Evolutionary simulation
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Evolutionary Simulation. 9:10-10:00 Tracey Heath (UT-Austin) and Sheng Guo (UPenn) “Million-Taxon Macroevolution Simulation for Phylogenetic Algorithm Benchmarks” 10:00-10:50 Paul Higgs (McMaster) “Mitochondrial Genomes—Evolution at Three Scales” 11:10-12:00 Claus Wilke (UT-Austin)

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Evolutionary Simulation

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Evolutionary simulation

Evolutionary Simulation

9:10-10:00Tracey Heath (UT-Austin) and Sheng Guo (UPenn)

“Million-Taxon Macroevolution Simulation for Phylogenetic Algorithm Benchmarks”

10:00-10:50Paul Higgs (McMaster)

“Mitochondrial Genomes—Evolution at Three Scales”

11:10-12:00 Claus Wilke (UT-Austin)

“What are the Determinants of Protein Evolutionary Rates in Yeast?”

1:30-2:20Christina Burch (UNC-Chapel Hill)

“Sexual Reproduction Selects for Robustness and Negative Epistasis in Artificial Gene Networks”

2:20-3:10Laura Landweber (Princeton)

"Scrambled genes as a model for the origin of complexity"

3:30-4:20John Yin (UW-Madison)

“Evolution on a Budget: Insights from Simulations of Virus Growth”

4:20-5:10Carlo Maley (Wistar)

“Multi-scale Modeling of Evolution in Tumors”


Evolutionary simulation

  • Major Goal: Develop the Computational Infrastructure for Reconstructing Phylogeny at the Level of the Entire Tree of Life

  • Funded by 5-year Information Technology Research (ITR) grant from NSF

  • Community Effort of Over 40 Biologists, Computer Scientists, Mathematicians from 17 Institutions in the US and 6 Abroad


Simulation modeling and benchmarks

Simulation, Modeling, and Benchmarks

U Penn: Junhyong Kim, Sampath Kannan, Susan Davidson

Yifeng Zheng, Steve Fisher, Sheng Guo, Lisan Wang, Shirley Cohen

U Texas : David Hillis, Lauren Meyers

Eric Miller, Tracy Heath, Derrick Zwickl

NC State: Spencer Muse

Errol Strain

Yale: Paul Turner

and

Bernard Moret

Tandy Warnow

Robert Jensen

Randy Linder


Evolutionary simulation

Goal: Develop validated datasets of sufficient complexity and scale to realistically benchmark latest tree algorithms


Problems

Problems

  • Typical simulation scheme requires combinatorial exploration of parameter space resulting in an experimental design that is extremely difficult to manage

  • Large-scale simulations is computationally demanding

  • Branching structure specification is critical but the options become limited for very large trees

  • Credible simulation model acceptable to the community is difficult to establish


Simulation design

Simulation Design

  • Generate a very large dataset (>106 positions) over a very large tree (>106 taxa) using a suite of complex models of evolution

  • Store the data in a database

  • Retrieve subsets of the data by various sampling schemes


Simulation and data access

Simulation and Data Access

Model

Characterization

Simulators

  • Character Evolution Simulators

  • HyPhy

  • Micro-evolution

  • Others

Taxon Sampling

Database

  • Tree Topology Simulators

  • Pure Birth

  • Birth-Death

  • Empirical Fit

  • Others

Data Subset with Associated Subtree

Model Sampling

  • Others

  • Tree/Char Combined

  • Experimental Evolution

  • Virtual Cell

  • etc

Format Translators

PAUP*, etc


Evolutionary simulation

“This isn’t real…”

Rationale:

Generate Community Support for Credible Phylogenetic Benchmarks

Learn from Experiences of the Broad Community Outside of Systematics


Thanks to

Tracy Heath

Sheng Guo

Paul Higgs

Claus Wilke

Laura Landweber

Christina Burch

John Yin

Carlo Maley

Sean Dalton

Penn Center for Bioinformatics

CIPRES members

NSF

Thanks to…


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