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.

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