Selective breeding is the deliberate pairing of individuals possessing preferred characteristics so that their offspring inherit and amplify those traits, thereby steering the genetic composition of a population across successive generations.
Directed evolution is an iterative, selection‑driven methodology that creates genetic diversity and empirically refines biomolecules or organisms toward a user‑defined functional phenotype. This encompasses techniques from molecular scales (e.g., protein engineering) to whole-organism breeding.
SELEX (Systematic Evolution of Ligands by EXponential Enrichment) is an iterative in‑vitro selection cycle that repeatedly partitions and amplifies nucleic‑acid molecules based on a defined functional interaction, thereby enriching a library for sequences possessing the desired binding or catalytic properties.
Compartmentalized self‑replication (CSR) is an in‑vitro selection method in which individual nucleic‑acid molecules are isolated within separate micro‑compartments (e.g., droplets or emulsions) so that each molecule serves as both genotype and phenotype, allowing only those sequences that can catalyze their own synthesis within the compartment to be amplified and enriched.
Methods and Techniques
Selective breeding at the organism level often involves:
- Multi-trait index selection: Assigning weights to multiple desirable traits and selecting individuals based on a composite score. See "Theory of index selection".
- Iterated embryo selection: Screening and selecting embryos for polygenic traits (e.g., intelligence, health) across multiple generations in vitro.
- High-throughput phenotyping: Combining breeding with microfluidics, robotics, or machine learning for rapid trait evaluation (e.g., robotic cell-picking for multidimensional evolution).
Related molecular techniques (bridging to directed evolution):
- In vitro compartmentalization (IVC): Enables selection for catalytic activities beyond binding, handling 105–109 variants.
- Phage-assisted continuous evolution (PACE): Continuous in vivo-like evolution of enzymes with negative selection for stringency control. See Negative selection and stringency modulation in PACE (Carlson et al., 2014).
- Machine learning-assisted evolution: Predicts fitness landscapes to guide variant selection, improving over random mutagenesis.
Challenges include navigating rugged fitness landscapes, managing mutation rates, and scaling to non-trivial traits.
Examples
- Mice selected over 140 generations for increased litter size (>20 pups per litter): bioRxiv 2021.
- Mice selected for high voluntary wheel-running activity: "Neurobiology of mice selected for high voluntary wheel-running activity" (Rhodes et al., 2005).
- Artificial selection for increased maternal defense behavior in mice: PMC2423941 (2008).
Foxes bred for tameness (Belyaev experiments): Demonstrates rapid behavioral shifts via selection on stress response.
Evolving microbial nootropics or brain-modifying organisms for cognitive enhancement (short sleep, long wakefulness, intelligence, learning, memory, brain uploading, ..).
- Polygenic selection using GWAS data for human-relevant traits (e.g., iterated embryo selection for intelligence)
References
Organism-Level Breeding
- "Theory of index selection"
- Mouse litter size selection: bioRxiv:10.1101/2021.05.28.446207
- Maternal defense in mice: PMC2423941
- Wheel-running mice: Rhodes et al., 2005
Directed Evolution Techniques
- "Directed evolution of polymerase function by compartmentalized self-replication" (Ghadessy et al., 2001)
- "Negative selection and stringency modulation in phage-assisted continuous evolution" (Carlson et al., 2014)
- "Machine-learning assisted directed protein evolution with combinatorial libraries" (Wu et al., 2019)
- "Virus-free continuous directed evolution in human cells using somatic hypermutation" (2024)
- "In vitro enzyme self-selection using molecular programs" (2023)
Reviews
- "Current methods in directed evolution" (2023)
- "A primer to directed evolution: current methodologies and future directions" (2023)
Extensions and Ideas
Nootropic microbes: Selectively breed gut bacteria or engineered microbes to produce intelligence-enhancing metabolites. This could also be done for gut bacteria that contribute to longevity or insulin resistance.
Sleep optimization: Evolve model organisms for altered sleep cycles or enhanced recovery.
long-term human family selective breeding project for cognitive traits and various phenotypes (high intelligence, learning, memory, short sleep, long wakefulness, longevity, ...).
long-term human family selective breeding for exceptional longevity. This could also possibly be done in cell culture instead, at least for the longevity of human cell cultures, which might correlate to whole body longevity in some aspects.
Let's propose a directed evolution project for the selective breeding of animals that are able to survive high levels of ischemia in the brain such that after ischemia they are able to be resuscitated and revived and they demonstrate learning and memory and personality retention such as learn commands. This would probably be done in dogs or cats or rabbits or some other animal. The idea would be to do a selected breeding project to select for traits that are favorable for long-term survival in situations of ischemia. This kind of trait could be useful for brain uploading or even cryonics.
Alternative terms
- applied evolution
- artificial evolution
- directed evolution
- selective breeding
- artificial selection
- selection engineering
- iterative selection methods
- applied selection effects
- darwinian engineering
- evolution hacking
- iterated embryo selection
See also
The nootropics page has several ideas for directed evolution of microbial nootropics to enhance human intelligence.
The sleep page has several ideas about using evolution and selection effects to investigate or control sleep processes.