Rheology of Pseudomonas fluorescens biofilms: From experiments to predictive DPD mesoscopic modeling
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2023
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American Institute of Physics
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Abstract
Bacterial biofilms mechanically behave as viscoelastic media consisting of micron-sized bacteria cross-linked to a self-produced network of extracellular polymeric substances (EPSs) embedded in water. Structural principles for numerical modeling aim at describing mesoscopic viscoelasticity without losing details on the underlying interactions existing in wide regimes of deformation under hydrodynamic stress. Here, we approach the computational challenge to model bacterial biofilms for predictive mechanics in silico under variable stress conditions. Up-to-date models are not entirely satisfactory due to the plethora of parameters required to make them functioning under the effects of stress. As guided by the structural depiction gained in a previous work with Pseudomonas fluorescens [Jara et al., Front. Microbiol. 11, 588884 (2021)], we propose a mechanical modeling by means of Dissipative Particle Dynamics (DPD), which captures the essentials of topological and compositional interactions between bacterial particles and cross-linked EPS-embedding under imposed shear. The P. fluorescens biofilms have been modeled under mechanical stress mimicking shear stresses as undergone in vitro. The predictive capacity for mechanical features in DPD-simulated biofilms has been investigated by varying the externally imposed field of shear strain at variable amplitude and frequency. The parametric map of essential biofilm ingredients has been explored by making the rheological responses to emerge among conservative mesoscopic interactions and frictional dissipation in the underlying microscale. The proposed coarse grained DPD simulation qualitatively catches the rheology of the P. fluorescens biofilm over several decades of dynamic scaling. Published under an exclusive license by AIP Publishing.
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B.O., I.L.-M., and C.V. acknowledge funding from Grant UCM/Santander PR26/16. C.V acknowledges funding from MINECO grants EUR2021-122001, PID2019-105343GB-I00, IHRC22/00002. F.M. acknowledges funding from MINECO under Grant Nos. PID2019-105606RB-I00, FIS2016-78847-P, PID2019-108391RB-I00, and FIS2015-70339; from the REACT-EU program PR38-21-28 ANTICIPA-CM, a grant by the Comunidad de Madrid and European Union under the FEDER Program; from EU in response to COVID-19 pandemics; and from Comunidad de Madrid under Grant Nos. S2018/NMT-4389 and Y2018/BIO-5207. C.V. acknowledges funding from MINECO under Grant No. PID2019-105343GB-I00. F.A acknowledges the support from the "Juan de la Cierva" program (Grant No. FJCI-2017-33580). A.K.M. is recipient of a Sara Borrell fellowship (Grant No. CD18/00206) financed by the Spanish Ministry of Health. V.B. acknowledges the support from the European Commission through Marie Sklodowska-Curie Fellowship No. 748170 ProFrost. Support from MINECO (Grant No. IRHC22/00002) is also acknowledged. The authors acknowledge the computer resources from the Red Española de Supercomputación (RES) under Grant Nos. FI-2020-1-0015 and FI-2020-2-0032 and from the Vienna Scientific Cluster (VSC).