Evolution and evolvability · Article

Evolvability

Evolvability concerns how living systems come to change by variation and selection, and why they do so with such success. The held texts approach it from several sides: the kinds of variation, accidental by-products, energetic niches and robust cell-level competencies. Fields and Levin (2022) argue that modular, self-modelling biological systems are highly evolvable.⁷

Earliest held: 1890, James, W.
Most discussed in: Elements of Physical Biology, 1925
In the library: 454 passages in 82 works
Rewritten: 2026-10-03

Variation and by-products

James (1890) argued that some emotional reactions are accidental in origin. They are incidental to other reactions evolved for utility, and would never have been evolved independently for any utility they might possess.¹ Bateson (1894) separated two classes of variation, meristic and substantive. In Bateson's account, either may occur independently of the other. Bateson called an appreciation of this distinction a first step towards understanding how the bodies of organisms are evolved.² Lotka (1925) defined evolution as the history of a system in the course of irreversible transformation.³

Energy and variation

Sousa and colleagues (2013) argued that in a world of genes and proteins, evolution via variation and selection can readily supplant chemical determinism.⁴ Wallace (2013) described an energetic route for variation. Continuously arising mtDNA functional variation lets subpopulations of animal species occupy a range of energetic environments within the species' niche.⁵ Wallace added that subpopulations in peripheral energetic environments amass multiple mtDNA variants, and that the slower-evolving nuclear bioenergetic genes then follow.⁵

Robustness in network models

Biswas, Clawson and Levin (2022) compared memory in models of biological transcriptional networks with memory in random networks. The biological models were much more robust to edge perturbation.⁶ The authors suggested that evolutionary pressure to handle changing environments may explain this. They framed it as a possibility, not a result.⁶ They also stated that the role of trainability in evolution and development is as yet unknown.⁶

Competency and evolvability

Fields and Levin (2022) argued that systems whose parts can be changed in a modular way, by evolution and by engineers, help clarify why biological systems are so highly evolvable.⁷ Shreesha and Levin (2023) introduced cellular competency as another contributor to phenotypic fitness. They set it apart from organism-level learning, which needs mechanisms such as a nervous system that evolution must build. Cellular competencies come by default, because cells carry capabilities evolved when their ancestors lived independently.⁸ The authors present this as complementary to work on learning and on evolvability mechanisms.⁹

SourcesEach quotation was checked word for word against the passage it opens.

many such reactions, incidental to others evolved for utility's sake, but which would never themselves have been evolved independentlyJames, W., 1890 · The Principles of Psychology, Vols. 1-2 · open at passage 2807
An appreciation -of this distinction is a first step towards a comprehension of the processes by which the bodies of organisms are evolved.Bateson, W., 1894 · Materials for the Study of Variation Treated with Especial Regard to… · open at passage 92
Evolution is the history of a system in the course of irreversible transformationLotka, A. J., 1925 · Elements of Physical Biology · open at passage 29
In a world of gene and proteins, evolution via variation and selection can readily supplant chemical determinism.Sousa FL, Thiergart T, Landan G, Nelson-Sathi S, Pereira…, 2013 · Early bioenergetic evolution · open at passage 37
The continuously arising mtDNA functional variation allows subpopulations of animal species to occupy a range of energetic environments within the species' niche.Wallace DC, 2013 · Bioenergetics in human evolution and disease: implications for the origins of… · open at passage 44
The memory capability of biological models is also much more robust to edge perturbation than those of random networks, as indicated in Figure 7, perhaps due to evolutionary pressure to handle changing environments.Biswas S, Clawson W, Levin M, 2022 · Learning in Transcriptional Network Models: Computational Discovery of… · open at passage 42
in which all of these aspects can be changed in a modular way: by evolution and by engineers. This helps clarify why biological systems are so highly evolvableFields C, Levin M, 2022 · Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing… · open at passage 73
In contrast, cellular competencies come “by default” because organisms consist of cellular components that already have many capabilities evolved during their ancestral lifetime as independent organisms.Shreesha L, Levin M, 2023 · Cellular Competency during Development Alters Evolutionary Dynamics in an… · open at passage 8
Thus, it is important to begin to study how cellular competencies affect evolution, to complement approaches focused on learning, evolvability mechanismsShreesha L, Levin M, 2023 · Cellular Competency during Development Alters Evolutionary Dynamics in an… · open at passage 8

Linked ideas

Basal cognitionrelated to

Biswas et al. link pressure to adapt to variable environments to basal cognition, and robust memory in biological networks to that pressure.

Scale of the selfrelated to

Fields and Levin tie evolvability to agents modelling themselves without privileged access to self-world boundaries, which they say dissolves fixed boundaries.

Regenerationrelated to

Bongard and Levin expect work on overloaded functions in evolved and designed substrates to affect regenerative medicine.

Homeostasis and the internal environmentrelated to

Robustness to perturbation, as in Biswas et al.'s network memory, concerns keeping function under disturbance, which is the area homeostasis covers.

Synthetic living machinesrelated to

Xenobots reach a novel replication method with no evolutionary selection for being a Xenobot, which Levin uses to argue for plasticity of standard cells.

Mitochondrial membrane potentialrelated to

Wallace links mtDNA variants that changed membrane potential and Ca2+ metabolism to lineages that moved into new climates.

Decision-making and learning in slime mouldrelated to

Passages link them only indirectly: Baluška & Levin say gene networks learn associatively, tying memory to evolvability; Levin 2023 cites Physarum learning as basal cognition for evolution of cognition. No direct link.

Discontinuous variationrelated to

Loeb's account of heritable mutations versus non-heritable fluctuations bears on how variation can feed evolution.

Goal-directednessrelated to

Fields and Levin treat agents that build actionable self-models, and high-level reward spaces, as part of why biological systems evolve so readily.

Vitalism and mechanismin tension with

Sousa and colleagues call thermodynamics chemistry's deterministic version of natural selection, a mechanistic framing of how evolving systems arise.

Ion channels and pumpsrelated to

Levin (2023) argues that voltage states give modularity, a known component of evolvability, between channel genes and anatomy.

Neurotransmitters before neuronsrelated to

Passages say neurons merely optimized ancient tricks (ion channels, gap junctions, neurotransmitters) for speed, and pre-neural signaling preceded nerves. That implies evolutionary co-option, but evolvability itself is never discussed alongside neurotransmitte

Collective intelligence of cellsrelated to

Levin 2023 argues cells' morphogenetic problem-solving competencies, inherited from unicellular ancestors, shape the evolutionary process itself, so collective cellular intelligence plausibly bears on evolvability. The passages stop short of a formal link.

Mitochondrial disease and mtDNA mutationparallels

Wallace treats mild mtDNA variants as functional variation that lets populations adapt to local energetic environments.

Exploratory adaptation and random-network searchrelated to

Random networks that often relax to fixed points bear on how readily evolved systems find workable solutions.

Inherency of form, mesoscopic materials and the physical origin of body plansrelated to

The evo-devo literature treats physical mesoscale forces as one source of developmental variation alongside gene regulatory networks.

Natural selection and the struggle for existencerelated to

Kouvaris and colleagues treat the capacity to vary usefully in new environments as a problem that selection alone seems unable to explain.

Free energy principle and active inferencerelated to

Friston 2013 suggests that persisting under the free energy principle implies adaptation and evolution; Levin 2019 and 2023 treat active inference as driving evolved homeostatic loops, and say its role in evolutionary advances needs study.

Niche constructionrelated to

Feedback between ecological and evolutionary dynamics, as in Power and colleagues, bears on how populations shape their own capacity to change.

Germ-plasm theory and chromosome individualityrelated to

Levin's 2023 argument on why somatic experience cannot simply be written back into a genome bears on how lineages change.

Downward causation and coarse-grainingrelated to

Levin passages link the two: polyploid newt tubule formation shows downward causation toward one large-scale outcome, which 'evolution does' not hardwire; anatomical homeostasis coarse-grains robustness. Evolvability is implied, not directly analysed.

Gene regulatory networks as Boolean networksrelated to

Kouvaris and colleagues link sparse GRN connectivity to enhanced evolvability as well as robustness.

Teleology and purposiveness in biologyin tension with

Bose says nyctitropic movement evolved stepwise from light-induced differentiation, not 'for the purpose' of plant advantage, warning against circular final-advantage arguments; this contrasts purposive explanation with evolutionary-process explanation.

Computational design automation and in-silico search of morphologyrelated to

Levin's Darwin's agential materials asks how evolution searches morphology space so effectively; Kriegman and Lobo apply evolutionary algorithms in silico to design organisms and infer networks, a computational analogue of biological evolvability.

Bacterial memory and history-dependent behaviorrelated to

Only B's passage (Wolf 2008) mentions fitness-enhancing memory strategies as possibly evolved, but explicitly offers no evidence; no passage discusses variation, selection, or evolvability, so the link is weak.

Life-as-it-could-be and the option space of possible organismsrelated to

Blackiston and colleagues say novel constructs help understand evolvability and plasticity beyond the Earthly data set.

Where it is discussedPassages matching evolvability, evolvable, robustness, developmental plasticity

1925	Elements of Physical Biology · Lotka, A. J.	36
1890	The Principles of Psychology, Vols. 1-2 · James, W.	25
2023	Darwin's agential materials: evolutionary implications of multiscale competency… · Levin M	24
2022	Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework… · Levin M	23
2019	The Computational Boundary of a "Self": Developmental Bioelectricity Drives… · Levin M	22
1915	Principles of General Physiology · Bayliss, W. M.	22
2023	Cellular Competency during Development Alters Evolutionary Dynamics in an… · Shreesha L, Levin M	14
2013	Early bioenergetic evolution · Sousa FL, Thiergart T, Landan G…	13
2022	Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing… · Fields C, Levin M	13
1894	Materials for the Study of Variation Treated with Especial Regard to… · Bateson, W.	13