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

Couce, Alejandro, et al. Changing fitness effects of mutations through long-term bacterial evolution.. Science. January, 2024. Michigan State University, Harvard Medica School and University of Paris biologists including Richard Lenski post a summary report for some years of laboratory test runs of computational organisms and their genetics code. As the Abstract says, and team interviews affirm, a general reliability does become evident.

The distribution of fitness effects of new mutations shapes evolution, but it is a challenge to observe how it changes as organisms adapt. Using Escherichia coli lineages spanning 50,000 evolutionary generations, we quantify the fitness effects of insertion mutations in every gene. Microscopically, changes in individual gene essentiality and deleterious effects often occurred in parallel. The identity and effect sizes of beneficial mutations changed rapidly over time, but many targets of selection remained predictable because of loss-of-function mutations. Taken together, these results reveal the dynamic—but statistically predictable—nature of mutational fitness effects.

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).

In the light of theoretic and empirical advances, we hosted an EMBO workshop at the European Molecular Biology Laboratory in June 2021 called ‘Predicting Evolution’ to ensure that this growing body of evolutionary knowledge moves toward mechanistic and causal accounts of biological processes. The conference explored the evolution of biological systems at different levels: from genes and molecules to organism development and ecology. As such, we invited leaders across various scales of evolution: molecular, network, microbial, developmental and community. The meeting explored biology at the interface of evolution, quantitative genetics, development and systems biology. (Editors)

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. (A. Cano)

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, 2022. 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.

We note these chapters to convey the book’s inclusive reach and deep veracity: Self-Organization Meets Evolution: Ernst Haeckel and Abiogenesis (Georgy Levit and Uwe Hossfeld, see review), Self-Organization in Embryonic Development (Stuart Newman, search), Biological Evolution of Microorganisms (Werner Arber) From Dissipative Structures to Biological Evolution: A Thermodynamic Perspective (Dilip Kondepudi, et al, see review), and Quantum Fractal Thermodynamics to Describe the Log-Periodicity Law in Species Evolution and Human Organizations (Diogo Queiros-Conde, et al). Anne Malasse then posts a final wrap as Sapiens and Cognition: The Last Threshold of Self-Organized and Self-Memorizing Increasing Complexity.

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)

Global warming, anthropocene extinctions along with astrobiology efforts to look for primitive life forms are prompting thinkers to view life’s evolution as the prime reality for species biodiversity and indeed our own civilization. This discernment leads to better understandings of the origin of the organization of dynamic forms and processes from the smallest cellular unit to the most complex interactions within the organism and then between organisms. Such novel insights and vista just coming into view can illume over geological and cosmic time scales how principles of self-organization of complex systems and generic laws of adaptation and complexification are at procreative work. (Anne Malasse, The Origin and Evolution of Living Organisms: A Convergence between Old and New Paradigms.)

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)

In recent years, however, exceptionally valuable insights into human development have also been contributed by researchers in fields that might seem at first to have nothing to do with the topic, such as mathematics, physics, computer science, and even philosophy. These efforts have not been focussed on precise details of which cell does what and when, but have instead tackled the profound, abstract questions that development raises, including, how can the simple become complex?, how can error-prone mechanisms construct something precise?, and is human development too complex for developed humans to understand completely? The jury is out on the last of these questions—the word ‘completely’ being the point of dispute—but significant progress has been made on the other two questions, both of which find an answer in the related concepts of ‘emergence’ and of ‘adaptive self-organization’. (4)

The terms are essentially two sides of the same thing, one viewed from above and one below. ‘Emergence’ tends to be used by people looking down from the perspective of high-level behaviors, and is the process by which complex structures and behaviors arise from simple components and rules. ‘Adaptive self-organization’ is a description grounded in the components and looks upwards, describing how the application of simple rules to these components can result in their collectively doing something large scale, clever, and subtle. The way in which adaptive self-organization allows non-living molecules to produce a living cell, and allows cells with very limited individual abilities to produce a very able multicellular body, will form a theme that runs through all of this book because it is the core of development. (4-5)

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)

Lying at the heart of vital eukaryotic processes are chromatin structure, organization and dynamics. Epigenetics provides striking examples of how bottom-up genetic and top-down epigenetic causation intermingle. The fundamental question then arises of how causal efficacy should be attributed to biological information. A proposal is made to implement explicit downward causation by coupling information directly to the dynamics of chromatin, thus permitting the coevolution of dynamical laws and states, and opening up a new sector of dynamical systems theory that promises to display rich self-organizing and self-complexifying behaviour. (Abstract, 1) The existence of a coded digital semantic (or, if preferred, contextual) information channel is a fundamental defining characteristic that separates living from non-living systems. (5)

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)

Self-organization as the spontaneous emergence of spatio-temporal patterns through physical or chemical processes has been described in many different systems, for example, in non-living reaction–diffusion systems, such as the Belousov–Zhabotinsky reaction. It was
used for an explanation of morphogenesis by Alan Turing. More recently, it came to prominence in embryology with the use of stem cells and their in vitro differentiation into various tissues, and self-organization has become a fashionable topic in studies of the development of patterns and form. (1)

Ute Deichmann received her Ph.D. in Genetics in 1991 at the University of Cologne with the thesis Biologists under Hitler: The Expulsion of Jewish Scientists and the Development of Biological Research in Germany. From 2003 to 2007, she was a research professor at the Leo-Baeck-Institute in London where, together with Ulrich Charpa, she was the head of the project "Jews in German-Speaking Academia, 19th and 20th centuries". (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)

This book contests the general view that natural selection constitutes the explanatory core of evolutionary biology. It invites the reader to consider an alternative view which favors a more complete and multidimensional interpretation. The 1950s Modern Synthesis to date ihas two main bases: (1) Gradual evolution due to small genetic variations oriented by natural selection, a process leading to adaptation and (2) Evolutionary trends and speciation events as macroevolutionary events from processes and mechanisms occurring at a microevolutionary level. But if one reads the new papers herein by biologists, historians and philosophers, this decades old school is being set aside in preparation for a dynamic developmental paradigm. (Natural Selection)

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