A machine learning based model accurately predicts cellular response to electric fields in multiple cell types.

ABSTRACT: Many cell types migrate in response to naturally generated electric fields. Furthermore, it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which do not generalize. Here, we present a machine learning model that can forecast directedness of cell migration given a timeseries of previous directedness and electric field values. This model is trained using time series galvanotaxis data of mammalian cranial neural crest cells obtained through time-lapse microscopy of cells cultured at 37 °C in a galvanotaxis chamber at ambient pressure. Next, we show that our modeling approach can be used for

SUBMITTER: Sargent B 

PROVIDER: S-EPMC9200721 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature