Browsing by Author "Hampson, D. J. D."

Now showing 1 - 2 of 2

Open Access
Investigation of multi-objective optimization criteria for RNA design
(IEEE, 2017-12) Hampson, D. J. D.; Sav, Sinem; Tsang, H. H.
RNA design is the inverse of RNA folding and it appears to be NP-hard. In RNA design, a secondary structure is given and the goal is to find a nucleotide sequence that will fold into this structure. To find such sequence(s) involves exploring the exponentially large sequence space. In literature, heuristic algorithms are the standard technique for tackling the RNA design. Heuristic algorithms enable effective and efficient exploration of the high-dimensional sequence-structure space when searching for candidates that fold into a given target structure. The main goal of this paper is to investigate the use of multi-objective criteria in SIMARD and Quality Pre-selection Strategy (QPS). The objectives that we optimize are Hamming distance (between designed structure and target structure) and thermodynamic free energy. We examine the different combinations of optimization criteria, and attempt to draw conclusions about the relationships between them. We find that energy is a poor primary objective but makes an excellent secondary objective. We also find that using multi-objective pre-selection produces viable solutions in far fewer steps than was previously possible with SIMARD. © 2016 IEEE.
Open Access
SIMARD: a simulated annealing based RNA design algorithm with quality pre-selection strategies
(IEEE, 2017-12) Sav, Sinem; Hampson, D. J. D.; Tsang, H. H.
Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy. © 2016 IEEE.