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  • Open Access

Generic Temperature Response of Large Biochemical Networks

Julian B. Voits1 and Ulrich S. Schwarz1,2,*

  • *Contact author: schwarz@thphys.uni-heidelberg.de

PRX Life 3, 043011 – Published 13 November, 2025

DOI: https://doi.org/10.1103/nyr3-5my3

Abstract

Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation, it is not clear how it affects large biochemical networks with complicated architectures. For developmental systems such as fly and frog, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions, but a rigorous explanation has not been given yet. Here we use a graph-theoretical interpretation of the mean first-passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state, then the Arrhenius plot is generically quadratic, in excellent agreement with numerical simulations for large networks as well as with experimental data for developmental times in fly. We also discuss under which conditions this generic response can be violated, for example for linear chains, which have only one spanning tree.

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References (97)

  1. C. M. Blatteis, Fever: Pathological or physiological, injurious or beneficial? J. Therm. Biol. 28, 1 (2003).
  2. IPCC, Synthesis Report of the IPCC Sixth Assessment Report (ar6) (IPCC, Geneva, Switzerland, 2023).
  3. F. García et al., Changes in temperature alter the relationship between biodiversity and ecosystem functioning, Proc. Natl. Acad. Sci. USA 115, 10989 (2018).
  4. S. Arrhenius, Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren, Z. Phys. Chem. 4, 226 (1889).
  5. S. Logan, The origin and status of the Arrhenius equation, J. Chem. Educ. 59, 279 (1982).
  6. H. Kramers, Brownian motion in a field of force and the diffusion model of chemical reactions, Physica 7, 284 (1940).
  7. P. Hänggi, P. Talkner, and M. Borkovec, Reaction-rate theory: Fifty years after Kramers, Rev. Mod. Phys. 62, 251 (1990).
  8. H. Eyring, The activated complex in chemical reactions, J. Chem. Phys. 3, 107 (1935).
  9. M. Evans and M. Polanyi, Some applications of the transition state method to the calculation of reaction velocities, especially in solution, Trans. Faraday Soc. 31, 875 (1935).
  10. J. C. Bischof and X. He, Thermal stability of proteins, Ann. N.Y. Acad. Sci. 1066, 12 (2006).
  11. M. L. Begasse et al., Temperature dependence of cell division timing accounts for a shift in the thermal limits of C. Elegans and C. Briggsae, Cell Rep. 10, 647 (2015).
  12. L. Fujise et al., Cell cycle dynamics of cultured coral endosymbiotic microalgae (symbiodinium) across different types (species) under alternate light and temperature conditions, J. Eukaryotic Microbiol. 65, 505 (20018).
  13. C. Rieder and H. Maiato, Stuck in division or passing through: Review what happens when cells cannot satisfy the spindle assembly checkpoint, Dev. Cell 7, 637, (2004).
  14. A. Moore et al., Effects of temperature shift on cell cycle, apoptosis and nucleotide pools in CHO cell batch cultues, Cytotechnology 23, 47 (1997).
  15. T. Hayashida et al., The CCGF/TAE250 gene is mutated in thermosensitive G1 mutants of the BHK21 cell line derived from golden hamster, Gene, 41, 247 (1994).
  16. P. Rao and J. Engelberg, Hela cells: Effects of temperature on the life cycle, Science 148, 1092 (1965).
  17. J. Sisken, L. Morasca, and S. Kibby, Effects of temperature on the kinetics of the mitotic cycle of mammalian cells in culture, Exp. Cell Res. 39, 103 (1965).
  18. I. Watanabe and S. Okada, Effects of temperature on growth rate of cultured mammalian cells (L5178Y), J. Cell Biol. 32, 309 (1967).
  19. H. Falahati et al., Temperature-induced uncoupling of cell cycle regulators, Dev Biol. 470, 147 (2021).
  20. H. J. Evans and J. R. K. Savage, The effect of temperature on mitosis and on the action of colchicine in root meristem cells of Vicia faba, Exp. Cell Res. 18, 51 (1959).
  21. O. Oleksiuk et al., Thermal robustness of signaling in bacterial chemotaxis, Cell 145, 312 (2011).
  22. M. Vanoni, M. Vai, and G. Frascotti, Effects of temperature on the yeast cell cycle analyzed by flow cytometry, Cytometry 5, 530 (1984).
  23. V. Zachleder et al., Cell cycle arrest by supraoptimal temperature in the alga Chlamydomonas reinhardtii, Cells 8, 1237 (2019).
  24. J. Crapse et al., Evaluating the Arrhenius equation for developmental processes, Mol. Syst. Biol. 2021, e9895 (2021).
  25. C. Bliss, Temperature characteristics for prepupal development in drosophila melanogaster, J. Gen. Physiol. 9, 467 (1926).
  26. L. Powsner, The effects of temperature on the durations of the developmental stages of drosophila melanogaster, Physiolog. Zoology 8, 474 (1935).
  27. D. Ratkowsky et al., Relationship between temperature and growth rate of bacterial cultures, ASM J. Bacteriol. 149, 1 (1982).
  28. F. Johnson, H. Eyring, and B. Stover, The Theory of Rate Processes in Biology and Medicine (John Wiley and Sons, New York, 1974).
  29. J. M. Membré et al., Temperature effect on bacterial growth rate: Quantitative microbiology approach including cardinal values and variability estimates to perform growth simulations on/in food, Int. J. Food Microbiol. 100, 179 (2005).
  30. H. Thormar, Effect of temperature on the reproduction rate of tetrahymena pyriformis, Exp. Cell. Res. 28, 269 (1962).
  31. F. A. M. Alberghina, Growth regulation in Neurospora crassa effects of nutrients and of temperature, Arch. Microbiol. 89, 83 (1973).
  32. T. Yoshida et al., Temperature effects on the egg development time and hatching success of three acartia species (Copepoda: Calanoida) from the Strait of Malacca, Zool. Stud. 51, 644 (2012).
  33. J. Choi et al., Probing and manipulating embryogenesis via nanoscale thermometry and temperature control, Proc. Nat. Acad. Sci. USA 117, 14636 (2020).
  34. A. Dell, S. Pawara, and V. Savage, Systematic variation in the temperature dependence of physiological and ecological traits, Proc. Natl. Acad. Sci. USA 108, 10591 (2011).
  35. J. Hastings and B. Sweeney, On the mechanism of temperature independence in a biological clock, Proc. Nat. Acad. Sci. USA 43, 804 (1957).
  36. P. Ruoff et al., Modeling temperature compensation in chemical and biological oscillators, Chronobiol. Int. 14, 499 (1997).
  37. P. Ruoff, M. Vinsjevik, and L. Rensing, Temperature compensation in biological oscillators: A challenge for joint experimental and theoretical analysis, Comments Theor. Biol. 5, 361 (2000).
  38. P. Ruoff et al., Temperature dependency and temperature compensation in a model of yeast glycolytic oscillations, Biophys. Chem. 106, 179 (2003).
  39. D. Virshup and D. Forger, Keeping the beat in the rising heat, Cell 137, 602 (2009).
  40. J. Leloup and A. Goldbeter, Temperature compensation of circadian rhythms: Control of the period in a model for circadian oscillations of the per protein in Drosophila, Chronobiol. Int. 14, 511 (1997).
  41. C. Hong and J. Tyson, A proposal for temperature compensation of the circadian rhythm in Drosophila based on dimerization of the per protein, Chronobiol. Int. 14, 521 (1997).
  42. C. Pittendrigh, On temperature independence in the clock system controlling emergence time in Drosophila, Proc. Nat. Acad. Sci. USA 40, 1018 (1954).
  43. P. Kidda, M. Younga, and E. Siggia, Temperature compensation and temperature sensation in the circadian clock, Proc. Nat. Acad. Sci. USA 112, E6284 (2015).
  44. C. Vibe, The temperature response of the medaka segmentation clock and its link to robustness in embryonic patterning, Ph.D. thesis, University of Heidelberg, Germany, 2021.
  45. K. Valeur and R. degli Agosti, Simulations of temperature sensitivity of the peroxidase–oxidase oscillator, Biophys. Chem. 99, 259 (2002).
  46. P. Ruoff, Introducing temperature-compensation in any reaction kinetic oscillator model, J. Interdiscipl. Cycle Res. 23, 92 (1992).
  47. J. Bourn and M. Dorrity, Degrees of freedom: Temperature's influence on developmental rate, Curr. Opin. Gen. Dev. 85, 102155 (2024).
  48. J. Rombouts Jr., F. Tavella, A. Vandervelde, C. Phong, J. E. Ferrell, Q. Yang, and L. Gelens, Mechanistic origins of temperature scaling in the early embryonic cell cycle, Nat. Commun. 16, 8045 (2025).
  49. J. J. Tyson, K. C. Chen, and B. Novak, Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell, Curr. Opin. Cell Biol. 15, 221 (2003).
  50. D. Ratkowsky, J. Olley, and T. Ross, Unifying temperature effects on the growth rate of bacteria and the stability of globular protein, J. Theor. Biol. 233, 351 (2005).
  51. R. Schoolfield, P. Sharpe, and C. Magnuson, Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory, J. Theor. Biol. 88, 719 (1981).
  52. T. Wagner et al., Modeling insect development rates: A literature review and application of a biophysical model, Ann. Entomol. Soc. Am. 77, 208 (1984).
  53. J. Knies and J. Kingsolver, Erroneous Arrhenius: Modified Arrhenius model best explains the temperature dependence of ectotherm fitness, Am. Nat. 176, 227 (2010).
  54. S. Iyer-Biswas et al., Scaling laws governing stochastic growth and division of single bacterial cells, Proc. Nat. Acad. Sci USA 111, 15912 (2014).
  55. B. Quinn, A critical review of the use and performance of different function types for modeling temperature-dependent development of arthropod larvae, J. Therm. Biol. 63, 65 (2017).
  56. F. Rebaudo and V. Rabhi, Modeling temperature-dependent development rate andphenology in insects: Review of major developments, challenges, and future directions, Entomol. Exp. Appl. 166, 607 (2018).
  57. B. Régnier, J. Legrand, and F. Rebaudo, Modeling temperature-dependent development rate in insects and implications of experimental design, Environ Entomol. 51, 132 (2022).
  58. X. Yin et al., A nonlinear model for crop development as a function of temperature, Agric. For. Meteorol. 77, 1 (1995).
  59. M. Zwietering et al., Modeling of bacterial growth as a function of temperature, Appl. Environ. Microbiol. 57, 1094 (1991).
  60. F. Guerrero, J. M. Blanco, and V. Rodríguez, Temperature-dependent development in marine copepods: A comparative analysis of models, J. Plankton Res. 16, 95 (1994).
  61. W. Müller-Esterl, Biochemie (Springer Spektrum, Berlin, 2018).
  62. W. Müller, Developmental Biology (Springer Science & Business Media, Berlin, 1996).
  63. T. Gillespie, A rigorous derivation of the chemical master equation, Physica A 188, 404 (1992).
  64. D. Schnoerr, G. Sanguinetti, and R. Grima, Approximation and inference methods for stochastic biochemical kinetics—a tutorial review, J. Phys. A: Math. Theor. 50, 093001 (2017).
  65. K. Nam, R. Martinez-Corral, and J. Gunawardena, The linear framework: Using graph theory to reveal the algebra and thermodynamics of biomolecular systems, Interface Focus 12, 20220013 (2022).
  66. S. Redner, A Guide to First-Passage Processes (Cambridge University Press, Cambridge, 2001).
  67. N. van Kampen, Stochastic Processes in Physics and Chemistry (North-Holland, Amsterdam, 2004).
  68. T. Kanesaki, C. M. Edwards, U. S. Schwarz, and J. Grosshans, Dynamic ordering of nuclei in syncytial embryos: A quantitative analysis of the role of cytoskeletal networks, Integr. Biol. 3, 1112 (2011).
  69. J. Rodenfels, K. M. Neugebauer, and J. Howard, Heat oscillations driven by the embryonic cell cycle reveal the energetic costs of signaling, Dev. Cell 48, 646 (2019).
  70. J. E. Ferrell, T. Y.-C. Tsai, and Q. Yang, Modeling the cell cycle: Why do certain circuits oscillate? Cell 144, 874 (2011).
  71. R. Lyons and Y. Peres, Probability on Trees and Networks (Cambridge University Press, Cambridge, 2017), Vol. 42.
  72. J. Honerkamp, Stochastic Dynamical Systems: Concepts, Numerical Methods, Data Analysis (John Wiley & Sons, New York, 1996).
  73. K. Nam, Algebraic approaches to molecular information processing, Ph.D. thesis, Harvard University, 2021.
  74. K. Nam and J. Gunawardena, The linear framework II: Using graph theory to analyze the transient regime of Markov processes, Front. Cell Dev. Biol. 11, 1233808 (2023).
  75. F. Khodabandehlou, C. Maes, and K. Netočný, Trees and forests for nonequilibrium purposes: An introduction to graphical representations, J. Stat. Phys. 189, 41 (2022).
  76. M. Bladt and B. Nielsen, Matrix-Exponential Distributions in Applied Probability (Springer, Berlin, 2017), Vol. 81.
  77. P. D. Leenheer, An elementary proof of a matrix tree theorem for directed graphs, SIAM Rev. 62, 716 (2020).
  78. T. Hill, Studies in irreversible thermodynamics IV: Diagrammatic representation of steady state fluxes for unimolecular systems, J. Theor. Biol. 10, 442 (1966).
  79. R. Zia and B. Schmittmann, Probability currents as principal characteristics in the statistical mechanics of non-equilibrium steady states, J. Stat. Mech.: Theory Exp. (2007) P07012.
  80. G. Bell, B. Munsky, and I. Nemenman, The simplicity of completion time distributions for common complex biochemical processes, Phys. Biol. 7, 016003 (2009).
  81. J. Chong, C. Amourda, and T. Saunders, Temporal development of Drosophila embryos is highly robust across a wide temperature range, J. R. Soc. Interface 15, 20180304 (2018).
  82. A. Cayley, A theorem on trees, Quart. J. Math. 23, 376 (1889).
  83. J. B. Voits and U. S. Schwarz, Code and Data for ‘The Generic Temperature Response of Large Biochemical Networks’, Github (2025), https://github.com/JVoits/networks.
  84. D. B. Wilson, Generating random spanning trees more quickly than the cover time, in Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing (Association for Computing Machinery (ACM), New York, 1996), pp. 296–303.
  85. J. Lepock, Measurement of protein stability and protein denaturation in cells using differential scanning calorimetry, Methods 35, 117 (2005).
  86. C. K. Ewe, E. M. Sommermann, J. Kenchel, S. E. Flowers, M. F. Maduro, P. M. Joshi, and J. H. Rothman, Feedforward regulatory logic controls the specification-to-differentiation transition and terminal cell fate during caenorhabditis elegans endoderm development, Development 149, dev200337 (2022).
  87. One can find mathematical counterexamples otherwise, e.g., ki=i2k, which converges to CV=210.
  88. R. C. Bradley, Introduction to Strong Mixing Conditions (Kendrick Press, Heber City, 2007).
  89. A. Mateus et al., Thermal proteome profiling in bacteria: Probing protein state in vivo, Mol. Syst. Biol. 14, e8242 (2018).
  90. V. Aquilanti et al., Temperature dependence of chemical and biophysical rate processes: Phenomenological approach to deviations from Arrhenius law, Chem. Phys. Lett. 498, 209 (2010).
  91. M. Elias, G. Wieczorek, S. Rosenne, and D. S. Tawfik, The universality of enzymatic rate–temperature dependency, Trends Biochem. Sci. 39, 1 (2014).
  92. S. Gomez Melo, D. Wörthmüller, P. Gönczy, N. Banterle, and U. S. Schwarz, Grand canonical brownian dynamics simulations of adsorption and self-assembly of SAS-6 rings on a surface, J. Chem. Phys. 158, 085102 (2023).
  93. D. T. Gillespie, Stochastic simulation of chemical kinetics, Annu. Rev. Phys. Chem. 58, 35 (2007).
  94. D. Kulasiri and R. Kosarwal, Chemical Master Equation for Large Biological Networks: State-Space Expansion Methods Using AI (Springer, Singapore, 2021).
  95. N. Polizzi, M. J. Therien, and D. Beratan, Mean first-passage times in biology, Isr. J. Chem. 56, 816 (2017).
  96. L. Ham et al., A stochastic vs deterministic perspective on the timing of cellular events, Nat. Commun. 15, 54286 (2024).
  97. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (MIT Press, Cambridge, 2022).

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