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

Huneman, Philippe and Denis Walsh, eds. Challenging the Modern Synthesis: Adaptation, Development, and Inheritance. Oxford: Oxford University Press, 2017. While holding to these three areas, an authoritative cast such as David Depew, Etienne Danchin, Patrick Batson, Alan Love, Stuart Newman, and Francesca Merlin press the envelope from a physical basis to evo-devo and evolvability. But its narrow compass yet allows little notice of epigenetics, cooperation, convergence, or self-organization.

Since its origin in the early 20th century, the Modern Synthesis theory of evolution has grown to become the orthodox view on the process of organic evolution. Its central defining feature is the prominence it accords to genes in the explanation of evolutionary dynamics. Since the advent of the 21st century, however, the Modern Synthesis has been subject to repeated and sustained challenges. In the last two decades, evolutionary biology has witnessed unprecedented growth in the understanding of those processes that underwrite the development of organisms and the inheritance of characters. The original essays collected in this volume survey the various challenges to the Modern Synthesis arising from the new biology of the 21st century. (Publisher edits)

Ingold, Tim. Between Evolution and History: Biology, Culture, and the Myth of Human Origins. Wheeler, Michael, et al, eds. The Evolution of Cultural Entities. Proceedings of the British Academy, 2002. In an effort to understand and diagram kinship relations, a British social anthropologist proposes a new view of evolution beyond Darwinian context-independent variation, selection and population genetics in order to integrate the self-organizing activity of a relational, environmental field in which organisms actually live. Ingold provides a good summary of the larger revolution in biology and evolutionary theory as it adds complementary epigenetic, topological effects which can express the influence of dynamic developmental systems.

In brief, instead of thinking of evolution as the sequential modification, along one or more lines of descent, of the design specifications that are supposed to underwrite the construction of organisms or artifacts, we have to regard it as the unfolding of a total field of relationships – a web of life – with which forms come into being and are held in place. We can then see that what we are accustomed to call history, when speaking of human beings, is but one aspect of a total process of evolution that embraces the entire organic world. (43)

Jablonka, Eva. Extending Darwinism. Seed. October, 2008. The Tel Aviv University geneticist and author provides a concise guide to frontiers of evolutionary theory some 150 years after The Origin of Species. Although Charles could not have known of DNA genes, he got it as right as could be for the mid 19th century. The mid 20th century Modern Synthesis went on to integrate with Mendelian genetics, which still prevails today. An “epigenetic” revolution is now underway, for much more is actually going on than random mutations. With past co-author Marion Lamb, the metaphor of a musical score is of utility in this regard. While an original score abides, its expression or performance quite depends on the external arrangement, instruments, orchestra maestro, and so on. Denis Noble in The Music of Life also evokes this fertile analogy.

My colleagues and I have argued that various types of epigenetic inheritance have played key roles in all the major evolutionary transitions. For example, the symbiotic relations with bacteria that gave rise to modern cells would have been impossible without epigenetic mechanisms allowing their cell membranes to reproduce; cellular epigenetic inheritance mechanisms were necessary for the transition from single-celled creatures to complex multicellular organisms with many cell types; a new non-genetic system of information transmission (symbolic language) was crucial for the transition to human culture. (26)

Jablonka, Eva and Marion Lamb. Evolution in Four Dimensions. Cambridge: MIT Press, 2005. Jablonka, Tel Aviv University and Lamb, University of London, gather a decade of research and articles into a meticulous argument that much more is going on than molecular mutations and blind selection. In actuality, a sequence of “inheritance systems” can be observed from genetic to epigenetic, behavioral and symbolic-linguistic realms. This view is said to accord with the Major Transitions scale of Maynard Smith and Szathmary since it likewise describes new, more effective modes of hereditary information transfer. The organization of these systems is both modular and holistic as the transmission proceeds both vertically and horizontally among relative carriers. As genetic information gains better templates and modes of expression, it is increasingly under the active control of nucleated cell, animal, human person. Altogether the book provides a comprehensive synthesis which defines a revised evolutionary synthesis as the emergence of a genetic script from DNA to knowledge. But the authors cite a caveat: since it is “very improper” to suggest anything progressive, even though it might look that way, such is not the case. An updated Precis by the authors, with peer comments, can be found in Behavioral and Brain Sciences 30/353, 2007.

This significant contribution, along with the major evolutionary transitions scale, has gained much acceptance since as a standard model for all manner of studies. A 2014 second edition has now come out from MIT Press with extensive updates. A main advance is the recognition of epigenetic effects far beyond nucleotides, along with systems biology integrations and a renewed importance of developmental biology. Prolific findings of regulatory, neural, and societal networks further contribute to a comprehensive scenario. An affinity is cited with the innovative theories of geneticist James Shapiro in his 2013 Evolution: A View from the 21st Century. Further substantiation is recorded for our symbolic, linguistic dimensions, by which regnant life is becoming recognized as self-organizing in kind. A topical bibliography for the intervening years is also included.

Our basic claim is that biological thinking about heredity and evolution is undergoing a revolutionary change. What is emerging is a new synthesis, which challenges the gene-centered version of neo-Darwinism that has dominated biological thought for the last fifty years. (1)

This change in perspective is not peculiar to molecular biology. A more integrated, developmental view is now being adopted in many other areas of biology. Attention is focused less on the individual components of a system and more on their organization and the collective properties that emerge from their interactions. Disciplinary boundaries are being crossed, and subjects like behavioral epigenetics, ecological epigenetics, and cultural epigenetics, are growing. (2014, 378)

Jaeger, Johannes and Nick Monk. Bioattractors: Dynamical Systems Theory and the Evolution of Regulatory Processes. Journal of Physiology. 592/11, 2014. In this special issue, a Universitat Pompeu Fabra, Barcelona, systems biologist and a University of Sheffield mathematician offer an exercise in how to perceive and express the actual presence of such agency, as if the missing natural genotype prior to any selective winnowing. In a Glossary, “ontogeny” is defined as “the generation of being” which includes not only development but also metabolic and physiological processes that produce phenotypes. For a later synopsis by the authors, see Everything Flows: A Process Perspective on Life in EMBO Reports (16/9, 2015).

In this paper, we illustrate how dynamical systems theory can provide a unifying conceptual framework for evolution of biological regulatory systems. Our argument is that the genotype–phenotype map can be characterized by the phase portrait of the underlying regulatory process. We show how the geometric analysis of phase space connects Waddington's epigenetic landscape to recent computational approaches for the study of robustness and evolvability in network evolution. Finally, we demonstrate how the active, self‐organizing role of the environment in phenotypic evolution can be understood in terms of dynamical systems concepts. A systematic exploration of such systems will enable us to understand better the nature and origin of the phenotypic variability, which provides the substrate for evolution by natural selection. (Abstract excerpts)

Jaeger, Johannes and Nick Monk. Dynamic Modules in Metabolism, Cell and Developmental Biology. Interface Focus. April, 2021. A paper for an Interdisciplinary Approaches to Dynamics in Biology issue, Complexity Science Hub, Vienna and University of Sheffield systems biologists (search) advance their 2020s studies by more insights how nature’s complex adaptive system procreativity is composed of distinct modular units. As they proceed to nest and join into whole entities, their diversity can contribute vital features. An array of clever graphics conveys how effective this method is, and how consistently it is availed. A philoSophia view would strongly imply that all these innate appearances must arise from and exemplify a greater genesis. See also Homology of Process: Developmental Dynamics in Comparative Biology by James de Frisco and J. Jaeger in this same issue.

Modularity is an essential feature of any adaptive complex system. Phenotypic traits are modules in the sense that they have a distinguishable structure or function. Since phenotypic traits are the product of regulatory dynamics, the generative processes that constitute the genotype–phenotype map must also be modular. Here, we propose an approach that parses such a complex regulatory system into elementary activity-functions. We illustrate by way of examples from metabolism, cellular processes, as well as development and pattern formation. We argue that dynamical modules provide a shared conceptual foundation for developmental and evolutionary biology, and can found a new account of process homology, see also DiFrisco and Jaeger in this focus issue. (Abstract)

Jaeger, Johannes, et al. The Inheritance of Process: A Dynamical Systems Approach. Journal of Experimental Zoology B. 318/8, 2012. In this journal edited by Gunter Wagner, mathematical biologists Jaeger, Universtitat Pompeu Fabra, Barcelona, with David Irons, University of Sheffield, and Nick Monk, University of Nottingham, propose a concerted, innovative effort toward a 21st century developmental evolutionary synthesis, which many agree is overdue. As the quotes discuss, a missing theoretical basis is the presence of innate nonlinear complex phenomena, as much a factor as biomolecular genes. A major import is that such genotype-like self-organization is at work prior to post phenotype selection. In regard, this work accords with similar 2012 projects across nature such as D. Aerts, et al for quantum potentials, J. Schneider, et al, animal societies, and K. Doron, et al for cerebral processes, (search all) where each seeks to admit the active role of these independent, universally applicable propensities as they serve to join discrete elements into dynamic interconnective networks. As a precedent, the authors say that this synthesis would fulfill the vision of holistic biologist Brain Goodwin (search).

A central unresolved problem of evolutionary biology concerns the way in which evolution at the genotypic level relates to the evolution of phenotypes. This genotype–phenotype map involves developmental and physiological processes, which are complex and not well understood. In this study, we argue that an explicit treatment of this map in terms of dynamical systems theory can provide an integrated understanding of evolution and development. We use a simple conceptual model to illustrate how the regulatory dynamics of the genotype–phenotype map can be passed on from generation to generation, and how heredity itself can be treated as a dynamic process. Our model yields explanations for punctuated evolutionary dynamics, the difference between micro- and macroevolution, and for the role of the environment in major phenotypic transitions. We propose a quantitative research program in evolutionary developmental systems biology—combining experimental methods with mathematical modeling—which aims at elaborating our conceptual framework by applying it to a wide range of evolving developmental systems. This requires a large and sustained effort, which we believe is justified by the significant potential benefits of an extended evolutionary theory that uses dynamic molecular genetic data to reintegrate development and evolution. (Abstract)

This lack of unity and understanding is not simply an issue of incompatible research programs or insufficient evidence. We argue that the problem is conceptual: we urgently need a mechanistic understanding of the nature of phenotypic variability for inherited traits if we are to gain an integrative understanding of evolution. By mechanistic, we mean causative explanations – in terms of dynamic interactions between genes or other relevant factors – rather than correlations between genotypes and phenotypes. Here, we present an outline of such a conceptual framework, expanding on the earlier work by (Brian) Goodwin (which) treats development and heredity as two different aspects (occurring at different time scales) of the same underlying evolutionary process. (593)

Epigenetic processes – physiology and development – co-determine the phenotype of an organism. While it is hardly controversial to treat these processes in terms of their dynamics, we show that heredity can be interpreted as a dynamical system as well, and that it is dynamic process itself that is inherited. We adopt the view that development and heredity can be combined explicitly by introducing a simple conceptual model based on dynamical systems theory. This model illustrates how the regulatory architecture of a developmental system is passed from generation to generation, and acts to integrate genetic, maternal, and environmental factors to produce a phenotype, which in turn is the primary target of natural selection. This regulatory structure holds the central ground between evolution and development, genotype, and phenotype. (593)

Lastly, our work is an attempt at integrating the great variety of approaches and subjects that have been proposed to be central to an extended evolutionary synthesis. This extension not only expands the scope of the original theory of evolution, but also shifts its focus away from genes towards evolving developmental systems, embedded in their genetic and environmental context. Darwin’s original theory suffered from two great deficits. One was the lack of a theory of inheritance. The integration of genetics into evolutionary theory solved this. Now it is time to tackle the second one: the lack of a mechanistic theory on the nature of phenotypic variability. Such a theory is now achievable. It will enable us to establish empirically whether there are regularities, or even laws, governing major phenotypic transitions. (608)

Jain, Kavita and Luca Peliti. Special Issue on a Statistical Theory of Biological Evolution. Journal of Statistical Physics. 172/1, 2018. An introduction by Jawaharfal Nehru Centre for Advanced Scientific Research, India and Santa Marinella Research Institute, Italy system physicists. Such a global collaboration and this edition are a good example of late 2010s syntheses as cosmic and living nature increasingly cross-fertilizes, informs, and becomes a unified vital procreation. Among the dozen papers are Stochastic Spatial Models in Ecology, Universality Classes of Interaction Structures for NK Fitness Landscapes, and Environmental Stochasticity and the Speed of Evolution. Of notice is how these expansive views of life’s development can easily meld together as they allow and imply guidance from a mathematical, genetic-like source. See also 2018 papers in Natural Algorithms (Bernini, et al) for another portal by our Earthropic sapience.

Jeong, Hawoong, et al. The Large-Scale Organization of Metabolic Networks. Nature. 407/651, 2001. A report from the Univerisity of Norte Dame physics group which has discovered a universality of invariant network principles.

Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks. (651)

Johnson, Brian and Sheung Kwan Lam. Self-organization, Natural Selection, and Evolution: Cellular Hardware and Genetic Software. BioScience. 60/11, 2010. Circa late 2010, the pursuit of a more viable evolutionary theory, which begs a wholly revised synthesis, seems to be reaching a critical juncture and advance. As this site tries to document, it is increasingly evident that something else is going-on than random, gradual selection alone. In some real way, a prior, mathematical, informational, intrinsically generative force is being realized to impel and guide life’s nested ascent. While an old guard may give a nod, or not, and speak of “extended or expanded” versions, this large shift has not occurred. One reason has been that the multi-faceted complexity sciences, covered in A Cosmic Code, were in embryonic maturation until now.

In this paper, University of California, Berkeley, postdoctoral researchers give new admission and credence to these active, developmental influences, and show how they are indeed at work from proteins to organisms. Not to get ahead of themselves, this is done within a view of selective results, and phrased in mechanistic terms. As other ventures in this transition, it is then couched as a computerese “software,” which serves to proscribe creaturely “hardware.” Astutely, the authors situate this revolution not as an either-or divide between selection or organization, but a formative blend of both modes.

Self-organization is sometimes presented as an alternative to natural selection as the primary mechanism underlying the evolution of function in biological systems. Here we argue that although self-organization is one of selection's fundamental tools, selection itself is the creative force in evolution. The basic relationship between self-organization and natural selection is that the same self-organizing processes we observe in physical systems also do much of the work in biological systems. Consequently, selection does not always construct complex mechanisms from scratch. However, selection does capture, manipulate, and control self-organizing mechanisms, which is challenging because these processes are sensitive to environmental conditions. Nevertheless, the often-inflexible principles of self-organization do strongly constrain the scope of evolutionary change. Thus, incorporating the physics of pattern-formation processes into existing evolutionary theory is a problem significant enough to perhaps warrant a new synthesis, even if it will not overturn the traditional view of natural selection. (879)

The computer science concepts of hardware and software provide a useful metaphor for understanding the nature of biological systems. All the chemical compounds within a cell, and the stable organizational relationships among them, form the hardware of the biological system; this includes the cell membranes, organelles, and the DNA molecules. These structures and processes encompass information stored in genes, as well as information inherent in their organization. The software of the cell corresponds to programs implemented on the hardware for adaptive responses to the environment and for information storage and transmission. Such programs are contained not only in DNA but also in stable self-organization patterns, which are inherited across generations as ongoing processes. (883)

Jose, Marco. Rhythms Found in Human DNA. Physics World. November, 2004. The revolutionary ability of computer-based bioinformatics to sequence genomes can reveal an independent, prior nonlinear dynamics at work in their organization. This is seen as a challenge to the “belief that biological processes are governed by random mutations, genetic drift and natural selection.”

Joshi, Niknil, et al. The Minimal Complexity of Adapting Agents Increases with Fitness. PLoS Computational Biology. 9/7, 2013. As the Abstract cites, Joshi, Cal Tech, with Giulio Tononi, University of Wisconsin, and Christof Koch, Allen Institute for Brain Sciences, discern a parallel track between evolved organic intricacy and informed nervous systems. That is, the potential of any creature to survive and reproduce depends on its relative cognitive capacity. Pierre Teilhard would be pleased by this 21st century affirmation of his central vision of a tandem emergence of complexity and consciousness.

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness. (Abstract)

It has often been asserted that as organisms adapt to natural environments with many independent forces and actors acting over a variety of different time scales, they become more complex. We investigate this question from the point of view of information theory as applied to the nervous systems of simple creatures evolving in a stereotyped environment. We performed a controlled in silico evolution experiment to study the relationship between complexity, as measured using different information-theoretic measures, and fitness, by evolving animats with brains of twelve binary variables over 60,000 generations. We compute the complexity of these evolved networks using three measures based on mutual information and one measure based on the extent to which their brain contain states that are both differentiated and integrated. All measures show the same trend - the minimal complexity at any one fitness level increases as the organisms become more adapted to their environment, that is, as they become fitter. Above this minimum, there exists a large degree of degeneracy in evidence. (Author Summary)

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