The generative capacity of probabilistic splicing systems

Mathuri Selvarajoo, Sherzod Turaev, Wan Heng Fong, Nor Haniza Sarmin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The concept of probabilistic splicing system was introduced as a model for stochastic processes using DNA computing techniques. In this paper we introduce splicing systems endowed with different continuous and discrete probabilistic distributions and call them as probabilistic splicing systems. We show that any continuous distribution does not increase the generative capacity of the probabilistic splicing systems with finite components, meanwhile, some discrete distributions increase their generative capacity up to context-sensitive languages. Finally, we associate certain thresholds with probabilistic splicing systems and this increases the computational power of splicing systems with finite components.

Original languageEnglish
Pages (from-to)1191-1198
Number of pages8
JournalApplied Mathematics and Information Sciences
Issue number3
Publication statusPublished - 2015
Externally publishedYes


  • DNA computing
  • Generative capacity
  • Probabilistic splicing systems
  • Splicing systems

ASJC Scopus subject areas

  • Analysis
  • Numerical Analysis
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics


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