Introduction
Several models of the whole chemoton have been produced. Each model concentrates on answering a particular question about how the various subsystems of the chemoton could function and evolve. Sometimes one of the subsystems is ignored.
Template Evolution in Protocells: The Evolution of Increasing Information Transmission.
One would like to know how increasing amounts of information can be transmitted down the generations. One way is to be able to replicate longer and longer templates, i.e. increasing the information coded on one template. This option suffers from the problem that no known mechanism for long non-enzymatic template replication is known, and that for enzymatic methods of long template replication (i.e. replication mechanisms that depend on sequence specific second order autocatalysis), Eigen's paradox imposes a threshold to template length, Eigen (1971).
Another way is for shorter RNA templates (that could have arisen by non-enzyamtic means) to form a catalytic network of second-order autocatalysis, e.g. a hypercycle, Eigen (1971), Eigen & Schuster (1979), Zintzaras et al .,(2002). This option suffers from the fact that the hypercycle is unstable because template concentrations fluctuate, threatening extinctions (Eigen & Schuster, 1979), (Nuño & Tarazona, 1994), and external parasites and short-circuiting mutants can have a selective advantage, Maynard Smith (1979). No known hypercycle exists.
Another way is for 'bags of genes' to be inherited in protocells, Szathmáry & Demeter (1987), Niesert et al. (1981), that possess a non-specific replicase. These differ from hypercycle models since in hypercycles each template is a specific replicase for its own replication and for the neighbouring template. Non-specific replicase models without structured second-order autocatalysis are known as 'stochastic-corrector-models'. Of-course, this assumes that a replicase ribozyme has already evolved. But there is no demonstrated replicase ribozyme! Even if such a ribozyme existed, one of the recognized complications is that competition between templates within a protocell can result in short selfish templates replicating faster than longer slower templates that perhaps would have been catalytically more useful for the protocell, (Swetina & Schuster, 1982), Suzuki & Ono, (2003). This is a problem even for short non-enzymatic template replication within protocells. These problems limit template diversity and so reduce information content once again. Another problem is that even in the absence of competition, drift and mutation can result in loss of template diversity and hense loss of information. High group selection effects (i.e. poor vesicle replication rates without homogenous template concentrations, and high vesicles replication rates when all templates are present at equal concentration) are required to counteract template competition, drift and mutation, if survival of > 4 templates is to be maintained, under the reasonable assumption that different templates in a vesicle have different replication rates (Fontanari et al 2005, in press). It is an open question how such high group selection effects could be produced. What could a group of templates do to a vesicle that would mean that only with all the templates present, would the vesicle survive? If vesicles were capable of escaping this replication constraint imposed by template homogenaity, then vesicles with lower g, would be selected for.
It should be remembered that "scm" type models as well as the hypercycle type models assume the existence of a replicase. Both models therefore 'beg the question' of how such a replicase could arise. This is worrying, because it is the evolution of such a replicase that such models seem to be trying to explain.
Read Doudna & Lorsch, 2005. and Johnston et al. (2001).
Other approaches: Scheuring (2000) and Szabó et al. (2002).
Bibliography.
Ono, N. Emergence of Proto-cells in Marginal Environments. (2003) Download pdf.
Ono, N. Ikegami, T. Selection of Catalysts through Cellular Reproduction. (2002) Download pdf.
Schwehm, M. Parallel Stochastic Simulation of Whole-Cell Models. Download pdf.
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