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

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

Kim, Hyobin and Hiroki Sayama. Criticality of Gene Regulatory Networks and the Resulting Morphogenesis. Knibbe, Carole, et al, eds. Proceedings of the ECAL 2017. Cambridge: MIT Press, 2017. We note this paper presented at the 14th European Conference on Artificial Life, Lyon, France (this book is posted in full on the publisher’s site) because SUNY Binghamton systems theorists find persistent tendencies to reach critical states are evident even in genomes. Just as brains are now known to reside in and prefer this condition (Sporns 2017), it appears that genetic phenomena equally seek this optimum phase. The authors have posted an update How Criticality of Gene Regulatory Networks Affects the Resulting Morphogeneis under Genetic Perbutions at arXiv:1801.04919, and Hiroki Sayama has a 2018 paper, Complexity, Development, and Evolution in Morphogenetic Collective Systems, at arXiv:1801.02086.

We present morphogenetic systems using Kauffman’s NK random Boolean network (RBN) as a gene regulatory network (GRN) and spring-mass-damper kinetics for cellular movements. We investigate what role the criticality of GRNs plays in morphogenetic pattern formation. Our model represents a cell aggregation, where all cells have identical GRNs. The properties of GRNs are varied from ordered, through critical, to chaotic by node in-degree K. We found that nontrivial spatial patterns were generated most frequently when the GRNs were critical. Our finding indicates that the criticality of GRNs facilitates the formation of nontrivial morphologies in GRN-based morphogenetic systems. (Abstract excerpt)

Koonin, Eugene. The Turbulent Network Dynamics of Microbial Evolution and the Statistical Tree of Life. Journal of Molecular Evolution. 80/5-6, 2015. We note this entry by the National Center for Biotechnology Information, NIH geneticist (search 2017) for its citation of a genome-wide network structure and activation. Whereas in the early 2000s a systems (re)assembly was underway, into the mid 2010s a further presence of common network topologies is being added everywhere, here evinced for genomes.

The wide spread and high rate of gene exchange and loss in the prokaryotic world translate into “network genomics”. The rates of gene gain and loss are comparable with the rate of point mutations but are substantially greater than the duplication rate. Thus, evolution of prokaryotes is primarily shaped by gene gain and loss. These processes are essential to prevent mutational meltdown of microbial populations by stopping Muller’s ratchet and appear to trigger emergence of major novel clades by opening up new ecological niches. At least some bacteria and archaea seem to have evolved dedicated devices for gene transfer. Despite the dominance of gene gain and loss, evolution of genes is intrinsically tree-like. The significant coherence between the topologies of numerous gene trees, particularly those for (nearly) universal genes, is compatible with the concept of a statistical tree of life, which forms the framework for reconstruction of the evolutionary processes in the prokaryotic world. (Abstract)

Kuzmin, Elena, et al. Systematic Analysis of Complex Genetic Interactions. Science. 360/eaao1729, 2918. We cite this entry by thirty-one researchers from Canada, the USA, Switzerland, and Japan among many nowadays as an example of how genomes are being treated as a whole dynamic complex system. It also can exhibit the broad and deep technical sophistication which can be achieved by a global collaborative groups.

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship. (Abstract)

Lai, Qiang, et al. Monostability, Bistability, Periodicity and Chaos in Gene Regulatory Network. European Physical Journal Special Topics. 227/7-9, 2018. In a special issue on Nonlinear Effects in Life Sciences, a five member team from China, Vietnam and Ethiopia provide another example of this “connectivity” advance as genetic phenomena, as everywhere else, becomes understood as a reciprocal complementarity of DNA nodes with AND linkages, which altogether carries their generative informational program.

Gene regulatory network (GRN) is a group of molecular connections which controls the gene expression levels of mRNAs and proteins in the cell. The regulators can be deoxyribonucleic acid (DNA), ribonucleic acid (RNA), messenger ribonucleic acid (mRNA), protein and other substances involved in regulation process. Their connections are very diverse and dynamically evolving. The gene expression commonly has two important processes: transcription and translation. The genes on DNA are first transcribed into mRNAs, and then mRNAs are translated into proteins. To understand the mechanism of gene expression, scientist study the GRN rather than a single genes, since it is now known as the key factor in determining the morphogenesis and phylogenesis of living organisms. As a strongly nonlinear complex system, gene regulatory network often produces various types of dynamic properties, such as multistability, synchronizatio, periodic oscillation, bifurcation, chaos, etc. (719)

Li, Wentian, et al. Principles for the Organization of Gene-Sets. Computational Biology and Chemistry. 59/B, 2015. In an issue on Advances in Systems Biology (search Wentian), Feinstein Institute for Medical Research, Long Island, theorists show how genomes are increasingly being perceived as complex, dynamic systems which arise from and exemplify independent dynamic phenomena.

A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. (Abstract)

Lieberman-Aiden, Erez, et al. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science. 326/289, 2009. A 19 member team from Harvard and MIT, including Eric Lander, provide further evidence of intrinsic self-similar topologies that distinguish and aid dynamic genetic structures. See also A Fractal Model for Nuclear Organization in Nucleic Acid Research (40/8783, 2012).

We describe Hi-C, a method that probes the three-dimensional architecture of whole genomes by coupling proximity-based ligation with massively parallel sequencing. These maps confirm the presence of chromosome territories and the spatial proximity of small, gene-rich chromosomes. We identified an additional level of genome organization that is characterized by the spatial segregation of open and closed chromatin to form two genome-wide compartments. At the megabase scale, the chromatin conformation is consistent with a fractal globule, a knot-free, polymer conformation that enables maximally dense packing while preserving the ability to easily fold and unfold any genomic locus.

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)

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