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

Fagerstrom, T., et al. Biologists Put on Mathematical Glasses. Science. 274/2039, 1996. With an opening nod to Galileo’s statement about the book of nature written in mathematics, four theoretical biologists make note of a dawning discovery and articulation of a universal pattern and process.

What causes Nature to build more complex things in a hierarchical manner with principles that are recurrent at all levels? Genes, for example, are integrated into genomes, cells into multicellular organisms, and individuals into societies. Recurrence of the same principle gives rise to ever higher forms of complex life in an apparently open-ended evolutionary process. (2039)

Fangerau, Heiner, et al, eds. Classification and Evolution in Biology, Linguistics and the History of Science. Stuttgart: Franz Steiner Verlag, 2013. A collection with chapters such as Reconstructing the Lateral Component of Language History and Genome Evolution Using Network Approaches which discern innate commonalities between these archetypal prescriptive realms.

While Darwin’s grand view of evolution has undergone many changes and shown up in many facets, there remains one outstanding common feature in its 150-year history: since the very beginning, branching trees have been the dominant scheme for representing evolutionary processes. Only recently, network models have gained ground reflecting contact-induced mixing or hybridization in evolutionary scenarios. In biology, research on prokaryote evolution indicates that lateral gene transfer is a major feature in the evolution of bacteria. In the field of linguistics, the mutual lexical and morphosyntactic borrowing between languages seems to be much more central for language evolution than the family tree model is likely to concede. In the humanities, networks are employed as an alternative to established phylogenetic models, to express the hybridization of cultural phenomena, concepts or the social structure of science. However, an interdisciplinary display of network analyses for evolutionary processes remains lacking. Therefore, this volume includes approaches studying the evolutionary dynamics of science, languages and genomes, all of which were based on methods incorporating network approaches.

Feigin, Charles, et al. The GRN Concept as a Guide for Evolutionary Developmental Biology. Journal of Experimental Zoology B. 340/2, 2023. In a special Animal Gene Regulatory Network Evolution section, see intro by Mark Rebeiz and Thomas Williams, Princeton University and University of Melbourne biologists provide a latest survey of the formative importance that these interconnective anatomies serve. As the quote says their active presence as they join and arrange nucleotides as life grows and quickens.

Organismal phenotypes result from inherited developmental programs, carried out during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and the evolutionary trajectories of species. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown along with advances in “omic” techniques. In this Perspective, we go on to discuss how experiments and projects can be designed and enhanced in light of the vital GRN concept. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design.

Fernandez, Jose, et al. Emergent Diversity in an Open-Ended Evolving Virtual Community. Artificial Life. 18/2, 2012. We cite this as an example of growing abilities to simulate evolutionary and ecological communities by way of algorithmic, computational dynamics. With coauthors Daniel Lobo, Gema Martin, Francisco Vico, and Rene Doursat, University of Malaga and Ecole Polytechnique researchers study digital flora as multi-agent plants across genetic, developmental, and physiological domains. Diverse viable habitats are then seen to flourish via mutually interacting individuals within their biotic environment.

Thus, evolution is being increasingly studied through alternative approaches involving the theoretical reconstruction and simulation of living systems at multiple scales: genetic, cellular, developmental, population, and ecosystem. In particular, computational models have the great benefit of potentially producing a large number of experiments that can condense long evolutionary periods into short computing time frames, while making vast collections of data available for the analysis and extraction of relevant properties. (200)

On the other hand, agent-based models (ABMs) propose to describe communities in a bottom-up fashion through the properties of their individual members, the developmental and behavioral rules they obey, and their interactions with one another and the environment. The collective behavior of the system, originating from complex multi-agent dynamics, is then obtained by computer simulation. Compared to analytical frameworks, two major properties are distinctive of the agent-based computational approach: emergence and adaptation. New properties at higher scales, which were not explicitly included in the model, appear in the system as the individuals interact. (200)

Greater computer power available at a lower cost provides new means to simulate complex worlds of evolving and interacting organism populations. Today, large computational resources are now accessible to conventional research groups, and more projects are undertaken in the field of artificial life, which is potentially open to any degree of model sophistication and realism. Theoretical ecology, in particular, can greatly benefit from this approach, as several open problems of population genetics can be formulated and studied in virtual communities. (220)

Fields, Chris and Michael Levin. Scale-Free Biology: Integrating Evolutionary and Developmental Thinking. BioEssays. June, 2020. As a 2020 integrative phase goes forward, a veteran philosopher of biology now based in France and a Tufts University, Allen Discovery Center developmental biologist propose and scope out an array of unifying perspectives which are guided by an insight that life’s oriented emergence repeats in similar ways and means across the nested phases it engenders.

When the history of life on Earth is viewed as a history of cell division, all of life becomes a single cell lineage. The growth and differentiation of this lineage in reciprocal interaction with its environment can be viewed as a developmental process; hence the evolution of life can also be seen as the development of life. Here some fruitful research directions suggested by this perspective are highlighted. Variation and selection become bidirectional information flows between scales, while “cooperation” and “competition” become scale relative. The language of communication, inference, and information processing are more useful than the language of causation to describe homogeneous and heterogeneous living systems. Emerging scale‐free theories such as predictive coding and active inference can provide conceptual tools for the study of a unified, multiscale dynamical system. (Abstract)

Fisher, Daniel. Asexual Evolution Waves: Fluctuations and Universality. Journal of Statistical Mechanics. Online January, 2013. Another article in the “Statistical Mechanics and the Dynamics of Evolution” issue noted in Nourmohammad below. Here a Stanford University biophysicist seems to find, allude to, in so many words, something is going on by itself. Could one ask what kind of greater reality does this such spontaneous structuring come from? And another deep insight might thus accord. As complexity science and statistical physics marry, it suggests that for a local existence, whether particle or person, a chancy contingency does go on, but for globally collective populations, a necessary “universality” of eventual, orderly patterning will prevail. (See also Christof Koch 2012) By this advisory, might we earth kinfolk join altogether, therefore choose to succeed, which may even influence our genesis cosmos amongst the stochastic multiverse?

In large asexual populations, multiple beneficial mutations arise in the population, compete, interfere with each other, and accumulate on the same genome, before any of them fix. The resulting dynamics, although studied by many authors, is still not fully understood, fundamentally because the effects of fluctuations due to the small numbers of the fittest individuals are large even in enormous populations. In this paper, branching processes and various asymptotic methods for analyzing the stochastic dynamics are further developed and used to obtain information on fluctuations, time dependence, and the distributions of sizes of subpopulations, jumps in the mean fitness, and other properties. The focus is on the behavior of a broad class of models: those with a distribution of selective advantages of available beneficial mutations that falls off more rapidly than exponentially. For such distributions, many aspects of the dynamics are universal—quantitatively so for extremely large populations. On the most important time scale that controls coalescent properties and fluctuations of the speed, the dynamics is reduced to a simple stochastic model that couples the peak and the high-fitness 'nose' of the fitness distribution. (Abstract)

Fodor, Jerry and Massimo Piattelli-Palmarini. What Darwin Got Wrong. New York: Farrar, Straus and Giroux, 2010. What tangled webs we weave. These senior philosophers and cognitive scientists declare that the textbook gospel of “random mutation and adaptive selection” is a historical relic, and in no way an adequate explanation of how life evolves. But Ptolemaic epicycles are spun as the authors try to update and expand evolution within the tacit material machine paradigm. From the get-go (first quote) they claim that there is really no abiding, self-developmental nature to philosophize about in such a pointless, insensate universe. After some dense early chapters, an evo-devo window is rightly opened to admit the entire life span or ‘ontogenesis’ of an organism as where the evolutionary action is. For an example of further argumentation, Michael Ruse tears into the book in the March 7, 2010 issue of The Chronicle of Higher Education.

Of course, Charles could not have known this, nor other influences as per the second quote. And however much F & P-Ps later chapters on “whole genomes, networks, modules, and laws of form” allude to an innate, mathematical drive and direction, any admission is blocked by this prevailing mindset. And re the third quote, CD himself has been quite distorted and misappropriated in recent decades. He was an epitome of his age, exchanging letters with Alexander von Humboldt, immersed in the recapitulation or ‘universal gestation’ view of the day (see Robert Richards). With such baggage, the book again befogs in latter chapters. For example a March 2010 article in Scientific American (Organic Cosmos) by Robert Hazen can just as readily envision a greater genesis universe trying to discover itself.

We therefore seek thoroughly naturalistic explanations of the facts of evolution, although we that they will turn out to be quite complex, as scientific explanations often are. It is our assumption that evolution is a mechanical process through and through. We take that to rule out not just divine causes but final causes, élan vital, entelechies, the interventions of extraterrestrial aliens and so forth. (xiii)

But, as we have just seen, there are some instances of optimal (or near-optimal) solutions to problems in biology; so, if natural selection cannot optimize, then something else must be involved. Very plausibly, the ‘something else’ includes: physics, chemistry, autocatalytic processes, dissipative structures and principles of self-organization, and surely other factors that the progress of science will in due time reveal. (92)

Darwin pointed the direction to a thoroughly naturalistic – indeed a thoroughly atheistic – theory of phenotype formation; but he didn’t see how to get the whole way there. He killed off God, if you like, but Mother Nature and other pseudo-agents got away scot-free. We think it’s time now to get rid of them too. (163)

Fontana, Walter and Leo Buss. What Would be Conserved if ‘The Tape Were Played Twice’? Proceedings of the National Academy of Sciences. 91/757, 1994. A response to Stephen Jay Gould’s claim that if earth evolution happened again, because of blind variation and contingent selection as the only mechanism, human beings would not appear. But this article contends that the prior, independent existence of self-organizing dynamics, unknown to Darwin, introduces a novel source of emergent order. What would reoccur each time is the nested scale of self-maintaining organizations.

Moreover, separating the problem of the emergence of self-maintenance from the problem of self-reproduction leads to the realization that there exist routes to the generation of biological order other than that of natural selection. (761)

Forestiero, Saveiro. The Historical Nature of Biological Complexity and the Ineffectiveness of the Mathematical Approach. Theory in Biosciences. 141/213, 2022. A University of Rome biotheorist clarifies and contributes to overdue, course correction, revisions of life’s inherent vitalities and oriented emergence as they naturally arise from biomolecules to ourselves. Four main aspects are identified as Organization, Individuality,
Diverse Variation and Relationality. By an overall surmise, it becomes evident, once again, some orthogenetic self-organization seems to be organizing her/his self.

Contemporary scientific knowledge is mainly based on a reductionism epistemology and method. But its limitations prevent mathematical treatment of physical properties such as complex patterns and processes. The article will review the biological complexity debate and differences between living and inert matter. With these preparations, biological complexity can be viewed as a global, relational, and historical phenomenon at the individual and species level. (Abstract edits)

Along with that between order and disorder (life as per “Entre le cristal et la fumée” by Henri Atlan 1979), there is the opposition between stability and instability, variation an its opposite, differentiation and integration, openness and closure (with respect to the internal organization), there are the (internal metabolic oscillators, the seasonal rhythms, periodicity or aperiodicity of signals external phase-shifted from the internal rhythms), and many more. (1-2)

Frank, Steven. Natural Selection V: How to Read the Fundamental Equations of Evolutionary Change in Terms of Information Theory. Journal of Evolutionary Biology. 25/2377, 2012. The UC Irvine biologist continues his series of essays such as Selection vs. Transmission, Levels of Selection, and Kin Selection Theory. In Part V a reach is made to reorient life’s development in a more physical agreement with nature’s apparent essence and vitality by way of content and communication. As other areas, this somewhat statistical process seems akin to Bayesian probabilities (see below), which can illuminate how post-selection is involved.

The equations of evolutionary change by natural selection are commonly expressed in statistical terms. Fisher’s fundamental theorem emphasizes the variance in fitness. Quantitative genetics expresses selection with covariances and regressions. Population genetic equations depend on genetic variances. How can we read those statistical expressions with respect to the meaning of natural selection? One possibility is to relate the statistical expressions to the amount of information that populations accumulate by selection. However, the connection between selection and information theory has never been compelling. Here, I show the correct relations between statistical expressions for selection and information theory expressions for selection. Those relations link selection to the fundamental concepts of entropy and information in the theories of physics, statistics and communication. We can now read the equations of selection in terms of their natural meaning. Selection causes populations to accumulate information about the environment. (Abstract)

I show that natural selection can be described by the same measure of information that provides the conceptual foundations of physics, statistics and communication. (2377) In my view, information is a primary quantity with intuitive meaning in the study of selection, whereas the genetic variance just happens to be an algebraic equivalence for the measure of information. The history of evolutionary theory has it backwards, using statistical expressions of variances and covariances in place of the equivalent and more meaningful expressions of information. To read the fundamental equations of evolutionary change, one must learn to interpret the standard expressions of variances and covariances as expressions of information. (2377)

Bayesian Interpretations of Selection: Bayesian updating combines prior information with new information to improve prediction. The Bayesian process makes an obvious analogy with selection. The initial population encodes predictions about the fit of characters to the environment. Selection through differential fitness provides new information. The updated population combines the prior information in the initial population with the new information from selection to improve the fit of the new population to the environment. I am sure this Bayesian analogy has been noted many times. But it has never developed into a coherent framework that has contributed significantly to understanding selection. (2384)

Fussy, Siegfried, et al. Irreversibility in Models of Macroevolution. Cybernetics and Systems. 32/3-4, 2001. A theoretical exercise that finds a “hierarchically emergent fractal evolution” founded on invariant power laws by which can be defined the radiation of species.

Gallo, Elisa, et al. The Core & Periphery Hypothesis: A Conceptual Basis for Generality in Cell and Developmental Biology. arXiv:2306.09534. University of Zurich, European Molecular Biology Lab, University College London and Northwestern University including Roberto Mayor first note an overdue concern for the biological sciences that while a great array of vital data findings have been achieved in recent years, a project to discern a consequent presence of general, integrative patterns across life’s evolution is not yet underway. In regard, as the quotes say, as a starter it is offered that specific aspects (core) could well be seen to form an holistic constancy (periphery). (This C & P version is different from its neural net usage.)

The discovery of general principles underlying the complexity and diversity of cellular and developmental systems is a prime goal of biological studies. Whilst new technologies collect data at an accelerating rate, conceptual progress has not kept pace due to an absence of viable general theories of mesoscale biological phenomena. In exploring this issue, we have laid out one such framework, termed the Core and Periphery (C&P) hypothesis, which reveals hidden commonalities across the diverse, complex behaviors by cells and tissues. Here, we view its applicability across multiple scales, its consistency with evolution, and discuss key implications. (Abstract)

We refer to systems with this architecture – consisting of an inherently versatile system core embedded in a function-specific system periphery that "programs" it – as Core & Periphery systems (Fig. 2a-c). To be exact, we define a system core to be a subset of a biological system that has the intrinsic capacity to generate a wide range of non-trivial behaviors. Conversely, we define a system Periphery to be the subset of a biological system that is not part of the core and instead triggers or programs it to perform one specific functional behavior out of the many that it potentially could. We expect cores to have a highly non-linear and integrated structure (such as the tight feedback within a Turing system) providing the "computation-like" behavior that underpins their versatility, whereas peripheries will tend to be structured hierarchically or linearly around their core. (5)

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