Book contents
- Frontmatter
- Contents
- Preface
- 1 The Potential of Universal Biology
- 2 Methodology in Universal Biology
- 3 Reproduction of Cells: Dynamic and Thermodynamic Characteristics, Dormancy, Fluctuation, and Requisites of Protocells
- 4 Generic Adaptation of Cells
- 5 Adaptation and Cellular Homeostasis
- 6 Cellular Memory
- 7 Cell Differentiation through Development
- 8 Evolution of Phenotypic Plasticity and Robustness in Terms of Phenotypic Fluctuations
- 9 Direction and Constraint in Phenotypic Evolution
- 10 Summary and Future Issues
- References
- Index
- References
References
Published online by Cambridge University Press: 11 November 2025
Book contents
- Frontmatter
- Contents
- Preface
- 1 The Potential of Universal Biology
- 2 Methodology in Universal Biology
- 3 Reproduction of Cells: Dynamic and Thermodynamic Characteristics, Dormancy, Fluctuation, and Requisites of Protocells
- 4 Generic Adaptation of Cells
- 5 Adaptation and Cellular Homeostasis
- 6 Cellular Memory
- 7 Cell Differentiation through Development
- 8 Evolution of Phenotypic Plasticity and Robustness in Terms of Phenotypic Fluctuations
- 9 Direction and Constraint in Phenotypic Evolution
- 10 Summary and Future Issues
- References
- Index
- References
Summary
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- Universal BiologyThe Physics of Life through the Macro-Micro Consistency Principle, pp. 327 - 342Publisher: Cambridge University PressPrint publication year: 2025
References
Adachi, S., Hori, K., and Hiraga, S. (2006). Subcellular positioning of F plasmid mediated by dynamic localization of SopA and SopB. Journal of Molecular Biology, 356(4), 850–863.10.1016/j.jmb.2005.11.088CrossRefGoogle ScholarPubMed
Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K., and Watson, J. D. (1983, 1989, 1994, 2002). The Molecular Biology of the Cell. Garland Science.Google Scholar
Altmeyer, S., and McCaskill, J. S. (2001). Error threshold for spatially resolved evolution in the quasispecies model. Physical Review Letters, 86, 5819–5822.10.1103/PhysRevLett.86.5819CrossRefGoogle ScholarPubMed
Alon, U. (2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. CRC Press.10.1201/9781420011432CrossRefGoogle Scholar
Alon, U., Surette, M. G., Barkai, N., and Leibler, S. (1999). Robustness in bacterial chemotaxis. Nature, 397, 168–171.10.1038/16483CrossRefGoogle ScholarPubMed
Alon, U., Barkai, N., Notterman, D. A., et al. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America, 96, 6745–6750.10.1073/pnas.96.12.6745CrossRefGoogle ScholarPubMed
Ancel, L. W., and Fontana, W. (2002). Plasticity, evolvability, and modularity in RNA. Journal of Experimental Zoology, 288, 242–283.10.1002/1097-010X(20001015)288:3<242::AID-JEZ5>3.0.CO;2-O3.0.CO;2-O>CrossRefGoogle Scholar
Aoki, H., and Kaneko, K. (2013). Slow stochastic switching by collective chaos of fast elements. Physical Review Letters, 111(14), 144102.10.1103/PhysRevLett.111.144102CrossRefGoogle ScholarPubMed
Ariizumi, T., and Asashima, M. (2001). In vitro induction systems for analyses of amphibian organogenesis and body patterning. The International Journal of Developmental Biology, 45, 273–279.10.1387/ijdb.11291857CrossRefGoogle ScholarPubMed
Asakura, S., and Honda, H. (1984). Two-state model for bacterial chemoreceptor proteins: The role of multiple methylation. Journal of Molecular Biology, 176(3), 349–367.10.1016/0022-2836(84)90494-7CrossRefGoogle Scholar
Awazu, A., and Kaneko, K. (2007). Discreteness-induced transition in catalytic reaction networks. Physical Review E, 76(4), 041915.10.1103/PhysRevE.76.041915CrossRefGoogle ScholarPubMed
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., and Leibler, S. (2004). Bacterial persistence as a phenotypic switch. Science, 305(5690), 1622–1625.10.1126/science.1099390CrossRefGoogle ScholarPubMed
Bar-Even, A., Paulsson, J., Maheshri, N., et al. (2006). Noise in protein expression scales with natural protein abundance. Nature Genetics, 38(6), 636.10.1038/ng1807CrossRefGoogle ScholarPubMed
Barkai, N., and Leibler, S. (1997). Robustness in simple biochemical networks. Nature, 387(6636), 913.10.1038/43199CrossRefGoogle ScholarPubMed
Beisson, J., and Sonneborn, T. M. (1965). Cytoplasmic inheritance of the organization of the cell cortex in Paramecium aurelia. Proceedings of the National Academy of Sciences of the United States of America, 53(2), 275–282.10.1073/pnas.53.2.275CrossRefGoogle ScholarPubMed
Blelloch, R. H., Hochedlinger, K., Yamada, Y., et al. (2004). Nuclear cloning of embryonal carcinoma cells. Proceedings of the National Academy of Sciences of the United States of America, 101, 13985–13990.Google Scholar
Benner, S. A., and Sismour, A. M. (2005). Synthetic biology. Nature Reviews Genetics, 6(7), 533–543.10.1038/nrg1637CrossRefGoogle ScholarPubMed
Berg, H. C. (1975). Chemotaxis in bacteria. Annual Review of Biophysics and Bioengineering, 4(1), 119–136.10.1146/annurev.bb.04.060175.001003CrossRefGoogle ScholarPubMed
Berthier, L., and Biroli, G. (2011). Theoretical perspective on the glass transition and amorphous materials. Reviews of Modern Physics, 83(2), 587.10.1103/RevModPhys.83.587CrossRefGoogle Scholar
Biancalani, T., Rogers, T., and McKane, A. J. (2012). Noise-induced metastability in biochemical networks. Physical Review E, 86(1), 010106.10.1103/PhysRevE.86.010106CrossRefGoogle ScholarPubMed
Boerlijst, M., and Hogeweg, P. (1991). Spiral wave structure in pre-biotic evolution – hypercycles stable against parasites. Physica, 48D, 17.Google Scholar
Braun, E. (2015). The unforeseen challenge: from genotype-to-phenotype in cell populations. Reports on Progress in Physics, 78(3), 036602.10.1088/0034-4885/78/3/036602CrossRefGoogle ScholarPubMed
Bremer, H. (1981). Simulated bacterial growth. Journal of Theoretical Biology, 92, 23–38.10.1016/0022-5193(81)90158-2CrossRefGoogle Scholar
Callahan, H. S., Pigliucci, M., and Schlichting, C. D. (1997), Developmental phenotypic plasticity: where ecology and evolution meet molecular biology. Bioessays, 19, 519–525.10.1002/bies.950190611CrossRefGoogle ScholarPubMed
Campbell, A. (1957). Synchronization of cell division. Bacteriological Reviews, 21(4), 263–272.10.1128/br.21.4.263-272.1957CrossRefGoogle ScholarPubMed
Campbell, K. H. S., McWhir, J., Ritchie, W. A., and Wilmut, I. (1996). Sheep cloned by transfer from a cultured cell line. Nature, 380, 64–66.10.1038/380064a0CrossRefGoogle ScholarPubMed
Cannon, W. B. (1932). Wisdom of the Body. W.W. Norton and Company Inc.10.1097/00000441-193212000-00028CrossRefGoogle Scholar
Carathéodory, C. (1976). Investigations into the foundations of thermodynamics. The Second Law of Thermodynamics, 5, 229–256.Google Scholar
Cavagna, A. (2009). Supercooled liquids for pedestrians. Physics Reports, 476(4–6), 51–124.10.1016/j.physrep.2009.03.003CrossRefGoogle Scholar
Chambers, I., Silva, J., Colby, D., et al. (2007). Nanog safeguards pluripotency and mediates germline development. Nature, 450(7173), 1230.10.1038/nature06403CrossRefGoogle ScholarPubMed
Chang, H. H., Hemberg, M., Barahona, M., Ingber, D. E., and Huang, S. (2008). Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature, 453(7194), 544–547.10.1038/nature06965CrossRefGoogle ScholarPubMed
Chow, M., Yao, A., and Rubin, H. (1994). Cellular epigenesis: Topochronology of progressive spontaneous transformation of cells under growth constraint. Proceedings of the National Academy of Sciences, 91, 599–603.10.1073/pnas.91.2.599CrossRefGoogle Scholar
Ciliberti, S., Martin, O. C., and Wagner, A. (2007). Robustness can evolve gradually in complex regulatory gene networks with varying topology. PLOS Computational Biology, 3, e15.10.1371/journal.pcbi.0030015CrossRefGoogle ScholarPubMed
Daniels, B. C. Chen, Y. J. Sethna, J. P. Gutenkunst, R. N., and Myers, C. R. (2008). Sloppiness, robustness, and evolvability in systems biology. Current Opinion in Biotechnology, 19(4), 389–395.10.1016/j.copbio.2008.06.008CrossRefGoogle ScholarPubMed
Darwin, C. (1859). On the Origin of Species by Means of Natural Selection or the Preservation of Favored Races in the Struggle for Life (Murray, London).Google Scholar
Debenedetti, P. G., and Stillinger, F. H. (2001). Supercooled liquids and the glass transition. Nature, 410(6825), 259.10.1038/35065704CrossRefGoogle ScholarPubMed
Derrida, B., and Pomeau, Y. (1986). Random networks of automata: a simple annealed approximation. Europhysics Letters, 1(2), 45.10.1209/0295-5075/1/2/001CrossRefGoogle Scholar
Dodd, I. B. Micheelsen, M. A. Sneppen, K., and Thon, G. (2007). Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell, 129, 8132.Google ScholarPubMed
Duboule, D. (1994). Temporal colinearity and the phylotypic progression: a basis for the stability of a vertebrate Bauplan and the evolution of morphologies through heterochrony. Development, (Supplement), 135–142.10.1242/dev.1994.Supplement.135CrossRefGoogle ScholarPubMed
Dunn, S. J., Martello, G., Yordanov, B., Emmott, S., and Smith, A. G. (2014). Defining an essential transcription factor program for naive pluripotency. Science, 344(6188), 1156–1160.10.1126/science.1248882CrossRefGoogle ScholarPubMed
Edwards, A. W. F. (2000). Foundations of Mathematical Genetics, Cambridge University Press.Google Scholar
Eigen, M., and Schuster, P. (1979). The Hypercycle (Springer).10.1007/978-3-642-67247-7CrossRefGoogle Scholar
Einstein, A. (1905). Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Annalen der Physik, 17, 549–560.10.1002/andp.19053220806CrossRefGoogle Scholar
Einstein, A. (1906). Zur Theorie der Brownschen Bewegung. Annalen der Physik, 19, 371–381.10.1002/andp.19063240208CrossRefGoogle Scholar
Elena, S. F., and Lenski, R. E. (2003). Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nature Reviews Genetics, 4(6), 457.10.1038/nrg1088CrossRefGoogle ScholarPubMed
Elowitz, M. B., and Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338.10.1038/35002125CrossRefGoogle ScholarPubMed
Elowitz, M. B., Levine, A. J., Siggia, E. D., and Swain, P. S. (2002). Stochastic gene expression in a single cell. Science, 297, 1183–1186.10.1126/science.1070919CrossRefGoogle Scholar
Frentz, Z., Kuehn, S., and Leibler, S. (2015). Strongly deterministic population dynamics in closed microbial communities. Physical Review X, 5(4), 041014.10.1103/PhysRevX.5.041014CrossRefGoogle Scholar
Fisher, R. A. (1930;1958). The Genetical Theory of Natural Selection. Oxford University Press.CrossRefGoogle Scholar
Forgacs, G., and Newman, S. A. (2005). Biological Physics of the Developing Embryo. Cambridge University Press.10.1017/CBO9780511755576CrossRefGoogle Scholar
Fujimoto, K., and Kaneko, K. (2003). How fast elements can affect slow dynamics. Physica D: Nonlinear Phenomena, 180(1–2), 1–16.10.1016/S0167-2789(03)00046-0CrossRefGoogle Scholar
Furusawa, C., and Kaneko, K. (1998a). Emergence of rules in cell society: differentiation, hierarchy, and stability. Bulletin of Mathematical Biology, 60(4), 659–687.10.1006/bulm.1997.0034CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (1998b). Emergence of multicellular organisms with dynamic differentiation and spatial pattern. Artificial Life, 4, 78–89.10.1162/106454698568459CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2000a). Origin of complexity in multicellular organisms. Physical Review Letters, 84, 6130–6133.10.1103/PhysRevLett.84.6130CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2000b). Complex organization in multicullarity as a necessity in evolution. Artificial Life, 6, 265–281.10.1162/106454600300103638CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2001). Theory of robustness of irreversible differentiation in a stem cell system: Chaos hypothesis. Journal of Theoretical Biology, 209(4), 395–416.10.1006/jtbi.2001.2264CrossRefGoogle Scholar
Furusawa, C., and Kaneko, K. (2002). Origin of multicellular organisms as an inevitable consequence of dynamical systems. Anatomical Record, 268, 327–342.CrossRefGoogle Scholar
Furusawa, C., and Kaneko, K. (2003a). Zipf’s Law in gene expression. Physical Review Letters, 90, 088102.10.1103/PhysRevLett.90.088102CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2003b). Robust development as a consequence of generated positional information. Journal of Theoretical Biology, 224, 413–435.10.1016/S0022-5193(03)00189-9CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2008). A generic mechanism for adaptive growth rate regulation. PLoS Computational Biology, 4(1), e3.10.1371/journal.pcbi.0040003CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2009). Chaotic expression dynamics implies pluripotency: when theory and experiment meet. Biology Direct, 4(1), 17.10.1186/1745-6150-4-17CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2012a). A dynamical-systems view of stem cell biology. Science, 338(6104), 215–217.10.1126/science.1224311CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2012b). Adaptation to optimal cell growth through self-organized criticality. Physical Review Letters, 108(20), 208103.10.1103/PhysRevLett.108.208103CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2013). Epigenetic feedback regulation accelerates adaptation and evolution. PloS One, 8(5), e61251.10.1371/journal.pone.0061251CrossRefGoogle Scholar
Furusawa, C., and Kaneko, K. (2015). Global relationships in fluctuation and response in adaptive evolution. Journal of The Royal Society Interface, 12(109), 20150482.10.1098/rsif.2015.0482CrossRefGoogle ScholarPubMed
Furusawa, C., and Kaneko, K. (2018). Formation of dominant mode by evolution in biological systems. Physical Review E, 97(4), 042410.10.1103/PhysRevE.97.042410CrossRefGoogle ScholarPubMed
Furusawa, C., Suzuki, T., Kashiwagi, A., Yomo, T., and Kaneko, K. (2005). Ubiquity of log-normal distributions in intra-cellular reaction dynamics. Biophysics, 1, 25.10.2142/biophysics.1.25CrossRefGoogle ScholarPubMed
Futuyma, D. J. (1986). Evolutionary Biology Second Edition. Sinauer Associates Inc., Sunderland, Mass.Google Scholar
Ganti, T. (1975). Organization of chemical reactions into dividing and metabolizing units: The chemotons. Biosystems, 7, 189.10.1016/0303-2647(75)90038-6CrossRefGoogle ScholarPubMed
Gefen, O., Fridman, O., Ronin, I., and Balaban, N. Q. (2014). Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. Proceedings of the National Academy of Sciences, 111(1), 556–561.10.1073/pnas.1314114111CrossRefGoogle ScholarPubMed
Gehring, W. (1998). Master Control Genes in Development and Evolution: The Homeobox Story. Yale University Press.Google Scholar
Gerdes, K., Howard, M., and Szardenings, F. (2010). Pushing and pulling in prokaryotic DNA segregation. Cell, 141(6), 927–942.10.1016/j.cell.2010.05.033CrossRefGoogle ScholarPubMed
Glass, L., and Kauffman, S. (1973). The logical analysis of continuous nonlinear biochemical control networks. Journal of Theoretical Biology, 39, 103–129.10.1016/0022-5193(73)90208-7CrossRefGoogle ScholarPubMed
Goentoro, L., Shoval, O., Kirschner, M. W., and Alon, U. (2009). The incoherent feedforward loop can provide fold-change detection in gene regulation. Molecular Cell, 36(5), 894–899.10.1016/j.molcel.2009.11.018CrossRefGoogle Scholar
Goldenfeld, N. (1992). Lectures on phase transitions and the renormalization group. CRC Press.Google Scholar
Goldford, J. E., Lu, N., Bajić, D., et al. (2018). Emergent simplicity in microbial community assembly. Science, 361(6401), 469–474.CrossRefGoogle ScholarPubMed
Goto, Y., and Kaneko, K. (2013). Minimal model for stem-cell differentiation. Physical Review E, 88(3), 032718.CrossRefGoogle ScholarPubMed
Gould, S. J., and Eldredge, N. (1977). Punctuated equilibria: the tempo and mode of evolution reconsidered. Paleobiology, 3, 115–151 (1977).10.1017/S0094837300005224CrossRefGoogle Scholar
Grainger, R. M., and Gurdon, J. B. (1989). Loss of competence in amphibian induction can take place in single nondividing cells. Proceedings of the National Academy of Sciences of the USA, 86, 1900904.Google ScholarPubMed
Grey, D., Hutson, V., and Szathmary, E. (1995). A Re-examination of the stochastic corrector model. Proceedings of the Royal Society of London. Series B, 262, 29–35.Google Scholar
Gurdon, J. B., Laskey, R. A., and Reeves, O. R. (1975). The developmental capacity of nuclei transplanted from keratinized skin cells of adult frogs. Journal of Embryology and Experimental Morphology, 34, 93–112.Google ScholarPubMed
Haeckel, E. (1866). Generelle morphologie der organismen: Allgemeine grundzuge der organischen formen – wissenschaft, mechanisch begrundet durch die von Charles Darwin reformirte Descendenz – Theorie. Georg Reimer.10.1515/9783110848281CrossRefGoogle Scholar
Haldane, J. B. S. (1949). Suggestion as to quantitative measurement of the rate of evolution. Evolution, 3, 51–56.10.2307/2405451CrossRefGoogle Scholar
Hall, B. K. (2012). Evolutionary Developmental Biology. Springer Science and Business Media.Google Scholar
Hammerschmidt, K., Rose, C. J., Kerr, B., and Rainey, P. B. (2014). Life cycles, fitness decoupling and the evolution of multicellularity. Nature, 515(7525), 75.10.1038/nature13884CrossRefGoogle ScholarPubMed
Hartl, D. L., and Clark, A. G. (2007). Principles of Population Genetics, 4th ed. Sinauer Associates, MA, USA.Google Scholar
Hashimoto, M., Nozoe, T., Nakaoka, et al. (2016). Noise-driven growth rate gain in clonal cellular populations. Proceedings of the National Academy of Sciences, 113(12), 3251–3256.10.1073/pnas.1519412113CrossRefGoogle ScholarPubMed
Hasty, J., Pradines, J., Dolnik, M., and Collins, J. J. (2000). Noise-based switches and amplifiers for gene expression. Proceedings of the National Academy of Sciences USA, 97, 2075–2080.10.1073/pnas.040411297CrossRefGoogle ScholarPubMed
Hatakeyama, T. S., and Kaneko, K. (2012). Generic temperature compensation of biological clocks by autonomous regulation of catalyst concentration. Proceedings of the National Academy of Sciences, 109(21), 8109–8114.10.1073/pnas.1120711109CrossRefGoogle ScholarPubMed
Hatakeyama, T. S., and Kaneko, K. (2014a). Kinetic memory based on the enzyme-limited competition. PLoS Computational Biology, 10(8), e1003784.10.1371/journal.pcbi.1003784CrossRefGoogle Scholar
Hatakeyama, T. S., and Kaneko, K. (2014b). Homeostasis of the period of post-translational biochemical oscillators. FEBS Letters, 588(14), 2282–2287.10.1016/j.febslet.2014.05.015CrossRefGoogle Scholar
Hatakeyama, T. S., and Kaneko, K. (2015). Reciprocity between robustness of period and plasticity of phase in biological clocks. Physical Review Letters, 115(21), 218101.10.1103/PhysRevLett.115.218101CrossRefGoogle ScholarPubMed
Hatakeyama, T. S., and Kaneko, K. (2017). Robustness of spatial patterns in buffered reaction-diffusion systems and its reciprocity with phase plasticity. Physical Review E, 95(3), 030201.10.1103/PhysRevE.95.030201CrossRefGoogle ScholarPubMed
Hatakeyama, T. S., and Kaneko, K. (2020). Transition in relaxation paths in allosteric molecules: Enzymatic kinetically constrained model. Physical Review Research, 2(1), 012005.10.1103/PhysRevResearch.2.012005CrossRefGoogle Scholar
Hatakeyama, T. S., and Kaneko, K. (2023). A linear reciprocal relationship between robustness and plasticity in homeostatic biological networks. Plos One, 18(1),e0277181.10.1371/journal.pone.0277181CrossRefGoogle ScholarPubMed
Hayashi, K., de Sousa Lopes, S. M. C., Tang, F., and Surani, M. A. (2008). Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states. Cell Stem cell, 3(4), 391–401.10.1016/j.stem.2008.07.027CrossRefGoogle ScholarPubMed
Hebb, D. O. (1949). The organization of behavior. A neuropsychological theory. John Wiley & Sons.Google Scholar
Himeoka, Y., and Kaneko, K. (2014). Entropy production of a steady-growth cell with catalytic reactions. Physical Review E, 90(4), 042714.10.1103/PhysRevE.90.042714CrossRefGoogle ScholarPubMed
Himeoka, Y., and Kaneko, K. (2017). Theory for transitions between exponential and stationary phases: universal laws for lag time. Physical Review X, 7(2), 021049.10.1103/PhysRevX.7.021049CrossRefGoogle Scholar
Himeoka, Y., and Kaneko, K. (2020). Epigenetic ratchet: Spontaneous adaptation via stochastic gene expression. Scientific Reports, 10(1), 459.10.1038/s41598-019-57372-0CrossRefGoogle ScholarPubMed
Hirsch, M. W., Smale, S., and Devaney, R. L. (2003). Differential Equations, Dynamical Systems, and an Introduction to Chaos. Academic Press.Google Scholar
Hochedlinger, K., Blelloch, R., Brennan, C., et al. (2004). Reprogramming of a melanoma genome by nuclear transplantation. Genes & Development, 18(15), 1875–1885.10.1101/gad.1213504CrossRefGoogle ScholarPubMed
Hogeweg, P. (1994). Multilevel evolution: replicators and the evolution of diversity. Physica 75 D, 275–291.10.1016/0167-2789(94)90288-7CrossRefGoogle Scholar
Hopfield, J. J. (1974). Kinetic proofreading: a new mechanism for reducing errors in biosynthetic processes requiring high specificity. Proceedings of the National Academy of Sciences, 71(10), 4135–4139.10.1073/pnas.71.10.4135CrossRefGoogle Scholar
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8), 2554–2558.10.1073/pnas.79.8.2554CrossRefGoogle ScholarPubMed
Horinouchi, T., Suzuki, S., Hirasawa, T., et al. (2015). Phenotypic convergence in bacterial adaptive evolution to ethanol stress. BMC Evolutionary Biology, 15(1), 180.10.1186/s12862-015-0454-6CrossRefGoogle Scholar
Hosokawa, T., Koga, R., Kikuchi, Y., Meng, X. Y., and Fukatsu, T. (2010). Wolbachia as a bacteriocyte-associated nutritional mutualist. Proceedings of the National Academy of Sciences, 107(2), 769–774.10.1073/pnas.0911476107CrossRefGoogle ScholarPubMed
Hu, H., Uesaka, M., Guo, S., et al. (2017). Constrained vertebrate evolution by pleiotropic genes. Nature Ecology & Evolution, 1(11), 1722.10.1038/s41559-017-0318-0CrossRefGoogle ScholarPubMed
Huang, S., and Ingber, D. E. (2006). A non-genetic basis for cancer progression and metastasis: self-organizing attractors in cell regulatory networks. Breast Diseases, 26, 27–54.CrossRefGoogle ScholarPubMed
Huang, S. (2009). Reprogramming cell fates: reconciling rarity with robustness. Bioessays, 31(5), 546–560.10.1002/bies.200800189CrossRefGoogle Scholar
Huang, S., Ernberg, I., and Kauffman, S. (2009). Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Seminars in Cell and Developmental Biology, 20, 869–876.10.1016/j.semcdb.2009.07.003CrossRefGoogle ScholarPubMed
Hubbell, S. P. (2001). The Unified Neutral Theory of Biodiversity and Biogeography. Princeton University Press.Google Scholar
Ichihashi, N., Matsuura, T., Kita, H., Sunami, T., Suzuki, H., and Yomo, T. (2010). Constructing partial models of cells. Cold Spring Harbor Perspectives in Biology, a004945.10.1101/cshperspect.a004945CrossRefGoogle ScholarPubMed
Ichihashi, N., Usui, K., Kazuta, Y., Sunami, T., Matsuura, T., and Yomo, T. (2013). Darwinian evolution in a translation-coupled RNA replication system within a cell-like compartment. Nature Communications, 4, 2494.10.1038/ncomms3494CrossRefGoogle Scholar
Ichikawa, K., and Kaneko, K. (2024). Bayesian inference is facilitated by modular neural networks with different time scales. PLOS Computational Biology, 20(3), e1011897.10.1371/journal.pcbi.1011897CrossRefGoogle ScholarPubMed
Inoue, M., and Kaneko, K. (2011). Weber’s law for biological responses in autocatalytic networks of chemical reactions. Physical Review Letters, 107(4), 048301.10.1103/PhysRevLett.107.048301CrossRefGoogle ScholarPubMed
Inoue, M., and Kaneko, K. (2013). Cooperative adaptive responses in gene regulatory networks with many degrees of freedom. PLoS Computational Biology, 9(4),e1003001.10.1371/journal.pcbi.1003001CrossRefGoogle ScholarPubMed
Irie, N., and Kuratani, S. (2011). Comparative transcriptome analysis reveals vertebrate phylotypic period during organogenesis. Nature Communications, 2, 248.10.1038/ncomms1248CrossRefGoogle ScholarPubMed
Ishihara, S., and Kaneko, K. (2005). Magic number 7 ± 2 in networks of threshold dynamics. Physical Review Letters, 94, 058102.10.1103/PhysRevLett.94.058102CrossRefGoogle ScholarPubMed
Ito, Y., Toyota, H., Kaneko, K., and Yomo, T. (2009). How selection affects phenotypic fluctuation. Molecular Systems Biology, 5(1), 264.10.1038/msb.2009.23CrossRefGoogle ScholarPubMed
Ito-Miwa, K., Furuike, Y., Akiyama, S., and Kondo, T. (2020). Tuning the circadian period of cyanobacteria up to 6.6 days by the single amino acid substitutions in KaiC. Proceedings of the National Academy of Sciences, 117(34), 20926–20931.10.1073/pnas.2005496117CrossRefGoogle ScholarPubMed
Jennings, H. S. (1906). Behavior of the Lower Organisms. The Colombia University press, New York.Google Scholar
Jones, P. A., and Baylin, S. B. (2002). The fundamental role of epigenetic events in cancer. Nature Reviews Genetics, 3, 415–426.10.1038/nrg816CrossRefGoogle ScholarPubMed
Jordan, D. J., and Miska, E. A. (2023). Canalisation and plasticity on the developmental manifold of Caenorhabditis Belegans. Molecular Systems Biology, 19 e11835.10.15252/msb.202311835CrossRefGoogle Scholar
Kadanoff, L. P. (1966). Scaling laws for Ising models near Tc. Physics Physique Fizika, 2(6), 263.10.1103/PhysicsPhysiqueFizika.2.263CrossRefGoogle Scholar
Kadanoff, L. P. (2000). Statistical Physics: Statics, Dynamics and Renormalization (World Scientific).10.1142/4016CrossRefGoogle Scholar
Kaern, M., Elston, T. C., Blake, W. J., and Collins, J. J. (2005). Stochasticity in gene expression: from theories to phenotypes. Nature Reviews Genetics, 6, 451–464.10.1038/nrg1615CrossRefGoogle Scholar
Kalinka, A. T., Varga, K. M., Gerrard, D. T., et al. (2010). Gene expression divergence recapitulates the developmental hourglass model. Nature, 468(7325), 811.10.1038/nature09634CrossRefGoogle ScholarPubMed
Ken-ichiro, F. K., Kobayashi-Kirschvink, K. J., Nozoe, T., Nakaoka, H., Umetani, M., and Wakamoto, Y. (2025). Revealing global stoichiometry conservation architecture in cells from Raman spectral patterns. eLife, 14.Google Scholar
Kamimura, A., and Kaneko, K. (2010). Reproduction of a protocell by replication of a minority molecule in a catalytic reaction network. Physical Review Letters, 105(26), 268103.10.1103/PhysRevLett.105.268103CrossRefGoogle Scholar
Kamimura, A., and Kaneko, K. (2015). Transition to diversification by competition for multiple resources in catalytic reaction networks. Journal of Systems Chemistry, 6(1), 5.10.1186/s13322-015-0010-1CrossRefGoogle ScholarPubMed
Kamimura, A., and Kaneko, K. (2016). Negative scaling relationship between molecular diversity and resource abundances. Physical Review E, 93(6), 062419.CrossRefGoogle ScholarPubMed
Kamimura, A., and Kaneko, K. (2018). Exponential growth for self-reproduction in a catalytic reaction network: relevance of a minority molecular species and crowdedness. New Journal of Physics, 20(3), 035001.10.1088/1367-2630/aaaf37CrossRefGoogle Scholar
van Kampen, N. G. (1971). The case against linear response theory. Phys. Norveg. 5, 279.Google Scholar
Kaneko, K. (1990). Clustering, coding, switching, hierarchical ordering, and control in network of chaotic elements. Physica D: Nonlinear Phenomena, 137–172.10.1016/0167-2789(90)90119-ACrossRefGoogle Scholar
Kaneko, K. (1997a). Dominance of milnor attractors and noise-induced selection in a multi-attractor system. Physical Review Letters, 78, 2736–2739.10.1103/PhysRevLett.78.2736CrossRefGoogle Scholar
Kaneko, K. (1997b). Coupled maps with growth and death: An approach to cell differentiation. Physica D: Nonlinear Phenomena, 103, 505–527.10.1016/S0167-2789(96)00282-5CrossRefGoogle Scholar
Kaneko, K. (1998a). Life as complex systems: Viewpoint from intra-inter dynamics. Complexity, 3, 53–60.10.1002/(SICI)1099-0526(199807/08)3:6<53::AID-CPLX7>3.0.CO;2-93.0.CO;2-9>CrossRefGoogle Scholar
Kaneko, K. (1998b). On the strength of attractors in a high-dimensional system: Milnor attractor network, robust global attraction, and noise-induced selection. Physica D, 124, 322–344.10.1016/S0167-2789(98)00205-XCrossRefGoogle Scholar
Kaneko, K. (2002a). Symbiotic sympatric speciation: Compliance with Interaction-driven phenotype differentiation from a single genotype. Population Ecology, 44, 71–85.10.1007/s101440200009CrossRefGoogle Scholar
Kaneko, K. (2002b). Dominance of milnor attractors in globally coupled dynamical systems with more than 7 ± degrees of freedom. Physical Review E. 66, 055201(R).10.1103/PhysRevE.66.055201CrossRefGoogle Scholar
Kaneko, K. (2005). On recursive production and evolvabilty of cells: Catalytic reaction network approach. Advances in Chemical Physics, 130, 543–598.Google Scholar
Kaneko, K. (2006). Life: An Introduction to Complex Systems Biology. Springer.10.1007/978-3-540-32667-0CrossRefGoogle Scholar
Kaneko, K. (2007). Evolution of robustness to noise and mutation in gene expression dynamics. PLoS One, 2(5), e434.10.1371/journal.pone.0000434CrossRefGoogle ScholarPubMed
Kaneko, K. (2009). Relationship among phenotypic plasticity, phenotypic fluctuations, robustness, and evolvability; Waddington’s legacy revisited under the spirit of Einstein. Journal of Biosciences, 34(4), 529.10.1007/s12038-009-0072-9CrossRefGoogle ScholarPubMed
Kaneko, K. (2011a). Characterization of stem cells and cancer cells on the basis of gene expression profile stability, plasticity, and robustness. Bioessays, 33(6), 403–413.10.1002/bies.201000153CrossRefGoogle ScholarPubMed
Kaneko, K. (2011b). Proportionality between variances in gene expression induced by noise and mutation: consequence of evolutionary robustness. BMC Evolutionary Biology, 11(1), 27.10.1186/1471-2148-11-27CrossRefGoogle ScholarPubMed
Kaneko, K. (2012a). Phenotypic plasticity and robustness: evolutionary stability theory, gene expression dynamics model, and laboratory experiments in Evolutionary systems biology (pp. 249–278). Springer New York.Google Scholar
Kaneko, K. (2012b). Evolution of robustness and plasticity under environmental fluctuation: Formulation in terms of phenotypic variances. Journal of Statistical Physics, 148(4), 687–705.10.1007/s10955-012-0563-1CrossRefGoogle Scholar
Kaneko, K. (2014). Dynamical systems++ for a theory of biological system in Chaos, Information Processing and Paradoxical Games: The Legacy of John S Nicolis (ed.), Nicolis, G. and Vasileois, B., World Scientific Publishing Co. Pte. Ltd.Google Scholar
Kaneko, K. (2016). Scenario for the Origin of Multicellular Organisms: Perspective from Multilevel Consistency Dynamics in Multicellularity: Origins and evolution. MIT Press., Niklas, K. J., Newman, S. A., eds.Google Scholar
Kaneko, K. (2024). Constructing universal phenomenology for biological cellular systems: an idiosyncratic review on evolutionary dimensional reduction. Journal of Statistical Mechanics: Theory and Experiment, 2024(2), 024002.CrossRefGoogle Scholar
Kaneko, K., and Furusawa, C. (2006). An evolutionary relationship between genetic variation and phenotypic fluctuation. Journal of Theoretical Biology, 240(1), 78–86.10.1016/j.jtbi.2005.08.029CrossRefGoogle ScholarPubMed
Kaneko, K., and Furusawa, C. (2018). Macroscopic theory for evolving biological systems akin to thermodynamics. Annual Review of Biophysics, 47, 273–290.10.1146/annurev-biophys-070317-033155CrossRefGoogle ScholarPubMed
Kaneko, K., Furusawa, C., and Yomo, T. (2015). Universal relationship in gene-expression changes for cells in steady-growth state. Physical Review X, 5(1), 011014.10.1103/PhysRevX.5.011014CrossRefGoogle Scholar
Kaneko, K., Sato, K., Michiue, T., et al. (2008). Developmental potential for morphogenesis in vivo and in vitro. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, 310(6), 492–503.10.1002/jez.b.21222CrossRefGoogle ScholarPubMed
Kaneko, K., and Tsuda, I. (1994). Constructive complexity and artificial reality: an introduction. Physica D: Nonlinear Phenomena, 1–10.10.1016/0167-2789(94)90272-0CrossRefGoogle Scholar
Kaneko, K., and Tsuda, I. (2000). Complex Systems: Chaos and Beyond —–A Constructive Approach with Applications in Life Sciences. Springer.Google Scholar
Kaneko, K., and Tsuda, I. (2003). Chaotic itinerancy. Chaos, 13(3), 926.10.1063/1.1607783CrossRefGoogle ScholarPubMed
Kaneko, K., and Yomo, T. (1994). Cell division, differentiation, and dynamic clustering. Physica D: Nonlinear Phenomena, 89–102.10.1016/0167-2789(94)90277-1CrossRefGoogle Scholar
Kaneko, K., and Yomo, T. (1997). Isologous diversification: a theory of cell differentiation. Bulletin of Mathematical Biology, 59, 139–196.10.1007/BF02459474CrossRefGoogle Scholar
Kaneko, K., and Yomo, T. (1999). Isologous diversification for robust development of cell society. Journal of Theoretical Biology, 199, 243–256.10.1006/jtbi.1999.0952CrossRefGoogle ScholarPubMed
Kaneko, K., and Yomo, T. (2000). Symbiotic speciation from a single genotype. Proceedings of the Royal Society B, 267, 2367–2373.10.1098/rspb.2000.1293CrossRefGoogle ScholarPubMed
Kaneko, K., and Yomo, T. (2002a). On a kinetic origin of heredity: minority control in replicating molecules. Journal of Theoretical Biology, 312, 563–576.10.1006/jtbi.2001.2481CrossRefGoogle Scholar
Kaneko, K., and Yomo, T. (2002b). Symbiotic sympatric speciation through interaction-driven phenotype differentiation. Evolutionary Ecology Research, 4, 317–350.Google Scholar
Kasemeier-Kulesa, J. C., Teddy, J. M., Postovit, L. M., et al. (2008). Reprogramming multipotent tumor cells with the embryonic neural crest microenvironment. Developmental Dynamics, 237, 2657–2666.10.1002/dvdy.21613CrossRefGoogle ScholarPubMed
Kashiwagi, A., Urabe, I., Kaneko, K., and Yomo, T. (2006). Adaptive response of a gene network to environmental changes by fitness-induced attractor selection. PloS One, 1(1), e49.10.1371/journal.pone.0000049CrossRefGoogle ScholarPubMed
Kataoka, N., and Kaneko, K. (2000). Functional dynamics. I: Articulation process. Physica D: Nonlinear Phenomena, 138(3–4), 225–250.10.1016/S0167-2789(99)00230-4CrossRefGoogle Scholar
Kataoka, N., and Kaneko, K. (2001). Functional dynamics: II: Syntactic structure. Physica D: Nonlinear Phenomena, 149(3), 174–196.10.1016/S0167-2789(00)00203-7CrossRefGoogle Scholar
Kataoka, N., and Kaneko, K. (2003). Dynamical networks in function dynamics. Physica D: Nonlinear Phenomena, 181(3–4), 235–251.10.1016/S0167-2789(03)00100-3CrossRefGoogle Scholar
Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology, 22, 437.10.1016/0022-5193(69)90015-0CrossRefGoogle ScholarPubMed
Kauffman, S. (1971). Differentiation of malignant to benign cells. Journal of Theoretical Biology, 31, 429–451.10.1016/0022-5193(71)90020-8CrossRefGoogle ScholarPubMed
Kauffman, S. A. (1993). The Origin of Order, Oxford University Press.10.1093/oso/9780195079517.001.0001CrossRefGoogle Scholar
Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., and Arieli, A. (2003). Spontaneously emerging cortical representations of visual attributes. Nature, 425(6961), 954.10.1038/nature02078CrossRefGoogle ScholarPubMed
Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cambridge University Press.10.1017/CBO9780511623486CrossRefGoogle Scholar
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.10.1126/science.220.4598.671CrossRefGoogle ScholarPubMed
Kobayashi, T., Mizuno, H., Imayoshi, I., Furusawa, C., Shirahige, K., and Kageyama, R. (2009). The cyclic gene Hes1 contributes to diverse differentiation responses of embryonic stem cells. Genes & Development, 23(16), 1870–1875.10.1101/gad.1823109CrossRefGoogle ScholarPubMed
Kohsokabe, T., and Kaneko, K. (2016). Evolution–development congruence in pattern formation dynamics: Bifurcations in gene expression and regulation of networks structures. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, 326(1), 61–84.10.1002/jez.b.22666CrossRefGoogle Scholar
Kohsokabe, T., and Kaneko, K. (2022). Dynamical systems approach to evolution–development congruence: Revisiting Haeckel’s recapitulation theory. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, 338(1–2), 62–75.10.1002/jez.b.23031CrossRefGoogle Scholar
Kohsokabe, T., Kuratanai, S., and Kaneko, K. (2024). Developmental hourglass: Verification by numerical evolution and elucidation by dynamical-systems theory. PLOS Computational Biology, 20(2), e1011867.10.1371/journal.pcbi.1011867CrossRefGoogle ScholarPubMed
Komatsu, S. (1968). Tsugu-no-wa dareka (Who is the successor) in Japanese (Hayakawa Pub.)Google Scholar
Kondepudi, D., and Prigogine, I. (2014). Modern Thermodynamics: From Heat Engines to Dissipative Structures. John Wiley and Sons.10.1002/9781118698723CrossRefGoogle Scholar
Kondo, Y., and Kaneko, K. (2011). Growth states of catalytic reaction networks exhibiting energy metabolism. Physical Review E, 84(1), 011927.10.1103/PhysRevE.84.011927CrossRefGoogle Scholar
Koshland, D. E., Goldbeter, A., and Stock, J. B. (1982). Amplification and adaptation in regulatory and sensory systems. Science, 217, 220–225.10.1126/science.7089556CrossRefGoogle ScholarPubMed
Kotte, O., Volkmer, B., Radzikowski, J. L., and Heinemann, M. (2014). Phenotypic bistability in Escherichia coli’s central carbon metabolism. Molecular Systems Biology, 10(7), 736.10.15252/msb.20135022CrossRefGoogle ScholarPubMed
Kubo, R. (1957). Linear response theory and fluctuafion. Dissipation Theorem. J. Phys. Soc. Japan, 12, 570–586.10.1143/JPSJ.12.570CrossRefGoogle Scholar
Kubo, R., Toda, M., and Hashitsume, N. (1985). Statistical Physics II: Nonequilibrium Statistical Mechanics (Vol. 31). Springer Science and Business Media.10.1007/978-3-642-96701-6CrossRefGoogle Scholar
Kuratani, S. (2017). Dobutsu-Shinka-Keitaigaku. University of Tokyo Press, in Japanese.Google Scholar
Kurikawa, T., and Kaneko, K. (2012). Associative memory model with spontaneous neural activity. Europhysics Letters, 98(4), 48002.10.1209/0295-5075/98/48002CrossRefGoogle Scholar
Kurikawa, T., and Kaneko, K. (2013). Embedding responses in spontaneous neural activity shaped through sequential learning. PLoS Computational Biology, 9(3), e1002943.10.1371/journal.pcbi.1002943CrossRefGoogle ScholarPubMed
Kurikawa, T., and Kaneko, K. (2015). Memories as bifurcations: Realization by collective dynamics of spiking neurons under stochastic inputs. Neural Networks, 62, 25–31.10.1016/j.neunet.2014.07.005CrossRefGoogle ScholarPubMed
Kurikawa, T., and Kaneko, K. (2024). Fluctuation-learning relationship in neural networks. arXiv preprint arXiv:2409.13597.Google Scholar
Landry, C. R., Lemos, B., Rifkin, S. A., Dickinson, W. J., and Hartl, D. L. (2007). Genetic properties influencing the evolvability of gene expression. Science, 317(5834), 118–121.10.1126/science.1140247CrossRefGoogle ScholarPubMed
Lane, N. (2015). The Vital Question: Energy, Evolution, and the Origins of Complex Life. WW Norton and Company.Google Scholar
Levy, S. F. Ziv, N., and Siegal, M. L. (2012). Bet Hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol, 10(5), e1001325.10.1371/journal.pbio.1001325CrossRefGoogle ScholarPubMed
Li, F., Long, T., Lu, Y., Ouyang, Q., and Tang, C. (2004). The yeast cell-cycle network is robustly designed. Proceedings of the National Academy of Sciences of the United States of America, 101(14), 4781–4786.10.1073/pnas.0305937101CrossRefGoogle ScholarPubMed
Li, L., Connelly, M. C. Wetmore, C., et al. (2003). Mouse embryos cloned from brain tumors. Cancer Research, 63, 2733–2736.Google Scholar
Lieb, E. H., and Yngvason, J. (1999). The physics and mathematics of the second law of thermodynamics. Physics Reports, 310(1), 1–96.10.1016/S0370-1573(98)00082-9CrossRefGoogle Scholar
Lisman, J. E., and Goldring, M. A. (1988). Feasibility of long-term storage of graded information by the Ca2+/calmodulin-dependent protein kinase molecules of the postsynaptic density. Proceedings of the National Academy of Sciences, 85(14), 5320–5324.10.1073/pnas.85.14.5320CrossRefGoogle ScholarPubMed
Lisman, J., Schulman, H., and Cline, H. (2002). The molecular basis of CaMKII function in synaptic and behavioural memory. Nature Reviews Neuroscience, 3, 1750.10.1038/nrn753CrossRefGoogle ScholarPubMed
López-Maury, L., Marguerat, S., and Bähler, J. (2008). Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nature Reviews Neuroscience, 9(8), 583–593.10.1038/nrg2398CrossRefGoogle Scholar
Luczak, A., Bartho, P., and Harris, K. D. (2009). Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron, 62(3), 413–425.10.1016/j.neuron.2009.03.014CrossRefGoogle ScholarPubMed
MacArthur, R., and Wilson, E. O. (1967). The Theory of Island Biogeography. Princeton University Press.Google Scholar
Maeda, Y., Mizuuchi, R., Shigenobu, S., et al. (2022). Experimental evidence for the correlation between RNA structural fluctuations and the frequency of beneficial mutations. RNA, 28(12), 1659–1667.Google ScholarPubMed
Matsumoto, Y., Murakami, Y., Tsuru, S., Ying, B-Y., and Yomo, T. (2013). Growth rate-coordinated transcriptome reorganization in bacteria. BMC Genomics, 14, 808.10.1186/1471-2164-14-808CrossRefGoogle Scholar
Matsuura, T., Ichihashi, N., Sunami, T., Kita, H., Suzuki, H., and Yomo, T. (2011). Evolvability and Self-Replication of Genetic Information in Liposomes in The Minimal Cell (pp. 275–287). Springer Netherlands.10.1007/978-90-481-9944-0_15CrossRefGoogle Scholar
Matsuura, T., Yomo, T., Yamaguchi, M., et al. (2002). Importance of compartment formation for a self-encoding system. Proceedings of the National Academy of Sciences USA, 99, 7514–7517.10.1073/pnas.062710399CrossRefGoogle ScholarPubMed
Maynard-Smith, J. (1979). Hypercycles and the origin of life. Nature, 280, 445–446.10.1038/280445a0CrossRefGoogle Scholar
Maynard-Smith, J., and Szathmary, E. (1995). The Major Transitions in Evolution (W.H. Freeman).Google Scholar
McCulloch, W. S., and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.10.1007/BF02478259CrossRefGoogle Scholar
Mezard, M., Parisi, G., and Virasoro, M. A. eds. (1987). Spin Glass Theory and Beyond. World Scientific Publishing.Google Scholar
Mikhailov, A., and Hess, B. (1995). Fluctuations in living cells and intracellular traffic. Journal of Theoretical Biology, 176, 185–192.10.1006/jtbi.1995.0188CrossRefGoogle ScholarPubMed
Milo, R., and Phillips, R. (2015). Cell Biology by the Numbers. Garland Science.10.1201/9780429258770CrossRefGoogle Scholar
Mintz, B., and Ilmensee, K. (1975). Normal genetically mosaic mice produced from malignant teratocarcinoma cells. Proceedings of the National Academy of Sciences of the USA, 72, 3585–3589.10.1073/pnas.72.9.3585CrossRefGoogle ScholarPubMed
Mizuuchi, R., and Ichihashi, N. (2018). Sustainable replication and coevolution of cooperative RNAs in an artificial cell-like system. Nature Ecology & Evolution, 2(10), 1654.10.1038/s41559-018-0650-zCrossRefGoogle Scholar
Mjolsness, E., Sharp, D. H., and Reinitz, J. (1991). A connectionist model of development. Journal of Theoretical Biology, 152, 429–453.10.1016/S0022-5193(05)80391-1CrossRefGoogle Scholar
Monod, J. (1949). The growth of bacterial cultures. Annual Reviews in Microbiology, 3(1), 371–394.10.1146/annurev.mi.03.100149.002103CrossRefGoogle Scholar
Moran, J., and Tikhonov, M. (2022). Defining coarse-grainability in a model of structured microbial ecosystems. Physical Review X, 12(2), 021038.10.1103/PhysRevX.12.021038CrossRefGoogle Scholar
Mori, H. (1965). Transport, collective motion, and Brownian motion. Progress of Theoretical Physics, 33(3), 423–455.10.1143/PTP.33.423CrossRefGoogle Scholar
Nakajima, A., and Kaneko, K. (2008). Regulative differentiation as bifurcation of interacting cell population. Journal of Theoretical Biology, 253(4), 779–787.10.1016/j.jtbi.2008.04.007CrossRefGoogle ScholarPubMed
Nakajima, M., Imai, K., Ito, H., et al. (2005). Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro. Science, 308, 414–415.10.1126/science.1108451CrossRefGoogle ScholarPubMed
Nakaoa, Y., Tokui, H., Gion, Y., Inoue, S., and Oosawa, F. (1982). Behavioral adaptation of Paramecium caudatum to environmental temperature. Proceedings of the Japan Academy, Series B, 58(7), 213–217.10.2183/pjab.58.213CrossRefGoogle Scholar
Newman, S. A. (1994). Generic physical mechanisms of tissue morphogenesis: a common basis for development and evolution. Journal of Evolutionary Biology, 7, 467–488.10.1046/j.1420-9101.1994.7040467.xCrossRefGoogle Scholar
Newman, S. A., and Comper, W. D. (1990). Generic physical mechanisms of morphogenesis and pattern formation. Development, 110, 1–18.10.1242/dev.110.1.1CrossRefGoogle ScholarPubMed
Nicolis, G., and Prigogine, I. (1977). Self-organization in Nonequilibrium Systems (Wiley).Google Scholar
Ohno, S. (1970). Evolution by Gene Duplication. Springer Berlin.10.1007/978-3-642-86659-3CrossRefGoogle Scholar
Ohta, T. (1992). The nearly neutral theory of molecular evolution. Annual Review of Ecology and Systematics, 23(1), 263–286.10.1146/annurev.es.23.110192.001403CrossRefGoogle Scholar
Ohta, T. (2011). Near-neutrality, robustness, and epigenetics. Genome Biology and Evolution, 3, 1034–1038.10.1093/gbe/evr012CrossRefGoogle ScholarPubMed
Opper, M., and Diederich, S. (1992). Phase transition and 1/f noise in a game dynamical model. Physical Review Letters, 69, 1616.10.1103/PhysRevLett.69.1616CrossRefGoogle Scholar
Oono, Y. (2012). The Nonlinear World: Conceptual Analysis and Phenomenology. Springer Science and Business Media.Google Scholar
Pfeuty, B., and Kaneko, K. (2014). Reliable binary cell-fate decisions based on oscillations. Physical Review E, 89(2), 022707.10.1103/PhysRevE.89.022707CrossRefGoogle ScholarPubMed
Pfeuty, B., and Kaneko, K. (2016). Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition. Physical Biology, 13(2), 026007.10.1088/1478-3975/13/2/026007CrossRefGoogle ScholarPubMed
Pham, M. T., and Kaneko, K. (2023). Double-replica theory for evolution of genotype-phenotype interrelationship. Physical Review Research, 5, 023049.10.1103/PhysRevResearch.5.023049CrossRefGoogle Scholar
Pham, T. M., and Kaneko, K. (2023). Theory for adaptive systems: collective robustness of genotype-phenotype evolution. arXiv:2306.01403.Google Scholar
Pirt, S. J. (1965). The maintenance energy of bacteria in growing cultures. Proceedings of the Royal Society B, 163(991), 224–231.Google ScholarPubMed
Price, G. R. (1970). Selection and covariance. Nature, 227, 520–521.10.1038/227520a0CrossRefGoogle ScholarPubMed
Price, G. R. (1972). Extension of covariance selection mathematics. Annals of Human Genetics, 35(4), 485–490.10.1111/j.1469-1809.1957.tb01874.xCrossRefGoogle ScholarPubMed
Rashevsky, N. (1940). Physicomathematical aspects of some problems of organic form. The Bulletin of Mathematical Biophysics, 2(3), 109–121.10.1007/BF02478175CrossRefGoogle Scholar
Rashevsky, N. (1960). Mathematical biophysics: physico-mathematical foundations of biology. Literary Licensing, LLC.Google Scholar
Raff, R. A. (1996). The Shape of Life: Genes, Development, and the Evolution of Animal Form. University of Chicago Press.10.7208/chicago/9780226256573.001.0001CrossRefGoogle Scholar
Rafols, I., Sawada, Y., Amagai, A., Maeda, Y., and MacWilliams, H. K. (2001). Cell type proportioning in Dictyostelium slugs: lack of regulation within a 2.5-fold tolerance range. Differentiation, 67(4–5), 107–116.10.1046/j.1432-0436.2001.670403.xCrossRefGoogle ScholarPubMed
Rainey, P. B., and Rainey, K. (2003). Evolution of cooperation and conflict in experimental bacterial populations. Nature, 425(6953), 72.10.1038/nature01906CrossRefGoogle ScholarPubMed
Ratcliff, W. C., Denison, R. F., Borrello, M., and Travisano, M. (2012). Experimental evolution of multicellularity. Proceedings of the National Academy of Sciences, 109(5), 1595–1600.10.1073/pnas.1115323109CrossRefGoogle ScholarPubMed
Reya, T., Morrison, S. J., Clarke, M. F., and Weissman, I. L. (2001). Stem cells, cancer, and cancer stem cells. Nature, 414, 105–111.10.1038/35102167CrossRefGoogle ScholarPubMed
Rice, S. H. (2004). Evolutionary Theory: Mathematical and Conceptual Foundations. Sunderland, MA: Sinauer Associates.Google Scholar
Ritort, F., and Sollich, P. (2003). Glassy dynamics of kinetically constrained models. Advances in Physics, 52(4), 219–342.10.1080/0001873031000093582CrossRefGoogle Scholar
Rohner, P. T., and Berger, D. (2023). Developmental bias predicts 60 million years of wing shape evolution. Proceedings of the National Academy of Sciences, 120(19), e2211210120.10.1073/pnas.2211210120CrossRefGoogle ScholarPubMed
Ronen, M., Rosenberg, R., Shraiman, B., and Alon, U. (2002). Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proceedings of the National Academy of Sciences of the USA, 99, 10555–10560 (2002).Google Scholar
Rubin, H. (1990). The significance of biological heterogeneity. Cancer and Metastasis Reviews, 9 (1990), 1–20.10.1007/BF00047585CrossRefGoogle ScholarPubMed
Rubin, H. (1994a). Cellular epigenetics: control of the size, shape, and spatial distribution of transformed foci by interactions between the transformed and nontransformed cells. Proceedings of the National Academy of Sciences, 91, 1039–1043.10.1073/pnas.91.3.1039CrossRefGoogle ScholarPubMed
Rubin, H. (1994b). Experimental control of neoplastic progression in cell populations; Fould’s rules revisited. Proceedings of the National Academy of Sciences, 91, 6619–6623.10.1073/pnas.91.14.6619CrossRefGoogle Scholar
Russell, J. B., and Baldwin, R. L. (1979). Comparison of maintenance energy expenditures and growth yields among several rumen bacteria grown on continuous culture. Applied and Environmental Microbiology, 37(3), 537–543.10.1128/aem.37.3.537-543.1979CrossRefGoogle ScholarPubMed
Sadtler, P. T., Quick, K. M., Golub, M. D., et al. (2014). Neural constraints on learning. Nature, 512(7515), 423.10.1038/nature13665CrossRefGoogle ScholarPubMed
Saito, N., Sughiyama, Y., and Kaneko, K. (2016). Motif analysis for small-number effects in chemical reaction dynamics. The Journal of Chemical Physics, 145(9), 094111.10.1063/1.4961675CrossRefGoogle ScholarPubMed
Saito, K., Tsuboi, M., and Takahashi, Y. (2024). Developmental noise and phenotypic plasticity are correlated in Drosophila simulans. Evolution Letters, 8(3), 397–405.10.1093/evlett/qrad069CrossRefGoogle ScholarPubMed
Sakata, A., Hukushima, K., and Kaneko, K. (2009). Funnel landscape and mutational robustness as a result of evolution under thermal noise. Physical Review Letters, 102(14), 148101.10.1103/PhysRevLett.102.148101CrossRefGoogle ScholarPubMed
Sakata, A., and Kaneko, K. (2020). Dimensional reduction in evolving spin-glass model: correlation of phenotypic responses to environmental and mutational changes. Physical Review Letters, 124(21), 218101.10.1103/PhysRevLett.124.218101CrossRefGoogle ScholarPubMed
Sakata, A., and Kaneko, K. (2023). Evolutionary shaping of low-dimensional path facilitates robust and plastic switching between phenotypes. Physical Review Research, 5(4), 043296.10.1103/PhysRevResearch.5.043296CrossRefGoogle Scholar
Salazar-Ciudad, I. Newman, S. A., and Sole, R. V. (2001). Phenotypic and dynamical transitions in model genetic networks I. Evolution and Development, 3, 84.10.1046/j.1525-142x.2001.003002084.xCrossRefGoogle Scholar
Salazar-Ciudad, I., Sole, R. V., and Newman, S. A. (2001). Phenotypic and dynamical transitions in model genetic networks II. Application to the evolution of segmentation mechanisms. Evolution & Development, 3(2), 95–103.10.1046/j.1525-142x.2001.003002095.xCrossRefGoogle Scholar
Sato, K., Ito, Y., Yomo, T., and Kaneko, K. (2003). On the Relation between Fluctuation and Response in Biological Systems. Proceedings of the National Academy of Sciences of the USA, 100, 14086–14090.Google ScholarPubMed
Sato, K., and Kaneko, K. (2006). On the distribution of state values of reproducing cells. Physical Biology, 3(1), 74.10.1088/1478-3975/3/1/008CrossRefGoogle ScholarPubMed
Sato, T., and Kaneko, K. (2020). Evolutionary dimension reduction in phenotypic space. Physical Review Research, 2, 013197.10.1103/PhysRevResearch.2.013197CrossRefGoogle Scholar
Sato, T. U., Furusawa, C., & Kaneko, K. (2023). Prediction of cross-fitness for adaptive evolution to different environmental conditions: Consequence of phenotypic dimensional reduction. Physical Review Research, 5, 043222.10.1103/PhysRevResearch.5.043222CrossRefGoogle Scholar
Schmidt, A., Kochanowski, K., Vedelaar, S., et al. (2016). The quantitative and condition-dependent Escherichia coli proteome. Nature Biotechnology, 34(1), 104.10.1038/nbt.3418CrossRefGoogle ScholarPubMed
Schreier, H. I. Soen, Y., and Brenner, N. (2017). Exploratory adaptation in large random networks. Nature Communications, 8, 14826.10.1038/ncomms14826CrossRefGoogle ScholarPubMed
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z., and Hwa, T. (2010). Interdependence of cell growth and gene expression: origins and consequences. Science, 330(6007), 1099–1102.10.1126/science.1192588CrossRefGoogle ScholarPubMed
Shimizu, Y., Kanamori, T., and Ueda, T. (2005). Protein synthesis by pure translation systems. Methods, 36(3), 299–304.10.1016/j.ymeth.2005.04.006CrossRefGoogle ScholarPubMed
Shoval, O., Goentoro, L., Hart, Y., Mayo, A., Sontag, E., and Alon, U. (2010). Fold-change detection and scalar symmetry of sensory input fields. Proceedings of the National Academy of Sciences, 107(36), 15995–16000.10.1073/pnas.1002352107CrossRefGoogle ScholarPubMed
Silva, A. J. Kogan, J. H. Frankland, P. W., and Kida, S. (1998). CREB and memory. Annual Review of Neuroscience, 21, 1278.10.1146/annurev.neuro.21.1.127CrossRefGoogle Scholar
Sompolinsky, H., Crisanti, A., and Sommers, H. J. (1988). Chaos in random neural networks. Physical Review Letters, 61, 259.10.1103/PhysRevLett.61.259CrossRefGoogle ScholarPubMed
Sprinzak, D., and Elowitz, M. B. (2005). Reconstruction of genetic circuits. Nature, 438, 443–448.10.1038/nature04335CrossRefGoogle ScholarPubMed
Spudich, J. L., and Koshland, D. E. Jr. (1976). Non-genetic individuality: chance in the single cell. Nature, 262, 467–471.10.1038/262467a0CrossRefGoogle ScholarPubMed
Stearns, S. C., Kaiser, M., and Kawecki, T. J. (1995). The differential genetic and environmental canalization of fitness components in Drosophila melanogaster. Journal of Evolutionary Biology, 8, 539.10.1046/j.1420-9101.1995.8050539.xCrossRefGoogle Scholar
Stern, S., Dror, T., Stolovicki, E., Brenner, N., and Braun, E. (2007). Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Molecular Systems Biology, 3(1), 106.10.1038/msb4100147CrossRefGoogle ScholarPubMed
Stern, S., Fridmann-Sirkis, Y., Braun, E., and Soen, Y. (2012). Epigenetically heritable alteration of fly development in response to toxic challenge. Cell Reports, 1(5), 528–542.10.1016/j.celrep.2012.03.012CrossRefGoogle ScholarPubMed
Steward, F. C., Mapes, M. O., and Mears, K. (1958). Growth and organized development of cultured cells. II. Organization in cultures from freely suspended cells. American Journal of Botany, 45, 705–708.10.1002/j.1537-2197.1958.tb10599.xCrossRefGoogle Scholar
Strogatz, S. (2001). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Perseus Books.Google Scholar
Sugawara, T., and Kaneko, K. (2011). Chemophoresis as a driving force for intracellular organization: theory and application to plasmid partitioning. Biophysics, 7, 77–88.10.2142/biophysics.7.77CrossRefGoogle ScholarPubMed
Surovtsev, I. V., and Jacobs-Wagner, C. (2018). Subcellular organization: a critical feature of bacterial cell replication. Cell, 172(6), 1271–1293.10.1016/j.cell.2018.01.014CrossRefGoogle ScholarPubMed
Suzuki, N., Furusawa, C., and Kaneko, K. (2011). Oscillatory protein expression dynamics endows stem cells with robust differentiation potential. PLoS One, 6(11), e27232.10.1371/journal.pone.0027232CrossRefGoogle ScholarPubMed
Suzuki, S., Horinouchi, T., and Furusawa, C. (2014). Prediction of antibiotic resistance by gene expression profiles. Nature Communications, 5, 5792.10.1038/ncomms6792CrossRefGoogle ScholarPubMed
Szathmary, E., and Demeter, L. (1987). Group selection of early replicators and the origin of life. Journal of Theoretical Biology, 128, 463–486.10.1016/S0022-5193(87)80191-1CrossRefGoogle ScholarPubMed
Szathmary, E., and Maynard-Smith, J. (1997). From replicators to reproducers: the first major transitions leading to life. Journal of Theoretical Biology, 187, 555–571.10.1006/jtbi.1996.0389CrossRefGoogle ScholarPubMed
Takagi, H., and Kaneko, K. (2005). Dynamical Systems Basis of Metamorphosis: Diversity and Plasticity of Cellular States in Reaction Diffusion Network. Journal of Theoretical Biology, 234, 173–186.10.1016/j.jtbi.2004.11.030CrossRefGoogle Scholar
Takagi, H., Furusawa, C., Sawai, S., and Kaneko, K. (2025). Theoretical Biology of the Cell – Dynamical Systems Perspective (Cambridge University Press).Google Scholar
Takahashi, K., and Yamanaka, S. (2006). Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell, 126(4), 663–676.10.1016/j.cell.2006.07.024CrossRefGoogle ScholarPubMed
Takahashi, Y., Kataoka, N., Kaneko, K., and Namiki, T. (2001). Function dynamics. Japan Journal of Industrial and Applied Mathematics, 18(2), 405.10.1007/BF03168583CrossRefGoogle Scholar
Takeuchi, N., Kaneko, K., and Hogeweg, P. (2016). Evolutionarily stable disequilibrium: endless dynamics of evolution in a stationary population in Proceedings of the Royal Society B (Vol. 283, No. 1830, p. 20153109). The Royal Society.Google Scholar
Takeuchi, N., Hogeweg, P., and Kaneko, K. (2017). The origin of a primordial genome through spontaneous symmetry breaking. Nature Communications, 8(1), 250.10.1038/s41467-017-00243-xCrossRefGoogle ScholarPubMed
Takeuchi, N., and Kaneko, K. (2019). The origin of the central dogma through conflicting multilevel selection. Proceedings of the Royal Society B, 286(1912), 20191359.10.1098/rspb.2019.1359CrossRefGoogle ScholarPubMed
Takeuchi, N., Kaneko, K., and Koonin, E. V. (2014). Horizontal gene transfer can rescue prokaryotes from Mullers ratchet: benefit of DNA from dead cells and population subdivision. G3: Genes, Genomes, Genetics, 4(2), 325–339.10.1534/g3.113.009845CrossRefGoogle ScholarPubMed
Takeuchi, N., Mitarai, N., and Kaneko, K. (2022). A scaling law of multilevel evolution: how the balance between within-and among-collective evolution is determined. Genetics, 220(2), iyab182.10.1093/genetics/iyab182CrossRefGoogle ScholarPubMed
Tan, C., Marguet, P., and You, L. (2009). Emergent bistability by a growth-modulating positive feedback circuit. Nature Chemical Biology, 5(11), 842–848.10.1038/nchembio.218CrossRefGoogle ScholarPubMed
Tang, Q. Y., and Kaneko, K. (2021). Dynamics-evolution correspondence in protein structures. Physical Review Letters, 127(9), 098103.10.1103/PhysRevLett.127.098103CrossRefGoogle ScholarPubMed
Taniguchi, Y., Choi, P. J., Li, G. W., et al. (2010). Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science, 329(5991), 533–538.10.1126/science.1188308CrossRefGoogle ScholarPubMed
Tlusty, T., Libchaber, A., and Eckmann, J. P. (2017). Physical model of the genotype-to-phenotype map of proteins. Physical Review X, 7(2), 021037.10.1103/PhysRevX.7.021037CrossRefGoogle Scholar
Togashi, Y., and Kaneko, K. (2001). Transitions induced by the discreteness of molecules in a small autocatalytic system. Physical Review Letters, 86, 2459.10.1103/PhysRevLett.86.2459CrossRefGoogle Scholar
Togashi, Y., and Kaneko, K. (2005). Discreteness-induced stochastic steady state in reaction diffusion systems: Self-consistent analysis and stochastic simulations. Physica D, 205, 87–99.10.1016/j.physd.2004.12.008CrossRefGoogle Scholar
Togashi, Y., and Mikhailov, A. S. (2007). Nonlinear relaxation dynamics in elastic networks and design principles of molecular machines. Proceedings of the National Academy of Sciences of the United States of America, 104(21), 8697–8702.10.1073/pnas.0702950104CrossRefGoogle ScholarPubMed
Tsuda, I. (1991). Chaotic itinerancy as a dynamical basis of Hermeneutics in brain and mind. World Futures, 32, 167.10.1080/02604027.1991.9972257CrossRefGoogle Scholar
Tsuda, I. (1992). Dynamic link of memory–chaotic memory map in nonequilibrium Neural networks. Neural Networks, 5, 313.10.1016/S0893-6080(05)80029-2CrossRefGoogle Scholar
Tsuru, S., Ichinose, J., Kashiwagi, A., Ying, B. W., Kaneko, K., and Yomo, T. (2009). Noisy cell growth rate leads to fluctuating protein concentration in bacteria. Physical Biology, 6(3), 036015.10.1088/1478-3975/6/3/036015CrossRefGoogle Scholar
Tsuru, S., Yasuda, N., Murakami, Y., et al. (2011). Adaptation by stochastic switching of a monostable genetic circuit in Escherichia coli. Molecular Systems Biology, 7(1), 493.10.1038/msb.2011.24CrossRefGoogle Scholar
Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society B, 237, 37–72.Google Scholar
Uchida, Y., Takeda, H., Furusawa, C., and Irie, N. (2023). Stability in gene expression and body-plan development leads to evolutionary conservation. EvoDevo, 14(4).10.1186/s13227-023-00208-wCrossRefGoogle ScholarPubMed
de Visser, J. A. G. M., Hermisson, J., Wagner, G. P., et al. (2003). Evolution and detection of genetic robustness. Evolution, 57, 1959–1972.Google ScholarPubMed
van Zon, J. S. Lubensky, D. K. Altena, P. R. H., and ten Wolde, P. R. (2007). An allosteric model of circadian KaiC phosphorylation. Proceedings of the National Academy of Sciences of the United States of America, 104, 7420–7425.Google Scholar
Veening, J. W., Hamoen, L. W., and Kuipers, O. P. (2005). Phosphatases modulate the bistable sporulation gene expression pattern in Bacillus subtilis. Molecular Microbiology, 56, 1481–1494.10.1111/j.1365-2958.2005.04659.xCrossRefGoogle ScholarPubMed
Waddington, C. H. (1957). The Strategy of the Genes. George Allen and Unwin LTD., Bristol.Google Scholar
Wagner, A. (2007). Robustness and Evolvability in Living Systems. Princeton University Press.Google Scholar
Wakamoto, Y., Dhar, N., Chait, R., Schneider, K., Signorino-Gelo, F., Leibler, S., and McKinney, J. D. (2013). Dynamic persistence of antibiotic-stressed mycobacteria. Science, 339(6115), 91–95.10.1126/science.1229858CrossRefGoogle ScholarPubMed
Wang, P., Robert, L., Pelletier, J., Dang, W. L., Taddei, F., Wright, A., and Jun, S. (2010). Robust growth of Escherichia coli. Current Biology, 20(12), 1099–1103.10.1016/j.cub.2010.04.045CrossRefGoogle ScholarPubMed
West-Eberhard, M. J. (2003). Developmental Plasticity and Evolution, Oxford University Press.10.1093/oso/9780195122343.001.0001CrossRefGoogle Scholar
Wilson, K. G. (1983). The renormalization group and critical phenomena. Reviews of Modern Physics, 55(3), 583.10.1103/RevModPhys.55.583CrossRefGoogle Scholar
Yamagishi, J. F., and Kaneko, K. (2024). Universal transitions between growth and dormancy via intermediate complex formation. Physical Review Letters, 132(11), 118401.10.1103/PhysRevLett.132.118401CrossRefGoogle ScholarPubMed
Yamagishi, J. F., Saito, N., and Kaneko, K. (2016). Symbiotic cell differentiation and cooperative growth in multicellular aggregates. PLoS Computational Biology, 12(10), e1005042.10.1371/journal.pcbi.1005042CrossRefGoogle ScholarPubMed
Yamagishi, J. F., Saito, N., and Kaneko, K. (2020). Advantage of leakage of essential metabolites for cells. Physical Review Letters, 124(4), 048101.10.1103/PhysRevLett.124.048101CrossRefGoogle Scholar
Yamagishi, J. F., Saito, N., and Kaneko, K. (2021). Adaptation of metabolite leakiness leads to symbiotic chemical exchange and to a resilient microbial ecosystem. PLOS Computational Biology, 17(6), e1009143.10.1371/journal.pcbi.1009143CrossRefGoogle ScholarPubMed
Yoshida, T., Murayama, Y., Ito, H., Kageyama, H., and Kondo, T. (2009). Nonparametric entrainment of the in vitro circadian phosphorylation rhythm of cyanobacterial KaiC by temperature cycle. Proceedings of the National Academy of Sciences of the United States of America, 106, 1648–1653.10.1073/pnas.0806741106CrossRefGoogle ScholarPubMed
Zhuangzi (Chuang Tzu) (around 2C BC), in Chinese; English translation, by Watson, B. (1968). The Complete Works of Chuang Tzu. New York: Columbia University Press.Google Scholar
Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley, Cambridge.Google Scholar
Zwanzig, R. (1973). Nonlinear generalized Langevin equations. Journal of Statistical Physics, 9, 215–220.10.1007/BF01008729CrossRefGoogle Scholar
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