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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The automated inference of physically interpretable (bio)chemical reactionnetwork models from measured experimental data is a challenging problem whosesolution has significant commercial and academic ramifications. It isdemonstrated, using simulations, how sets of elementary reactions comprisingchemical reaction networks, as well as their rate coefficients, may beaccurately recovered from non-equilibrium time series concentration data, suchas that obtained from laboratory scale reactors. A variant of an evolutionaryalgorithm called differential evolution in conjunction with least squarestechniques is used to search the space of reaction networks in order to inferboth the reaction network topology and its rate parameters. Properties of thestoichiometric matrices of trial networks are used to bias the search towardsphysically realisable solutions. No other information, such as chemicalcharacterisation of the reactive species is required, although where availableit may be used to improve the search process.

Reverse Engineering Chemical Reaction Networks from Time Series Data