RNA folding kinetics using Monte Carlo and Gillespie algorithms?

• DOI: 10.48550/arxiv.1707.03922
• Publication Date: 2017-01-01
• Authors: Peter Clote; Amir H. Bayegan
• Keywords: Folding (DSP implementation); Monte Carlo algorithm; Boltzmann constant

RNA secondary structure folding kinetics is known to be important for the biological function of certain processes, such as hok/sok system in E. coli. Although linear algebra provides an exact computational solution with respect Turner energy model tiny (~ 20 nt) sequences, larger sequences can only approximated by binning structures into macrostates a coarse-grained model, or repeatedly simulating either Monte Carlo algorithm Gillespie algorithm. Here we investigate relation between and We prove that asymptotically, expected time K-step trajectory equal <N> times algorithm, where denotes Boltzmann network degree. If regular (i.e. every node has same degree), then mean first passage (MFPT) computed MFPT multiplied <N>; however, this not true non-regular networks. In particular, kinetics, although are roughly correlated. Simulation software according Gille- spie algorithms publicly available, our compute degree net- work given sequence { see http://bioinformatics.bc.edu/clote/ RNAexpNumNbors.