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

C. Our Own HumanVerse (Epi) Genomic Heredity

Danchin, Antoine. Bacteria as Computers Making Computers. FEMS Microbiology Reviews. 33/1, 2009. The Pasteur Institute geneticist and author endorses systems biology’s emphasis upon life’s deep informational and computational basis. In such regard, a cell is akin to a computer since both have dual domains of a manifest “machinery” and a “program” that serves to generate and run it. Compare with Koon-Kiu Yan, et al, for a similar take.

Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments. (3)

Using a variety of sources, I show that a cell can be seen as a computer (a machine expressing a program), and review the evidence in support of the cell having the properties required to reproduce the computing machine while replicating its program. This view takes into account the important paradox raised by the obvious observation that computers do not make computers (yet). It provides an entry point for the category of information as a fundamental category of nature that all future developments of systems biology need to include. (3)

Danchin, Antoine. The Delphic Boat. Cambridge: Harvard University Press, 2002. The Directeur de Recherche, Institut Pasteur advises that after a half century of genetic research, there is a growing sense that an organism’s genome is more than a collection of discrete molecules, rather it is a holistic system much like a written text. In this nascent model, how a gene expresses itself depends much on its location within the entire program.

We would like to add a July 2009 update from Antoine Danchin's website: http://www.normalesup.org/~adanchin/AD/summary_AD.html from which the second quote.

We then come to the heart of the book, as we realize – and this is surely so obvious that we can only be amazed not to find it stated more often – that what counts in life is not objects themselves, but the relationships between them. (4)

All the research developed by AD is centered around one unique question: is it possible to uncover rules that would account for the fact that genes function as a whole in the cell and contribute to its consistent and reproducible development? When one tries to isolate some of the important trends of AD's past research, one produces a picture that culminates in what can be considered as "symplectic biology", a biology where the relationships between objects is of more conceptual importance than the objects themselves. (Website)

Danchin, Étienne and Wagner, R. H. Inclusive Heritability: Combining Genetic and Nongenetic Information to Study Animal Behavior and Culture. Oikos. 119/2, 2010. Evolution & Diversite Biologique Laboratory, CNRS, Toulouse, and Konrad Lorenz Institute for Ethology, Vienna, researchers contribute to the growing evidence for “genetic” influences that occur much beyond molecular limits to these psychological and social domains. Might one perceive that nature’s informational essence in fact ascends in relative mode through life’s evolutionary course, as the major transitions model attests, to our linguistic genre? See Mesoudi, et al, 2013 for a strong stand in its defense.

Evolutionary ecologists acknowledge that many behaviors are adaptations produced by selection. However, most of us do not yet perceive behavior as a major vector of information inheritance, and thus of evolution. For instance, behavioral biologists often seek genetic causes of behavioral variance, while overlooking the potential role of environmental inheritance. Genetic information rather, should be viewed as producing the plastic template on which behavior can develop and thus vary according to the multiple forms of information obtained during development. (216)

Davidson, Eric. The Regulatory Genome: Gene Regulatory Networks in Development and Evolution. New York: Elsevier, 2006. The Cal Tech geneticist presents an initial volume of his pioneer studies, as the subtitle notes, of pervasive networks which link the component strands of DNA molecules. These ubiquitous nets possess their own dynamic properties such as modular structures. For a consummate volume see Genomic Control Process: Development and Evolution by Isabelle Peter and Davidson (Academic Press, 2015.

Davidson, Eric and Douglas Erwin. Gene Regulatory Networks and the Evolution of Animal Body Plans. Science. 311/796, 2006. These pervasive genetic systems which control phenotype bauplans are found to exhibit a modular, hierarchical organization.

Davies, Neil and Dawn Field, et al. The Founding Charter of the Genomic Observatories Network. GigaScience. 3/2, 2014. . In this BioMed Central online journal (see below), this posting is a manifesto by some 80 scientists led by these authors of Biocode (search) with the aim to establish a worldwide facility to sequence every Metazoan creaturely genetic code possible. The initiative has a grand goal to achieve a composite “global genome” for a Gaian biosphere.

The co-authors of this paper hereby state their intention to work together to launch the Genomic Observatories Network (GOs Network) for which this document will serve as its Founding Charter. We define a Genomic Observatory as an ecosystem and/or site subject to long-term scientific research, including (but not limited to) the sustained study of genomic biodiversity from single-celled microbes to multicellular organisms. (Abstract)

Key outcomes of 21st century science include an Earth with its essential life support systems intact, and a planet where human society has achieved sustainable development. Achieving these challenges, however, requires a greatly improved understanding of human interactions with the natural environment. Towards that end, the GOs Network aims to observe DNA sequences – the biocode – across the principal levels of biological organization (cell, organism, ecosystem) up to the planetary genome. (2)

Planetary Genome: A special case of the biocode: the sum of all genomes that exist on Earth at a given time. Biocoding the Earth: The aspirational target of sequencing every genome on the planet. Biocode: The totality of DNA sequences in a given unit of biological organization, such as a: Cell (e.g., the Yeast Biocode includes both its nuclear and mitochondrial genomes); organism (e.g., the Human Biocode includes both the Human Genome and the Human Microbiome); ecosystem (e.g., the Moorea Biocode includes all the genomes on the island); Planet (e.g., the Earth Biocode includes all the genomes on the planet.) (3)

GigaScience aims to revolutionize data dissemination, organization, understanding, and use. An online open-access open-data journal, we publish 'big-data' studies from the entire spectrum of life and biomedical sciences. To achieve our goals, the journal has a novel publication format: one that links standard manuscript publication with an extensive database that hosts all associated data and provides data analysis tools and cloud-computing resources. Our scope covers not just 'omic' type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale sharable data.

Diehl, Adam and Alan Boyle. Deciphering ENCODE. Trends in Genetics. 32/4, 2016. University of Michigan computational geneticists review the progress since the later 2000s of this multitudinous database collaboration which covers these prime areas: genome interactions, chromatin structure, DNA-protein interactions, DNA methylation, transcription, gene expression, and RNA protein interactions.

The ENCODE project represents a major leap from merely describing and comparing genomic sequences to surveying them for direct indicators of function. The astounding quantity of data produced by the ENCODE consortium can serve as a map to locate specific landmarks, guide hypothesis generation, and lead us to principles and mechanisms underlying genome biology. Despite its broad appeal, the size and complexity of the repository can be intimidating to prospective users. We present here some background about the ENCODE data, survey the resources available for accessing them, and describe a few simple principles to help prospective users choose the data type(s) that best suit their needs, where to get them, and how to use them to their best advantage. (Abstract)

Dokholyan, Nikolay, et al. Expanding Protein Universe and its Origin from the Biological Big Bang. Proceedings of the National Academy of Sciences. 99/14132, 2002. Russian-American researchers begin to detect an inherent convergence in the evolution of protein geometries.

The bottom-up approach to understanding the evolution of organisms is by studying molecular evolution. With the large number of protein structures identified in the past decades, we have discovered peculiar patterns that nature imprints on protein structural space in the course of evolution. In particular, we have discovered that the universe of protein structures is organized hierarchically into a scale-free network. (14132) We discovered that the structure of the PDUG (protein domain universe graph) is, by far, nonrandom, but rather represents a scale-free network featuring the poser-law distribution of number of edges per node. (14136)

Durand, Pierre and Richard Michod. Genomics in the Light of Evolutionary Transitions. Evolution. 64/6, 2010. As research projects increasingly enlist and assimilate this scalar sequence of Maynard-Smith and Szathmary, a major transformation in evolutionary theory that it implies cannot be ignored any longer. The gradual, aimless drift of the modern synthesis is being replaced by a discernibly “progressive” development, which is best tracked by informational and cognitive advances. Here University of Arizona biologists propose that the whole genome, now found to be suffused by dynamic, relational systems, ought to be properly situated and appreciated in this regard.

Eastman, Peter and Vijay Pande. Predicting Gene Expression between Species with Neural Networks. arXiv:1907.03041. This study is a proof of concept that a neural network can predict gene expression levels in one species based on experimental data from a different species. We cite this entry by Stanford University bioengineers to report how geno-informatic phenomena can be treated with these the same cerebral dynamics found to well apply everywhere else. The second author is a leading, creative source in this endeavor, which is evident by his Pande Lab website at pande.stanford.edu. See also, e.g., Physical Machine Learning Outperforms “Human Learning” in Quantum Chemistry by Pande and Anton Sinitskiy at arXiv:1908.00971.

We train a neural network to predict human gene expression levels based on experimental data for rat cells. The network is trained with paired human/rat samples from the Open TG-GATES database, where paired samples were treated with the same compound at the same dose. When evaluated on a test set of held out compounds, the network successfully predicts human expression levels. On the majority of the test compounds, the list of differentially expressed genes determined from predicted expression levels agrees well with the list of differentially expressed genes determined from actual human experimental data. (Abstract)

Eggermont, J. J. Is There a Neural Code? Neuroscience & Biobehavioral Reviews. 22/2, 1998. On the possibility of a cerebral version of the molecular genetic code.

The neural code can be loosely defined as the way information (in the syntactic, semantic and pragmatic sense) is represented in the activity of neurons. (358)

El-Hani, Charbel, et al. A Semiotic Analysis of the Genetic Information System. Semiotica. 160/1-4, 2006. With co-authors Joao Queiroz and Claus Emmeche, a long exercise on how the DNA code could be best understood, via a “Peircean biosemiotics,” in terms of its sign-bearing, communicative qualities. Other like studies propose the whole genesis universe ought to be appreciated this way.

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