Abstract
Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways. We seek an analogous computational process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. A single organism is represented by a set of exectuing sequences. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate “invasion when rare”.
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Hickinbotham, S. et al. (2011). Molecular Microprograms. In: Kampis, G., Karsai, I., Szathmáry, E. (eds) Advances in Artificial Life. Darwin Meets von Neumann. ECAL 2009. Lecture Notes in Computer Science(), vol 5777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21283-3_37
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DOI: https://doi.org/10.1007/978-3-642-21283-3_37
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