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Quantifying the impact of electric fields on single-cell motility

View ORCID ProfileTP Prescott, View ORCID ProfileK Zhu, View ORCID ProfileM Zhao, View ORCID ProfileRE Baker
doi: https://doi.org/10.1101/2021.01.22.427762
TP Prescott
1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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  • For correspondence: tpprescott@gmail.com
K Zhu
2Department of Ophthalmology and Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Sacramento, CA 95817, USA
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M Zhao
2Department of Ophthalmology and Vision Science, Department of Dermatology, Institute for Regenerative Cures, University of California, Sacramento, CA 95817, USA
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RE Baker
1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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ABSTRACT

Cell motility in response to environmental cues forms the basis of many developmental processes in multicellular organisms. One such environmental cue is an electric field (EF), which induces a form of motility known as electrotaxis. Electrotaxis has evolved in a number of cell types to guide wound healing, and has been associated with different cellular processes, suggesting that observed electrotactic behaviour is likely a combination of multiple distinct effects arising from the presence of an EF. In order to determine the different mechanisms by which observed electrotactic behaviour emerges, and thus to design EFs that can be applied to direct and control electrotaxis, researchers require accurate quantitative predictions of cellular responses to externally-applied fields. Here, we use mathematical modelling to formulate and parametrise a variety of hypothetical descriptions of how cell motility may change in response to an EF. We calibrate our model to observed data using synthetic likelihoods and Bayesian sequential learning techniques, and demonstrate that EFs bias cellular motility through only one of a selection of hypothetical mechanisms. We also demonstrate how the model allows us to make predictions about cellular motility under different EFs. The resulting model and calibration methodology will thus form the basis for future data-driven and model-based feedback control strategies based on electric actuation.

SIGNIFICANCE Electrotaxis is attracting much interest and development as a technique to control cell migration due to the precision of electric fields as actuation signals. However, precise control of electrotactic migration relies on an accurate model of how cell motility changes in response to applied electric fields. We present and calibrate a parametrised stochastic model that accurately replicates experimental single-cell data and enables the prediction of input–output behaviour while quantifying uncertainty and stochasticity. The model allows us to elucidate and quantify how electric fields perturb the motile behaviour of the cell. This model and the associated simulation-based calibration methodology will be central to future developments in the control of electrotaxis.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Additional data collected; conclusions on model calibration and parameter values updated in the light of new data; model predictions validated against held-back test sets.

  • https://github.com/tpprescott/electro

  • https://zenodo.org/record/4749429

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 28, 2021.
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Quantifying the impact of electric fields on single-cell motility
TP Prescott, K Zhu, M Zhao, RE Baker
bioRxiv 2021.01.22.427762; doi: https://doi.org/10.1101/2021.01.22.427762
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Quantifying the impact of electric fields on single-cell motility
TP Prescott, K Zhu, M Zhao, RE Baker
bioRxiv 2021.01.22.427762; doi: https://doi.org/10.1101/2021.01.22.427762

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