4.8 Article

A general theory for temperature dependence in biology

出版社

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2119872119

关键词

temperature kinetics; scaling; metabolic theory

资金

  1. Beca de Doctorado Nacional Agencia Nacional de Investigacion y Desarrollo (ANID) [21130515]
  2. ANID-Fondo de Desarrollo Cient'ifico y Tecnologico (FONDECYT) [1200925]
  3. Centro de Modelamiento Matematico (CMM) [FB210005]
  4. BASAL funds for centers of excellence from ANID-Chile [ACE210006, ACE210010]
  5. Grant EcoDep [PSI-AAP2020-0000000013]
  6. NSF [1838420, 1840301]
  7. Charities Aid Foundation of Canada
  8. ANID-FONDECYT [1150171, 1190998]
  9. [AFB 17008]

向作者/读者索取更多资源

This study proposes a general theory for temperature dependence in biology based on chemical reaction rates, which can describe the temperature response of various biological quantities from quantum to classical scales and fits well with empirical data.
At present, there is no simple, first principles-based, and general model for quantitatively describing the full range of observed biological temperature responses. Here we derive a general theory for temperature dependence in biology based on Eyring-EvansPolanyi's theory for chemical reaction rates. Assuming only that the conformational entropy of molecules changes with temperature, we derive a theory for the temperature dependence of enzyme reaction rates which takes the form of an exponential function modified by a power law and that describes the characteristic asymmetric curved temperature response. Based on a few additional principles, our model can be used to predict the temperature response above the enzyme level, thus spanning quantum to classical scales. Our theory provides an analytical description for the shape of temperature response curves and demonstrates its generality by showing the convergence of all temperature dependence responses onto universal relationships-a universal data collapse-under appropriate normalization and by identifying a general optimal temperature, around 25.C, characterizing all temperature response curves. The model provides a good fit to empirical data for a wide variety of biological rates, times, and steady-state quantities, from molecular to ecological scales and across multiple taxonomic groups (from viruses to mammals). This theory provides a simple framework to understand and predict the impact of temperature on biological quantities based on the first principles of thermodynamics, bridging quantum to classical scales.

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