V. Life's Corporeal Evolution Encodes and Organizes Itself: An EarthWinian Genesis Synthesis
Cornwell, John, ed. Nature’s Imagination. New York: Oxford University Press, 1995. Papers from a conference to explore ways to expand 20th century science beyond its limited reductionism.
Twentieth-century discoveries of new phenomena at successive levels in matter, living organisms and mind-brain relationships have led to a more dynamic, emergent, relational view of nature. (v)
Crocker, Justin, et al. Interdisciplinary Approaches to Predicting Evolutionary Biology. Royal Society Philosophical Transactions B. 376/1877 April, 2023. JC and Joshua Payne, European Molecular Biology Laboratory, Aleksandra Walczak, Sorbonne University, and Patricia Wittkopp, University of Michigan system biologists introduce a special issue with this integral purposeful quest. As the quote says, topical discussions were about The Predictable Genome, Gene Regulation and Networks, Community Evolution, and more. Some entries are Global Epistasis on Fitness Landscapes (Juan Diaz-Colunga, et al), Mutation Bias and Predictability of Evolution (Alejandro Cano, et al) and. Evolutionary Repeatability of Emergent Properties of Ecological Communities (S. Venkataram and S. Kryazhimskiy). See also Evolution Towards Increasing Complexity through Functional Diversification in a Protocell Model of the RNA World by Suvam Roy and Supratim Sengupta in Royal Society Proceedings B. (October 2021).
Crutchfield, James and Peter Schuster, eds. Evolutionary Dynamics. Oxford: Oxford University Press, 2003. Conference proceedings which consider how to apply complex systems theory to evolution. By this method, a far-from-equilibrium, self-organizing emergence can be defined which acts prior to selection.
As a result, evolution has come to be modeled as an intrinsically stochastic and (nonlinear) dynamical system in which a population of structured individuals, monitored as a set of genotypes, diffuses through the space of all possible genotypes. The diffusion is far from random, but instead is driven by genetic developmental processes and selection according to phenotypic fitness, for example. (xv) Another, complementary approach to the evolution of biological complexity originates from the observation that rich dynamical behavior and intricate structures emerge when a few simple rules are applies over and over again. (xvii)
Cuesta, Jose et al. Evolutionary Modeling and Experimental Evolution. Europhysics Letters. Online January, 2018. European mathematical theorists JC, Joachim Krug and Susanna Manrubia post a Focus Issue with this title as a home, as the summary says, for papers broadly about life’s latest phase of retro empirical quantification via our homo to anthropo worldwise sapience. Amongst the 13 entries by September are Playing Evolution in the Laboratory: From the First Major Evolutionary Transition to Global Warming by Ines Fragata, et al (122/3), Non-Deterministic Genotype-Phenotype Maps of Biological Self-Assembly by S. Tesoro, S. and S. E. Ahnert (123/3) and Statistical Theory of Phenotype Abundance by Juan Garcia-Martin, et al (123/2).
Evolutionary thinking is undergoing major changes due to new experimental techniques which are providing us with a huge amount of data to an unprecedented microscopic detail. As a consequence the new research field of Experimental Evolution has emerged, where populations of simple organisms are propagated over thousands of generations under well-controlled laboratory conditions. The possibility to test the predictions of evolutionary theory on a quantitative level has lead to an upsurge of interest in evolutionary modelling, which is strongly driven by researchers with a background in statistical physics and complex systems studies. At the same time physicists have become increasingly involved in the design and realisation of evolution experiments. Together these developments promise to transform evolutionary biology from a largely retrospective, historical endeavour to a predictive science based on quantitative theory. (Summary)
Dambricourt Malasse, Anne, ed.
Self-Organization as a New Paradigm in Evolutionary Biology.
International: Springer Frontiers,
The editor is a senior paleo-anthropologist at the French National Center for Scientific Research. The volume appears in a new Springer series Evolutionary Biology: New Perspectives (search Richard Delisle) and can represent a latest, strongly evident affirmation of this missing innate, common source force for life’s oriented, emergent development. In regard the work well serves to establish an absent, animating, informative, genome-like basis which can at last inform, explain, qualify and brace a valid 2020s genesis synthesis within a revolutionary ecosmos uniVerse.
A new evolutionary synthesis is proceeding to integrate the scientific models of self-organization in occurrence since the later 20th century as based on the laws of physics, thermodynamics, and mathematics. This book shows how self-organization is by now integrated across a 21st century span from life’s origins to our human phase. The first part attends to the modern observations in paleontology and biology, with prior presciences such as Immanuel Kant, d’Arcy Thompson, Henri Bergson, and Ilya Prigogine. The second part views emergent evolutionary models drawn from the complexity sciences, the non-linear dynamical systems, fractals, attractors, epigenesis, and other system approaches such as embryogenesis-morphogenesis phenomena. (Publisher)
Davies, Jamie. Life Unfolding: How the Human Body Creates Itself. Oxford: Oxford University Press, 2014. Almost a century after fellow Scotsman and theoretical biologist D’Arcy Thompson (1960-1948) wrote his classic On Growth and Form, the University of Edinburgh, Center for Integrative Physiology, embryologist achieves a similar synopsis. Just as Thompson argued that selection alone cannot explain bodily formation, Davies reaches the same conclusion as he melds physiological studies with 21st century physical and structural theories of dynamic development. Today these prior forces are “adaptive self-organization and emergence,” as drawn from the mathematical sciences of complex dynamic systems. A companion work is Davies’ technical text Mechanisms of Morphogenesis (2nd edition 2013). The significance of Davies’ laboratory and conceptual views are noted in Morphogenesis: Origins of Patterns and Shapes, Paul Bourgine, Annick Lesne, editors, (search) another volume where a novel confirmation of an evolutionary embryogeny is documented.
Where did I come from? How did my brain learn to learn? Questions like these remain biology's deepest and most ancient challenges. They force us to confront a fundamental biological problem: how can something as large and complex as a human body organize itself from the simplicity of a fertilized egg? A convergence of ideas from embryology, genetics, physics, networks, and control theory has begun to provide real answers. Based on the central principle of 'adaptive self-organization,' it explains how the interactions of many cells, and of the tiny molecular machines that run them, can organize tissue structures vastly larger than themselves, correcting errors as they go along and creating new layers of complexity where there were none before. (Publisher)
Davies, P. C. W. The Epigenome and Top-Down Causation. Journal of the Royal Society Interface. Online November 15, 2011. A few scientists with a philosophical bent, who are also good writers, can venture far afield and achieve novel insights across nature’s realm. Francis Crick could wax on neuroscience, Freeman Dyson lucidly on everything, and here physicist Paul Davies broadens and deepens genetic phenomena. The molecular nucleotide code thus gains a new dimension as a self-organizing network, while a real and pervasive “epigenetic” domain of behavioral and environmental influences takes on a real formative importance. Might we ever move closer to allowing such universal complex dynamics to gain a semblance as a true cosmic genetic code? In an organic universe surely there must be such a essential dimension.
Genes store heritable information, but actual gene expression often depends on many so called epigenetic factors, both physical and chemical, external to DNA. Epigenetic changes can be both reversible and heritable. The genome is associated with a physical object (DNA) with a specific location, whereas the epigenome is a global, systemic, entity. Furthermore, genomic information is tied to specific coded molecular sequences stored in DNA. Although epigenomic information can be associated with certain non-DNA molecular sequences, it is mostly not. Therefore, there does not seem to be a stored ‘epigenetic programme’ in the information-theoretic sense. Instead, epigenomic control is—to a large extent—an emergent self-organizing phenomenon, and the real-time operation of the epigenetic ‘project’ lies in the realm of nonlinear bifurcations, interlocking feedback loops, distributed networks, top-down causation and other concepts familiar from the complex systems theory. (Abstract, 1)
de Oliveira, P. M. C. Why Do Evolutionary Systems Stick to the Edge of Chaos. Theory in Bioscience. 120/1, 2001. Insights into Darwinian selection as a critically poised dynamic system searching for new forms and conditions due to “scale-free probability distribution power laws.”
De Vladar, Harold, and Nicholas Barton. The Contribution of Statistical Physics to Evolutionary Biology. Trends in Ecology & Evolution. 26/8, 2011. In tune with the growing realization that basic pursuits of physical science have a deep affinity with the properties of dynamic complex systems, Institute of Science and Technology, Austria, and University of Edinburgh biologists sketch out an initial entry to life’s developmental emergence. Typical points of engagement are stochastic diffusion of allele frequencies, and statistical mechanics of quantitative genetics of finite populations. See also Barton and J. B. Coe “On the Application of Statistical Physics to Evolutionary Biology” in Journal of Theoretical Biology (259/317, 2009).
Evolutionary biology shares many concepts with statistical physics: both deal with populations, whether of molecules or organisms, and both seek to simplify evolution in very many dimensions. Often, methodologies have undergone parallel and independent development, as with stochastic methods in population genetics. Here, we discuss aspects of population genetics that have embraced methods from physics: non-equilibrium statistical mechanics, travelling waves and Monte-Carlo methods, among others, have been used to study polygenic evolution, rates of adaptation and range expansions. (424)
Deacon, Terrence. Relaxed Selection and the Role of Epigenesis in the Evolution of Language. Blumberg, Mark, et al, eds. Oxford Handbook of Developmental Behavioral Neuroscience. Oxford: Oxford University Press, 2010. The University of California at Berkeley professor of biological anthropology and linguistics searches for ways to join and synthesize the evidential presence of prior self-organization forces and post selective winnowing. Genes are said to be “lazy” at times, so as to hand off tasks to epigenetic domains and to loosen restraints on such a spontaneity.
On the other hand, it suggests a powerful non-Darwinian mechanism that may contribute to the evolution of complex functional synergies and to the emergence of highly distributed control of development. Because spontaneous self-organizing biases arise irrespective of natural selection, the two processes can complement one another. (743)
Deichmann, Ute. Self-Organization and Genomic Causality in Models of Morphogenesis. Entropy. 25/6, 2023. The Ben-Gurion University of the Negev historian of science author was founding director of the Jacques Loeb Centre for the History and Philosophy of the Life Sciences in 2007 (see below). Once again this year, a strong overview statement is posted by which to ground and install this 21st revolutionary procreative paradigm as the main way that living systems proceed to develop and evolve. So to scene-set, the paper cites prior versions such as D’Arcy Thompson’s structures, Alan Turing’s morphogenesis, Ilya Prigogine’s non-equilibrium complexities, and others. A next section is about current revivals of this prescience by informing them with self-organizing theories and genomic agencies. The 2010s work of Tom Misteli, Eric Karsenti, Ellen Rosenberg and James Briscoe is entered to lead into the gene regulatory network corpus as conceived by Eric Davidson. Altogether the paper shows how a well-quantified affirmation of the past 50 and 25 years of this whole dynamic scenario has be accomplished.
The ancient issue of the generation of form and structure in embryological development lately considers whether the occasion of patterns is a self-organized process or is mainly due to complex gene regulations. This paper surveys these versions with a special emphasis on Alan Turing’s 1952 reaction–diffusion model. I show that from the year 2000, his work became was increasingly cited as it was updated to account for genetic biological patterns. I next bring in Eric Davidson’s theory of early embryogenesis based on gene-regulatory networks analysis which but also the effects of evolution and organisms’ and species stability. (Abstract excerpt)
Delisle, Richard, ed.. Natural Selection: Revisiting its Explanatory Role in Evolutionary Biology. Switzerland: Springer, 2021. The subject book is part of a new Springer series Evolutionary Biology: New Perspectives which is also edited by the University of Lethbridge, Canada philosopher. As the quotes allude, the intent is to gather and report a historic, overdue revolution far beyond Darwinism which is open to and can include the many advances of the later 20th century and in the 21st century. (See our Chapter V for a long litany and documentation.)
Evolutionary biology has been a remarkably dynamic area since its foundation. Its true complexity, however, has been concealed in the last 50 years under an assumed opposition between the "Extended Evolutionary Synthesis" and an "Alternative to the Evolutionary Synthesis". This multidisciplinary book series aims to move beyond the notion that the development of evolutionary biology is structured around a lasting tension between a Darwinian tradition and a non-Darwinian tradition, once dominated by categories like Darwinian Revolution, Eclipse of Darwinism, Evolutionary Synthesis, and Post-Synthetic Developments. (Series summary)