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Vanni Bucci

Population dynamics of enteric bacteria in surface water: role of mutation and growth. Laboratory experiments and agent based modeling. Vanni Bucci. PhD Candidate Dept. of Civil and Environmental Engineering Northeastern University, Boston (MA) vbucci@coe.neu.edu.

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Vanni Bucci

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  1. Population dynamics of enteric bacteria in surface water: role of mutation and growth. Laboratory experiments and agent based modeling VanniBucci PhD Candidate Dept. of Civil and Environmental Engineering Northeastern University, Boston (MA) vbucci@coe.neu.edu

  2. Surface water pathogen pollution • 13% of streams, 3% of lakes and 11% of estuaries (EPA, 2009). • In 2006, 32% of beaches more than one advisory (EPA, 2007). • 3.4 million people die from water-related diseases each year – (WHO, 2003). • Waterborne diseases: bacteria, viruses and protozoa • Transmission mostly via feces.

  3. Questions • Why are enteric bacteria important in surface water? • Used as indicators (e.g. Escherichia coli) of fecal contamination • Pathogens for humans (E. coli O157:H7) • Why model enteric bacteria fate and transport in surface water? • Modeling can help understand patterns not reproducible in laboratory or field studies (larger scale) • Tools for water bodies management

  4. Historical modeling approach Martin and Wool,2002; Chapra and Pellettier, 2003 .

  5. Biphasic decay pattern Percent Remaining Time • Commonly observed, • but unexplained!! Frost and Streeter ,1924

  6. Hypothesis Biphasic decay pattern due to a density effect (quorum sensing) Threshold Density

  7. Density effect: results and conclusions K1= 1.9 K2= -0.15 • A density effect is not the responsible mechanism for the biphasic patter in surface water • The second part of the decay is characterized by a statistically significant increase in cells density? (also in Dutka-Kwan et al.,1980) Hellweger et al. 2009, ASCE J. Env. Eng.135:372-376. .

  8. Hypothesis The dynamics of enteric bacteria populations in surface water lab culture are due to a GASP-type mutation • Spontaneous mutation • Growth on lysedcells • LB culture: attenuation of stress-response rpoS (σ38) Zambranoet al.,1993; Vulíc and Kolter, 2001; Finkel, 2006.

  9. Growing sub-population … is the sub-population resulting from a random phenomena of growth and death in homogeneous populations or it is due to a GASP-type mutation? Bucci et al. 2010, JAWRA [In Revision] .

  10. Selection of GASP mutants Phosphate Buffer Autoclaved River Water Natural River Water (Long-term survivors over-compete naïve population) Bucci et al. 2010, JAWRA [In Revision] .

  11. Conclusions GASP and next steps • GASP-type mutation is observed in SW lab cultures and can explain the biphasic decay pattern • Modeling: Integrate GASP mechanism into Charles River model to test its relevance in a real SW body

  12. Model Development

  13. Agent Based vs. Lumped State

  14. ABMs for microbes

  15. Model Application 1 Agent based modeling prediction of heterogeneity in biological phosphorous removal microbe populations

  16. Conceptual model Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  17. SBR-Extracellular Nutrients • Randomization: • State Variables • Parameters Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  18. SBR-quota/size distributions • Randomization: • State Variables • Parameters Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  19. Heterogeneity maintenance • Randomization: • State Variables • Parameters Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  20. Model optimization Model • CVV,MAX • CVKM • CVkd • CVKM • CVkdT=CVkd ± 0.05 • CVV,MAX • CVkd • CVV,MAX • CVKM • CVKM • CVkd • CVV,MAX • CVKM • CVkd • CVV,MAXT=CVV,MAX±0.05 • CVKM =CVKM± 0.05 • CVkd • CVV,MAX RMSE < RMSEMIN CVKM=CVKMT RMSE > RMSEMIN CVKM=CVKM RMSE < RMSEMIN CVkd=CVkdT RMSE > RMSEMIN CVkd=CVkd RMSE < RMSEMIN CVV,MAX=CVV,MAXT RMSE > RMSEMIN CVV,MAX=CVV,MAX Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  21. Model optimization Bucci et al. 2010, Env.Sci.Tech. [in Revision] .

  22. Conclusions wastewater model • Developed ABM framework for heterotrophic bacteria • Applied to P-removal process • Model predictions resolve lab observations (extra/intra-cellular nutrients) • Parameter randomization better than state variable randomization • G.A. for model calibration optimization • Model was successfully validated to data from CSTR system (not shown)

  23. Model Application 2 Incorporating mutation and growth in enteric bacteria fate and transport models Hypothesis • GASP-type mutation drives dynamics of enteric bacteria in a real water body

  24. Conceptual model • mG= Growth • mR= Endogenous Respiration • kA= Death in pure culture • kB= Death due to autochthons biota • f = Mutation M1 WT Bucci et al. 2010, [In Preparation] .

  25. Division and mutation Divide Mom D2 h = f x SR D1 m= m0 SR= SRMother m= m0 SR= ? ≥ 200 < 200 m=2m0 SR= SRMother Box-Mueller Algorithm R1,R2 (generated numbers) Z =(-2LogR1)1/2 cos(2pR2) X = Z (h)1/2 + h Straight Poisson p0 = e-h px = p(x-1)h/x CDFx = Σpx X= X+1 Loop Until CDF > RND D2A D2B m= m0 SR= X m= m0 SR= SRMother-X

  26. Laboratory application (I) Bucci et al. 2010, [In Preparation] .

  27. Laboratory application (II): WT vs. M1 characterization Bucci et al. 2010, [In Preparation] .

  28. Charles River model • Hydrodynamic model • ECOMSED • 3D (not really needed for this application) • Time-variable • 3,066 boxes, 10 layers • Water quality model • RCA • (descends from WASP) Hellweger, 2007, Wat. Sci. Technol. 56:39-46; Hellweger and Masopust, 2008, JAWRA44:509-522.

  29. Charles River model

  30. Model vs. data: temporal series Bucci et al. 2010, [In Preparation] .

  31. Model vs. data: spatial transects Bucci et al. 2010, [In Preparation] .

  32. Conclusions E. coli modeling • Applied ABM to study dynamics of E. coli in real water body • Included growth and developed an algorithm to simulate first round GASP mutation • Model applied and calibrated to laboratory data • Model validated to field observations • Predictions resolve observed temporal and spatial density patterns • Findings are consistent with the presence of rpoSatt in the Charles River (Gupta 1997).

  33. Contribution to the field Determined mechanism responsible for biphasic decay Developed model Tested by application to field data Advanced knowledge of water quality modeling More accurate models Better water bodies management

  34. Future research needs • Field • Develop method to discern WT vs. M1 cells (based on differential resistance to other stresses) • Lab • Identification the mutation loci • Characterization of mortality rates for mutants and wild-type cells under other stresses ( i.e. acid resistance) • Determine if GASP-type mutation happens in SW for all other enteric bacteria strains showing biphasic decay • Modeling • Develop metabolic models (Systems Bio-Ecology approach) which account for real genetic mechanism to test ecological hypotheses

  35. induce induce Population rpoMH rpoMH DNA RpoD RNAP R P T Cell rpoD rpoD rptMH rptMH Cell Division polMH polMH Pol DOCout ftsMH ftsMH Fts dumMH dumMH Dum Enz2MH Enz2MH Enz2MH DOCin HPr HPr HPr Pyruvate Enz1 Enz1 Enz1 CACycMH CACycMH m RpoS CACyc rpoS rpoS RelA RelA ppGpp Proteins make make DNA mRNA

  36. Acknowledgements • Ferdi • April, Marin and Stewart • Luca and Lauren • Mamma and Babbo • All my family and friends

  37. QUESTIONS?

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