Network theory tools for RNA modeling

Namhee Kim, Louis Petingi, Tamar Schlick

Research output: Contribution to journalArticle

Abstract

An introduction into the usage of graph or network theory tools for the study of RNA molecules is presented. By using vertices and edges to define RNA secondary structures as tree and dual graphs, we can enumerate, predict, and design RNA topologies. Graph connectivity and associated Laplacian eigenvalues relate to biological properties of RNA and help understand RNA motifs as well as build, by computational design, various RNA target structures. Importantly, graph theoretical representations of RNAs reduce drastically the conformational space size and therefore simplify modeling and prediction tasks. Ongoing challenges remain regarding general RNA design, representation of RNA pseudoknots, and tertiary structure prediction. Thus, developments in network theory may help advance RNA biology.

Original languageEnglish (US)
Pages (from-to)941-955
Number of pages15
JournalWSEAS Transactions on Mathematics
Volume12
Issue number9
StatePublished - Sep 2013

Keywords

  • In Vitro Selection
  • Network Theory
  • RNA Prediction
  • RNA-As-Graphs

ASJC Scopus subject areas

  • Algebra and Number Theory
  • Endocrinology, Diabetes and Metabolism
  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Management Science and Operations Research
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

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