, Volume 24, Issue 5, pp 583–596 | Cite as

Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization

  • Katarzyna H. Kucharzyk
  • Terence Soule
  • Thomas F. Hess
Original Paper


Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases.


Genetic algorithm Perchlorate degradation Consortium Resazurin 



This material is based on work supported by the United States Army Corps of Engineers, Humphreys Engineering Center Support Activity under Contract No. W912HQ-07-C-0014. Views, opinions, and/or findings contained in this report are those of the authors and should not be construed as an official department of defense position decision unless so designated by other official documentation.


  1. Achenbach L, Bender K, Sun Y, Coates J (2006) The biochemistry and genetics of microbial perchlorate reduction. In: Perchlorate environmental occurrence, interactions and treatment. Springer, New YorkGoogle Scholar
  2. Bae W, Rittmann B (1996) A structural model of dual-limitation kinetics. Biotechnol Bioeng 49:683–689PubMedCrossRefGoogle Scholar
  3. Brenner K, You L, Arnold F (2008) Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol 26(9):483–489PubMedCrossRefGoogle Scholar
  4. Bruce RA, Achenbach L, Coates J (1999) Reduction of (per)chlorate by a novel organism isolated from paper mill waste. Environ Microbiol 1(4):319–329PubMedCrossRefGoogle Scholar
  5. Coates J, Michaelidou U, O’Connor S, Bruce R, Achenbach L (2000) The diverse microbiology of (per)chlorate reduction. In: Perchlorate in the environment. Kluwer/Plenum, New YorkGoogle Scholar
  6. Emborg C, Jepsen PK, Biedermann K (1989) Two-level factorial screening of new plasmid/strain combinations for production of recombinant-DNA products. Biotechnol Bioeng 33(11):1393–1399. doi: 10.1002/bit.260331105 PubMedCrossRefGoogle Scholar
  7. Herman D, Frankenberger W (1998) Microbial mediated reduction of perchlorate in groundwater. J Environ Qual 27:750–754CrossRefGoogle Scholar
  8. Kambam P, Eriksen D, Lajoie J, Sayut D, Sun L (2008) Design and mathematical modeling of a synthetic symbiotic ecosystem. IET Syst Biol 2:33–38PubMedCrossRefGoogle Scholar
  9. Kucharzyk K, Crawford R, Paszczynski A, Hess T (2010) A method for assaying perchlorate concentration in microbial cultures using the fluorescent dye resazurin. J Microbiol Methods 81:26–32PubMedCrossRefGoogle Scholar
  10. Kucharzyk KH, Crawford RL, Paszczynski AJ, Soule T, Hess TF (2012) Maximizing microbial degradation of perchlorate using a genetic algorithm: media optimization. J Biotechnol 157(1):189–197. doi: 10.1016/j.jbiotec.2011.10.011 PubMedCrossRefGoogle Scholar
  11. Liu Y (2007) Overview of some theoretical approaches for derivation of the Monod equation. Appl Microbiol Biotechnol 73:1241–1250PubMedCrossRefGoogle Scholar
  12. Liu C, Zachara J (2001) Uncertainties of Monod kinetic parameters nonlinearly estimated from batch experiments. Environ Sci Technol 35:133–141PubMedCrossRefGoogle Scholar
  13. Liu C, Zachara J, Gorby Y, Szecsody J, Brown C (2001) Microbial reduction of Fe(III) and sorption/precipitation of Fe(II) on bacteria, S. putrefaciens CN32. Environ Sci Technol 35:1385–1393PubMedCrossRefGoogle Scholar
  14. Liu Y, Lin Y, Yang S (2003) A thermodynamic interpretation of the Monod equation. Curr Microbiol 46:233–234PubMedCrossRefGoogle Scholar
  15. Monod J (1949) The growth of bacterial cultures. Annu Rev Microbiol 3:371–393CrossRefGoogle Scholar
  16. Rittmann B, McCarty P (2001) Environmental biotechnology: principles and applications. McGraw Hill, New YorkGoogle Scholar
  17. Rittmann B, VanBriesen J (1996) Microbiological processes in reactive modeling. Rev Mineral 34:311–334Google Scholar
  18. Robinson J, Tiedje J (1983) Nonlinear estimation of Monod growth kinetic parameters from a single substrate depletion curve. Appl Environ Microbiol 45:1453–1458PubMedGoogle Scholar
  19. Scow K, Merica R, Alexander M (1990) Kinetic analysis of enhanced biodegradation of carbofuran. J Agric Food Chem 38:908–912CrossRefGoogle Scholar
  20. Simkins S, Alexander M (1984) Models for mineralization kinetics with the variables of substrate concentration and population density. Appl Environ Microbiol 47:1299–1306PubMedGoogle Scholar
  21. Simkins S, Alexander M (1985) Nonlinear estimation of the parameters of Monod kinetics that best describe mineralization of several substrate concentrations by dissimilar bacterial densities. Appl Environ Microbiol 50:816–824PubMedGoogle Scholar
  22. Spear J, Figueroa L, Honeyman B (1999) Modeling the removal of uranium U(VI) from aqueous solutions in the presence of sulfate reducing bacteria. Environ Sci Technol 33:2667–2675CrossRefGoogle Scholar
  23. Tchobanoglous G, Burton F (1991) Wastewater engineering. McGraw Hill, New YorkGoogle Scholar
  24. Vandecasteele F, Hess T, Crawford R (2003) Constructing microbial consortia with optimal biomass production using a genetic algorithm. Paper presented at the genetic and evolutionary computation conference: late-breaking papersGoogle Scholar
  25. Vandecasteele F, Hess T, Crawford R (2004) Constructing Microbial Consortia with minimal growth using a genetic algorithm. Paper presented at the EvoBIO2004, 2nd European workshop on evolutionary bioinformatics. Springer, BerlinGoogle Scholar
  26. Wallace W, Ward T, Breen A, Attaway H (1996) Identification of an anaerobic bacterium which reduces perchlorate and chlorate as Wolinella succinogenes. J Ind Microbiol 16:68–72CrossRefGoogle Scholar
  27. Wang Y, Shen H (1997) Modeling Cr(VI) reduction by pure bacterial cultures. Water Res 31:727–732CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Katarzyna H. Kucharzyk
    • 1
    • 4
  • Terence Soule
    • 3
  • Thomas F. Hess
    • 1
    • 2
  1. 1.Environmental Biotechnology Institute, Environmental Science ProgramUniversity of IdahoMoscowUSA
  2. 2.Department of Biological & Agricultural EngineeringUniversity of IdahoMoscowUSA
  3. 3.Department of Computer ScienceUniversity of IdahoMoscowUSA
  4. 4.Civil and Environmental EngineeringDuke UniversityDurhamUSA

Personalised recommendations