Thu, 21 Aug 2008 09:22:23 BST CiteULike: austins modeling http://www.citeulike.org/user/austin/tag/modeling CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/austin/article/150261 <i>Proc Natl Acad Sci U S A, Vol. 100, No. 26. (23 December 2003), pp. 15310-15315.</i><br /><br />Ab initio RNA secondary structure predictions have long dismissed helices interior to loops, so-called pseudoknots, despite their structural importance. Here we report that many pseudoknots can be predicted through long-time-scale RNA-folding simulations, which follow the stochastic closing and opening of individual RNA helices. The numerical efficacy of these stochastic simulations relies on an O(n2) clustering algorithm that computes time averages over a continuously updated set of n reference structures. Applying this exact stochastic clustering approach, we typically obtain a 5- to 100-fold simulation speed-up for RNA sequences up to 400 bases, while the effective acceleration can be as high as 105-fold for short, multistable molecules (<or=150 bases). We performed extensive folding statistics on random and natural RNA sequences and found that pseudoknots are distributed unevenly among RNA structures and account for up to 30% of base pairs in G+C-rich RNA sequences (online RNA-folding kinetics server including pseudoknots: http://kinefold.u-strasbg.fr). Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations. A Xayaphoummine T Bucher F Thalmann H Isambert doi:10.1073/pnas.2536430100 Proc Natl Acad Sci U S A, Vol. 100, No. 26. (23 December 2003), pp. 15310-15315. 2005-04-06T11:19:46-00:00 2003 Proc Natl Acad Sci U S A 0027-8424 100 26 15310 15315 kinefold kinetics modeling pseudoknot rna http://www.citeulike.org/user/austin/article/1885378 <i>Nat Biotech, Vol. 25, No. 11. (November 2007), pp. 1239-1249.</i> Executable cell biology Jasmin Fisher Thomas Henzinger doi:10.1038/nbt1356 Nat Biotech, Vol. 25, No. 11. (November 2007), pp. 1239-1249. 2007-11-08T17:06:30-00:00 2007 Nat Biotech 25 11 1239 1249 Nature Publishing Group modeling http://www.citeulike.org/user/austin/article/1691183 <i>Bioinformatics, Vol. 23, No. 18. (15 September 2007), pp. 2415-2422.</i><br /><br />MOTIVATION: The goal of synthetic biology is to design and construct biological systems that present a desired behavior. The construction of synthetic gene networks implementing simple functions has demonstrated the feasibility of this approach. However, the design of these networks is difficult, notably because existing techniques and tools are not adapted to deal with uncertainties on molecular concentrations and parameter values. RESULTS: We propose an approach for the analysis of a class of uncertain piecewise-multiaffine differential equation models. This modeling framework is well adapted to the experimental data currently available. Moreover, these models present interesting mathematical properties that allow the development of efficient algorithms for solving robustness analyses and tuning problems. These algorithms are implemented in the tool RoVerGeNe, and their practical applicability and biological relevance are demonstrated on the analysis of the tuning of a synthetic transcriptional cascade built in Escherichia coli. AVAILABILITY: RoVerGeNe and the transcriptional cascade model are available at http://iasi.bu.edu/%7Ebatt/rovergene/rovergene.htm. Robustness analysis and tuning of synthetic gene networks. G Batt B Yordanov R Weiss C Belta doi:10.1093/bioinformatics/btm362 Bioinformatics, Vol. 23, No. 18. (15 September 2007), pp. 2415-2422. 2007-09-25T01:56:45-00:00 2007 Bioinformatics 1460-2059 23 18 2415 2422 modeling syntheticbiology