Greg Carter Galitski Lab Institute for Systems Biology (Seattle)
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Transcript of Greg Carter Galitski Lab Institute for Systems Biology (Seattle)
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Greg Carter
Galitski LabInstitute for Systems Biology (Seattle)
Maximal Extraction of Biological Information from
Genetic Interaction Data
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Genetic Interaction
Pairwise perturbation
two genes combine to affect phenotype
Hereford & Hartwell 1974
Measure a phenotype for 4 strains:
1. Wild-type reference genotype
2. Perturbation of gene A
3. Perturbation of gene B
4. Double perturbation of A and B
• Loss-of-function, gain-of-function, dominant-negative, etc.
• Interaction depends on phenotype measured.
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Example: flo11 and sfl1 for yeast invasion.
WT flo11 sfl1 flo11sfl1
pre-
was
hpo
st-w
ash
Invasion Assay
~2000 interactions measured
(Drees et al, 2005)
Genetic Interaction
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45 possible phenotype inequalities
Classified into 9 rules (Drees, et al. 2005)
Classification of Interactions
WT=A=B=AB, WT=A<B=AB, A=B=WT<AB, A<B<WT=AB, AB<A<WT=B, WT=A=AB<B, WT=A=AB<B, A<B<WT<AB, etc…
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Distribution of Rules
2000 interactions among 130 genes
Yeast Invasion Network
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Extracting Biological Statements
Statistical associations of a gene interacting with a function
PhenotypeGenetics plug-in for Cytoscape
www.cytoscape.org
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WT=A=B=AB, WT=A<B=AB, A=B<WT<AB, A<B<WT=AB, AB<A<WT=B, WT=A=AB<B, WT=A=AB<B, A<B<WT<AB, etc…
?
Can the 45 interactions be classified in a more informative way?
How many rules?
Distribution of interactions?
Classification Problem
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Requirements for a complexity metric :
1. Adding a gene with random interactions adds no information
2. Duplicating a gene adds no information
3. Should depend on
(i) the information content of each gene’s interactions, and
(ii) the information content of all gene-gene relationships.
General requirements for biological information (see poster).
Context-dependent Complexity
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= Ki mij (1 – mij )
Ki is the information of node i,
mij is the mutual information between i and j,
0 ≤ mij ≤ 1and
0 ≤ ≤ 1
Applied to (see poster):
• Sets of bit strings (sequences)• Network architecture• Dynamic Boolean networks• Genetic interaction networks…
pairs ij
Context-dependent Complexity
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Genetic Interaction Networks
• Invasion network of Drees, et al. Genome Biology 2005
130 genes, 2000 interactions
• MMS fitness network of St Onge, et al. Nature Genetics 2007
26 genes, 325 interactions
Determined networks of maximum complexity .
Network Classification Scheme
Invasion Data MMS Fitness Data
biological
statements
biological statements
Drees, et al. 0.57 52 0.27 28Segré, et al. 0.52 47 0.32 19St Onge, et al. - - 0.16 10Maximum 0.79 72 0.62 32
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Complexity and Biological Information
Number of biological statements is correlated with
115k possible MMS fitness networks, r = 0.80
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Genetic Interaction Networks
Maximally complex MMS fitness network
Rule Frequency InequalitiesClassical Interpretation
(Drees et al. 2005)
1 120 PAB = PA < PB < PWTepistatic
2 55 PAB < PA = PB < PWTadditive
3 92 PAB < PA < PB < PWTadditive
4 30PAB = PA = PB < PWT
PAB = PA < PB = PWT
asynthetic
non-interactive
5 26
PAB < PA = PB = PWT
PA < PAB = PB < PWT
PAB = PA = PB = PWT
PAB < PA < PB = PWT
PA < PAB < PB < PWT
synthetic
epistatic
non-interactive
conditional
single-nonmonotonic
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gene interacts via with genes P
SGS1 Rule 5 error-free DNA repair 0.00014
SWC5 Rule 2 error-free DNA repair 0.00056CSM2 Rule 4 error-free DNA repair 0.0026
SHU2 Rule 4 error-free DNA repair 0.0030SHU1 Rule 4 error-free DNA repair 0.0065
Genetic Interaction Networks
Biological statements from the
maximally complex MMS fitness network
gene interacts via with genes P
PSY3 Rule 1 meiotic recombination 0.0011
St Onge, et al. Figure 5d
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Conclusion and Future Work
For a given data set, maximizing facilitates unsupervised, maximal information extraction by balancing over-generalized and over-specific classifications schemes.
Need network-based methods to interpret the maximally complex interaction rules. Interpretations will depend on the system, specific to phenotype measured and perturbations performed.
See poster for more details
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Becky DreesAlex Rives
Marisa RaymondIliana Avila-Campillo
Paul ShannonJames TaylorSusanne Prinz
Vesteinn ThorssonTim Galitski
Matti NykterNathan Price
Ilya ShmulevichDavid Galas
Thanks to