Computational Modeling of Bacterial Biofilms

With recent advances in experimental imaging and image analysis techniques, highly time-resolved measurements of complex bacterial communities at single-cell resolution are now possible to obtain. Guided by these rich experimental data sets, we improve a recently proposed three-dimensional individual-based simulation framework to uncover governing microscopic dynamics at single-cell level that drive the structural developments in growing biofilms. Our individual-based model incorporates the essential biophysical processes of cell growth and division, viscous drag, attractive-repulsive cell-surface interactions, attractive-repulsive cell-cell interactions and external forces and torques (e.g. from surrounding flow field). Codes employing graphics processing units (GPUs) are developed to perform simulations to achieve a high degree of parallelization. To validate our simulations with single-cell experimental data, we develop quantitative methods to effectively summarize biofilm architectural properties by a feature vector. With this simulation framework, we investigate the collective dynamics of Vibrio cholerae biofilm formation in various flow intensities. Our experimental and numerical results imply that mechanical cell-cell interactions, combined with the effect of flow when flow intensity is high, account for the emergence of order and structure seen in growing biofilms. In addition, this framework is used to identify the single-cell level mechanisms in the breakdown of Vibrio cholerae biofilm architecture during exposure to antibiotics. We further apply this framework to identify universal mechanical properties that determine early-stage biofilm architectures of four widely studied bacterial species.This work shows an enhanced understanding of the microscopic physics governing biofilm development, which is essential to control and inhibit bacterial populations.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mathematics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Song, Boya

Advisor dc:contributor.advisor

• Dunkel, Jörn

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139009

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139009