Download AvidaEd Research - Sara Hettenbach

HOW DOES A REAL
PHYLOGENETIC TREE OF
AVIDA ED INDIVIDUALS
COMPARE TO A
SIMULATED TREE?
Sara Hettenbach
Background
• AvidaEd is a computer program developed by
Michigan state that uses digital organisms to show
evolution.
• When placed in an environment and given rewards
for more complex functions, these individuals evolve
to gain new functions, such as number recognition
and addition functions.
• These individuals replicate asexually using a 50 letter
genome that mutates at a pre-determined rate.
Background
• Phylogenetics = The study of evolutionary relationships among
groups of individuals.
• Phylogenetic analysis programs use DNA sequencing to identify
when and where mutations happen.
• Using these sequences, phylogeny programs determine how closely
related individuals are.
• There are different ways to form an evolutionary tree:
• Maximum likelihood: This type of program searches for the most likely tree given
the DNA genetic information.
• Parsimony: Forms an evolutionary tree based on the process that would take the
fewest evolutionary steps
• Distance phylogeny: Uses pairwise distance matrices to form the phylogenetic
tree
• Bayesian inference: Uses algorithms and posterior distributions on a set of
parameters to infer a phylogentic tree.
Interest
• When approaching this inquiry, I knew of the AvidaEd
program, but was unaware of all of the possibilities and uses
for the program.
• Upon more reading, I became interested in classroom
applications of this program, as well as other possible
experiments.
• Brad introduced me to a previous study similar to mine that
used a know phylogenetic tree of E.Coli to test phylogenetic
analyses.
• After reading this, I was interested to see if the AvidaEd
organisms could be used to do something similar.
Methods Overview
• I formed my own evolutionary tree of AvidaEd
individuals by isolating the two most fit individuals
after 500 updates (or generations).
• These isolated individuals were used to start the next
generation.
• Each generation had the same environment with the
same rewards, mutation rate settings, and time.
Methods Overview Continued
• After my known tree was formed, I exported all of
the individuals’ genomic data.
• The AvidaEd organisms do not have a DNA genome.
• Each individual has a 50 letter long genome that uses
all 26 letters of the alphabet.
• I translated each of these letters into a three-letter
DNA codon.
letter
Three codon code
a
GCU
b
UAU
c
UGU
d
GAU
e
GAG
f
UUU
g
GGU
h
CAU
i
AUU
j
UGC
k
AAA
l
CUU
m
AUG
n
AAU
o
UCC
p
CCU
q
CAA
r
AGA
s
UCU
t
ACU
u
UGG
v
GUU
w
CAC
x
AUC
y
GUC
z
GGC
Methods Overview Continued
• After converting the final genome DNA sequences so that they
were in the correct format for alignment programs, I was finally
able to input the information into the phylogenetics programs.
• Once the DNA sequencing data is input and the parameters are set,
the program aligns the data and the “spits out” a tree!
• Statistical tests are run in the program
• Bootstrapping: I set all of the programs to replicate 100 different
phylogenetic trees. The bootstrap value displayed in the key or on the tree
branch shows the percentage that that branch was the same in all of the
100 replicates. Bootstrap values above 70 are considered well supported.
Results
Maximum likelihood tree
Results
Parsimony Tree
Results
Distance phylogeny
Results
Bayesian inference
Results Details
• The two trees that do not show bootstrap values had bootstrap
values greater than 70 on all branches, and therefore are
considered well-supported.
• The parsimony tree got the evolution of my Avida organisms
perfect!
• The other two methods of analyses did categorize the right
organisms as being most closely related, but some of the branching
points off in time or placement.
Conclusion/Implications
• I was very excited by my results!
• Shows how to think critically about evolution; in cases other than this one,
we don’t know the lineage of evolution.
• Also demonstrates that the AVidaEd individuals are an actual instance of
evolution, not a simulation of one.
• If I was to re-create another tree, the individuals would most likely not have the
same genomes!
• This whole project was a learning process for me, but by the end of it I felt
much more comfortable with the phylogenetic analyses programs.
• I also felt that I had a better understanding of phylogenetics; this was great
practice.
• All of these things that I learned, students could learn too!
Acknowledgements
• Huge thank you to Brad, who persuaded me to do this project, helped me
along the way, and received a lot of late-night emails.
• I’d also like to thank Helen, Bob, and the rest of my peers in this class who
provided feedback and support.
• Dr. Paulyn Cartwright for a brief introduction into phylogenetics.
• French phylogeny website that provided the software/analyses programs.
• Dereeper A., Audic S., Claverie J.M., Blanc G. BLAST-EXPLORER helps you building datasets for phylogenetic
analysis. BMC Evol Biol. 2010 Jan 12;10:8. (PubMed)
• Dereeper A.*, Guignon V.*, Blanc G., Audic S., Buffet S., Chevenet F., Dufayard J.F., Guindon S., Lefort V.,
Lescot M., Claverie J.M., Gascuel O. Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic
Acids Res. 2008 Jul 1;36(Web Server issue):W465-9. Epub 2008 Apr 19. (PubMed) *: joint first authors
• Thompson J.D., Higgins D.G., Gibson T.J. CLUSTAL W: improving the sensitivity of progressivemultiple
sequence alignment through sequence weighting, position-specific gap penalties and weight matrix
choice. Nucleic Acids Res. 1994, Nov 11;22(22):4673-80. (PubMed)
Goloboff P., Farris S., Nixon K. TNT (Tree analysis using New Technology) ver. 1.1 2000, Published by the
authors, Tucumán, Argentina.
• Chevenet F., Brun C., Banuls AL., Jacq B., Chisten R. TreeDyn: towards dynamic graphics and annotations for
analyses of trees. BMC Bioinformatics. 2006, Oct 10;7:439. (PubMed)