V. Life's Corporeal Evolution Develops, Encodes and Organizes Itself: An Earthtwinian Genesis Synthesis

B. Systems Biology Unites: EvoDevo, Genomes, Cells, Networks, Symbiosis, Homology, Inherency

Garcia-Ojalvo, Jordi. Physical Approaches to the Dynamics of Genetic Circuits. Contemporary Physics. 52/5, 2011. As another instance of both the discovery in genomes of pervasive regulatory networks, and their association with and rooting in statistical mechanics, a Universitat Politècnica de Catalunya, Terrassa, Spain physicist provides a lengthy tutorial introduction. A growing number of such projects and papers are on their way to a whole scale reconception of the nature of genotype and phenotype, and by an affinity with a nonlinear materiality, to imply a conducive genesis cosmos.

Cellular behaviour is governed by gene regulatory processes that are intrinsically dynamic and nonlinear, and are subject to non-negligible amounts of random fluctuations. Such conditions are ubiquitous in physical systems, where they have been studied for decades using the tools of statistical and nonlinear physics. The goal of this introductory tutorial is to show how approaches traditionally used in physics can help in reaching a systems-level understanding of living cells. (Abstract, 439)

One of the main questions to be answered in the quest towards understanding life is how structure relates with function in living systems, in particular in cells. A substantial amount of evidence has recently pointed to the relevance of a ‘mesoscopic’ description, at the level of networks of interacting genes and proteins that coordinately govern most cellular processes. This picture has relegated the notion of ‘one gene, one function’ (and frequently one disease) that guided much of genetics and molecular biology in most of the twentieth century. (439)

Cells, both in unicellular and multicellular organisms, possess multiple mechanisms of communicating with one another, in a process that is critical for the survival of any species. From bacterial biofilms to the more sophisticated animal tissues, cells coordinate their behavior in order to function and dynamics is not an exception. (458)

Garcia-Sancho, Miguel. Biology, Computing, and the History of Molecular Sequencing. New York: Palgrave Macmillan, 2012. A Spanish National Research Council historian of genetics provides a thorough course from Frederick Sanger’s 1950s rudimentary techniques to 1960s basic instrumentation, later 1980-1990s automation onset and onto 21st century rapid, large-scale machine computations. But we include because this worldwide collaborative, cumulative project can quite appear as our human phenomenal way that a genesis uniVerse tries to read its own genetic code.

Gazestani, Vahid and Nathan Lewis. From Genotype to Phenotype: Augmenting Deep Learning with Networks and Systems Biology. Current Opinion in Systems Biology. 15/68, 2019. UC San Diego bioscientists consider a timely synthesis of these genetic, systems, network, and AI aspects and methods, which appear to have a natural, innate affinity with each other. A subsection is Generalizability, Transferability, and Interpretability as our worldwise learning phase proceeds to reconstruct how we came to be, so as to better go forward.

Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep methods now allow one to encode this existing knowledge in the architecture of the learning procedure. By encoding biological networks this way, one can develop flexible techniques that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an impact on the cell phenotype. (Abstract excerpt)

Geontoro, Lea. Cross-Hierarchy Systems Principles. Current Opinion in Systems Biology. 1/80, 2017. In an inaugural Future of Systems Biology issue, a CalTech biologist alludes to nascent perceptions of an innate geometry and mathematics at work that serves to guide cells, organisms, and species. A common cycle of initial, diverse explorations, later formation of relational networks, and an emergence of integral wholes is described. Akin to Martin Davis in cosmic computations, and Jan Boeyens for periodic elements, recurrent similarities are seen to grace life’s ascent unto our retrospect witness.

One driving motivation of systems biology is the search for general principles that govern the design of biological systems. But questions often arise as to what kind of general principles biology could have. Concepts from engineering such as robustness and modularity are indeed becoming a regular way of describing biological systems. Another source of potential general principles is the emerging similarities found in processes across biological hierarchies. In this piece, I describe several emerging cross-hierarchy similarities. Identification of more cross-hierarchy principles, and understanding the implications these convergence have on the construction of biological systems, I believe, present exciting challenges for systems biology in the decades to come. (Abstract)

Gilbert, Scott, et al. Resynthesizing Evolutionary and Developmental Biology. Developmental Biology. 173/357, 1996. A report on initial efforts to forge a more complete picture informed by the concepts of embryology, homology, Bauplan, and morphogenetic fields.

This gets us into a newly discovered and fascinating realm of homology - the homology of process. Whereas classic homology has been one of structure - be it skeletons or genes - the homology of process goes into the very mechanisms of development. Whereas classical homology looks at the similarities between entities, the homology of process concerns the similarities of dynamic interactions. (364)

Gilpin, William, et al. Learning Dynamics from Large Biological Data Sets: Machine Learning Meets Systems Biology. Current Opinion in Systems Biology. July 30, 2020. As is the current case for many scientific fields, Harvard and Dartmouth researchers scope out ways by which a suitable apply of AI deep neural net techniques can effectively interface with life studies so to enhance research methods and results.

In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis can provide a complementary, beneficial approach to traditional modeling. Recent applications of machine learning have been used to study biological dynamics in diverse systems from neuroscience to animal behavior. We propose several avenues for bridging dynamical systems theory with large-scale analysis enabled by machine learning. (Abstract excerpt)

Taken together, these results introduce the question of whether universal mathematical constraints determine certain aspects of large biological systems whose interacting units spontaneously collapse onto a low-dimensional manifold. Has evolution driven complex biological networks toward these emergent motifs, and do they confer adaptive benefits such as stabilizing an ecosystem against an invasive predator, or suppressing unwanted fluctuations in a genetic circuit? We hope that further development of models at the intersection of machine learning and dynamical theory will provide unified insight into these questions. (6)

Gontier, Nathalie. Testing the “(Neo-) Darwinian” Principles against Reticulate Evolution. Information. 11/7, 2020. The University of Lisbon evolutionary epistemologist has been at the conceptual forefront (search) of a 2010s revision of life’s developmental emergence. This paper continues her 2015 edited Reticulate Evolution volume by noting exemplary network topologies in symbiosis, lateral gene transfer, adaptive fitness, infective (viral) heredity, organismic mobility, species affordances, hybridization and more. A distinct approach of reticulate studies is proposed as an overdue phase of interconnective linkages between all the prior parts. In regard, an inclusion and endorsement of symbiotic mutual unions in their role as a prime evolutionary property is achieved. A history of symbiogenesis from the 1900s to the work of Lynn Margulis to current holobiont models braces the claim. See also Towards a Dynamic Interaction Network of Life to Unify and Expand the Evolutionary Theory by Eric Bapteste and Philip Huneman in BMC Biology (16/56, 2018) for another confirmation.

Variation, adaptation, heredity and fitness, constraints and affordances, speciation, and extinction form the building blocks of the (Neo-)Darwinian research program. Several of these aspects have been called “Darwinian principles.” However, we will here describe the important role played by reticulate evolutionary mechanisms and processes in also bringing about these phenomena. Reticulate mechanisms and processes include symbiosis, symbiogenesis, lateral gene transfer, infective heredity mediated by genetic and organismal mobility, and hybridization. Because “Darwinian principles” are brought about by both vertical and reticulate processes, they should contribute to a more pluralistic theory of evolution, one that surpasses the Modern and Neo-Darwinian Synthesis. Instead, these general principles of evolution need to be understood as common goods that come about through interactions between different units and levels of evolutionary hierarchies, and they are exherent rather than inherent properties of individuals. (Abstract excerpt)

Reticulate: the formation of a net or web- work topology, or the presence of net structures such as veins of a leaf, or a nervous system.

Nathalie Gontier: I’m a philosopher of evolutionary sciences, with special interest in the nature and scope of evolutionary explanations, how they evolved within the overall genealogy of thought; how they are applied within the biological, sociocultural and linguistic sciences; and how they are depicted in hierarchical diagrams such as cycles, timelines, trees and networks.

Green, Sara, et al. Explanatory Integration Challenges in Evolutionary Systems Biology. Biological Theory. Online July, 2014. Philosophers of science Sara Green, Melinda Fagan, and Johannes Jaeger, from the USA, Spain and Denmark, are engaged with colleagues in a project to discern the presence general biological principles. Since an reductionist phase has run its course, there is much need, for example in stem cell research, to define unifying properties and propensities which seem to be truly there. In regard, Jaeger has studied holistic biology with the late Brian Goodwin, so a neo-rational, structural, generative vista guides the effort. This school and view is then contrasted with the narrow, inadequate neo-Darwinian focus. It is then said that evolutionary theory can be integrated by virtue of common code-like agencies evident in their independence and as instantiated in organisms.

Green, Sara, et al. Network Analyses in Systems Biology: New Strategies for Dealing with Biological Complexity. Synthese. Online January, 2017. As many fields such as systems chemistry (Peter Stadler) are lately integrating network features, so Green and Maria Serban, University of Copenhagen, Raphael Scholl, University of Geneva, Nicholaos Jones, University of Alabama, along with Ingo Brigandt and William Bechtel, UC San Diego proceed with an expansion of this title endeavor. A better report of interactive linkages, computational strategies, epigenetic stabilities, and more, can be achieved. One benefit, it is noted, might be perceptions of a “cancer attractor.”

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity. (Abstract)

Gross, Fridolin and Sara Green. The Sum of the Parts: Large-Scale Modeling in Systems Biology. Philosophy, Theory, and Practice in Biology. Volume 9, 2017. University of Kassel, Germany and University of Copenhagen philosophers provide an update review as this 21st century integral turn and method proceeds to put life back together again.

Hall, Brian. Evolutionary Developmental Biology: Past, Present, and Future. Evolution: Education and Outreach. 5/2, 2012. The Dalhousie University, Nova Scotia, biologist has been a leading advocate of this once and future Evo-Devo endeavor. As the article explains, while in the later 19th century, and ages before, life’s earthly course was tacitly a sequential gestation, at the start of the 20th century, embryology and evolution parted ways into separate domains. This disconnect is now realized as detrimental to evolutionary theory, and in the past decade or so, drawing on vastly more knowledge, a robust reunion is underway. As a result, life’s nested, recurrent emergence can be truly appreciated an embryonic maturation, might we even be at planetary term.

Evolutionary developmental biology (evo–devo) is that part of biology concerned with how changes in embryonic development during single generations relate to the evolutionary changes that occur between generations. Charles Darwin argued for the importance of development (embryology) in understanding evolution. After the discovery in 1900 of Mendel’s research on genetics, however, any relationship between development and evolution was either regarded as unimportant for understanding the process(es) of evolution or as a black box into which it was hard to see. Research over the past two decades has opened that black box, revealing how studies in evo–devo highlight the mechanisms that link genes (the genotype) with structures (the phenotype). This is vitally important because genes do not make structures. Developmental processes make structures using road maps provided by genes, but using many other signals as well—physical forces such as mechanical stimulation, temperature of the environment, and interaction with chemical products produced by other species. (Abstract)

Not only do genes not make structures (the phenotype), but new properties and mechanisms emerge during embryonic development: genes are regulated differentially in different cells and places; aggregations of similar cells provide the cellular resources (modules) from which tissues and organs arise; modules and populations of differently differentiated cells interact to set development along particular tracks; and organisms interact with their environment and create their niche in that environment. (Abstract)

Hall, Brian and Wendy Olson, eds. Keywords and Concepts in Evolutionary Developmental Biology. Cambridge: Harvard University Press, 2003. A 1992 Harvard Press work covered the core definitions of Evolutionary Biology (Evelyn Fox Keller and Elisabeth Lloyd, eds.). This present work considers the reconvergence of embryological ontogeny and evolutionary phylogeny, aka “evo-devo.” Fifty or so topics include Atavism, Developmental Systems Theory, Epigenesis and Epigenetics, Homology and Homoplasy, Segmentation. But as a review in Evolution by Ehab Abouheif (58/12, 2004) notes, the book in fact challenges the neo-Darwinian tenet that gene mutation and recombination alone drives evolution. Many other factors such as environmental influences, topological constraints, self-organization, modularity, and developmental processes are seen in effect as organisms develop from genotype to phenotype.

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