🚨 Late preprint alert! Why do biological process rates scale nonlinearly with temperature, deviating from the straight line on an Arrhenius plot? The key may lie in their inherent complexity! More in thread and here: doi.org/10.1101/2025...

[2/8] Our model is relatively straightforward: a cascade of n (reversible) Markov jump processes, each transition rate following Arrhenius-like temperature scaling, with forward transitions favored near a reference temperature.

[3/8] The completion time of such multi-step processes, i.e., reaching step n from step 1, was found to follow a quadratic-exponential function of inverse temperature. This scaling, seen in experiments, naturally emerges from averaging over many independent intermediate steps.

[4/8] Interestingly, this quadratic-exponential behavior holds within a range around the reference temperature, where forward transitions dominate and averaging applies.

[5/8] At temperature extremes, timing deviates from the quadratic-exponential law. It becomes dominated by the slowest rate-limiting step, where forward transitions turn slower than backward ones – effectively trapping the process in a loop.

[6/8] Our scaling equation – a quadratic-exponential function with single-exponential deviations at extremes – fits 100+ temperature-response curves across traits and taxa, offering a sound estimate of mean activation energy.

[7/8] Our model, a step toward a deeper mechanistic understanding of how biological processes scale with temperature, offers an alternative perspective that embraces the underlying complexity of biochemical regulation.