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Maximal Extraction of Biological Information from Genetic Interaction Data Greg Carter Galitski Lab Institute for Systems Biology (Seattle) 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. Genetic Interaction Example: flo11 and sfl1 for yeast invasion. post-wash pre-wash Invasion Assay WT flo11 sfl1 flo11sfl1 ~2000 interactions measured (Drees et al, 2005) Classification of Interactions 45 possible phenotype inequalities Classified into 9 rules (Drees, et al. 2005) WT=A=B=AB, A=B=WT<AB, AB<A<WT=B, WT=A=AB<B, etc… WT=A<B=AB, A<B<WT=AB, WT=A=AB<B, A<B<WT<AB, Yeast Invasion Network Distribution of Rules 2000 interactions among 130 genes Extracting Biological Statements Statistical associations of a gene interacting with a function PhenotypeGenetics plug-in for Cytoscape www.cytoscape.org Classification Problem Can the 45 interactions be classified in a more informative way? How many rules? Distribution of interactions? WT=A=B=AB, A=B<WT<AB, AB<A<WT=B, WT=A=AB<B, etc… WT=A<B=AB, A<B<WT=AB, WT=A=AB<B, A<B<WT<AB, ? Context-dependent Complexity Requirements for a complexity metric Y: 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 Y = S Ki mij (1 – mij ) pairs ij Ki is the information of node i, mij is the mutual information between i and j, 0 ≤ mij ≤ 1 and 0 ≤ Y≤ 1 Applied to (see poster): • Sets of bit strings (sequences) • Network architecture • Dynamic Boolean networks • Genetic interaction networks… 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 Y. Network Classification Scheme Invasion Data MMS Fitness Data Y biological statements Y biological statements Drees, et al. 0.57 52 0.27 28 Segré, et al. 0.52 47 0.32 19 - - 0.16 10 0.79 72 0.62 32 St Onge, et al. Maximum Y Complexity and Biological Information Number of biological statements is correlated with Y 115k possible MMS fitness networks, r = 0.80 Genetic Interaction Networks Maximally complex MMS fitness network Frequency 1 120 PAB = PA < PB < PWT epistatic 2 55 PAB < PA = PB < PWT additive 3 92 PAB < PA < PB < PWT additive 30 PAB = PA = PB < PWT asynthetic 4 PAB = PA < PB = PWT non-interactive PAB < PA = PB = PWT synthetic PA < PAB = PB < PWT epistatic PAB = PA = PB = PWT non-interactive PAB < PA < PB = PWT conditional PA < PAB < PB < PWT single-nonmonotonic 5 26 Inequalities Classical Interpretation Rule (Drees et al. 2005) Genetic Interaction Networks Biological statements from the maximally complex MMS fitness network gene interacts via PSY3 Rule 1 gene SGS1 SWC5 CSM2 SHU2 SHU1 with genes meiotic recombination interacts via with genes Rule 5 Rule 2 Rule 4 Rule 4 Rule 4 error-free DNA repair error-free DNA repair error-free DNA repair error-free DNA repair error-free DNA repair P 0.0011 P 0.00014 0.00056 0.0026 0.0030 0.0065 St Onge, et al. Figure 5d Conclusion and Future Work For a given data set, maximizing Y facilitates unsupervised, maximal information extraction by balancing over-generalized and overspecific 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 Thanks to Becky Drees Alex Rives Marisa Raymond Iliana Avila-Campillo Paul Shannon James Taylor Susanne Prinz Vesteinn Thorsson Tim Galitski Matti Nykter Nathan Price Ilya Shmulevich David Galas