Predictive Patterns Among Microorganisms: Data Sciences for Screening Smart Bacteria for Methanogenesis and Wastewater Treatment

  • Charles C. ZhouEmail author
  • Shuo Han
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


“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.


Artificial intelligence Big data Swarm intelligence Microbes Wastewater Anaerobic digestion 


  1. 1.
    Fleischer, M.: Foundations of Swarm Intelligence: From Principles to Practice (2005).
  2. 2.
    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)Google Scholar
  3. 3.
    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)ADSCrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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 9,792,404Google Scholar
  9. 9.
    Kates, M., Kushner, D.J., Matheson, A.T. (eds.): The Biochemistry of Archaea (Archaebacteria). Elsevier, Amsterdam (1993)Google Scholar
  10. 10.
    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)ADSCrossRefGoogle Scholar
  11. 11.
    Zhou, C., Killgrow, S., Hill, C.: Cascade Clean Energy System for Water & Wastewater Treatment.
  12. 12.
    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, 1989Google Scholar
  13. 13.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Cascade Clean Energy, Inc.CupertinoUSA
  2. 2.Chemistry DepartmentMissouri S&TRollaUSA

Personalised recommendations