A new heuristic method for approximating the number of local minima in partial RNA energy landscapes

Albrecht, Andreas A., Day, Luke, Souki, Ouala Abdelhadi Ep and Steinhofel, Kathleen (2016) A new heuristic method for approximating the number of local minima in partial RNA energy landscapes. Computational Biology and Chemistry, 60 . pp. 43-52. ISSN 1476-9271 [Article] (doi:10.1016/j.compbiolchem.2015.11.002)


The analysis of energy landscapes plays an important role in mathematical modelling, simulation and optimisation. Among the main features of interest are the number and distribution of local minima within the energy landscape. Granier and Kallel proposed in 2002 a new sampling procedure for estimating the number of local minima. In the present paper, we focus on improved heuristic implementations of the general framework devised by Granier and Kallel with regard to run-time behaviour and accuracy of predictions. The new heuristic method is demonstrated for the case of partial energy landscapes induced by RNA secondary structures. While the computation of minimum free energy RNA secondary structures has been studied for a long time, the analysis of folding landscapes has gained momentum over the past years in the context of co-transcriptional folding and deeper insights into cell processes. The new approach has been applied to ten RNA instances of length between 99 nt and 504 nt and their respective partial energy landscapes defined by secondary structures within an energy offset ΔE above the minimum free energy conformation. The number of local minima within the partial energy landscapes ranges from 1440 to 3441. Our heuristic method produces for the best approximations on average a deviation below 3.0% from the true number of local minima.

Item Type: Article
Additional Information: Available online 19 November 2015
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 18755
Depositing User: Andreas Albrecht
Date Deposited: 14 Jan 2016 10:49
Last Modified: 12 Jun 2019 12:24
URI: https://eprints.mdx.ac.uk/id/eprint/18755

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