PIRSA:16050031

Growth dynamics and scaling laws across levels of biological organization.

Hatton, I. (2016). Growth dynamics and scaling laws across levels of biological organization.. Perimeter Institute. https://pirsa.org/16050031

Hatton, Ian. Growth dynamics and scaling laws across levels of biological organization.. Perimeter Institute, May. 16, 2016, https://pirsa.org/16050031 

          @misc{ pirsa_PIRSA:16050031,
            doi = {10.48660/16050031},
            url = {https://pirsa.org/16050031},
            author = {Hatton, Ian},
            keywords = {Other},
            language = {en},
            title = {Growth dynamics and scaling laws across levels of biological organization.},
            publisher = {Perimeter Institute},
            year = {2016},
            month = {may},
            note = {PIRSA:16050031 see, \url{https://pirsa.org}}
          }

Ian Hatton

McGill University

Talk number
PIRSA:16050031
Collection
Talk Type
Subject
Abstract

Recent findings on quantitative growth patterns have revealed striking generalities across the tree of life, and recurring over distinct levels of organization. Growth-mass relationships in 1) individual growth to maturity, 2) population reproduction, 3) insect colony enlargement and 4)  community production across wholeecosystems of very different types, often follow highly robust near ¾ scaling laws. These patterns represent some of the most general relations in biology, but the reasons they are so strangely similar across levels of organization remains a mystery. The dynamics of these distinct levels are connected, yet their scaling can be shown to arise independently, and free of system-specific properties. Numerous experiments in prebiotic chemistry have shown that minimal self-replicating systems that undergo template-directed synthesis, typically show reaction orders (ie. growth-mass exponents) between ½ and 1. I will outline how modifications to these simplified reaction schemes can yield growth-mass exponents near ¾, which may offer insight into dynamical connections across hierarchical systems.