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

The genetic code has been shown to be very error robust compared to randomlyselected codes, but to be significantly less error robust than a certain codefound by a heuristic algorithm. We formulate this optimisation problem as aQuadratic Assignment Problem and thus verify that the code found by theheuristic is the global optimum. We also argue that it is strongly misleadingto compare the genetic code only with codes sampled from the fixed block model,because the real code space is orders of magnitude larger. We thus enlarge thespace from which random codes can be sampled from approximately 2.433 x 10^18codes to approximately 5.908 x 10^45 codes. We do this by leaving the fixedblock model, and using the wobble rules to formulate the characteristicsacceptable for a genetic code. By relaxing more constraints three larger spacesare also constructed. Using a modified error function, the genetic code isfound to be more error robust compared to a background of randomly generatedcodes with increasing space size. We point out that these results do notnecessarily imply that the code was optimized during evolution for errorminimization, but that other mechanisms could explain this error robustness.

Some mathematical refinements concerning error minimization in the genetic code