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Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population
Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554727/ http://dx.doi.org/10.1098/rsif.2022.0412 |
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author | Messenger, Daniel A. Wheeler, Graycen E. Liu, Xuedong Bortz, David M. |
author_facet | Messenger, Daniel A. Wheeler, Graycen E. Liu, Xuedong Bortz, David M. |
author_sort | Messenger, Daniel A. |
collection | PubMed |
description | Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (WSINDy) methodology has been shown to be computationally efficient for identifying the governing equations of complex systems from noisy data. Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for the second-order IPS to learn equations for communities of cells. Our approach learns the directional interaction rules for each individual cell that in aggregate govern the dynamics of a heterogeneous population of migrating cells. To sort a cell according to the active classes present in its model, we also develop a novel ad hoc classification scheme (which accounts for the fact that some cells do not have enough evidence to accurately infer a model). Aggregated models are then constructed hierarchically to simultaneously identify different species of cells present in the population and determine best-fit models for each species. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments. |
format | Online Article Text |
id | pubmed-9554727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-95547272022-10-25 Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population Messenger, Daniel A. Wheeler, Graycen E. Liu, Xuedong Bortz, David M. J R Soc Interface Life Sciences–Mathematics interface Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (WSINDy) methodology has been shown to be computationally efficient for identifying the governing equations of complex systems from noisy data. Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for the second-order IPS to learn equations for communities of cells. Our approach learns the directional interaction rules for each individual cell that in aggregate govern the dynamics of a heterogeneous population of migrating cells. To sort a cell according to the active classes present in its model, we also develop a novel ad hoc classification scheme (which accounts for the fact that some cells do not have enough evidence to accurately infer a model). Aggregated models are then constructed hierarchically to simultaneously identify different species of cells present in the population and determine best-fit models for each species. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments. The Royal Society 2022-10-12 /pmc/articles/PMC9554727/ http://dx.doi.org/10.1098/rsif.2022.0412 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Messenger, Daniel A. Wheeler, Graycen E. Liu, Xuedong Bortz, David M. Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title_full | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title_fullStr | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title_full_unstemmed | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title_short | Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
title_sort | learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554727/ http://dx.doi.org/10.1098/rsif.2022.0412 |
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