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

C. Our Own HumanVerse (Epi) Genomic Heredity

Moss, Lenny. What Genes Can’t Do. Cambridge: MIT Press, 2002. In a review of the 20th century field of genetics, a philosopher argues that the concept of particulate genes and a predetermined program has been superseded by their inclusion within complex, dynamic, epigenetic systems which are inherited in parallel with the genomic sequence. These aspects then form a complementarity of discrete and systemic influences.

Mukherjee, Siddhartha. The Gene: An Intimate History. New York: Scribner, 2016. The author is an Indian-born American physician, Pulitzer Prize winner, Columbia University professor of medicine and a CU/NYU Presbyterian Hospital cancer specialist. This 500 page tome represents a premier study of a long unknown, often intimated, bioinformatic nucleotide genome as an increasingly vital element of our existence. Chapters span The Missing Science of Heredity 1865 (Mendel) – 1935, The Dreams of Geneticists 1970 – 2001, to Post Genome 2015. Its copious content covers the main scientists, ethical issues with guidance from the Nobel laureate geneticist Paul Berg, now 90, epigenetics, eugenics, the human genome sequence, and onto CRISPR advances. On a Charlie Rose show (July 1), Dr. Mukherjee compared genomes to a vast encyclopedia. A major entry to welling historic realizations about how significant is this presence of a programmic genotype to our phenomenal nature.

Ndifon, Wilfred. A Complex Adaptive Systems Approach to the Kinetic Folding of RNA. BioSystems. 82/3, 2005. Prior to selection, RNA sequences act as self-organizing CAS as they fold into structural arrangements.

Specifically, a complex adaptive system (CAS) is characterized by the presence of a diverse ensemble of components that engage in local interactions and an autonomous process that selects a subset of those components for enhancement based on the results of the local interactions. (258)

Neumann-Held, Eva and Christoph Rehmann-Sutter, eds. Genes in Development: Re-reading the Molecular Paradigm. Durham, NC: Duke University Press, 2006. A stellar collection of papers which convey the range and depth of ferment and revolution in evolutionary theory. Stuart Newman, Gerd Muller, Brian Goodwin, Susan Oyama, Paul Griffiths, Evelyn Fox Keller, Sahorta Sarkar, those quoted below, and others describe a quite different genetic “code” than the mid to late 20th century molecular determinism. Much more is going on in terms of developmental systems, epigenetics, environmental contexts, processes along with parts, an informational vector, a malleable phenotype, constructivist interactionism, and so on. The work then begs a common agenda and nomenclature, properly attributed to a composite humankind. As a result, we gain not just another “synthesis” but a genesis cosmos with its own “self-organization, cohesiveness, emergence and selfhood” (Hoffmeyer).

Biosemiotics suggests that our universe has a built-in tendency (originating in the second law of thermodynamics) to produce organized systems possessing increasingly more semiotic freedom in the sense that the semiotic aspect of the system’s activity becomes more autonomous relative to its material basis. (Jesper Hoffmeyer, 139)

Process perspectives model phenomena by representing processes rather that players in structural or functional terms. Processualists identify units of evolution with processes rather than with objects or functions. (James Griesemer, 208)

Nicolau, Miguel and Marc Schoenauer. On the Evolution of Scale-free Topologies with a Gene Regulatory Network Model. BioSystems. In Press Online, 2009. University of Paris scientists contribute to the worldwide reconception of genomes in terms of complex systems. Much more than a collection of molecular objects, they are distinguished by equally real nested interrelations and topologies.

The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also require only a few evolutionary cycles to achieve a satisfactory error value.

Genetic regulatory networks (GRNs) are biological interaction networks among the genes in a chromosome and the proteins they produce: each gene encodes a specific type of protein, and some of those, termed Transcription Factors, regulate (either enhance or inhibit) the expression of other genes and hence the generation of the protein those genes encode.

Nijhout, H. Frederik. The Importance of Context in Genetics. American Scientist. September-October, 2003. An article on the new understanding of what genes are and how they are expressed. Rather than isolated, determinant molecules, strands of DNA interact as complex systems within cellular landscapes, often under environmental influences.

Noble, Denis. Genes and Causation. Philosophical Transactions of the Royal Society A. 366/3001, 2008. A contribution to the epoch rethinking of how we conceive informative, genetic domains from before the time of Gregor Mendel. As evoked by his 2006 The Music of Life, the salient shift moves from point-like, “digital” molecules to a “analogue” sense of genomic expression as if constantly edited and recast sentences and paragraphs. Once again, a complementarity of particle and wave, node and link in dynamic nets accrues, could one imagine father and mother.

Nussimov, Ruth, et al. Protein Ensembles Link Genotype to Phenotype. PLoS Computational Biology. June, 2019. National Cancer Institute researchers contribute a latest insight into how genetic phenomena proceeds to actively inform and array into evolving organisms. Rather than a prior one gene to one trait, now mostly set aside, it is “ensembles” of biochemical generative guidance which are the pathway by which life forms and vivifies itself. See also The Energy Landscapes of Biomolecular Function by Nussimov and Peter Wolynes in Physical Chemistry Chemical Physics (16/6321, 2014) for a setup piece.

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods from genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene actually encodes ensembles of conformations. A dynamic ensemble view can better reveal the linkage between genetic change and observable physical or biochemical features. An ensemble view, rather than the genotype–phenotype paradigm, clarifies how even small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function. (Abstract excerpts)

The terms genotype and phenotype have been in use at least since the turn of the last century. Genotype has been defined as the genetic makeup of an organism or of a specific characteristic. Phenotype has been construed as the composite of the organism’s observable characteristics or traits, such as morphology, development, biochemical, and physiological properties. Classically, the genotype of an organism has been described as the inherited genetic material coding for all processes in the organism’s life. (1)

Oiwa, Nestor and James Glazier. The Fractal Structure of the Mitochondrial Genomes. Physica A. 311/1-2, 2002. An identical scale-free genetic pattern is discovered across a wide range of plants and animals from algae to sharks and homo sapiens, another sign of a universal recurrence.

The mitochondrial DNA genome has a definite multifractal structure. We show that loops, hairpins and inverted palindromes are responsible for this self-similarity. (221) We thus see true multifractality in all 35 mtDNAs analyzed showing that self-similarity is independent of level of evolutionary complexity. (229)

Olsen, Peter, et al, eds. Next Generation Systematics. Cambridge: Cambridge University Press, 2016. As the blurb notes, this volume 78 of the Systematics Association series considers how their phylogenetics endeavors (second quote) can be advanced by the latest sequencing abilities. Some chapters are Systematics in the Age of Genomics by Antonis Rojas, and The Role of NGS for Integrative Approaches in Evolutionary Biology by Ralf Sommer. The edition is another instance of nature’s every aspect gaining a genetic essence

We live in an age of ubiquitous genomics. Next generation sequencing (NGS) technology, both widely adopted and advancing at pace, has transformed the data landscape, opening up an enormous source of heritable characters to the comparative biologist. Its impact on systematics, like many other fields of biology, has been felt throughout its breadth: from defining species boundaries to estimating their evolutionary histories. This volume examines the broad range of ways in which NGS data are being used in systematics and in the fields that it underpins, from biodiversity prospecting to evo-devo. Experts in their fields draw on contemporary case studies to demonstrate state-of-the-art applications of NGS data. These, along with novel analyses, comprehensive reviews and lively perspectives, are combined to produce an authoritative account of contemporary issues in systematics that have been impacted by the adoption of NGS (Publisher).

Biological systematics is the study of the diversification of living forms, both past and present, and the relationships among living things through time. Relationships are visualized as evolutionary trees (synonyms: cladograms, phylogenetic trees, phylogenies). Phylogenies have two components, branching order (showing group relationships) and branch length (showing amount of evolution). (Wikipedia)

Pagel, Mark. Rise of the Digital Machine. Nature. 452/699, 2008. In this note, the University of Reading biologist cites a common affinity between genomes and language. Our speech might thus be genetic in kind, while the molecular code can appear as a written text. In each parallel case, the total number of words or DNA genes is not important but how they are ‘grammatically’ used or regulated in a complex adaptive system. From our vantage I add, this rise of evolutionary information might be seen to proceed from analogue to digital (alphabetic) and on a nascent bicameral synthesis of both modes. What kind of a Universe evolves its own Reader? Are ‘we the people’ in some way acting as ‘genes’ trying to learn and continue the cosmic genetic message?

Human societies and multicellular organisms share a puzzling feature. They seem to be under-specified. Our societies depend on many more interactions among group members than there are members. Multicellular organisms have many more parts, and connections among those parts, that they have genes. This points to a principle of regulation in the evolution of such complex adaptive systems: complexity areses not from the number of genes or actors but from how those elements are expressed or deployed. (699) Deep down, language may be just the latest form of gene regulation – the voice of our genes. Information management, not lats of parts, is the key to complexity. (699)

Pah, Adam, et al. Use of a Global Metabolic Network to Curate Organismal Metabolic Network. Nature Scientific Reports. 3/1695, 2013. Via Google, the word Curate has dual meanings – “a person invested with the care or cure of souls,” or “to organize, sort, arrange, such as a museum.” A “Curator” is an overseer or caretaker. As the quotes explain, with Roger Guimera, A. M. Mustoe, and Luis Amaral, Northwestern University systems biologists propose a novel sophistication to further limn and parse complex genomes. As scientists proceed with this literacy project, as if “cosmic curators,” we seem to fulfill a phenomenal role as an intended agency by which a genesis uniVerse tries to consciously read its own genetic code.

The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks. (Abstract)

Because data reliability is such a pressing problem for experimental and computational researchers alike, there has been a push in research to consider the analysis of metabolic networks from novel perspectives. A promising new framework is to consider metabolism in the context of a global network. This framework has been successfully applied in assessing the emergence of biological carbon fixation in phylometabolism and, more generally, to understand the regulation of metabolism. A global network has also been recently used in conjunction with probabilistic methods to predict metabolic networks on a small scale with experimental verification. While the motivation for the global network approach has been mostly pragmatic, it is reminiscent of the ‘‘Res Potentia’’ framework proposed by (Alfred North) Whitehead. Wherein he proposes that which does exist—termed the Res Extenta or in the case of metabolism the set of organismal metabolic network—are specific realizations of a ‘‘universal’’ framework—the Res Potentia or the global network in our analysis—that defines what is possible. (1)

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