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 dynamics of living systems often serves the purpose of reachingfunctionally important target states. We previously proposed a theory toanalyze stochastic biological dynamics evolving towards target states inreverse time. However, a large class of systems in biology can only beadequately described using state-dependent noise, which had not been discussed.For example, in gene regulatory networks, biochemical signaling networks orneuronal circuits, count fluctuations are the dominant noise component. Wecharacterize such dynamics as an ensemble of target state aligned (TSA)trajectories and characterize its temporal evolution in reverse-time bygeneralized Fokker-Planck and stochastic differential equations withmultiplicative noise. We establish the classification of boundary conditionsfor target state modeling for a wide range of power law dynamics, and derive auniversal low-noise approximation of the final phase of target stateconvergence. Our work expands the range of theoretically tractable systems inbiology and enables novel experimental design strategies for systems thatinvolve target states.

Reverse-time analysis and boundary classification of directional biological dynamics with multiplicative noise