Experimental and Computational Investigation of Biofilm Formation by Rhodopseudomonas palustris Growth under Two Metabolic Modes

• Published in: PloS one 2015-06, Vol.10 (6), p.e0129354-e0129354
• Main Authors: Kernan, Chase, Chow, Philicia P, Christianson, Rebecca J, Huang, Jean
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bacteria; Bioengineering; Biofilms; Biofilms - growth & development; Computation; Computer applications; Confocal microscopy; Engineering schools; Growth conditions; Hypoxia - metabolism; Image analysis; Image processing; Influence; Learning algorithms; Light; Machine learning; Metabolism; Microscopy; Microscopy, Confocal; Models, Theoretical; Oxygen; Phototrophic bacteria; Rana palustris; Rhodopseudomonas - growth & development; Rhodopseudomonas - metabolism; Rhodopseudomonas palustris; Scanning microscopy; Science
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0129354

We examined biofilms formed by the metabolically versatile bacterium Rhodopseudomonas palustris grown via different metabolic modes. R. palustris was grown in flow cell chambers with identical medium conditions either in the presence or absence of light and oxygen. In the absence of oxygen and the presence of light, R. palustris grew and formed biofilms photoheterotrophically, and in the presence of oxygen and the absence of light, R. palustris grew and formed biofilms heterotrophically. We used confocal laser scanning microscopy and image analysis software to quantitatively analyze and compare R. palustris biofilm formation over time in these two metabolic modes. We describe quantifiable differences in structure between the biofilms formed by the bacterium grown heterotrophically and those grown photoheterotrophically. We developed a computational model to explore ways in which biotic and abiotic parameters could drive the observed biofilm architectures, as well as a random-forest machine-learning algorithm based on structural differences that was able to identify growth conditions from the confocal imaging of the biofilms with 87% accuracy. Insight into the structure of phototrophic biofilms and conditions that influence biofilm formation is relevant for understanding the generation of biofilm structures with different properties, and for optimizing applications with phototrophic bacteria growing in the biofilm state.