IV. Ecosmomics: A Survey of Animate Complex Network Systems

B. Our Own HumanVerse Genome Studies

Morange, Michel. Genome as a Multipurpose Structure Built by Evolution.. Persepctives in Biology and Medicine.. 57/1, 2015. In a special issue on The Changing Concept of the Gene, the French biologist and philosopher reviews the history of genetics as a “progressive discovery of the complexity of the genome” from biomolecular nucleotides to gene regulatory networks to an integral system lately taking on epigenetic functions.

Morowitz, Harold. Phenetics, A Born Again Science. Complexity. 8/1, 2003. Another report of a major paradigm shift in biology from random, particulate genes to a systems integration of a phenotypic genetics which can generate metabolic networks.

If biology is governed by a hierarchy of phenetic laws, then replaying the tape (of life) might lead to a rather similar outcome particularly at the unicellular level. Our task as biologists is then to search for these laws rather than focusing on the thousand billion elements of sequence that must characterize the biosphere. (13)

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field of genetics, a philosopher argues that the concept of particulate genes and a predetermined program has been superseded by their inclusion within complex, dynamic, epigenetic systems which are inherited in parallel with the genomic sequence.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.

Mozziconacci, Julien, et al. The 3D Genome Shapes the Regulatory Code of Developmental Genes. arXiv:1911.04779. Drawing upon the latest research results, Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensé theoretical geneticists JM, Melody Merle and Annick Lesne contribute a deeper conceptual appreciation of nature’s pervasive, semantic, prescriptive source program.

We revisit the notion of gene regulatory code in embryonic development in the light of new findings about genome spatial organisation. By analogy with the genetic code, we posit that the concept of code can only be used if the corresponding adaptor can clearly be identified. An adaptor is here defined as an intermediary physical entity mediating the correspondence between codewords and objects. In our context, the encoded objects are gene expression levels, while specific transcription factors in the cell nucleus provide the codewords. We propose that an adaptor for this code is the gene domain, that is, the genome segment comprising the gene and its enhancer regulatory sequences. (Abstract excerpt)

Our starting point is the definition of a code that will be used in the present text. Different meanings of this word are encountered in science, from the secret codes in cryptography, the source codes in computer science, to the neural codes and the genetic code. The latter is the emblematic example of a semantic code, in a biological context. The definition of a semantic code relies on three ingredients, namely codewords, objects, and adaptors: codewords are inputs to be interpreted; a single object is associated to each codeword; adaptors are physical entities that implement the association of each codeword with a unique object. (3)

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)

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