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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

Hacker, William, et al. Features of Genomic Organization in a Nucleotide-Resolution Molecular Model of Escherichia coli Chromosome. Nucleic Acids Research. Online July, 2017. We note this paper by University of Iowa biochemists because it cites the presence of globular domains with a fractal self-similarity. Here is another way that whole genomes are lately becoming known and treated as integral complex systems, akin to neural, quantum, and linguistic phenomena.

We describe structural models of the Escherichia coli chromosome in which the positions of all 4.6 million nucleotides of each DNA strand are resolved. In both types of model, the chromosome is partitioned into plectoneme-abundant and plectoneme-free regions, with plectoneme lengths and branching patterns matching experimental distributions, and with spatial distributions of highly-transcribed chromosomal regions matching recent experimental measurements of the distribution of RNA polymerases. The models exhibit characteristics similar to those of ‘fractal globules,’ and even the most genomically-distant parts of the chromosome can be physically connected, through paths combining linear diffusion and inter-segmental transfer, by an average of only ∼10 000 bp. We anticipate that the models will prove useful in exploring other static and dynamic features of the bacterial chromosome. (Abstract excerpts)

He, Bing and Kai Tan. Understanding Transcriptional Regulatory Networks Using Computational Models. Current Opinion in Genetics & Development. 37/101, 2016. We enter this paper by a University of Iowa geneticist and an oncologist as an example of whole genome studies by way of Bayesian complex networks and an array of algorithmic methods.

Transcriptional regulatory networks (TRNs) encode instructions for animal development and physiological responses. Recent advances in genomic technologies and computational modeling have revolutionized our ability to construct models of TRNs. Here, we survey current computational methods for inferring TRN models using genome-scale data. We discuss their advantages and limitations. We summarize representative TRNs constructed using genome-scale data in both normal and disease development. We discuss lessons learned about the structure/function relationship of TRNs, based on examining various large-scale TRN models. (Abstract)

Hu, Mengzhou, et al.. Evaluation of large language models for discovery of gene set function. arXiv:2309.04019. Seven UC San Diego computational geneticists including Trey Ideker carry out an initial, guided evaluation of this new machine intelligence method amd find it, at first usage, to be a reliable tool.

Gene set analysis is a mainstay of functional genomics, but it relies on partially curated databases. Here we evaluate the ability of OpenAI's GPT-4, a Large Language Model (LLM), to develop hypotheses about common gene functions from its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene sets with names that summarize their consensus functions, substantiated by analysis text and citations. The ability to rapidly synthesize common gene functions positions LLMs as valuable genomics assistants. (Excerpt)

Conclusions When applied to study gene function, we have suspected that LLMs might produce statements, hypotheses, and references that would be error-prone and unusable. In fact, in our evaluations, GPT-4 typically did not falter, often with exemplary performance. We thus conclude that, given appropriate framing, the current general platform provides researchers with a new and powerful tool for gene set interpretation. (7)

Huang, Lifang, et al. The Free-Energy Cost of Interaction between DNA Loops. Nature Scientific Reports. 7/12610, 2017. We cite this paper by Guangdong University and Sun Yat-Sen University mathematicians as an example of later 2010s quantifications of genomic functions by way of non-equilibrium thermodynamics, so as to further root and source them in a lively physical ecosmos.

From the viewpoint of thermodynamics, the formation of DNA loops and the interaction between them, which are all non-equilibrium processes, result in the change of free energy, affecting gene expression and further cell-to-cell variability as observed experimentally. However, how these processes dissipate free energy remains largely unclear. Here, by analyzing a mechanic model that maps three fundamental topologies of two interacting DNA loops into a 4-state model of gene transcription, we first show that a longer DNA loop needs more mean free energy consumption. Our studies provide insights into the understanding of gene expression regulation mechanism from the view of energy consumption. (Abstract excerpt)

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)

Lamm, Ehud and Sophie Juliane Veigl. Back to Chromatin: ENCODE and the Dynamic Epigenome. Biological Theory. November, 2022. Senior Tel Aviv University biophilosphers log in an update overview on the course of this premier 21st century genetic project from its 2001 onset to various finesses and expansions.

The “Encyclopedia of DNA Elements” (ENCODE) project was launched by the US National Human Genome Research Institute after the Human Genome Project (HGP). It aimed to wholly map the human transcriptome, and to identify regulatory regions and factor binding sites. Here we put the results of ENCODE and the work on epigenomics that followed over two decades in a theoretical and historical context with three strands of research. The first is the history of thinking about the organization of genomes. The second is ideas about gene regulation in eukaryotes. Finally, we consider the role of genetic material in physiology and development. (Excerpt)

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)

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