IV. Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

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

Daniels, Bryan, et al. Identifying a Developmental Transition in Honey Bees Using Gene Expression Data. bioRxiv. November 7, 2022. A latest paper by Arizona State University and Banner Health, Phoenix complexity theorists including Robert Page describes how dynamic genome studies now reveal critically poised bistable states even in this prescriptive phase. This –omic occurrence of self-organized criticalities can well establish nature’s 2020s universal preference for this optimum poise. See also Social Networks Predict the Life and Death of Honey Bees by Benjamin Wile, et al in Nature Communications 12/1, 2021 and Self-Organization and the Evolution of Division of Labor by R. Page and Sandra Mitchell in Apidologie (29/1, 1998).

In many organisms, interactions among genes lead to multiple functional states, while other interactions can transition into new modes, maybe by way of critical bifurcations in dynamical systems. Here, we develop a statistical theory to identify a bistability near a transition event from gene expression data. We apply the method to honey bees where a known developmental occurrence between bees performing tasks in the nest and leaving to forage. Our approach is able to predict the emergence of bistability and link it to genes involved in the behavioral transition. (Abstract excerpt)

Social insects represent well-known examples of adaptive collective systems, combining the efforts of many individual actors to produce robust and adaptive aggregate behavior. The allocation of tasks to individuals often displays a sophisticated organization that promotes collective success. This distributed coordination of effort is the result of a complicated process reaching from the level of gene regulation to social relations. (1) To summarize, the generality of this phenomenology suggests that such critical transitions may be a common mechanism within biology, making use of the emergent properties of strongly interacting dynamical networks to generate reproducible diversity. (14)

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)

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)

Fang, Jing-Kai, et al. Divide-and-Conquer Quantum Algorithm for Hybrid de novo Genome Assembly of Short and Long Reads.. PRX Life. 2/023006, 2024. We note this contribution by BGI Research, Shenzhen, China computational geneticists as a frontier example of how genetic studies are being taken to a new dimension by virtue of quantum capabilities. An evidential result implies that life’s genomic proscription can gain an affinity with this fundamental physical phase.

Researchers have begun to apply quantum computing in genome assembly implementation, but the issue of repetitive sequences remains unresolved. Here, we propose a hybrid assembly quantum algorithm using short reads and long reads which utilizes divide-and-conquer strategies to approximate the ground state of a larger Hamiltonian while conserving quantum resources. The convergence speed is improved via the problem-inspired Ansatz based on the known information. In addition, we verify that entanglement within quantum circuits may accelerate the assembly path optimization. (Excerpt)

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)

Ghorbani, Mahboobeh, et al. Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Network.. Frontiers in Physiology. October, 2018. University of Southern California system theorists including Paul Bogdan describe how of a self-similar topology is evident in complex genomes.

Understanding the dynamics of gene expression is crucial to unraveling the physical complexities of this process. Here, we report the scaling properties of gene expression time series in Escherichia coli. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. The interplay between genes and transcription factors in regulatory networks are also fractal and cross-correlated. (Excerpt)

Lastly, mathematical and analytical investigation of the relation between structure and dynamics of processes are also fundamental in theory. Answering to the question of how long-range dependency transfers between structure and dynamics and how the degree of fractality/multifractality of structure and dynamics are like each other would have a huge impact on predicting the behavior of complex systems.

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)

Gorfinkiel, Nicole and Alfonso Martinez Arias. The Cell in the Age of the Genomic Revolution: Cell Regulatory Networks. Cells & Development. Vol. 168, 2021. By the current profusion of bioinformatic insights, Universidad Complutense, Madrid systems biologists first argue that gene regulatory networks (GRNs) do not specify how cells builds organs. They then propose a further finesse via cell regulatory nets (CRNs) which can generate shapes during morphogenesis.. Both modes go on to interact through feedback loops and tissue tectonics.

Over the last few years an intense activity in the areas of advanced microscopy and quantitative cell biology has focused on the morphogenetic events that shape embryos. In regard, here we seek to integrate the activity of genes with that of cells based on cellular processes akin to the formulation of Gene Regulatory Networks (GRNs). We begin to do this by suggesting elements for building Cell Regulatory Networks (CRNs). In the same manner that GRNs create schedules of gene expression that give rise to cellular states over time, CRNs are meant to engender tissues and organs in a spatial milieu. (Abstract)

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