Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Bayesian Reconstruction of Coevolutionary Histories

Arman Bilge
December 09, 2013

Bayesian Reconstruction of Coevolutionary Histories

Slides for the presentation that I gave at the George Washington University for the National Finals of the Siemens Competition. I earned second place in the individual category and a $50,000 scholarship for my project. A video recording of my talk is available at the following link. http://youtu.be/a3aAQ8sUJMY

Arman Bilge

December 09, 2013
Tweet

More Decks by Arman Bilge

Other Decks in Research

Transcript

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

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

    to Life Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  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 Arman Bilge
  4. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  5. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  6. Introduction Methods Simulation Results Closing Remarks Phylogenetic Trees are Evolutionary

    Models Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  7. Introduction Methods Simulation Results Closing Remarks Coevolution is a Complex

    Process Hafner & Nadler (1988), Nature 332: 258–259 Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  8. Introduction Methods Simulation Results Closing Remarks Reconstruction Methods Make Several

    Assumptions Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  9. Introduction Methods Simulation Results Closing Remarks Evolutionary Processes are Studied

    Probabilistically 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 Arman Bilge
  10. Introduction Methods Simulation Results Closing Remarks Evolutionary Processes are Studied

    Probabilistically 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 Arman Bilge
  11. 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 Arman Bilge
  12. 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 Arman Bilge
  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 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 Arman Bilge
  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 = 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 Arman Bilge
  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 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 Arman Bilge
  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) = 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 Arman Bilge
  17. 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 Arman Bilge
  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 Approximation by considering only observed events Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  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 Observation of duplication, host-switch, and loss events are modeled as independent Poisson processes Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  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 Cospeciation assumed to occur whenever host speciates Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  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 Algorithm identifies between 7 cases to calculate probability of observation Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  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 Algorithm identifies between 7 cases to calculate probability of observation Any uncertainties are integrated out Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  23. Introduction Methods Simulation Results Closing Remarks The Cases x x

    x x Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  24. Introduction Methods Simulation Results Closing Remarks An Example Case x

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

    Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  26. Introduction Methods Simulation Results Closing Remarks An Example Case x

    Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  27. Introduction Methods Simulation Results Closing Remarks An Example Case x

    Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  28. Introduction Methods Simulation Results Closing Remarks An Example Case x

    x Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  29. Introduction Methods Simulation Results Closing Remarks An Example Case x

    Host / Symbiont Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  30. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  31. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically Approximated using Markov chain Monte Carlo (MCMC) MCMC uses a random walk to explore the parameter-space Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  32. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically Approximated using Markov chain Monte Carlo (MCMC) MCMC uses a random walk to explore the parameter-space Algorithm implemented as a Java plug-in for BEAST, an existing program for evolutionary analysis via Bayesian MCMC Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  33. Introduction Methods Simulation Results Closing Remarks Bayesian MCMC Implementation Probability

    of a reconstruction cannot be determined analytically Approximated using Markov chain Monte Carlo (MCMC) MCMC uses a random walk to explore the parameter-space Algorithm implemented as a Java plug-in for BEAST, an existing program for evolutionary analysis via Bayesian MCMC All parameters estimated simultaneously host tree symbiont tree reconciliation Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  34. 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 Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  35. 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 0.998 1 1 1 1 1 symbiont4.1 symbiont8.1 symbiont2.1 symbiont3.1 symbiont1.1 symbiont7.1 symbiont6.1 symbiont5.1 0.079 0.053 0.365 symbiont5.1 0.615 0.079 0.984 0.053 0.365 0.366 symbiont3.3 symbiont7.1 symbiont8.1 symbiont5.1 symbiont5.2 symbiont3.1 0.607 symbiont8.2 Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  36. Introduction Methods Simulation Results Closing Remarks Contributions Formulated an expression

    for the probability of a coevolutionary reconstruction Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  37. 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 Arman Bilge
  38. 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 Arman Bilge
  39. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  40. Introduction Methods Simulation Results Closing Remarks Future Work Analysis of

    real datasets Model for preferential host-switching Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  41. 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 Arman Bilge
  42. 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 Arman Bilge
  43. Introduction Methods Simulation Results Closing Remarks Acknowledgements My mentors, Dr.

    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 and College Board George Washington University Bayesian Reconstruction of Coevolutionary Histories Arman Bilge
  44. 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 Arman Bilge