title: Network Theory Tools for RNA Modeling.
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.
authors.affiliation: New York University Department of Chemistry Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY 10012, USA.
authors.forename: Namhee
authors.initials: N
authors.lastname: Kim
authors.affiliation: College of Staten Island City University of New York Department of Computer Science 2800 Victory Boulevard Staten Island, NY 10314, USA louis.petingi@csi.cuny.edu.
authors.forename: Louis
authors.initials: L
authors.lastname: Petingi
authors.affiliation: New York University Department of Chemistry Courant Institute of Mathematical Sciences 251 Mercer Street New York, NY 10012, USA schlick@nyu.edu.
authors.forename: Tamar
authors.initials: T
authors.lastname: Schlick
keywords: In Vitro Selection; Network Theory; RNA Prediction; RNA-As-Graphs