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

Wagner, Andreas. The Origins of Evolutionary Innovations: A Theory of Transformative Change in Living Systems. Oxford: Oxford University Press, 2011. It is now widely, begrudgingly, admitted that random variation and selection alone is not able to account for life’s somatic and behavioral profligacy. The University of Zurich biochemist and author here courses through 21st century advances, not realized or understood much earlier, such as ubiquitous network topologies and dynamics, that promise an intrinsic answer. For example, Chapter 6 “Genotype Networks, Self-organization, and Natural Selection,” from which the quotes, opens wide windows on a nascent genesis synthesis.

Self-organization and natural selection. The word self-organization has many meanings. Here I use it is the sense that collections of objects and their interactions bring forth structures on a higher level of organization. Such structures form from the bottom-up, merely through properties of the objects and their interactions, and without any order imposed from the outside. (91) Genotype networks are examples of self-organized structures. Here, the lower level objects are genotypes of molecules, regulatory circuits, and metabolic networks. The higher order structures are individual genotype networks, and their organization in genotype space. (91) For more than a century, evolutionary biology has focused on natural selection as the key process explaining life’s enormous diversity. The occasional suggestion that self-organization may be equally, or more, important than natural selection has been decidedly heterodox. In light of what I have said thus far, it is useful to re-examine the relationship between natural selection and self-organization. (91)

Genotype networks that are large, occupy a small fraction of genotype space, traverse a large fraction of this space, and show highly diverse phenotypic neighborhoods are self-organized, emergent features of genotype spaces. (92) Natural selection and self-organization are both essential for evolutionary innovation. In Chapter 1, I quoted the geneticist de Vries (Hugo, 1848-1935) who stated that natural selection cannot explain the origin of novel phenotypes. More that 100 years later we can say this: Genotype networks help explain the arrival of the fittest, and natural selection permits their survival. (92)

Wagner, Andreas. The Role of Randomness in Darwinian Evolution. Philosophy of Science. 79/1, 2012. The University of Zurich evolutionary biologist and author (search), per his laboratory webpage, researches “evolution and innovation from genes and genomes, to biological networks and whole organisms as well at their communities.” In this philosophical journal Wagner takes on the hoary neoDarwinian doctrine that randomness alone rules. It is broached that if one factors discoveries flowing in from 21st century systems biology, inklings of an inherent directedness are increasingly being found. The real presence, beyond mutating molecules, of metabolic networks, regulatory circuits, and protein topologies, is seen to impart a “nonrandom” bias as they actively serve to structure phenotypes. And one wonders why it is so difficult, with trepidation, to shift from the all accident camp to something actually is going on. For one example, a review by Michel Morange in Studies in History and Philosophy of Biological and Biomedical Sciences (Online, January 2012) of James Shapiro’s 2011 Evolution book (search) chides him for suggesting that genomes might know what they are doing, because such “teleology and finality” is not to be excluded from biological science.

I discussed three very different system classes: metabolic networks, regulatory circuits, and macromolecules. These systems have complex phenotypes involved in many evolutionary innovations. In all three of them, we can study the relationship between genotype and phenotype systematically. All three systems show highly intertwined and connected genotype networks with diverse phenotypic neighborhoods. Such networks allow the exploration of novel phenotypes while preserving existing phenotypes and thus facilitate evolutionary adaption and innovation. No statistical model or null hypothesis that could reproduce all or most aspects of genotype space organization currently exists. On the basis of what we know today, mutations affect both DNA and also complex phenotypes ronrandomly. (115)

Wagner, Gunter. Genetics and the Origin of Organismal Complexity. http://capabilities.templeton.org/2008/LS/gooc.html. This is a Templeton grant program from 2008 to 2012 to the Yale University chair of ecology and evolutionary biology. As its banner quote next alludes, a core aim is to allow and seek an imagination that cosmic and earthly nature has as its essence a propensity to form and foster increasingly complex and conscious biological life. But the project, affirms Wagner, is to be pursued with the utmost scientific rigor. View his Yale publication page for recent papers, such as “Protein Structural Modularity and Robustness are Associated with Evolvability,” coauthored with Mary Rorick, in Genome Biology and Evolution (3/456, 2011) see quote below.

If we can show that the emergence of a new cell type is underwritten by a causally cohesive mechanism, then we shall give back to organismic life, in its various forms, its dignity as an irreducible part of reality.

Theory suggests that biological modularity and robustness allow for maintenance of fitness under mutational change, and when this change is adaptive, for evolvability. Empirical demonstrations that these traits promote evolvability in nature remain scant however. This is in part because modularity, robustness, and evolvability are difficult to define and measure in real biological systems. Here, we address whether structural modularity and/or robustness confer evolvability at the level of proteins by looking for associations between indices of protein structural modularity, structural robustness, and evolvability. (456) We find that protein evolvability is positively associated with structural modularity as well as structural robustness and that the effect of structural modularity on evolvability is independent of the structural robustness index. (Rorick & Wagner, Abstract, 456)

Wahl, L. M. Evolving the Division of Labour: Generalists, Specialists and Task Allocation. Journal of Theoretical Biology. 219/371, 2002. A similar evolutionary sequence appears over and over whenever a colonial cluster, whether bacteria, cells or social insects, becomes more differentially complex.

Walker, Sara Imari, et al. Evolutionary Dynamics and Information Hierarchies in Biological Systems. Annals of the New York Academy of Sciences. 1305/1, 2013. An extensive review of a 2012 Aspen Center for Physics summer seminar. As evolution was “in the air” in Darwin’s time, so today we seem on the verge of a 21st century evolutionary genesis synthesis. Life’s procession from prokaryote microbe to homo sapiens is now realized as a nested multilevel iteration, due to dynamic networks and communicative media instead of chancy mutations. By these lights, as many theorists perceive, this expansion accords with revisions of its physical substrate as a similarly fertile, self-organizing ground. Specific topics included the vital role of chromatin (DNA and proteins in a cell’s nucleus), how genetic “programs” are akin to computer software, and ways that a deep order can mitigate randomness. In regard, one may note that while genes still mutate and species diversify, these regulatory networks provide an intrinsic measure of stability.

The study of evolution has entered a revolutionary new era, where quantitative and predictive methods are transforming the traditionally qualitative and retrospective approaches of the past. Genomic sequencing and modern computational techniques are permitting quantitative comparisons between variation in the natural world and predictions rooted in neo-Darwinian theory, revealing the shortcomings of current evolutionary theory, particularly with regard to large-scale phenomena like macroevolution. Current research spanning and uniting diverse fields and exploring the physical and chemical nature of organisms across temporal, spatial, and organizational scales is replacing the model of evolution as a passive filter selecting for random changes at the nucleotide level with a paradigm in which evolution is a dynamic process both constrained and driven by the informational architecture of organisms across scales, from DNA and chromatin regulation to interactions within and between species and the environment.

Walker, Sara Imari, et al. Evolutionary Transitions and Top-Down Causation. Adami, Christoph, et al, eds. Artificial Life 13. Cambridge: MIT Press, 2012. These Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems, Michigan State University, July 2012, are available in full at its MIT Press webpage. Walker, NASA Astrobiology Institute, along with Luis Cisneros, Arizona State University, and Paul Davies, BEYOND Center, ASU, as the quote notes, propose that a definitive metric for life’s complexity and consciousness could be the processive rise of its algorithmic program from bottom-up origins to an increasing top-down influence. In so doing, an independent, formative information “gains causal efficacy over higher levels of organization.” The onset of multicellularity, for example, is one result. This agency is then suggested as a driver of evolution’s episodic emergence.

However, given that we do not know the specific sequence of events leading to the emergence of the first known life, a more pragmatic perspective is to assume that when life as we know it first emerged, it was surely characterized by the same distinctive hierarchical and causal structure as all known life. Adopting this viewpoint, Walker and Davies have recently suggested that a transition in causal structure, from bottom-up to top-down, was the critical step in the origin of life. In this context, the origin of life is associated with the emergence of a collective contextual information processing system with top-down causal efficacy over the matter it is instantiated in. The transition from non-living to living matter may therefore be identified when information gains causally efficacy. (289)

Here we have proposed that increasing levels of biological complexity, corresponding to increased depth in the hierarchical organization of living systems, correspond to information gaining causal efficacy over increasingly higher levels of organization. Each major evolutionary transition leading to the emergence of genuinely new, higher-level entities form lower-level units, should therefore be characterized be a transition in causal structure mediated by a reversal in the dominant direction of information flow from bottom-up to top-down. (289-290)

Watson, Richard A. Compositional Evolution. Cambridge: MIT Press, 2006. A biologist and computer scientist at the University of Southampton applies algorithmic and complexity theory to show that much more is going on than a gradual drift of random mutations, a simple “hill climbing.” Rather nature employs additional computations which generate a constant modularity engaged in symbiotic assemblies. This approach supports Lynn Margulis’ theory of active endosymbiosis and John Maynard-Smith’s major transitions. A scale-invariant evolutionary process results as such “symbiotic encapsulation” repeats in a nested emergence. But the advance, I add, ought to be appreciated as more than another mechanism. It implies an alternative evolution or development with its own internal, self-organizing impetus which seems to know where it is headed.

Watson, Richard and Jordan Pollack. A Computational Model of Symbiotic Composition in Evolutionary Transitions. BioSystems. 69/2-3, 2003. Brandeis University system scientists attempt to explain the hierarchical emergence of symbiotically composed whole units in evolution. This procession is seen as a consistent source of novelty versus the gradual accumulation of random variations. A “symbiogenic evolutionary adaptation model” or SEAM algorithm which is active on a fractal fitness landscape is proposed. As this produces bounded units composed of semi-independent, modular components such as the eukaryotic cell, it allows evolution to exhibit the general properties of a scale invariant, self-organized dynamical system.

Weber, Bruce and David Depew. Does the Second Law of Thermodynamics Refute the Neo-Darwinian Synthesis? Peter Koslowski, ed. Sociobiology and Bioeconomics. Berlin: Springer, 1999. Thoughts on the “Neo-Humankind” discovery of a thermodynamically driven self-organization of life and societies.

Before proceeding, it should be noted that there exists an alternative evolutionary research tradition to Darwinism that we dub ‘evolutionary developmentalism,’ which existed before Darwin and still thrives today. The main theme of the evolutionary developmentalist tradition is that autonomous ontogenetic, ecological and phylogenetic dynamics, rather than selection, are the creative forces in the hierarchical complexification of life. (54)

Weber, Bruce and David Depew, eds. Evolution and Learning. Cambridge: MIT Press, 2003. A century later, a reconsideration of the Baldwin effect which contends that learned adaptive behavior can influence an organism’s genome and hence the rate and direction of evolutionary change is bringing fruitful insights. By this view, a better understanding of the influence of developmental systems and the emergence of language and consciousness can be gained.

Webster, Gerald and Brian Goodwin. Form and Transformation. New York: Cambridge University Press, 1996. On the “rational morphology” viewpoint of a primacy of the developmental processes of an organism over purely genetic dictates. In a review of the book (Biology and Philosophy. 13/4, 1998) philosopher David Hull concludes: “If ever there is a time that the intimations of a science of organic form sensed by certain biologists from Aristotle and Owen to the present are to be realized, it is now.” (591)

Weinreich, Daniel, et al. Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins. Science. 312/111, 2006. From the Department of Organismic and Evolutionary Biology at Harvard, a precise study which finds that although 120 mutational states are available for the ß-lactamase allele, due to structural constraints only 4 or 5 are really possible. For this reason genes are seen to evolve not with unlimited options but as channeled along a few open pathways.

This is important because it draws attention to the mechanistic basis of selective inaccessibility. It now appears that intramolecular interactions render many mutational trajectories selectively inaccessible, which implies that replaying the protein tape of life might be surprisingly repetitive. (113)

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