"Living" under the challenge of information decay: the stochastic corrector model vs. hypercycles.
The combined problem of having a large genome size when the accuracy of replication was a limiting factor is probably the most difficult transition to explain at the late stages of RNA world. One solution has been to suggest the existence of a cyclically coupled system of autocatalytic and cross-catalytic molecular mutualists, where each member helps the following member and receives help from the preceding one (i.e., a "hypercycle"). However, such a system is evolutionarily unstable when mutations are taken into account because it lacks individuality. In time, the cooperating networks of genes should have been encapsulated in a cell-like structure. But once the cell was invented, it closely aligned genes' common interests and helped to reduce gene selfishness, so there was no need for hypercycles. A simple package of competing genes, described by the "stochastic corrector model" (SCM), could have provided the solution. Until now, there is no clear demonstration that the proposed mechanisms (compartmentalized hypercycles and the stochastic corrector model) do in fact solve the error threshold problem. Here, we present a Monte Carlo model to test the viability of protocell populations that enclose a hypercyclic (HPC) or a non-hypercyclic (SCM) system when faced with realistic mutation rates before the evolution of efficient enzymic machinery for replication. The numerical results indicate that both systems are efficient information integrators and are able to overcome the danger of information decay in the absence of accurate replication. However, a population of SCM protocells can tolerate higher deleterious mutation rates and reaches an equilibrium mutational load lower than that in a population of HPC protocells.
Zintzaras
Elias
E
Collegium Budapest, Institute for Advanced Study, Szentháromság u. 2, H-1014, Budapest, Hungary. zintza@nagref.gr
Santos
Mauro
M
Szathmary
Eors
E
D000818:Animals; D005075:Biological Evolution; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D009154:Mutation; D012313:RNA / Q000235:genetics*
D012313:RNA