title: The simulated emergence of distributed environmental control in evolving microcosms.
abstract: This work continues investigation into Gaia theory [Lovelock, (1995) The ages of Gaia, Oxford University Press] from an artificial life perspective [Downing, (2000) in Proceedings of the 7th International Conference on Artificial Life, (pp. 90-99) MIT Press], with the aim of assessing the general compatibility of emergent distributed environmental control with conventional natural selection. Our earlier system, GUILD [Downing and Zvirinsky, (1999) Artificial Life, 5, 291-318], displayed emergent regulation of the chemical environment by a population of metabolizing agents, but the chemical model underlying those results was trivial, essentially admitting all possible reactions at a single energy cost. The new model, METAMIC, utilizes abstract chemistries that are both (a) constrained to a small set of legal reactions, and (b) grounded in basic fundamental relationships between energy, entropy, and biomass synthesis/breakdown. To explore the general phenomena of emergent homeostasis, we generate 100 different chemistries and use each as the basis for several METAMIC runs, as part of a Gaia hunt. This search discovers 20 chemistries that support microbial populations capable of regulating a physical environmental factor within their growth-optimal range, despite the extra metabolic cost. Case studies from the Gaia hunt illustrate a few simple mechanisms by which real biota might exploit the underlying chemistry to achieve some control over their physical environment. Although these results shed little light on the question of Gaia on Earth, they support the possibility of emergent environmental control at the microcosmic level.
authors.affiliation: Department of Computer Science, Norwegian University of Science and Technology (NTNU), 7020 Trondheim, Norway. keithd@idi.ntnu.no
authors.forename: Keith L
authors.initials: KL
authors.lastname: Downing
mesh_terms: D003198:Computer Simulation / Q000706:statistics & numerical data*; D004734:Energy Metabolism / Q000502:physiology; D004777:Environment; D008956:Models, Chemical