References

Full author-date bibliography for the constrained evolution thesis.

Architecture-ready

Evolutionary Biology & Extremophiles

Anderson, R. E. (2021). Tracking microbial evolution in the subseafloor biosphere. mSystems, 6(4), e00731-21. https://doi.org/10.1128/mSystems.00731-21

Anderson, R. E., Graham, E. D., Huber, J. A., & Tully, B. J. (2022). Microbial population dynamics are dominated by stochastic forces in a low biomass subseafloor habitat. mBio, 13(1), e00354-22. https://doi.org/10.1128/mbio.00354-22

Anderson, R. E., Reveillaud, J., Reddington, E., Delmont, T. O., Eren, A. M., McDermott, J. M., Seewald, J. S., & Huber, J. A. (2017). Genomic variation in microbial populations inhabiting the marine subseafloor at deep-sea hydrothermal vents. Nature Communications, 8, 1114. https://doi.org/10.1038/s41467-017-01228-6

Anderson, R. E., Sogin, M. L., & Baross, J. A. (2014). Evolutionary strategies of viruses and cells in hydrothermal systems revealed through metagenomics. PLoS ONE, 9(10), e109696. https://doi.org/10.1371/journal.pone.0109696

Anderson, R. E., Sogin, M. L., & Baross, J. A. (2015). Biogeography and ecology of the rare and abundant microbial lineages in deep-sea hydrothermal vents. FEMS Microbiology Ecology, 91(1), fiu016. https://doi.org/10.1093/femsec/fiu016

Bartlett, D. H. (2002). Pressure effects on in vivo microbial processes. Biochimica et Biophysica Acta, 1595(1–2), 367–381.

Boden, J. S., Zhong, J., Anderson, R. E., & Stüeken, E. (2024). Timing the evolution of phosphorus-cycling enzymes through geological time. Nature Communications, 15, 3703. https://doi.org/10.1038/s41467-024-47914-0

Brock, T. D., & Freeze, H. (1969). Thermus aquaticus gen. n. and sp. n., a nonsporulating extreme thermophile. Journal of Bacteriology, 98(1), 289–297.

Campbell, K. M., Kouris, A., England, W., Anderson, R. E., McCleskey, R. B., Nordstrom, D. K., & Whitaker, R. J. (2017). Sulfolobus islandicus meta-populations in Yellowstone National Park hot springs. Environmental Microbiology, 19(6), 2392–2405. https://doi.org/10.1111/1462-2920.13728

Chien, A., Edgar, D. B., & Trela, J. M. (1976). Deoxyribonucleic acid polymerase from the extreme thermophile Thermus aquaticus. Journal of Bacteriology, 127(3), 1550–1557.

Daly, M. J. (2009). A new perspective on radiation resistance based on Deinococcus radiodurans. Nature Reviews Microbiology, 7, 237–245.

Kashefi, K., & Lovley, D. R. (2003). Extending the upper temperature limit for life. Science, 301(5635), 934.

Mateos, K., Chappell, G., Klos, A., Le, B., Boden, J., Stüeken, E. E., & Anderson, R. E. (2023). The evolution and spread of sulfur-cycling enzymes reflect the redox state of the early Earth. Science Advances, 9(27), eade4847. https://doi.org/10.1126/sciadv.ade4847

Moulana, A., Anderson, R. E., Fortunato, C. S., & Huber, J. A. (2020). Selection is a significant driver of gene gain and loss in the pangenome of the bacterial genus Sulfurovum in geographically distinct deep-sea hydrothermal vents. mSystems, 5(2), e00673-19. https://doi.org/10.1128/mSystems.00673-19

Rothschild, L. J., & Mancinelli, R. L. (2001). Life in extreme environments. Nature, 409(6823), 1092–1101.

Schleper, C., Puehler, G., Holz, I., Gambacorta, A., Janekovic, D., Santarius, U., Klenk, H. P., & Zillig, W. (1995). Picrophilus gen. nov., fam. nov.: a novel aerobic, heterotrophic, thermoacidophilic genus and family comprising archaea capable of growth around pH 0. Journal of Bacteriology, 177(24), 7050–7059.

Vieille, C., & Zeikus, G. J. (2001). Hyperthermophilic enzymes: sources, uses, and molecular mechanisms for thermostability. Microbiology and Molecular Biology Reviews, 65(1), 1–43.

Whitaker, R. J., Grogan, D. W., & Taylor, J. W. (2003). Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science, 301(5635), 976–978.

Lenski LTEE

Barrick, J. E., Yu, D. S., Yoon, S. H., Jeong, H., Oh, T. K., Schneider, D., Lenski, R. E., & Kim, J. F. (2009). Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature, 461(7268), 1243–1247. https://doi.org/10.1038/nature08480

Blount, Z. D., Barrick, J. E., Davidson, C. J., & Lenski, R. E. (2012). Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature, 489(7417), 513–518. https://doi.org/10.1038/nature11514

Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008). Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences, 105(23), 7899–7906.

Haigh, J. (1978). The accumulation of deleterious genes in a population — Muller’s ratchet. Theoretical Population Biology, 14(2), 251–267.

Lenski, R. E., Rose, M. R., Simpson, S. C., & Tadler, S. C. (1991). Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2,000 generations. The American Naturalist, 138(6), 1315–1341.

Lenski, R. E., & Travisano, M. (1994). Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proceedings of the National Academy of Sciences, 91(15), 6808–6814.

Muller, H. J. (1964). The relation of recombination to mutational advance. Mutation Research, 1(1), 2–9.

Tenaillon, O., Barrick, J. E., Ribeck, N., Deatherage, D. E., Blanchard, J. L., Dasgupta, A., Wu, G. C., Wielgoss, S., Cruveiller, S., Médigue, C., Schneider, D., & Lenski, R. E. (2016). Tempo and mode of genome evolution in a 50,000-generation experiment. Nature, 536, 165–170. https://doi.org/10.1038/nature18959

Wiser, M. J., Ribeck, N., & Lenski, R. E. (2013). Long-term dynamics of adaptation in asexual populations. Science, 342(6164), 1364–1367.

Evolutionary Computation

Dolson, E. L., & Ofria, C. (2018). Ecological theory provides insights about evolutionary computation. GECCO 2018 Companion. https://doi.org/10.1145/3205651.3208237

Dolson, E. L., Vostinar, A. E., Wiser, M. J., & Ofria, C. (2019). The MODES toolbox: Measurements of open-ended dynamics in evolving systems. Artificial Life, 25(1), 50–73. https://doi.org/10.1162/artl_a_00280

Dolson, E. L., Pérez, S. G., & Goldsby, H. J. (2022). Artificial selection methods from evolutionary computing show promise for directed evolution of microbes. eLife, 11, e79665. https://doi.org/10.7554/eLife.79665

Dolson, E. L., Banzhaf, W., & Ofria, C. (2023). The ecology-evolution continuum and the origin of life. Journal of the Royal Society Interface, 20(208). https://doi.org/10.1098/rsif.2023.0346

Eiben, A. E., & Smith, J. E. (2003). Introduction to Evolutionary Computing. Springer.

Foreback, J., Bohm, C., & Dolson, E. (2025). Leveraging heterogeneous controller representations for evolutionary swarm robotics. IEEE.

Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.

Iram, S., Dolson, E., Chiel, J., Pelesko, J., Krishnan, N., Güngör, Ö., Kuber, B., Katza, J., Bonachela, J., Munsky, B., & Bhatt, D. (2020). Controlling the speed and trajectory of evolution with counterdiabatic driving. Nature Physics, 17, 135–142. https://doi.org/10.1038/s41567-020-0989-3

Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press.

Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.

Type Theory & Programming Language Design

Cardelli, L., & Wegner, P. (1985). On understanding types, data abstraction, and polymorphism. Computing Surveys, 17(4), 471–523.

Griffin, T. G. (1990). A formulae-as-types notion of control. Proceedings of the 17th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 47–58.

Hanenberg, S. (2010). An experiment about static and dynamic type systems: Doubts about the positive impact of static type systems on development time. ACM SIGPLAN Notices, 45(10), 22–35.

Howard, W. A. (1980). The formulae-as-types notion of construction. In To H.B. Curry: Essays on Combinatory Logic, Lambda Calculus and Formalism, 479–490. Academic Press.

Jung, R., Jourdan, J.-H., Krebbers, R., & Dreyer, D. (2017). RustBelt: Securing the foundations of the Rust programming language. Proceedings of the ACM on Programming Languages, 2(POPL), 66. https://doi.org/10.1145/3158154

Matsakis, N. D., & Klock, F. S., II. (2014). The Rust language. ACM SIGAda Ada Letters, 34(3), 103–104. https://doi.org/10.1145/2692956.2663188

Mayer, C., Hanenberg, S., Robbes, R., Tanter, É., & Stefik, A. (2012). An empirical study of the influence of static type systems on the usability of undocumented software. ACM SIGPLAN Notices, 47(10), 683–702.

Pierce, B. C. (2002). Types and Programming Languages. MIT Press.

Ray, B., Posnett, D., Filkov, V., & Devanbu, P. (2014). A large scale study of programming languages and code quality in GitHub. Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering, 155–165.

AI-Assisted Development

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. de O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.

Jesse, K., Ahmed, T., Devanbu, P. T., & Morgan, E. (2023). Large language models and simple, stupid bugs. IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), 563–575.

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., et al. (2022). Competition-level code generation with AlphaCode. Science, 378(6624), 1092–1097.

Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022). Asleep at the keyboard? Assessing the security of GitHub Copilot’s code contributions. IEEE Symposium on Security and Privacy (SP), 754–768.

Scientific Computing & Reproducibility

Ince, D. C., Hatton, L., & Graham-Cumming, J. (2012). The case for open computer programs. Nature, 482, 485–488. https://doi.org/10.1038/nature10836

Mesnard, O., & Barba, L. A. (2017). Reproducible and replicable computational fluid dynamics: It’s harder than you think. Computing in Science & Engineering, 19(4), 44–55.

Nickolls, J., Buck, I., Garland, M., & Skadron, K. (2008). Scalable parallel programming with CUDA. Queue, 6(2), 40–53.

Sellers, G. (2016). Vulkan Programming Guide. Addison-Wesley.

Stone, J. E., Gohara, D., & Shi, G. (2010). OpenCL: A parallel programming standard for heterogeneous computing systems. Computing in Science & Engineering, 12(3), 66–73.

Computational Physics (hotSpring)

Bazavov, A., et al. [HotQCD Collaboration]. (2014). Equation of state in (2+1)-flavor QCD. Nuclear Physics A, 931, 867–872.

Bazavov, A., et al. (2015). Gauge-invariant implementation of the Abelian Higgs model on optical lattices. Physical Review D, 92, 076003.

Bazavov, A., et al. (2016). Polyakov loop in 2+1 flavor QCD from low to high temperatures. Physical Review D, 93, 114502.

Bazavov, A., et al. (2025). Hadronic vacuum polarization for the muon g-2: Complete short and intermediate windows. Physical Review D, 111, 094508.

Diaw, A., Murillo, M. S., & Stanton, L. (2024). Learning transport properties of strongly coupled plasmas from neural surrogates. Nature Machine Intelligence.

Murillo, M. S., & Weisheit, J. C. (1998). Dense plasmas, screened interactions, and atomic ionization. Physics Reports, 302, 1–65.

Stanton, L. G., & Murillo, M. S. (2016). Ionic transport in high-energy-density matter. Physical Review E, 93, 043203.

Agriculture (airSpring)

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration — guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome.

Microbiology & Quorum Sensing (wetSpring)

Bruger, E. L., & Waters, C. M. (2018). Maximizing growth yield and dispersal via quorum sensing promotes cooperation in Vibrio bacteria. Applied and Environmental Microbiology, 84, e00402-18.

Hsueh, B. Y., Severin, G. B., Elg, C. A., Waldron, E. J., Kant, A., Wessel, A. J., Dover, J. A., Rhoades, C. R., Ridenhour, B. J., Parent, K. N., & Waters, C. M. (2022). A broadly conserved deoxycytidine deaminase protects bacteria from phage infection. Nature Microbiology, 7, 1210–1220.

Massie, J. P., Reynolds, E. L., Koestler, B. J., Cong, J. P., Agostoni, M., & Waters, C. M. (2012). Quantification of high-specificity cyclic diguanylate signaling. Proceedings of the National Academy of Sciences, 109, 12746–12751.

Waters, C. M., Lu, W., Rabinowitz, J. D., & Bhatt, S. (2008). Quorum sensing controls biofilm formation in Vibrio cholerae through modulation of cyclic di-GMP. Journal of Bacteriology, 190, 2527–2536.

Comparative Genomics (wetSpring / neuralSpring)

Liu, K., Raghavan, S., Nelesen, S., Linder, C. R., & Warnow, T. (2009). Rapid and accurate large-scale coestimation of sequence alignments and phylogenetic trees. Science, 324(5934), 1561–1564.

Liu, K., et al. (2014). An HMM-based comparative genomic framework for detecting introgression in eukaryotes. PLoS Computational Biology, 10, e1003649.

Wang, Y.-B., Ogilvie, H. A., & Liu, L. (2021). Build a better bootstrap and the RAWR shall beat a random path to your door. Bioinformatics, 37(Suppl 1), i111–i119.

Spectral Theory (hotSpring / groundSpring)

Bourgain, J., & Kachkovskiy, I. (2018). Anderson localization for two interacting quasiperiodic particles. Geometric and Functional Analysis (GAFA), 29, 3–43.

Filonov, N., & Kachkovskiy, I. (2018). On the structure of band edges of 2-dimensional periodic elliptic operators. Acta Mathematica, 221, 59–80.

Jitomirskaya, S., & Kachkovskiy, I. (2018). All couplings localization for quasiperiodic operators with Lipschitz monotone potentials. Journal of the European Mathematical Society (JEMS), 21, 777–795.

Kachkovskiy, I. (2016). On transport properties of isotropic quasiperiodic XY spin chains. Communications in Mathematical Physics, 345, 659–673.

Kachkovskiy, I., & Safarov, Y. (2016). Distance to normal elements in C*-algebras of real rank zero. Journal of the American Mathematical Society, 29, 61–80.

Cognitive Science & Creativity

Schwartz, B. (2004). The Paradox of Choice: Why More Is Less. Harper Collins.

Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Stokes, P. D. (2006). Creativity from Constraints: The Psychology of Breakthrough Thinking. Springer Publishing Company.

Physics-Informed ML (neuralSpring)

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.

Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature Machine Intelligence, 3, 218–229.

Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.

PCR & Molecular Biology

Mullis, K. B., & Faloona, F. A. (1987). Specific synthesis of DNA in vitro via a polymerase-catalyzed chain reaction. Methods in Enzymology, 155, 335–350.

Saiki, R. K., Gelfand, D. H., Stoffel, S., Scharf, S. J., Higuchi, R., Horn, G. T., Mullis, K. B., & Erlich, H. A. (1988). Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science, 239(4839), 487–491.

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