Validated In Silico Model for Biofilm Formation in Escherichia coli

• Published in: ACS synthetic biology 2022-02, Vol.11 (2), p.713-731
• Main Authors: Bhowmik, Purnendu, Rajagopal, Sreenath, Hmar, Rothangamawi Victoria, Singh, Purnima, Saxena, Pragya, Amar, Prakruthi, Thomas, Teby, Ravishankar, Rajani, Nagaraj, Savitha, Katagihallimath, Nainesh, Sarangapani, Ramanujan Kadambi, Ramachandran, Vasanthi, Datta, Santanu
• Format: Article
• Language: English
• Subjects: Bacteria; Biofilms; Computer Simulation; Escherichia coli - genetics; Escherichia coli Infections - microbiology; Humans
• ISSN: 2161-5063; 2161-5063
• DOI: 10.1021/acssynbio.1c00445

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.