StreAM-\(T_g\): Algorithms for Analyzing Coarse Grained RNA Dynamics Based on Markov Models of Connectivity-Graphs

  • Sven Jager
  • Benjamin Schiller
  • Thorsten Strufe
  • Kay Hamacher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9838)

Abstract

In this work, we present a new coarse grained representation of RNA dynamics. It is based on cliques and their patterns within adjacency matrices obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. Each adjacency matrix represents the interactions of k nucleotides. We then define transitions between states as changes in the adjacency matrices which form a Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-\(T_g\), a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. Here, we benchmark StreAM-\(T_g\) (a) for random and RNA unit sphere dynamic graphs. (b) we apply our method on a long term molecular dynamics simulation of a synthetic riboswitch (1,000 ns). In the light of experimental data our results show important design opportunities for the riboswitch.

Keywords

RNA Markovian dynamics Dynamic graphs Molecular dynamics Coarse graining Synthetic biology 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sven Jager
    • 1
  • Benjamin Schiller
    • 2
  • Thorsten Strufe
    • 2
  • Kay Hamacher
    • 1
    • 3
  1. 1.Computational Biology and Simulation, Department of BiologyTU DarmstadtDarmstadtGermany
  2. 2.Privacy and Data Security, Department of Computer ScienceTU DresdenDresdenGermany
  3. 3.Department of Physics, Department of Computer ScienceTU DarmstadtDarmstadtGermany

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