VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
D. The Ascent of Genetic Information: DNA/AND
In Part III, The Information Computation Turn, we saw how space, time, matter and energy is becoming seen as suffused by an informative, program-like quality. Part IV, Cosmic Code, went on to document such an independent procreative system, while Part V, A Quickening Evolution, reported humankind’s nascent integral synthesis of the oriented, gestation of life, mind and self-cognizance. This extensive Part VI, Earth Life Emergence, will attempt to show how these innate, genetic-like, complementary principles are in similar manifestation everywhere. To continue this scenario, its most familiar exemplar, of course, is the genome code that informs the form, function and life span of every organism and person. Since circa 2001, with the sequencing of the human genome, a whole scale revision has been underway as to what constitutes genetic phenomena, broadly considered, which is still being worked out. This section will try to chronicle the many concerted efforts, see also Systems Biology and Genetics above.
Aguilar-Rodriguez, Jose, et al. The Architecture of an Empirical Genotype-Phenotype Map. Evolution. 72/6, 2018. A bioinformatic team of JA-R, Leto Peel, Massimo Stella, Andreas Wagner and Joshua Payne with postings in Switzerland, Belgium and the UK consider expansions of genomic phenomena by way of network topologies and self-replicating computational programs. See for example Stochastic Turing Patterns in a Synthetic Bacterial Population by David Karig, at al (2018) for a concurrent project.
Recent advances in high‐throughput technologies are bringing the study of empirical genotype‐phenotype (GP) maps to the fore. Here, we use data from protein‐binding microarrays to study an empirical GP map of transcription factor (TF) ‐binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high‐resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are “small‐world” and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF‐binding sites in vivo. We discuss our findings in the context of regulatory evolution. (Abstract)
Allis, C. David, et al, eds. Epigenetics. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 2007. The first comprehensive text (500 pages) on all its aspects from historical to medical implications.
Almirantis, Yannis and Astero Provata. An Evolutionary Model for the Origin of Non-Randomness, Long-Range Order and Fractality in the Genome. BioEssays. 23/7, 2001. Mathematical self-similar, power law distributions characterize genomic systems.
Angier, Natalie. Scientists and Philosophers Find That ‘Gene’ Has a Multitude of Meanings. New York Times. November 11, 2008. A think piece in a Science Times issue dedicated to “Beyond the Gene.” Over the last decade, due to genome sequencings and the systems biology shift, largely unreported until now, an epochal revolution that predates Gregor Mendel has been underway. From beanbag textbook models and discrete, determinant DNA, the genetics has moved toward an increasing sense of equally real, “relational,” network dynamics. Inherited traits are influenced by more than double helix structures, today an “epigenetic” spectrum of topological to environmental effects weigh in. Single-strand RNA also gets into the act of making proteins, as Carl Zimmer explains in Now: The Rest of the Genome. The wider import is to offer a succinct example of a conceptual revolution across many scientific fields. But a deep dissonance persists since the physical ground of being and becoming remains stuck in a mechanical disconnect.
Arneodo, Alain, et al. Multi-scale Coding of Genomic Information: From DNA Sequence to Genome Structure and Function. Physics Reports. 498/2-3, 2010. In a lengthy technical treatise, French systems geneticists contribute to the current integral melding of nested physical and biological realms, each informing and revising the other, which bodes well for a self-animating genesis universe. With Goldenfeld, Shapiro, Kaschube, and others herein, life’s earthly and cosmic revolution closes on this grand, vital discovery.
Understanding how chromatin is spatially and dynamically organized in the nucleus of eukaryotic cells and how this affects genome functions is one of the main challenges of cell biology. Since the different orders of packaging in the hierarchical organization of DNA condition the accessibility of DNA sequence elements to trans-acting factors that control the transcription and replication processes, there is actually a wealth of structural and dynamical information to learn in the primary DNA sequence. In this review, we show that when using concepts, methodologies, numerical and experimental techniques coming from statistical mechanics and nonlinear physics combined with wavelet-based multi-scale signal processing, we are able to decipher the multi-scale sequence encoding of chromatin condensation–decondensation mechanisms that play a fundamental role in regulating many molecular processes involved in nuclear functions. (Abstract)
Avise, John. Evolving Genomic Networks: A New Look at the Language of DNA. Science. 294/86, 2001. A new metaphor is proposed for the 21st century genome as “a social collective whose DNA sequences display intricate divisions of labor and functional collaborations.” Rather than “beads on a string,” a better image would be a “miniature cellular ecosystem” or “interactive community.”
Beck, Stephan and Alexander Olek, eds. The Epigenome. Weinheim, GDR: Wiley-VCH, 2003. After the location and sequence of human genes by the Genome Project, it is now possible to move on to uncover their temporal and spatial expression, to be known at the Human Epigenome Project.
Beurton, Peter, et al, eds. The Concept of the Gene in Development and Evolution. Cambridge: Cambridge University Press, 2000. A historical review of genetics from an initial molecular emphasis to recent expansions to consider the genome as a integrative system. This new synthesis of particulate and developmental aspects takes on the guise of a complex adaptive system with discrete and epigenetic complements. Raphael Falk’s concluding essay is of special interest.
Bhardwaj, Nitin, et al. Analysis of Diverse Regulatory Networks in a Hierarchical Context Shows Consistent Tendencies for Collaboration in the Middle Levels. Proceedings of the National Academy of Sciences. 107/6841, 2010. A paper from Mark Gerstein’s (co-author) group at Yale that cites a common affinity between genomes and governments. The same, “strikingly similar,” geometries and dynamics, ostensibly springing from a common “nonrandom architecture,” are revealed in both disparate cases. An upshot not noted would then be an intrinsic “genetic” character for our social, communicative abidance. An extended quote is necessary to properly convey.
Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with well-defined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity. (6841)
Biro, Jan. The Proteomic Code. Theoretical Biology and Medical Modelling. 4/1, 2007. In this extensive, much cited, review, the Swedish-American geneticist now at the Homulus Foundation in San Francisco contributes to the 21st century revolution in understanding the expansive information processing dynamics of genetic molecular systems.
The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.
Caporale, Lynn, ed.
The Implicit Genome.
Oxford: Oxford University Press,
An edited contribution to a paradigm revision of our understanding of genetic codes. With the recent sequencing of genomes the initial DNA phase of identifying all the molecular components is fulfilled. But the dogma of an inert passivity before random mutations is now being discarded. Via a novel systems approach, the genetic realm is becoming seen as a nonrandom, dynamically responsive source. By these convergent insights, gene variation is able to focus and favorably guide itself. Rather than a “bag of letters,” it is the relationships between sequences that are important. In an Epilogue a parallel between genotype biology and the global Internet is proposed. Along with strings of nucleotides, nodes and links, sites and connections, protocol “rules” create a robust viability in both cases. As such they are highly mutable within changing environments. But these attributes, not evident until first parts and later waves were found, imply a new evolution and indeed universe with an innate spontaneity as it develops into a self-similar nest of complexity and consciousness.
Carroll, Sean B., et al. Regulating Evolution. Scientific American. May, 2008. A popular survey of an historic reconception in process of the functional nature of genes and genomes, that many other references herein report. We are surely familiar with the one to one attribution of DNA with a soma, trait, or malady. Yet it has lately perplexed for after the human genome sequence and a growing number of other creatures, how is it that animals differ so if they have similar gene complements? The prime distinction appears to be regulatory stretches in the double-helix that control when and where genes are turned on and off. It is mutations in these segments that then result in evolutionary change. As others such as Matt Ridley and Mark Pagel note, one gets an impression we could just as well be describing language with grammar and syntax. The same words exist, it is just how they are used in a sentence and paragraph.
Mutations in DNA “switches” that control body-shaping genes, rather than in the genes themselves, have been a significant source of evolving differences among animals. If humans want to understand what distinguishes animals, including ourselves, from one another, we have to look beyond genes. (61) If we really want to understand what makes the human form different from that of other apes or what makes an elephant distinct form a mouse, for that matter, much of that information lies not in our respective genes and proteins but in an entirely different realm of our genomes that remains to be explored. (67)