English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For predicting the kinetics of nucleic acid reactions, continuous-time Markovchains (CTMCs) are widely used. The rate of a reaction can be obtained throughthe mean first passage time (MFPT) of its CTMC. However, a typical issue inCTMCs is that the number of states could be large, making MFPT estimationchallenging, particularly for events that happen on a long time scale (rareevents). We propose the pathway elaboration method, a time-efficientprobabilistic truncation-based approach for detailed-balance CTMCs. It can beused for estimating the MFPT for rare events in addition to rapidly evaluatingperturbed parameters without expensive recomputations. We demonstrate thatpathway elaboration is suitable for predicting nucleic acid kinetics byconducting computational experiments on 267 measurements that cover a widerange of rates for different types of reactions. We utilize pathway elaborationto gain insight on the kinetics of two contrasting reactions, one being a rareevent. We then compare the performance of pathway elaboration with thestochastic simulation algorithm (SSA) for MFPT estimation on 237 of thereactions for which SSA is feasible. We further build truncated CTMCs with SSAand transition path sampling (TPS) to compare with pathway elaboration.Finally, we use pathway elaboration to rapidly evaluate perturbed modelparameters during optimization with respect to experimentally measured ratesfor these 237 reactions. The testing error on the remaining 30 reactions, whichinvolved rare events and were not feasible to simulate with SSA, improvedcomparably with the training error. Our framework and dataset are available athttps://github.com/ DNA-and-Natural-Algorithms-Group/PathwayElaboration.

The pathway elaboration method for mean first passage time estimation in  large continuous-time Markov chains with applications to nucleic acid  kinetics