Abstract
“Smart” microorganisms are named for their extraordinary ability to generate energy and materials like electricity, hydrogen, methane, cleaning wastewater and proteins. Unlocking predictive patterns between microorganisms’ genetic fingerprints and their metabolism from compiled databases could provide revolutionary screening methods for discovering smart microorganisms.
In this paper, we show a self-awareness concept and theory of natural swarm intelligence (SI) that can be used to discover authoritative and popular information as well as emerging and anomalous information when traditional connections among information nodes (e.g., hyperlinks or citations) are not available. The different categories of information can be all high-value depending on the application requirements. A self-awareness of swarm intelligence is a data-driven framework, modeled and measured using a recursive distributed infrastructure of machine learning. The combination of the machine learning and swarm intelligence are extended and enhanced in a completely new perspective. We built a data model from USPTO database, NCBI database, JGI (Joint Genomic Database) and KEGG database, as well as our own bio-database.
We applied our big data biotechnology called CASCADE to microorganism populations using a measure we termed average metabolic efficiency (AME), which highly correlates with real life metabolic capabilities. We used the data models to select microbial consortia for wastewater treatment using the swarm intelligence of microbes. The collective behaviors of the selected microbes are used for cleaning wastewater and convert bio-wastes to usable energy.
In methane experiments, we found that selected microbs are not only consistent with current scientific selection, but also allowed prediction for two additional microorganisms not previously selected. This technology can potentially identify mixtures of microorganisms that work more powerfully than single ones and dramatically speed up the discovery process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fleischer, M.: Foundations of Swarm Intelligence: From Principles to Practice (2005). http://arxiv.org/pdf/nlin/0502003.pdf
Zhao, Y., Zhou, C.: System self-awareness towards deep learning and discovering high-value information. In: Proceedings of the 7th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, October 20–22, New York, USA. pp. 109–116 (2016)
Logan, B.E., Hamelers, B., Rozendal, R., Schröder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., Rabaey, K.: Microbial fuel cells: methodology and technology. Environ. Sci. Technol. 40(17), 5181–5192 (2006)
Rabaey, K., Lissens, G., Siciliano, S.D., Verstraete, W.: A microbial fuel cell capable of converting glucose to electricity at high rate and efficiency. Biotechnol. Lett. 25, 1531–1535 (2003)
Niessen, J., Schröder, U., Rosenbaum, M., Scholz, F.: Fluorinated polyanilines as superior materials for electrocatalytic 8 anodes in bacterial batteries. Electrochem. Commun. 6, 571–575 (2004a)
Niessen, J., Schröder, U., Scholz, F.: Exploiting complexcarbohydrates for microbial electricity generation – a bacterial fuel cell operating on starch, Electrochem. Commun. 6, 955–958 (2004b)
Kim, M.J., Cho, H.S., Kim, J.Y.: Anaerobic biodegradability of plastic garbage bags based on starch polymer and aliphatic polyester. In: Proceedings of Sardinia 2007, Eleventh International Waste Management and Landfill Symposium, S. Margherita di Pula, Cagliari, Italy, 1–5 October 2007, pp. 517–518 (2007)
Zhou, C., Zhao Y.: Method and system for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic property or biological behavior. US patents 9,026,373 and 9,792,404
Kates, M., Kushner, D.J., Matheson, A.T. (eds.): The Biochemistry of Archaea (Archaebacteria). Elsevier, Amsterdam (1993)
Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., Magrini, V., Mardis, E.R., Gordon, J.I.: An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)
Zhou, C., Killgrow, S., Hill, C.: Cascade Clean Energy System for Water & Wastewater Treatment. www.energy.ca.gov/2015publications/CEC-500-2015-065.pdf
Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems, proceed. In: NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30, 1989
Zhao, Y., Wei, S., Oglesby, I., Zhou, C.: Utilizing the quantum intelligence system for drug discovery (QIS D2) for Anti-HIV and Anti-Cancer cocktail detection. J. Med. Chem. Bio. Res. Def., 7 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, C.C., Han, S. (2018). Predictive Patterns Among Microorganisms: Data Sciences for Screening Smart Bacteria for Methanogenesis and Wastewater Treatment. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-96661-8_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96660-1
Online ISBN: 978-3-319-96661-8
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)