title: In silico evolution of functional morphology: A test on bone tissue biomechanics.
abstract: Evolutionary algorithms (EAs) use Darwinian principles--selection among random variation and heredity--to find solutions to complex problems. Mostly used in engineering, EAs gain growing interest in ecology and genetics. Here, we assess their usefulness in functional morphology, introducing finite element modelling (FEM) as a simulated mechanical environment for evaluating the 'fitness' of randomly varying structures. We used this method to identify biomechanical adaptations in bone tissue, a long-lasting problem in skeletal morphology. The algorithm started with a bone tissue model containing randomly distributed vascular spaces. The EA randomly mutated the distribution of vascular spaces, and selected the new structure if its mechanical resistance was increased. After some thousands of generations, organized phenotypes emerged, containing vascular canals and sinuses, mimicking real bone tissue organizations. This supported the hypothesis that natural bone microstructures can result from biomechanical adaptation. Despite its limited faithfulness to reality, we discuss the ability of the EA+FEM method to assess adaptation in a dynamic evolutionary framework, which is not possible in the real world because of the generation times of macro-organisms. We also point out the interesting potential of EAs to simulate not only adaptation, but also concurrent evolutionary phenomenons such as historical contingency.
authors.affiliation: European Synchrotron Radiation Facility, BP 220, 6 rue Jules Horowitz, 38046 Grenoble Cedex, France. margerie.e.de@club.fr
authors.forename: Emmanuel
authors.initials: E
authors.lastname: de Margerie
authors.forename: Paul
authors.initials: P
authors.lastname: Tafforeau
authors.forename: Lalaonirina
authors.initials: L
authors.lastname: Rakotomanana
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D001696:Biomechanical Phenomena; D001842:Bone and Bones / Q000502:physiology*; D003198:Computer Simulation; D008954:Models, Biological