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 dynamics of cellular pattern formation is crucial for understandingembryonic development and tissue morphogenesis. Recent studies have shown thathuman dermal fibroblasts cultured on liquid crystal elastomers can exhibit anincrease in orientational alignment over time, accompanied by cellproliferation, under the influence of the weak guidance of a molecularlyaligned substrate. However, a comprehensive understanding of how this orderarises remains largely unknown. This knowledge gap may be attributed, in part,to a scarcity of mechanistic models that can capture the temporal progressionof the complex nonequilibrium dynamics during the cellular alignment process.The orientational alignment occurs primarily when cells reach a high densitynear confluence. Therefore, for accurate modeling, it is crucial to take intoaccount both the cell-cell interaction term and the influence from thesubstrate, acting as a one-body external potential term. To fill in this gap,we develop a hybrid procedure that utilizes statistical learning approaches toextend the state-of-the-art physics models for quantifying both effects. Wedevelop a more efficient way to perform feature selection that avoids testingall feature combinations through simulation. The maximum likelihood estimatorof the model was derived and implemented in computationally scalable algorithmsfor model calibration and simulation. By including these features, such as thenon-Gaussian, anisotropic fluctuations, and limiting alignment interaction onlyto neighboring cells with the same velocity direction, this modelquantitatively reproduce the key system-level parameters--the temporalprogression of the velocity orientational order parameters and the variabilityof velocity vectors, whereas models missing any of the features fail to capturethese temporally dependent parameters.

Data-driven model construction for anisotropic dynamics of active matter