What makes Racehorse fast? Predicting Competition Dynamics from Growth Curve Data Yoav Ram Stanford University Photo by Noah Silliman

Fitness The ability of a genotype to increase in frequency relative to other competing genotypes in the population 2

Who’s fitter? 3

How much fitter??? 4

Now who’s fitter? 5

Growth phases 6 Also… Death phase? Diauxic shift? Hall et al., MBE 2014

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

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

Competition experiments Strains must have a genotypic or phenotypic marker Laborious and Costly More so for non-model organisms 9 Ara- Ara+

Competition experiments Problem: Laborious and Costly Our Solution: Theoretical framework that predicts competition results 10 Ara- Ara+

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 )

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 )

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 )

Growth curves 14 DH5α vs. TG1 - model fit ● experiment

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

DH5α vs. TG1 without lag phase 16 - model fit ● experiment Growth curves

DH5α vs. TG1 without lag phase 17 -- model prediction ● experiment - model fit ● experiment Growth curves Competitions

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

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)

Acknowledgments 20 Hadany Lab / Tel-Aviv University Lilach Hadany Uri Obolski Eynat Dellus-Gur Berman Lab / Tel-Aviv University Judith Berman Maayan Bibi CONTACT yoav@yoavram.com @yoavram github.com/yoavram www.yoavram.com Israeli Ministry of Science & Technology