title: Selfishness versus functional cooperation in a stochastic protocell model.
abstract: How to design an "evolvable" artificial system capable to increase in complexity? Although Darwin's theory of evolution by natural selection obviously offers a firm foundation, little hope of success seems to be expected from the explanatory adequacy of modern evolutionary theory, which does a good job at explaining what has already happened but remains practically helpless at predicting what will occur. However, the study of the major transitions in evolution clearly suggests that increases in complexity have occurred on those occasions when the conflicting interests between competing individuals were partly subjugated. This immediately raises the issue about "levels of selection" in evolutionary biology, and the idea that multi-level selection scenarios are required for complexity to emerge. After analyzing the dynamical behaviour of competing replicators within compartments, we show here that a proliferation of differentiated catalysts and/or improvement of catalytic efficiency of ribozymes can potentially evolve in properly designed artificial cells where the strong internal competition between the different species of replicators is somewhat prevented (i.e., by choosing them with equal probability). Experimental evolution in these systems will likely stand as beautiful examples of artificial adaptive systems, and will provide new insights to understand possible evolutionary paths to the evolution of metabolic complexity.
authors.affiliation: Collegium Budapest, Institute for Advanced Study, Szentháromság u. 2, H-1014 Budapest, Hungary.
authors.forename: Elias
authors.initials: E
authors.lastname: Zintzaras
authors.forename: Mauro
authors.initials: M
authors.lastname: Santos
authors.forename: Eörs
authors.initials: E
authors.lastname: Szathmáry
mesh_terms: D058045:Artificial Cells / Q000378:metabolism*; D005075:Biological Evolution; D049109:Cell Proliferation; D003198:Computer Simulation; D008954:Models, Biological; D013269:Stochastic Processes; D013698:Templates, Genetic