Temperature is a crucial factor in every biological function. It holds true at all scales, from molecules to ecosystems, and in all environments, regardless of how little or vast they are.
There hasn’t been a universal hypothesis explaining how temperature affects life until now. Researchers under the direction of Jose Ignacio Arroyo, a Santa Fe Institute Postdoctoral Fellow, provide a straightforward framework that accurately predicts how temperature affects living organisms at all scales in an article published in the Proceedings of the National Academy of Sciences.
“It is very fundamental,” says SFI External Professor Pablo Marquet, an ecologist at the Pontifica Universidad Catolica de Chile, in Santiago. Marquet, Arroyo’s Ph.D. thesis advisor, also worked on the model. “You can apply this to pretty much every process that is affected by temperature. We hope it will be a landmark contribution.”
According to Marquet, a theory like this might aid scientists in making precise predictions about a variety of things, such as biological responses to climate change, the development of infectious illnesses, and food production.
According to Marquet, previous attempts to generalize the impact of temperature on biology missed the “big picture” implications included in the new model. The Arrhenius equation, for instance, is frequently used by biologists and ecologists to explain how temperature affects the speeds of chemical processes.
That method successfully approximates how temperature affects some biological processes, but it is unable to adequately explain many others, such as growth rate and metabolism.
Arroyo’s first goal was to create a universal mathematical model that could forecast any biological variable’s behavior. But he soon understood that temperature was a sort of all-purpose predictor and might direct the creation of a fresh model.
I think that our ability to systematize temperature response has the potential to reveal novel unification in biological processes in order to resolve a variety of controversies.
Professor Chris Kempes
Beginning with a chemical theory that describes the kinetics of enzymes, he expanded the model from the quantum-molecular level to larger, macroscopic dimensions by making a few additions and suppositions. The model, which is significant, incorporates three components that previous approaches lacked.
First, it is derived from first principles, just like its cousin in chemistry. Second, the model’s core consists of a single, straightforward equation with a minimal number of variables. (The majority of current models call for a large number of assumptions and parameters.) Third, “it’s universal in the sense that it can explain patterns and behaviors for any microorganisms or any taxa in any environment,” he says.
Across all processes, taxa, and sizes, temperature responses all condense into a single general functional form.
“I think that our ability to systematize temperature response has the potential to reveal novel unification in biological processes in order to resolve a variety of controversies,” says SFI Professor Chris Kempes assisted the team in bridging the quantum-to-classical scales, along with SFI Professor Geoffrey West.
According to the PNAS paper, the new model makes predictions that are consistent with empirical observations of a variety of phenomena, such as the metabolic rate of an insect, the relative germination of alfalfa, the growth rate of a bacterium, and the mortality rate of a fruit fly.
According to Arroyo, the team intends to use this model to generate fresh forecasts, many of which were intended for the initial publication.
“The paper was just getting too big,” he says.
