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

DOI 10.48660/16050031
Collection
Talk Type Scientific Series
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.