Darwin’s Agential Materials: evolutionary implications of multi-scale competency in developmental biology

Abstract

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multi-scale competency architecture where cells, tissues, and organs exhibit regulative plasticity - the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

Natural Induction: Spontaneous adaptive organisation without natural selection

“…Although the problem springs can represent a problem that is external to the learning springs, in the examples illustrated thus far all the state variables are shared. Inducing a model of a complex system that has hidden state can be much more difficult, and reflexively, a learned model that contains hidden state (or a ‘deep’ representation) can express relationships that a shallow model cannot (Caldwell, Watson et al 2018, Caldwell, Knowles et al 2021, Watson and Levin 2023).…”

“…These include: understanding how natural selection interacts with other biological and physical processes (e.g. development, niche construction, ecological dynamics, extended inheritance mechanisms, learning, organismic agency) (Laland, Uller et al 2015, Watson and Thies 2019, Levin 2023; understanding how natural selection got started (Cairns-Smith, Hartman andCairns-Smith 1986, Nowak andOhtsuki 2008); informing open questions such as how natural selection rescales from one level of organisation to another, i.e., evolutionary transitions in individuality (Maynard Smith andSzathmary 1997, Watson, Levin andBuckley 2022); and exploring the possibility of adaptation in biological systems that are not evolutionary units (Lovelock and Margulis 1974, Levin 1998, Power, Watson et al 2015, Wilson 2016. See (Watson and Lewens 2024) for further discussion.…”

“…Proceedings of the Royal Society B 290(1994): 20222409.. 12 Darwin suggested that some variation was developmentally-environmentally directed, but was not specific about its adaptive significance. The nature of developmental bias and phenotypic plasticity, and the potential of these and other factors to influence genetic evolution adaptively, is an active topic Levin, M. (2023). "Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology."…”

Aging as a morphostasis defect: a developmental bioelectricity perspective

“…These characteristics facilitate the precise orientation, scaling, and shaping of organs during embryogenesis, regeneration, and remodeling, through dynamic long-range coordination and anatomical decision-making [146]. Specifically, it has been argued [196,225] that these methods work because evolution exploits bioelectrical interfaces endogenously for modular control of anatomical homeostasis and repair processes [150]. Using molecular-genetic and pharmacological techniques, researchers have been able to manipulate the distribution of voltage gradients to effect alternative anatomical structure/location during development, increase regeneration after injury, and cancer suppression.…”

“…(E) Anatomical homeostasis is reached through an error-minimization scheme. Panel A-B is reproduced with permission from [229], panel C-D-E respectively from [148], [300], and [150]. The illustrations in panels A-C were created by Jeremy Guay of Peregrine Creative.…”