The mechanical properties of biofilms can be used to predict biofilm deformation under external forces, for example, under fluid flow. We used magnetic tweezers to spatially map the compliance of Pseudomonas aeruginosa biofilms at the microscale, then applied modeling to assess its effects on biofilm deformation. Biofilms were grown in capillary flow cells with Reynolds numbers (Re) ranging from 0.28 to 13.9, bulk dissolved oxygen (DO) concentrations from 1 mg/L to 8 mg/L, and bulk calcium ion (Ca2+) concentrations of 0 and 100 mg CaCl2/L. Higher Re numbers resulted in more uniform biofilm morphologies. The biofilm was stiffer at the center of the flow cell than near the walls. Lower bulk DO led to more stratified biofilms. Higher Ca2+ concentrations led to increased stiffness and more uniform mechanical properties. Using the experimental mechanical properties, fluid–structure interaction models predicted up to 64% greater deformation for heterogeneous biofilms, compared with a homogeneous biofilms with the same average properties. However, the deviation depended on the biofilm morphology and flow regime. Our results show significant spatial mechanical variability exists at the microscale, and that this variability can potentially affect biofilm deformation. The average biofilm mechanical properties, provided in many studies, should be used with caution when predicting biofilm deformation.

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Biofilms commonly develop in flowing aqueous environments, where the flow causes the biofilm to deform. Because biofilm deformation affects the flow regime, and because biofilms behave as complex heterogeneous viscoelastic materials, few models are able to predict biofilm deformation. In this study, a phase-field (PF) continuum model coupled with the Oldroyd-B constitutive equation was developed and used to simulate biofilm deformation. The accuracy of the model was evaluated using two types of biofilms: a synthetic biofilm, made from alginate mixed with bacterial cells, and a Pseudomonas aeruginosa biofilm. Shear rheometry was used to experimentally determine the mechanical parameters for each biofilm, used as inputs for the model. Biofilm deformation under fluid flow was monitored experimentally using optical coherence tomography. The comparison between the experimental and modeling geometries, for selected horizontal cross sections, after fluid-driven deformation was good. The relative errors ranged from 3.2 to 21.1% for the synthetic biofilm and from 9.1 to 11.1% for the P. aeruginosa biofilm. This is the first demonstration of the effectiveness of a viscoelastic PF biofilm model. This model provides an important tool for predicting biofilm viscoelastic deformation. It also can benefit the design and control of biofilms in engineering systems.

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Biofilms are typically heterogeneous in morphology, structure, and composition, resulting in nonuniform mechanical properties. The distribution of mechanical properties, in turn, determines the biofilm behavior, such as deformation and detachment. Most biofilm models neglect biofilm heterogeneity, especially at the microscale. In this study, an image‐based modeling approach was developed to transform two‐dimensional optical coherence tomography (OCT) biofilm images to a pixel‐scale non‐Newtonian viscosity map of the biofilm. The map was calibrated using the bulk viscosity data from rheometer tests, based on assumed maximum and minimum viscosities and a relationship between OCT image intensity signals and non‐Newtonian viscosity. While not quantitatively measuring biofilm viscosity for each pixel, it allows a rational spatial allocation of viscosities within the biofilm: areas with lower cell density, for example, voids, are assigned lower viscosities, and areas with high cell densities are assigned higher viscosities. The spatial distribution of non‐Newtonian viscosity was applied in an established Oldroyd‐B constitutive model and implemented using the phase‐field continuum approach for the deformation and stress analysis. The heterogeneous model was able to predict deformations more accurately than a homogenous one. Stress distribution in the heterogeneous biofilm displayed better characteristics than that in the homogeneous one, because it is highly dependent on the viscosity distribution. This study, using a pixel‐scale, image‐based approach to map the mechanical heterogeneity of biofilms for computational deformation and stress analysis, provides a novel modeling approach that allows the consideration of biofilm structural and mechanical heterogeneity. Future research should better characterize the relationship between OCT signal and viscosity, and consider other constitutive models for biofilm mechanical behavior.