A model for DNA computing using the recombination behaviour of DNA molecules known as a sticker system has been introduced by Adleman in 1994. A sticker model is an abstract computational model which uses the Watson-Crick complementary principle of DNA molecules. Starting from the axioms – incomplete double stranded sequences, and iteratively using sticking operations, complete double stranded sequences are obtained. It is known that sticker systems with finite sets of axioms and sticker rules generate only regular languages. Hence, different types of restrictions have been considered to increase the computational power of sticker systems. In this paper, we introduce probabilistic sticker systems in which probabilities are initially associated with the axioms, and the probability of the generated string is computed by multiplying the probabilities of all occurrences of the initial strings used in the computation of the string.

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The concept of splicing system was first introduced by Head in 1987. This model has been introduced to investigate the recombinant behavior of DNA molecules. Splicing systems with finite sets of axioms only generate regular languages. Hence, different restrictions have been considered to increase the computational power up to the recursively enumerable languages. Recently, probabilistic splicing systems have been introduced where probabilities are initially associated with the axioms, and the probability of a generated string is computed by multiplying the probabilities of all occurrences of the initial strings in the computation of the string. In this paper, some properties of probabilistic semi-simple splicing systems, which are special types of probabilistic splicing systems, are investigated. We prove that probabilistic semi-simple splicing systems can also increase the generative power of the generated languages.

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A splicing system, one of the early theoretical models for DNA computing was introduced by Head in 1987. Splicing systems are based on the splicing operation which, informally, cuts two strings of DNA molecules at the specific recognition sites and attaches the prefix of the first string to the suffix of the second string, and the prefix of the second string to the suffix of the first string, thus yielding the new strings. For a specific type of splicing systems, namely the simple splicing systems, the recognition sites are the same for both strings of DNA molecules. It is known that splicing systems with finite sets of axioms and splicing rules only generate regular languages. Hence, different types of restrictions have been considered for splicing systems in order to increase their computational power. Recently, probabilistic splicing systems have been introduced where the probabilities are initially associated with the axioms, and the probabilities of the generated strings are computed from the probabilities of the initial strings. In this paper, some properties of probabilistic simple splicing systems are investigated. We prove that probabilistic simple splicing systems can also increase the computational power of the splicing languages generated.