Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning
Data-driven PDEs are an important modeling tool when studying Chemotaxis at the macroscale, as they can learn bacterial motility from various data sources, fidelities (here, computational models, experiments) or coordinate systems. The resulting data-driven PDEs can then be simulated to reproduce/predict computational or experimental bacterial density profile data independent of the coordinate system, approximate meaningful parameters or functional terms, and even possibly estimate the underlying (unmeasured) chemonutrient field evolution.
Authors
- Juan M. Bello‐Rivas (ORCID: https://orcid.org/0000-0002-2640-0737)
- Yorgos M. Psarellis (ORCID: https://orcid.org/0000-0003-2540-0505)
- Ioannis G. Kevrekidis (ORCID: https://orcid.org/0000-0003-2220-3522)
- Tapomoy Bhattacharjee (ORCID: https://orcid.org/0000-0001-8899-1379)
- Sujit S. Datta (ORCID: https://orcid.org/0000-0003-2400-1561)
- Seungjoon Lee (ORCID: https://orcid.org/0000-0002-1714-7418)
Institutions
- Johns Hopkins University (US)
- Princeton University (US)
- Johns Hopkins Medicine (US)
- California State University, Long Beach (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1186/s12859-024-05929-w
- Citations
- 1
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.28
Funders
- National Science Foundation
- Pew Charitable Trusts
- Air Force Office of Scientific Research