Computational Models for Trapping Ebola Virus Using Engineered Bacteria
IEEE/ACM Transactions on Computational Biology and Bioinformatics
MetadataShow full item record
Martins, D., Barros, M., Pierobon, M., Kandhavelu, M., Lio, P., & Balasubramaniam, S. (2018). Computational Models for Trapping Ebola Virus Using Engineered Bacteria. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15 (6) https://doi.org/10.1109/TCBB.2018.2836430
IEEE The outbreak of Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60% of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into the future for treatment whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.
genetically engineered bacteria, Ebola virus, virus ecological trap
This work was partially funded by 1) Science Foundation Ireland via the CONNECT research centre (grant no. 13/RC/2077), 2) via the FiDiPro program of Academy of Finland (Nano communication Networks), 2012- 2016, 3) Academy of Finland Research Fellow grant, and 4) the US National Science Foundation through grant MCB-1449014.
External DOI: https://doi.org/10.1109/TCBB.2018.2836430
This record's URL: https://www.repository.cam.ac.uk/handle/1810/284377