Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Diagrammatic expressions for steady-state distribution and static responses in population dynamics

Koya Katayama1,*, Ryuna Nagayama1, and Sosuke Ito1,2

  • *Contact author: koya.katayama@ubi.s.u-tokyo.ac.jp

Phys. Rev. Research 8, 013312 – Published 24 March, 2026

DOI: https://doi.org/10.1103/fc35-47fs

Abstract

One of the fundamental questions in population dynamics is how biological populations respond to environmental perturbations. In population dynamics, the mean fitness and the fraction of a trait in the steady state are important because they indicate how well the trait and the population adapt to the environment. In this study, we examine the parallel mutation–reproduction model, which is one of the simplest models of an evolvable population. As an extension of the Markov chain tree theorem, we derive diagrammatic expressions for the static responses of the mean fitness and the steady-state distribution of the population. For the parallel mutation–reproduction model, we consider self-loops, which represent trait reproduction and are excluded from the Markov chain tree theorem for the linear master equation. To generalize the theorem, we introduce the concept of rooted 0/1 loop forests, which generalize spanning trees with loops. We demonstrate that the weights of rooted 0/1 loop forests yield the static responses of the mean fitness and the steady-state distribution. Our results provide exact expressions for the static responses and the steady-state distribution. Additionally, we discuss approximations of these expressions in cases where reproduction or mutation is dominant. We provide numerical examples to illustrate these approximations and exact expressions. We also demonstrate how our results can be used to design control strategies for harmful populations.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (78)

  1. R. A. Fisher, The Genetical Theory of Natural Selection (Clarendon Press, Oxford, 1930).
  2. J. F. Crow and M. Kimura, An Introduction to Population Genetics Theory (Harper & Row, New York, 1970).
  3. O. E. Sala et al., Global biodiversity scenarios for the year 2100, Science 287, 1770 (2000).
  4. M. R. Gardner and W. R. Ashby, Connectance of large dynamic (cybernetic) systems: Critical values for stability, Nature (London) 228, 784 (1970).
  5. R. M. May, Will a large complex system be stable? Nature (London) 238, 413 (1972).
  6. S. Allesina and S. Tang, Stability criteria for complex ecosystems, Nature (London) 483, 205 (2012).
  7. N. Q. Balaban, J. Merrin, R. Chait, L. Kowalik, and S. Leibler, Bacterial persistence as a phenotypic switch, Science 305, 1622 (2004).
  8. B. B. Aldridge, M. Fernandez-Suarez, D. Heller, V. Ambravaneswaran, D. Irimia, M. Toner, and S. M. Fortune, Asymmetry and aging of mycobacterial cells lead to variable growth and antibiotic susceptibility, Science 335, 100 (2012).
  9. Y. Wakamoto, N. Dhar, R. Chait, K. Schneider, F. Signorino-Gelo, S. Leibler, and J. D. McKinney, Dynamic persistence of antibiotic-stressed mycobacteria, Science 339, 91 (2013).
  10. J. Maltas, A. Huynh, and K. B. Wood, Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments, PLoS Biol. 23, e3002970 (2025).
  11. P. V. Markov, M. Ghafari, M. Beer, K. Lythgoe, P. Simmonds, N. I. Stilianakis, and A. Katzourakis, The evolution of SARS-CoV-2, Nat. Rev. Microbiol. 21, 361 (2023).
  12. C. Furusawa and K. Kaneko, Global relationships in fluctuation and response in adaptive evolution, J. R. Soc. Interface 12, 20150482 (2015).
  13. R. van den Bosch and V. Stern, The integration of chemical and biological control of arthropod pests, Annu. Rev. Entomol. 7, 367 (1962).
  14. G. P. Georghiou, The evolution of resistance to pesticides, Annu. Rev. Ecol. Syst. 3, 133 (1972).
  15. E. Baake, M. Baake, and H. Wagner, Ising quantum chain is equivalent to a model of biological evolution, Phys. Rev. Lett. 78, 559 (1997).
  16. J. Hermisson, O. Redner, H. Wagner, and E. Baake, Mutation–selection balance: Ancestry, load, and maximum principle, Theor. Popul Biol. 62, 9 (2002).
  17. E. Baake and H.-O. Georgii, Mutation, selection, and ancestry in branching models: A variational approach, J. Math. Biol. 54, 257 (2007).
  18. D. B. Saakian, A new method for the solution of models of biological evolution: Derivation of exact steady-state distributions, J. Stat. Phys. 128, 781 (2007).
  19. D. B. Saakian, O. Rozanova, and A. Akmetzhanov, Dynamics of the eigen and the Crow-Kimura models for molecular evolution, Phys. Rev. E 78, 041908 (2008).
  20. E. Muñoz, J.-M. Park, and M. W. Deem, Solution of the Crow-Kimura and eigen models for alphabets of arbitrary size by Schwinger spin coherent states, J. Stat. Phys. 135, 429 (2009).
  21. A. S. Bratus, A. S. Novozhilov, and Y. S. Semenov, Linear algebra of the permutation invariant Crow–Kimura model of prebiotic evolution, Math. Biosci. 256, 42 (2014).
  22. Y. S. Semenov and A. S. Novozhilov, Exact solutions for the selection–mutation equilibrium in the Crow–Kimura evolutionary model, Math. Biosci. 266, 1 (2015).
  23. L. Euler, Recherches générales sur la mortalité et la multiplication du genre humain, Mémoires de l'académie des sciences de Berlin 16, 144 (1767).
  24. A. J. Lotka, Relation between birth rates and death rates, Science 26, 21 (1907).
  25. E. O. Powell, Growth rate and generation time of bacteria, with special reference to continuous culture, Microbiology 15, 492 (1956).
  26. S. Pigolotti, Generalized Euler-Lotka equation for correlated cell divisions, Phys. Rev. E 103, L060402 (2021).
  27. W. J. Ewens, An interpretation and proof of the fundamental theorem of natural selection, Theor. Popul. Biol. 36, 167 (1989).
  28. S. A. Frank, The Price equation, Fisher's fundamental theorem, kin selection, and causal analysis, Evolution 51, 1712 (1997).
  29. M. Eigen, J. McCaskill, and P. Schuster, The molecular quasi-species, Adv. Chem. Phys. 75, 149 (1989).
  30. Y. Sughiyama, T. J. Kobayashi, K. Tsumura, and K. Aihara, Pathwise thermodynamic structure in population dynamics, Phys. Rev. E 91, 032120 (2015).
  31. H. Miyahara, Steady-state thermodynamics for population dynamics in fluctuating environments with side information, J. Stat. Mech. (2022) 013501.
  32. S. Leibler and E. Kussell, Individual histories and selection in heterogeneous populations, Proc. Natl. Acad. Sci. USA 107, 13183 (2010).
  33. T. J. Kobayashi and Y. Sughiyama, Fluctuation relations of fitness and information in population dynamics, Phys. Rev. Lett. 115, 238102 (2015).
  34. T. Nozoe, E. Kussell, and Y. Wakamoto, Inferring fitness landscapes and selection on phenotypic states from single-cell genealogical data, PLoS Genet. 13, e1006653 (2017).
  35. A. Genthon and D. Lacoste, Universal constraints on selection strength in lineage trees, Phys. Rev. Res. 3, 023187 (2021).
  36. R. García-García, A. Genthon, and D. Lacoste, Linking lineage and population observables in biological branching processes, Phys. Rev. E 99, 042413 (2019).
  37. A. Genthon and D. Lacoste, Fluctuation relations and fitness landscapes of growing cell populations, Sci. Rep. 10, 11889 (2020).
  38. M. Scott, C. W. Gunderson, E. M. Mateescu, Z. Zhang, and T. Hwa, Interdependence of cell growth and gene expression: Origins and consequences, Science 330, 1099 (2010).
  39. P. Wang, L. Robert, J. Pelletier, W. L. Dang, F. Taddei, A. Wright, and S. Jun, Robust growth of Escherichia coli, Curr. Biol. 20, 1099 (2010).
  40. G. Lambert and E. Kussell, Quantifying selective pressures driving bacterial evolution using lineage analysis, Phys. Rev. X 5, 011016 (2015).
  41. M. Hashimoto, T. Nozoe, H. Nakaoka, R. Okura, S. Akiyoshi, K. Kaneko, E. Kussell, and Y. Wakamoto, Noise-driven growth rate gain in clonal cellular populations, Proc. Natl. Acad. Sci. USA 113, 3251 (2016).
  42. R. E. Lenski, Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations, ISME J. 11, 2181 (2017).
  43. N. G. Van Kampen, Stochastic Processes in Physics and Chemistry (Elsevier, Amsterdam, 1992), Vol. 1.
  44. J. Moon, Counting Labelled Trees, Canadian Mathematical Monographs (Canadian Mathematical Congress, Montreal, 1970).
  45. S. Chaiken, A combinatorial proof of the all minors matrix tree theorem, SIAM J. Alg. Disc. Meth. 3, 319 (1982).
  46. A. Cayley, Note sur une formule pour la reversion des séries, J. Reine Angew. Math. 52, 276 (1856).
  47. J. J. Sylvester, On the change of systems of independent variables, Q. J. Pure Appl. Math. 1, 42, 126 (1857).
  48. C. W. Borchardt, Ueber eine der Interpolation entsprechende Darstellung der Eliminations-Resultante, J. Reine Angew. Math. 57, 111 (1860).
  49. G. Kirchhoff, Ueber die auflösung der gleichungen, auf welche man bei der untersuchung der linearen vertheilung galvanischer ströme geführt wird, Ann. Phys. 148, 497 (1847).
  50. J. C. Maxwell, A Treatise on Electricity and Magnetism, 3rd ed. (Clarendon Press, Oxford, 1892).
  51. T. L. Hill, Studies in irreversible thermodynamics IV. Diagrammatic representation of steady state fluxes for unimolecular systems, J. Theor. Biol. 10, 442 (1966).
  52. J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Rev. Mod. Phys. 48, 571 (1976).
  53. J. A. Owen, T. R. Gingrich, and J. M. Horowitz, Universal thermodynamic bounds on nonequilibrium response with biochemical applications, Phys. Rev. X 10, 011066 (2020).
  54. G. Fernandes Martins and J. M. Horowitz, Topologically constrained fluctuations and thermodynamics regulate nonequilibrium response, Phys. Rev. E 108, 044113 (2023).
  55. S. U. Pillai, T. Suel, and S. Cha, The Perron–Frobenius theorem: Some of its applications, IEEE Signal Process. Mag. 22, 62 (2005).
  56. C. Godsil and G. F. Royle, Algebraic Graph Theory (Springer Science & Business Media, New York, 2001), Vol. 207.
  57. D. B. West, Introduction to Graph Theory (Prentice Hall, Upper Saddle River, 2001), Vol. 2.
  58. M. Baym, L. K. Stone, and R. Kishony, Multidrug evolutionary strategies to reverse antibiotic resistance, Science 351, aad3292 (2016).
  59. M. Tyers and G. D. Wright, Drug combinations: A strategy to extend the life of antibiotics in the 21st century, Nat. Rev. Microbiol. 17, 141 (2019).
  60. J. Molina-Hernández, J. A. Cuesta, B. Pascual-Escudero, S. Ares, and P. Catalán, Optimization of sequential therapies to maximize extinction of resistant bacteria through collateral sensitivity, arXiv:2510.01808.
  61. R. B. Mokhtari, T. S. Homayouni, N. Baluch, E. Morgatskaya, S. Kumar, B. Das, and H. Yeger, Combination therapy in combating cancer, Oncotarget 8, 38022 (2017).
  62. B. A. Quinn et al., Pancreatic cancer combination therapy using a BH3 mimetic and a synthetic tetracycline, Cancer Res. 75, 2305 (2015).
  63. P. D. Tamma, S. E. Cosgrove, and L. L. Maragakis, Combination therapy for treatment of infections with Gram-negative bacteria, Clin. Microbiol. Rev. 25, 450 (2012).
  64. T.-C. Chou and P. Talalay, Generalized equations for the analysis of inhibitions of Michaelis-Menten and higher-order kinetic systems with two or more mutually exclusive and nonexclusive inhibitors, Eur. J. Biochem. 115, 207 (1981).
  65. R. Peña-Miller, A. Fuentes-Hernandez, C. Reding, I. Gudelj, and R. Beardmore, Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model, J. R. Soc. Interface 11, 20131035 (2014).
  66. H. L. David, Probability distribution of drug-resistant mutants in unselected populations of Mycobacterium tuberculosis, Appl. Microbiol. 20, 810 (1970).
  67. I. L. Bergval, A. R. Schuitema, P. R. Klatser, and R. M. Anthony, Resistant mutants of Mycobacterium tuberculosis selected in vitro do not reflect the in vivo mechanism of isoniazid resistance, J. Antimicrob. Chemother. 64, 515 (2009).
  68. C. Maes and K. Netočný, Heat bounds and the blowtorch theorem, Annales Henri Poincaré, 14, 1193 (2013).
  69. M. Polettini and M. Esposito, Effective thermodynamics for a marginal observer, Phys. Rev. Lett. 119, 240601 (2017).
  70. F. Khodabandehlou, C. Maes, and K. Netočný, Trees and forests for nonequilibrium purposes: An introduction to graphical representations, J. Stat. Phys. 189, 41 (2022).
  71. S. Dal Cengio, V. Lecomte, and M. Polettini, Geometry of nonequilibrium reaction networks, Phys. Rev. X 13, 021040 (2023).
  72. H.-M. Chun and J. M. Horowitz, Trade-offs between number fluctuations and response in nonequilibrium chemical reaction networks, J. Chem. Phys. 158, 174115 (2023).
  73. S. Liang, P. De Los Rios, and D. M. Busiello, Thermodynamic bounds on symmetry breaking in linear and catalytic biochemical systems, Phys. Rev. Lett. 132, 228402 (2024).
  74. P. E. Harunari, S. Dal Cengio, V. Lecomte, and M. Polettini, Mutual linearity of nonequilibrium network currents, Phys. Rev. Lett. 133, 047401 (2024).
  75. C. Floyd, A. R. Dinner, and S. Vaikuntanathan, Learning to control non-equilibrium dynamics using local imperfect gradients, arXiv:2404.03798.
  76. C. Floyd, A. R. Dinner, A. Murugan, and S. Vaikuntanathan, Limits on the computational expressivity of non-equilibrium biophysical processes, Nat. Commun. 16, 7184 (2025).
  77. P. J. Antsaklis and A. N. Michel, Linear Systems (Birkhäuser, Boston, 2006).
  78. F. Avanzini et al., Methods and conversations in (post) modern thermodynamics, SciPost Phys. Lect. Notes 80 (2024).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation