These have been observed to exhibit characteristic power law decays in many species. Here, we show that simple dynamical models consisting solely of duplication and mutation processes can already explain the characteristic features of MLDs observed in genomic sequences. Surprisingly, we find that these features are largely insensitive to details of the underlying mutational processes and do not necessarily rely on the action of natural selection. Our results demonstrate how analyzing statistical features of DNA sequences can help us reveal and quantify the different mutational processes that underlie genome evolution. (Abstract)

Mercer, Tim and John Mattick. Understanding the Regulatory and Transcriptional Complexity of the Genome through Structure. Genome Research. 23/1061, 2013. As global endeavors proceed to learn about and sequence an ever expansive genetic phenomena, Garvan Institute of Medical Research, Sydney, geneticists advise that a three dimensional, whole system, perspective is now possible and necessary for further progress.

An expansive functionality and complexity has been ascribed to the majority of the human genome that was unanticipated at the outset of the draft sequence and assembly a decade ago. We are now faced with the challenge of integrating and interpreting this complexity in order to achieve a coherent view of genome biology. We argue that the linear representation of the genome exacerbates this complexity and an understanding of its three-dimensional structure is central to interpreting the regulatory and transcriptional architecture of the genome. Chromatin conformation capture techniques and high-resolution microscopy have afforded an emergent global view of genome structure within the nucleus. Accordingly, we propose that the local and global three-dimensional structure of the genome provides a consistent, integrated, and intuitive framework for interpreting and understanding the regulatory and transcriptional complexity of the human genome. (Abstract excerpts)

Misteli, Tom. Self-Organization in the Genome. Proceedings of the National Academy of Sciences. 106/6885, 2009. A National Cancer Institute, NIH, commentary on a paper from the previous issue: Rajapakse, Indika, et al. The Emergence of Lineage-Specific Chromosomal Topologies from Coordinate Gene Regulation (106/6679). The latter team from the Fred Hutchinson Cancer Research Center, University of Washington, which included Mark Groudine and Steven Kosak, found that mathematical patterns observed in “nonrandom” dynamics of the mammalian cell nucleus matched a self-organizing computational model of the genome. The quotes are from the Rajapakse paper.

The shared insight from these different approaches is that biological processes are inclined to self-organize, in which a network of localized interactions yields an emergent structure that subsequently feeds back on and strengthens the original network. (6679) Our analysis demonstrates that the networks of coregulated gene expression and chromosomal association are indeed mutually related during differentiation, resulting in the self-organization of lineage-specific chromosomal topologies. (6679)

Misteli, Tom. The Self-Organizing Genome: Principles of Genome Architecture and Function. Cell. 183/1, 2020. The veteran Swiss-American systems biologist (search) is director of the NIH Center for Cancer Research. This paper describes a confirmation of his collegial 21st century project to reconceive life’s genetic and cellular phases by way of a primary self-organization. As the quotes say, this intrinsic developmental process is not random happenstance but a guided process which results in a reliable array of forms, units and features. A further significant finding is that even genetic phenomena can be seen to reach and take on a critical balance of conserved, stable states along with creative responses to external changes. We add several quotes for this consummate achievement.

Genomes have complex three-dimensional architectures. The recent convergence of genetic, biochemical, biophysical, and cell biological methods has uncovered several fundamental principles of genome organization. They highlight that genome function is a major driver of genome architecture and that structural features of chromatin act as modulators, rather than binary determinants, of genome activity. The interplay of these principles in the context of self-organization can account for the emergence of structural chromatin features, the diversity and single-cell heterogeneity of nuclear architecture in cell types and tissues, and explains evolutionarily conserved functional features of genomes, including plasticity and robustness. (Abstract)

An important realization from these studies has been that the organization of genomes is characterized by a high degree of order and non-randomness. An overt example is the physical segregation of transcriptionally active euchromatin from repressed heterochromatin into distinct regions in the cell nucleus of most eukaryotic cells. Other non-random features of genomes include the formation of chromatin domains and the positioning of genes to preferred locations within the nuclear space. In addition to the genetic material, many proteins are non-randomly distributed in the nucleus and are concentrated in sub-nuclear bodies. These observations highlight a considerable degree of order and non-randomness in genome organization. (28)

As outlined above, genomes are characterized by a high degree of order represented by ubiquitously conserved architectural features, such as chromatin loops, domains, and nuclear bodies, as well as by non-random patterns, such as the location of genes and chromosomes in 3D space. In addition, the transcriptional program of a given cell is stable and defines its overall state. (35)

At the same time, genome organization and gene expression are also highly dynamic, variable, and stochastic. How can these two apparently conflicting aspects of genome organization — steady-state stability and intrinsic variability — be reconciled? One hint comes from the realization that the major characteristics of genome organization, including a dynamic, stable steady state and a high degree of heterogeneity and variability, are hallmarks of a self-organizing system. The principle of self-organization is ubiquitous in nature and, when applied to the genome, provides a unifying mechanism to account for many of its structural and functional features. (35)

With the realization that genome architecture is an emergent property of a self-organizing system, the next phase of studying the genome is now upon us. (42)

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)

Pollen, Alex, et al. Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution.. Nature Reviews Genetics. September, 2023. UC San Francisco, University of Basel, and Duke University Medical School geneticists enter a comprehensive, illustrated, 30 page, 328 reference retro-reading of genomic varieties from primates to our own Earthumanity. Once again a grand scenario of a long evolutionary emergence of such editorial sequencings becomes evident so as to begin anew.

Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these abilities will provide insights into how we became human. This Review describes how the sequencing of genomes from modern and archaic hominins and great apes is revealing a course of human-specific heredity. It also cites new molecular cellular approaches, cell atlases and organoids are enabling exploration of the candidate causal factors that underlie human-specific traits. (Abstract)

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)

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