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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
18501875190019251950197520002025James, W., 18901Bateson, W., 18942Lotka, A. J., 19253Sousa FL, Thiergart T, Landan G…, 20134Wallace DC, 20135Biswas S, Clawson W, Levin M, 20226Fields C, Levin M, 20227Shreesha L, Levin M, 20238Shreesha L, Levin M, 20239
01

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

02

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

03

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

04

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.
  1. 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
  2. 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
  3. Evolution is the history of a system in the course of irreversible transformationLotka, A. J., 1925 · Elements of Physical Biology · open at passage 29
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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.
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.
Robustness to perturbation, as in Biswas et al.'s network memory, concerns keeping function under disturbance, which is the area homeostasis covers.
Xenobots reach a novel replication method with no evolutionary selection for being a Xenobot, which Levin uses to argue for plasticity of standard cells.
Wallace links mtDNA variants that changed membrane potential and Ca2+ metabolism to lineages that moved into new climates.
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.
Loeb's account of heritable mutations versus non-heritable fluctuations bears on how variation can feed evolution.
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.
Levin (2023) argues that voltage states give modularity, a known component of evolvability, between channel genes and anatomy.
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
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.
Where it is discussedPassages matching evolvability, evolvable, robustness, developmental plasticity
1925Elements of Physical Biology · Lotka, A. J.36
1890The Principles of Psychology, Vols. 1-2 · James, W.25
2023Darwin's agential materials: evolutionary implications of multiscale competency… · Levin M24
2022Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework… · Levin M23
2019The Computational Boundary of a "Self": Developmental Bioelectricity Drives… · Levin M22
1915Principles of General Physiology · Bayliss, W. M.22
2023Cellular Competency during Development Alters Evolutionary Dynamics in an… · Shreesha L, Levin M14
2013Early bioenergetic evolution · Sousa FL, Thiergart T, Landan G…13
2022Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing… · Fields C, Levin M13
1894Materials for the Study of Variation Treated with Especial Regard to… · Bateson, W.13