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Learning the dynamics of cell–cell interactions in confined cell migration
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| Zeitschriftentitel: | Proceedings of the National Academy of Sciences |
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| Personen und Körperschaften: | , , , , , |
| In: | Proceedings of the National Academy of Sciences, 118, 2021, 7 |
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| Sprache: | Englisch |
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Proceedings of the National Academy of Sciences
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Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. |
|---|---|
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Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. |
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Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. Proceedings of the National Academy of Sciences Learning the dynamics of cell–cell interactions in confined cell migration Multidisciplinary |
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brückner, david b. |
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Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.2016602118 <jats:title>Significance</jats:title> <jats:p>When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics.</jats:p> Learning the dynamics of cell–cell interactions in confined cell migration Proceedings of the National Academy of Sciences |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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Learning the dynamics of cell–cell interactions in confined cell migration |
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learning the dynamics of cell–cell interactions in confined cell migration |
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Multidisciplinary |
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<jats:title>Significance</jats:title>
<jats:p>When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics.</jats:p> |
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| description | <jats:title>Significance</jats:title> <jats:p>When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics.</jats:p> |
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| spelling | Brückner, David B. Arlt, Nicolas Fink, Alexandra Ronceray, Pierre Rädler, Joachim O. Broedersz, Chase P. 0027-8424 1091-6490 Proceedings of the National Academy of Sciences Multidisciplinary http://dx.doi.org/10.1073/pnas.2016602118 <jats:title>Significance</jats:title> <jats:p>When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics.</jats:p> Learning the dynamics of cell–cell interactions in confined cell migration Proceedings of the National Academy of Sciences |
| spellingShingle | Brückner, David B., Arlt, Nicolas, Fink, Alexandra, Ronceray, Pierre, Rädler, Joachim O., Broedersz, Chase P., Proceedings of the National Academy of Sciences, Learning the dynamics of cell–cell interactions in confined cell migration, Multidisciplinary |
| title | Learning the dynamics of cell–cell interactions in confined cell migration |
| title_full | Learning the dynamics of cell–cell interactions in confined cell migration |
| title_fullStr | Learning the dynamics of cell–cell interactions in confined cell migration |
| title_full_unstemmed | Learning the dynamics of cell–cell interactions in confined cell migration |
| title_short | Learning the dynamics of cell–cell interactions in confined cell migration |
| title_sort | learning the dynamics of cell–cell interactions in confined cell migration |
| title_unstemmed | Learning the dynamics of cell–cell interactions in confined cell migration |
| topic | Multidisciplinary |
| url | http://dx.doi.org/10.1073/pnas.2016602118 |