RESUMO
Restriction-modification (RM) systems are cognate gene complexes that code for an endonuclease and a methylase. They are often thought to have developed in bacteria as protection against invading genetic material, e.g., phage DNA. The high diversity of RM systems, as observed in nature, is often ascribed to the coevolution of RM systems (which 'invent' novel types) and phages. However, the extent to which phages are insensitive to RM systems casts doubts on the effectiveness of RM systems as protection against infection and thereby on the reason for the diversity of RM systems. We present an eco-evolutionary model in order to study the evolution of the diversity of RM systems. The model predicts that in general diversity of RM systems is high. More importantly, the diversity of the RM systems is expressed either at the individual level or at the population level. In the first case all individuals carry RM systems of all sequence specificities, whereas in the second case they carry only one RM system or no RM systems at all. Nevertheless, in the second case the same number of sequence specificities are present in the population.
Assuntos
Bactérias/crescimento & desenvolvimento , Bacteriófagos/fisiologia , Enzimas de Restrição-Modificação do DNA/genética , Variação Genética/genética , Modelos Biológicos , Bactérias/enzimologia , Bactérias/genética , Bacteriófagos/enzimologia , Bacteriófagos/genética , Evolução Biológica , Simulação por ComputadorRESUMO
Colicins are plasmids that are carried in Escherichia coli. They code for a toxic protein and for proteins that confer on the host immunity against this toxin. When bacteria carry plasmids their growth rate is reduced. At the same time, the production of toxins makes it possible for colicinogenic bacteria to invade bacterium strains that are not immune. In natural bacterium populations there is a high diversity of colicin types. The reason for the maintenance of this diversity has been the subject of much recent debate. We have studied a simple eco-evolutionary model of the interaction of bacteria with colicins and show that high diversity of colicins is to be expected. We find two different dynamical modes each with a high diversity: a hyperimmunity mode and a multitoxicity mode. Bacteria are immune to most toxins in the first mode but in fact produce very few toxins. In the second mode bacteria are immune only to those toxins that they actually produce. In the second mode toxin levels per bacterium are much higher, whereas immunity levels per bacterium are lower.
Assuntos
Plasmídeos de Bacteriocinas/genética , Colicinas/genética , Evolução Molecular , Variação Genética , Modelos Genéticos , Toxinas Bacterianas/genética , Toxinas Bacterianas/imunologia , Plasmídeos de Bacteriocinas/imunologia , Colicinas/imunologia , Contagem de Colônia Microbiana , Genes Bacterianos/imunologia , Variação Genética/imunologiaRESUMO
Most evolutionary optimization models incorporate a fitness evaluation that is based on a predefined static set of test cases or problems. In the natural evolutionary process, selection is of course not based on a static fitness evaluation. Organisms do not have to combat every existing disease during their lifespan; organisms of one species may live in different or changing environments; different species coevolve. This leads to the question of how information is integrated over many generations. This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between "genotypes" (the programs) and "phenotypes". We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalizable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.
