Bayesian Reconstruction of Coevolutionary Histories

3b90c9fa5c515daf192adfde6faffdbb?s=47 Arman Bilge
November 09, 2013

Bayesian Reconstruction of Coevolutionary Histories

Slides for the presentation that I gave at the University of Notre Dame for Region 3 of the Siemens Competition.

3b90c9fa5c515daf192adfde6faffdbb?s=128

Arman Bilge

November 09, 2013
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Transcript

  1. Introduction Methods Simulation Results Closing Remarks Bayesian Reconstruction of Coevolutionary

    Histories Arman Bilge Lexington High School Lexington, Massachusetts November 9, 2013 x Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  2. Introduction Methods Simulation Results Closing Remarks Symbiotic Interactions are Fundamental

    to Life Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  3. Introduction Methods Simulation Results Closing Remarks Pocket Gophers and Chewing

    Lice Thomomys bottae Botta’s Pocket Gopher Thomomydoecus sp. Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  4. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Hafner & Nadler (1988), Nature 332: 258–259 Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  5. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Hafner & Nadler (1988), Nature 332: 258–259 Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  6. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Hafner & Nadler (1988), Nature 332: 258–259 Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  7. Introduction Methods Simulation Results Closing Remarks Coevolution is a Complex

    Process Hafner & Nadler (1988), Nature 332: 258–259 Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  8. Introduction Methods Simulation Results Closing Remarks Coevolution is Event-Driven Bayesian

    Reconstruction of Coevolutionary Histories Lexington High School
  9. Introduction Methods Simulation Results Closing Remarks Reconstruction Methods Make Several

    Assumptions Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  10. Introduction Methods Simulation Results Closing Remarks We Need a Probabilistic

    Approach to Coevolution Molecular Evolution Felsenstein (1981) Population Genetics Kingman (1982) Gene Evolution Arvestad et al. (2003) Biogeography Lemey et al. (2009, 2010) Speciation Yang & Rannala (2010) Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  11. Introduction Methods Simulation Results Closing Remarks We Need a Probabilistic

    Approach to Coevolution Molecular Evolution Felsenstein (1981) Population Genetics Kingman (1982) Gene Evolution Arvestad et al. (2003) Biogeography Lemey et al. (2009, 2010) Speciation Yang & Rannala (2010) Coevolution? Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  12. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  13. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D ∝ θ likelihood P dH, dS | H, S, R, θ prior P (H, S, R, θ) dθ H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  14. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D ∝ θ likelihood P dH, dS | H, S, R, θ prior P (H, S, R, θ) dθ Likelihood P dH, dS | H, S, R, θ = P dH | H, S, R, θ P dS | H, S, R, θ H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  15. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D ∝ θ likelihood P dH, dS | H, S, R, θ prior P (H, S, R, θ) dθ Likelihood P dH, dS | H, S, R, θ = P dH | H, S, R, θ P dS | H, S, R, θ = P dH | H tree likelihood P dS | S tree likelihood H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  16. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D ∝ θ likelihood P dH, dS | H, S, R, θ prior P (H, S, R, θ) dθ Prior P (H, S, R, θ) = P S | H, R, θ P (H, R, θ) H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  17. Introduction Methods Simulation Results Closing Remarks A Bayesian Formulation for

    Coevolution probability of reconstruction P H, S, R | D ∝ θ likelihood P dH, dS | H, S, R, θ prior P (H, S, R, θ) dθ Prior P (H, S, R, θ) = P S | H, R, θ P (H, R, θ) = P S | H, R, θ ? P (H) P (R) P (θ) existing/trivial priors H = host tree, S = symbiont tree, R = reconciliation, θ = coevolutionary rates, D, dH, dS = sequence data Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  18. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  19. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Approximation by considering only observed events Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  20. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Approximation by considering only observed events Observation of duplication, host-switch, and loss events are modeled as independent Poisson processes Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  21. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Approximation by considering only observed events Observation of duplication, host-switch, and loss events are modeled as independent Poisson processes Cospeciation assumed to occur whenever host speciates Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  22. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Approximation by considering only observed events Observation of duplication, host-switch, and loss events are modeled as independent Poisson processes Cospeciation assumed to occur whenever host speciates For each ancestral symbiont, algorithm identifies between 7 situations to calculate probability of observation Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  23. Introduction Methods Simulation Results Closing Remarks Approximating the Symbiont Tree

    Prior, P S | H, R, θ Cannot be calculated easily because of infinite permutations of unobserved events Approximation by considering only observed events Observation of duplication, host-switch, and loss events are modeled as independent Poisson processes Cospeciation assumed to occur whenever host speciates For each ancestral symbiont, algorithm identifies between 7 situations to calculate probability of observation Any uncertainties are integrated out Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  24. Introduction Methods Simulation Results Closing Remarks The Cases x x

    x x Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  25. Introduction Methods Simulation Results Closing Remarks An Example Case x

    Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  26. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically, so approximated using Markov chain Monte Carlo Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  27. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically, so approximated using Markov chain Monte Carlo Algorithm implemented as a Java plug-in for BEAST, an existing program for evolutionary analysis via Bayesian MCMC Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  28. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically, so approximated using Markov chain Monte Carlo Algorithm implemented as a Java plug-in for BEAST, an existing program for evolutionary analysis via Bayesian MCMC All parameters estimated simultaneously, including host tree, symbiont tree, and the reconciliation between them Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  29. Introduction Methods Simulation Results Closing Remarks Simulation Results simulation 1

    simulation 2 Rate Actual Estimated Actual Estimated duplication 0.0 6.418 × 10−2 1.0 1.419 host-switch 0.0 6.295 × 10−2 1.0 2.471 loss 0.0 5.416 × 10−2 1.0 1.606 0 0.079 0.053 0.365 Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  30. Introduction Methods Simulation Results Closing Remarks Contributions Formulated an expression

    for the probability of a coevolutionary reconstruction Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  31. Introduction Methods Simulation Results Closing Remarks Contributions Formulated an expression

    for the probability of a coevolutionary reconstruction Developed an algorithm to approximate the probability of symbiont tree for a reconstruction Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  32. Introduction Methods Simulation Results Closing Remarks Contributions Formulated an expression

    for the probability of a coevolutionary reconstruction Developed an algorithm to approximate the probability of symbiont tree for a reconstruction Implemented the algorithm in a popular, widely-used phylogenetics program Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  33. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  34. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Model for preferential host-switching Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  35. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Model for preferential host-switching Integration with other evolutionary models (e.g., biogeography) Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  36. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Model for preferential host-switching Integration with other evolutionary models (e.g., biogeography) Hypothesis-testing of coevolutionary theories Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  37. Introduction Methods Simulation Results Closing Remarks Acknowledgements My mentors, Yi-Chieh

    Jessica Wu, Rachel Sealfon, and Prof. Mukul Bansal Andrew Brownjohn, Jon Sanders, Prof. Ran Libeskind-Hadas, Hayden Metsky, and Prof. Manolis Kellis, for their support Dr. Susan Offner, for inspiring me My family Siemens Foundation University of Notre Dame Bayesian Reconstruction of Coevolutionary Histories Lexington High School
  38. Introduction Methods Simulation Results Closing Remarks References Baum, D. A.

    & Offner, S. (2008). The American Biology Teacher, 70(4), 222–229. Bayes, T. & Price, R. (1763). Phil. Trans. 53, 370–418. Charleston, M. A. (2009). A new likelihood method for cophylogenetic analysis. Drummond, A. J., et al. (2012). Mol. Biol. Evol. 29(8), 1969–1973. Faria, N. R., et al. (2013). Phil. Trans. R. Soc. B, 368(1614). Felsenstein, J. (1981). J. Mol. Evol. 17(6), 368–376. Felsenstein, J. (2004). Inferring phylogenies. Sunderland, MA: Sinauer. Hafner, M. S. & Nadler, S. A. (1988). Nature, 332(6161), 258–259. Huelsenbeck, J. P., Rannala, B., & Larget, B. (2000). Evolution, 54(2), 352–364. Kingman, J. F. C. (1982). Stochastic Processes and their Applications, 13(3), 235–248. Segraves, K. A. (2010). Evo. Edu. Outreach, 3(1), 62–70. Sj¨ ostrand, J., et al. (In prep.). A Bayesian method for analyzing lateral gene transfer. Bayesian Reconstruction of Coevolutionary Histories Lexington High School