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In microbial communities, various cell types often coexist by occupying distinct spatial domains. What determines the shape of the interface between such domains—which, in turn, influences the interactions between cells and overall community function? Here, we address this question by developing a continuum model of a 2D spatially structured microbial community with two distinct cell types. We find that, depending on the balance of the different cell proliferation rates and substrate friction coefficients, the interface between domains is either stable and smooth or unstable and develops fingerlike protrusions. We establish quantitative principles describing when these different interfacial behaviors arise and find good agreement with both the results of previous experimental reports as well as new experiments performed here. Our work, thus, helps to provide a biophysical basis for understanding the interfacial morphodynamics of proliferating microbial communities as well as a broader range of proliferating active systems. Published by the American Physical Society2025
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Comparing parameter-reduction methods on a biophysical model of an auditory hair cell
Biophysical models describing complex cellular phenomena typically include systems of nonlinear differential equations with many free parameters. While experimental measurements can fix some parameters, those describing internal cellular processes frequently remain inaccessible. Hence, a proliferation of free parameters risks overfitting the data, limiting the model's predictive power. In this study, we develop systematic methods, applying statistical analysis and dynamical-systems theory, to reduce parameter count in a biophysical model. We demonstrate our techniques on a five-variable computational model designed to describe active, mechanical motility of auditory hair cells. Specifically, we use two statistical measures, the total-effect and PAWN indices, to rank each free parameter by its influence on selected, core properties of the model. With the resulting ranking, we fix most of the less influential parameters, yielding a five-parameter model with refined predictive power. We validate the theoretical model with experimental recordings of active hair-bundle motility, specifically by using Akaike and Bayesian information criteria after obtaining maximum-likelihood fits. As a result, we determine the system's most influential parameters, which illuminate the key biophysical elements of the cell's overall features. Even though we demonstrate with a concrete example, our techniques provide a general framework, applicable to other biophysical systems. Published by the American Physical Society2024
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- Award ID(s):
- 2210316
- PAR ID:
- 10631409
- Publisher / Repository:
- American Physical Society
- Date Published:
- Journal Name:
- Physical Review Research
- Volume:
- 6
- Issue:
- 3
- ISSN:
- 2643-1564
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1103/PhysRevResearch.6.033121
