Understanding the temperature response of biological systems: Part II -- Network-level mechanisms and emergent dynamics

TL;DR

This paper explores how network-level mechanisms and models explain the complex temperature responses observed in biological systems, linking molecular reactions to system-wide behaviors and adaptations.

Contribution

It introduces and reviews network-level models, both deterministic and stochastic, that elucidate how temperature effects emerge at the system level from molecular interactions.

Findings

Arrhenius-like dependence transforms into non-Arrhenius scaling

Models reveal thermal limits and temperature compensation mechanisms

Provides mechanistic insights into biological robustness and evolution

Abstract

Building on the phenomenological and microscopic models reviewed in Part I, this second part focuses on network-level mechanisms that generate emergent temperature response curves. We review deterministic models in which temperature modulates the kinetics of coupled biochemical reactions, as well as stochastic frameworks, such as Markov chains, that capture more complex multi-step processes. These approaches show how Arrhenius-like temperature dependence at the level of individual reactions is transformed into non-Arrhenius scaling, thermal limits, and temperature compensation at the system level. Together, network-level models provide a mechanistic bridge between empirical temperature response curves and the molecular organization of biological systems, giving us predictive insights into robustness, perturbations, and evolutionary constraints.