IV. Ecosmomics: An Independent, UniVersal, Source Code-Script of Generative Complex Network Systems
B. Our Own HumanVerse Genome Studies
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)
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.
Eraslan, Gokcen, et al. Deep Learning: New Computational Modelling Techniques for Genomics. Nature Reviews Genetics. 20/7, 2019. We review this paper by Technical University of Munich researchers along with Deep Neural Networks for Interpreting RNA-binding Protein Target Preferences by Mahsa Ghanbari and Uwe Ohler in Genome Research (January 2020) as an example of how frontier AI neural net techniques derived from our own cerebral cognition are being readily applied to model and analyze genetic phenomena. By this wide utility, they serve as an archetypal exemplar of self-organizing complexities which are similarly invariant from quantum to social systems. OK
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the increasing volume of genomics data requires more expressive machine learning models. By leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks such as the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing. (Erasian Abstract excerpt)
Field, Dawn and Neil Davies. Biocode: The New Age of Genomics. New York: Oxford University Press, 2015. As our 2010s worldwide sciencesphere proceeds to learn on its own, an especial advance is underway in this expansive field of genetic phenomena. Among a burst of books, an Oxford University senior research geneticist and the director of the UC Berkeley South Pacific Research Station here survey its frontiers. With an acknowledgement of its 1980s originator Barbara McClintock, a dynamic 3-dimensional genome of nucleotides and networks is the current version. A constant theme is the generative, systemic significance of this informational domain across life’s fauna and flora of evolving species. Since the 2000 human genome project, the sequencing of every creature from primates to invertebrates, along with extinct predecessors, continues apace. As a consequence, a novel project is proposed to achieve a composite global genome as the sum DNA total of biospheric beings, which would be a boon to an ecological sustainability. See also herein The Founding Charter of the Genomic Observatories Network by the authors, who lead a large international team.
The Meaning of Life.
The entry is a review of Carl Zimmer’s 2018 book She Has Her Mother’s Laugh: The Powers, Perversions and Potential of Heredity. We want to record this luminous volume, but also a deep quandary at the essence of natural science and philosophy. The reviewer is a renowned professor at the UCLA Brain Research Institute, who explains the above title early on. The “meaning” topic came up at a dinner party, to which his answer was: as a geneticist, I could confidently assure everyone that life has no meaning, all it does is transmit DNA. The expert essay closes by saying again while life may have heritable standards, it still has no meaning.