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Predicting Competition Dynamics from Growth Curve Data

Yoav Ram
February 23, 2017

Predicting Competition Dynamics from Growth Curve Data

Given at the 8th ILANIT/FISEB conference, Feb 22, 2017.

Fitness is not well estimated from growth curves of individual microbial isolates in monoculture. Rather, competition experiments must be performed to better infer relative fitness. However, competition experiments require unique genotypic or phenotypic markers, and thus are difficult to perform with isolates derived from a common ancestor or non-model organisms. I will present Curveball, a new computational approach for predicting competition dynamics and inferring relative fitness from growth curve data. We validated this approach using growth curve and competition experiments with bacteria. By integrating several growth phases into the fitness inference, Curveball offers a holistic approach to fitness inference from growth curve data.

Yoav Ram

February 23, 2017
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  1. What makes Racehorse fast? Predicting Competition Dynamics from Growth Curve

    Data Yoav Ram Stanford University Photo by Noah Silliman
  2. Fitness The ability of a genotype to increase in frequency

    relative to other competing genotypes in the population 2
  3. Who’s fitter? 3

  4. How much fitter??? 4

  5. Now who’s fitter? 5

  6. Growth phases 6 Also… Death phase? Diauxic shift? Hall et

    al., MBE 2014
  7. Competition experiments • Growth in a mixed culture • Captures

    all relevant growth phases • Fitness inferred from change in strain frequencies 7 Modified from Elena & Lenski, 2003 Strain 1 Strain 2
  8. Competition experiments 8 Ara- Ara+ Strains must have a genotypic

    or phenotypic marker • LTEE: arabinose utilization phenotype Lenski et al., Am Nat 1991 • Fluorescence and flow cytometry Gallet et al., Genetics 2012 • Lineage tracking with deep sequencing Bank et al., Evolution 2013 Levy et al., Nature 2015
  9. Competition experiments Strains must have a genotypic or phenotypic marker

    Laborious and Costly More so for non-model organisms 9 Ara- Ara+
  10. Competition experiments Problem: Laborious and Costly Our Solution: Theoretical framework

    that predicts competition results 10 Ara- Ara+
  11. Prediction and validation 11 • Two E. coli strains with

    fluorescent proteins (GFP, RFP) • Growth in mono- and mixed culture • Measure OD over time • Fit growth model • Predict competition results from fitted growth models • Use fluorescence to measure frequencies in mixed culture • Compare predictions to measurements Frequency Time (hr) Time (hr) Density (OD595 )
  12. Prediction and validation 12 • Two E. coli strains with

    fluorescent proteins (GFP, RFP) • Growth in mono- and mixed culture • Measure OD over time • Fit growth model • Predict competition results from fitted growth models • Use fluorescence to measure frequencies in mixed culture • Compare predictions to measurements Frequency Time (hr) Time (hr) Density (OD595 )
  13. Prediction and validation 13 • Two E. coli strains with

    fluorescent proteins (GFP, RFP) • Growth in mono- and mixed culture • Measure OD over time • Fit growth model • Predict competition results from fitted growth models • Use fluorescence to measure frequencies in mixed culture • Compare predictions to measurements Frequency Time (hr) Time (hr) Density (OD595 )
  14. Growth curves 14 DH5α vs. TG1 - model fit •

    experiment
  15. Competition 15 Time (hr) DH5α vs. TG1 -- model prediction

    • experiment
  16. DH5α vs. TG1 without lag phase 16 - model fit

    • experiment Growth curves
  17. DH5α vs. TG1 without lag phase 17 -- model prediction

    • experiment - model fit • experiment Growth curves Competitions
  18. Applications • Complex growth curves • Null model for detection

    of direct interactions • Interpret fitness differences • Predict adaptive evolution 18 Monod, Ann Rev Microbiol 1949 Monod, Nobel lecture 1965
  19. Summary 1. Fit growth models to growth curves 2. Predict

    competition results 3. Infer fitness Preprint: Ram et al. Predicting microbial relative growth in a mixed culture from growth curve data. bioRxiv, doi:10.1101/022640 Website: http://curveball.yoavram.com 19 Frequency Time (hr)
  20. Acknowledgments 20 Hadany Lab / Tel-Aviv University Lilach Hadany Uri

    Obolski Eynat Dellus-Gur Berman Lab / Tel-Aviv University Judith Berman Maayan Bibi CONTACT [email protected] @yoavram github.com/yoavram www.yoavram.com Israeli Ministry of Science & Technology