IV. Ecosmomics: A Survey of Genomic Complex Network System Sources
B. Our Own HumanVerse Genome Studies
Levin, Michael and Christopher Martyniuk. The Bioelectric Code: An Ancient Computational Medium for Dynamic Control of Growth and Form. Biosystems. Online August, 2017. Tufts University and University of Florida biologists advance a broadly conceived project, with colleagues, to seek out and specify novel prescriptive means, agencies or informed forces which serve evolutionary organisms. The paper and task is imaginative and engaging as it elucidates more ways that life avails beside genes alone. As the second quote alludes, by way of computational analogies a doubleness of an independent, malleable program and resultant biological form and function can be perceived. See also, e.g., Endogenous Bioelectric Signaling Networks: Exploiting Voltage Gradients for control of Growth and Form by Leven, Giovanni Pezzulo and Joshua Finkelstein in Annual Review of Biomedical Engineering (19/353, 2017) and Physiological Inputs Regulate Species-Specific Anatomy During Embryogenesis and Regeneration by Kelly Sullivan, Maya Emmons-Bell, and Levin in Communicative & Integrative Biology (9/4, 2016) for more applications.
What determines large-scale anatomy? DNA does not directly specify geometrical arrangements of tissues and organs, and a process of encoding and decoding for morphogenesis is required. Moreover, many species can regenerate and remodel their structure despite drastic injury. The ability to obtain the correct target morphology from a diversity of initial conditions reveals that the morphogenetic code implements a rich system of pattern-homeostatic processes. Here, we describe an important mechanism by which cellular networks implement pattern regulation and plasticity: bioelectricity. All cells, not only nerves and muscles, produce and sense electrical signals; in vivo, these processes form bioelectric circuits that harness individual cell behaviors toward specific anatomical endpoints. We review emerging progress in reading and re-writing anatomical information encoded in bioelectrical states, and discuss the approaches to this problem from the perspectives of information theory, dynamical systems, and computational neuroscience. (Abstract)
Lewontin, Richard. The Third Helix. Cambridge: Harvard University Press, 2000. As a response to the Human Genome hype, the renowned Harvard geneticist advises that DNA alone is not sufficient to specify even a folded protein much less an entire organism.
Longabaugh, William, et al. Computational Representation of Developmental Genetic Regulatory Networks. Developmental Biology. 283/1, 2005. Whereby ubiquitous complex system characteristics are similarly apparent in dynamic genomes. The authors then describe a freely available software package they have devised for their three-dimensional study: www.biotapestry.org.
Developmental genetic regulatory networks (GRNs) have unique architectural characteristics. They are typically large-scale, multilayered, and organized in a nested, hierarchy of regulatory network kernels, function-specific building blocks, and structural gene batteries. (1)
Marijuan, Pedro. Information and the Unfolding of Social Life: Molecular-Biological Resonances Reaching Up to the Economy. BioSystems. 46/1, 1998. A universal convergence is noted from “cellular signaling systems and vertebrate nervous systems” to “entrepreneurial accounting systems.”
Maynard Smith, John and Eors Szathmary. The Orgins of Life. Oxford: Oxford University Press, 1999. A popular update of the authors’ 1995 treatise on major nested, informed transitions, which is reviewed more in A Genesis Evolutionary Synthesis, and by 2010 has become a major structural contribution to this imminent advance.
McGillivray, Patrick, et al. Network Analysis as a Grand Unifier in Biomedical Data Science. Annual Review of Biomedical Data Science. Vol. 1, 2018. In this new Annual Review edition, a team of Yale University biochemists, bioinformaticians, and geneticists including Mark Gerstein show how common network processes and topologies can similarly be applied with benefit to genomic and physiological realms. Sections such as Networked Systems are at the Core of Human Biology, Making Sense of Complexity in Biomolecular Networks, Network Motifs, Logic, and Stability, and Prediction using Machine Learning and Neural Networks via text and graphic displays offer a state of the art tutorial for later 2010 advances. By so doing, once again a nascent sense of a universal recurrence across molecule, organelle, cell, organ, entity, and population phases, as illustrations depict, of the same intricate dynamics becomes evident.
Biomedical data scientists study many types of networks, ranging neural nets to those created by molecular interactions. However an issue of interpretation exists. Here we show that molecular biological networks can be read in several straightforward ways. First, we divide a network into smaller components with individual pathways and modules. Second, we compute global statistics describing the network as a whole. Third, we can compare networks which can be within the same context (e.g., gene regulatory networks) or cross-disciplinary (e.g. governmental hierarchies). By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease. (Abstract excerpt, edits)
Meadows, Jennifer and Kerstin Lindblad-Toh. Dissecting Evolution and Disease Using Comparative Vertebrate Genomics. Nature Reviews Genetics. 18/624, 2017. We cite this entry by Uppsala University and MIT/Harvard researchers (KLT credits below) to show how a worldwide biological science can achieve by theory and technique a retrospective reconstruction of the genetic endowment of prior evolutionary species. A full page graphic Figure 1 is entitled A Snapshot of Vertebrate Genome Sequencing Projects as they proceed from fish and reptiles to birds, mammals and onto human beings. Might one via a woman’s bicameral faculty ask and imagine what this whole scenario could be on its own? What kind of procreative ecosmos evolves to a sentient, collaborative global species able look back and do this?
With the generation of more than 100 sequenced vertebrate genomes in less than 25 years, the key question arises of how these resources can be used to inform new or ongoing projects. In the past, this diverse collection of sequences from human as well as model and non-model organisms has been used to annotate the human genome and to increase the understanding of human disease. In the future, comparative vertebrate genomics in conjunction with additional genomic resources will yield insights into the processes of genome function, evolution, speciation, selection and adaptation, as well as the quantification of species diversity. In this Review, we discuss how the genomics of non-human organisms can provide insights into vertebrate biology and how this can contribute to the understanding of human physiology and health. (Abstract)
Meinesz, Alexandre. How Life Began: Evolution’s Three Geneses. Chicago: University of Chicago, 2008. Reviewed in The Symbiotic Cell and noted here for this cogent quote of how well literature terms describe genetic activity.
To describe the characteristics of these modes of transmitting information, with their errors, mixings, and exchanges, scientists use printing terms: replication, transcription, recombination, transposition, translocation, reshuffling, inversion. These words apply to parts of the “book” (in this case, the nuclei) that constitutes the totality of the information of life: chapters (or, chromosomes), pages (parts of chromosomes), paragraphs (genes), lines (sequences of nucleotides), words (triplets of nucleotides), and letters (nucleotides). (106)
Miranda-Dominguez, Oscar, et al. Heritability of the Human Connectome. Network Neuroscience. 2/2, 2018. In an issue on New Trends in Connectomics, Oregon Health and Science University and Emory University behavioral neuroscientists propose a familial “connectotype” akin to a bodily phenotype to likewise represent a person’s cerebral endowment. In a similar way, ancestral histories can then be traced.
Misteli, Tom. Beyond the Sequence: Cellular Organization of Genome Function. Cell. 128/787, 2007. By the National Cancer Institute, NIH, researcher, a good review of the state of genetic rethinkings at the time from this novel whole systems perspective.
The similarities in spatial and temporal properties of the various nuclear processes indicate that the organizational principles involved in their biogenesis are universal. (790) It thus appears that compartmentalization of nuclear processes, likely via self-organization, into well-defined yet dynamically malleable sites is one of the fundamental principles of organizing genome function in vivo. (790)
Mitchell, Melanie. Complexity: A Guided Tour. Oxford: Oxford University Press, 2009. Reviewed more in A Cosmic Code, we note for still another view of genomes distinguished not by discrete molecules, but dynamical, communicative networks – which then, by inference, ought to be rightly seen as “genetic” in kind.
The complexity of living systems is largely due to networks of genes rather that the sum of independent effects of individual genes. (275) In the old genes-as-beads-on-a-string view, as in Mendel’s laws, genes are linear - each gene independently contributes to the entire phenotype. The new, generally accepted view, is that genes in a cell operate in nonlinear information-processing networks, in which some genes control the actions of other genes in response to changes in the cell’s state – that is, genes do not operate independently. (275-276)
Moghadam, S. Arbabi, et al. A Search for the Physical Basis of the Genetic Code. Biosystems. May, 2020. We cite because this entry by University of Alberta biophysicists including Jack Tuszynski discuss several ways that life’s genomic endowment can be rooted in and given a deeper substantial, innately fertile basis.
DNA contains the genetic code, which provides complete information about the synthesis of proteins in every living cell. Each gene encodes for a corresponding protein but most of the DNA sequence is non-coding. In addition to this non-coding part of the DNA, there is another redundancy, namely a multiplicity of DNA triplets (codons) corresponding to code for a given amino acid. In this paper we investigate possible physical reasons for the coding redundancy, by exploring free energy considerations and abundance probabilities as potential insights. (Abstract)