Evolutionary self-organization of cell-free genetic coding

Abstract

Genetic encoding provides a generic construction scheme for biomolecular functions. This paper addresses the key problem of coevolution and exploitation of the multiple components necessary to implement a replicable genetic encoding scheme. Extending earlier results on multicomponent replication, the necessity of spatial structure for the evolutionary stabilization of the genetic coding system is established. An individual-based stochastic model of interacting molecules in three-dimensional space is presented that allows the evolution of genetic coding to be analyzed explicitly. A massively parallel configurable computer (NGEN) is used to implement the model, on the time scale of millions of generations, directly in electronic hardware. The spatial correlations between components of the genetic coding system are analyzed and found to be essential for evolutionary stability.T he genetic code, as a wonder of ancient biological engineering, has excited much recent study (1). Its structure, origin, uniqueness, and information-processing capabilities have been the focus of biochemical and theoretical analysis. Its emergence has often been identified (2) with the origin of life itself in the long-standing debate as to whether genes or proteins came first. Genetic coding of function remains a central issue in an RNA world, in which RNA catalysts could act singly or jointly as polymerases to replicate other RNA as genes. It has long been realized that some form of effective compartmentation is necessary to allow the selection of the functional multicomponent replicable systems needed to implement a genetic code, and this has added an additional complexity to models of the evolution of such systems. Possibly for this reason, there have been few explicit models of the evolution of genetic coding. In this paper, we show that a multicomponent replication-translation system can evolve a stable genetic code in a continuous medium with no explicit compartmentation or control thereof.The model framework is simple and abstract, compared with our detailed understanding of the cellular translation process. However, it appears to capture the essential organizational problem associated with the simultaneous evolution of a generic coding mechanism and self-replicating entities. The basic assumptions of the model are:(i) Two distinct combinatorial families of chain molecules (catalysts and templates) are formed at a slow rate by random synthesis. (ii) Catalytic molecules (like proteins) are mostly not capable of self replication, whereas template molecules (like RNA) can be transcribed (or replicated) and translated generically. (iii) At least one rare polymer sequence exists that catalyzes the replication of templates; no recognition of specific template sequences is assumed. (iv) Rare polymer sequences exist that catalyze the translation of templates to potentially catalytic molecules according to one of a number of ''conflicting'' codes; no specific recognition of template sequences is assumed. (v) Molecular reactions are limited b...

Coding of Class I and II Aminoacyl-tRNA Synthetases

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Insuperable problems of the genetic code initially emerging in an RNA world

“…Much of the argument presented here rests on new comparisons of differential equations describing the translation dynamics of ribozymal, protein, and hybrid assignment catalysis and a generalization of the coupling within mathematical models for gene-replicase-translatase (GRT) systems (Füchslin and McCaskill, 2001; Markowitz et al, 2006). These elements proved too extensive to be compatible with a single publication, and are therefore developed in detail in this paper.…”

Bifurcations and phase transitions in spatially extended two-member hypercycles

“…Specifically, the local nature of interactions that takes place in reality (where systems are far from well mixed) pervade the emergence of new types of phenomena, from spatial structures to complex dynamics (Bascompte and Sole´, 1998). This is the case at all scales, from complex ecosystems (Bascompte and Sole´, 1995;Sole´and Bascompte, 2006) to molecular ecology (Breyer et al, 1998;Fu¨chslin and McCaskill, 2001;McCaskill, 1997). Space plays a key role in hypercycle dynamics, and spatial models of hypercyclic organization have been explored (Boerlijst, 2000;Chaco´n and Moran, 1993;Cronhjort, 1995;Cronhjort and Blomberg, 1994;Fontanari and Ferreira, 2002;Szaboé t al., 2002).…”