Understanding the temperature response of biological systems: Part II -- Network-level mechanisms and emergent dynamics
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914380474482688 |
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| author | Jacobs, Simen Voits, Julian B. Frolov, Nikita Schwarz, Ulrich S. Gelens, Lendert |
| author_facet | Jacobs, Simen Voits, Julian B. Frolov, Nikita Schwarz, Ulrich S. Gelens, Lendert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03307 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Understanding the temperature response of biological systems: Part II -- Network-level mechanisms and emergent dynamics Jacobs, Simen Voits, Julian B. Frolov, Nikita Schwarz, Ulrich S. Gelens, Lendert Molecular Networks Soft Condensed Matter Statistical Mechanics Adaptation and Self-Organizing Systems Biological Physics 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. |
| title | Understanding the temperature response of biological systems: Part II -- Network-level mechanisms and emergent dynamics |
| topic | Molecular Networks Soft Condensed Matter Statistical Mechanics Adaptation and Self-Organizing Systems Biological Physics |
| url | https://arxiv.org/abs/2601.03307 |