1. Greg Carter Galitski Lab Institute for Systems Biology (Seattle) Maximal Extraction of Biological Information from Genetic Interaction Data

2. 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.

3. Example: flo11 and sfl1 for yeast invasion. WTflo11sfl1flo11sfl1 pre-wash post-wash Invasion Assay ~2000 interactions measured (Drees et al, 2005) Genetic Interaction

4. 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…

5. Distribution of Rules 2000 interactions among 130 genes Yeast Invasion Network

6. Extracting Biological Statements Statistical associations of a gene interacting with a function PhenotypeGenetics plug-in for Cytoscape www.cytoscape.org

7. 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

8. 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

9.  =  K i m ij (1 – m ij ) K i is the information of node i, m ij is the mutual information between i and j, 0 ≤ m ij ≤ 1 and 0 ≤  ≤ 1 Applied to (see poster): Sets of bit strings (sequences) Network architecture Dynamic Boolean networks Genetic interaction networks… pairs ij Example: Shannon mutual information m ij =  p ij (a,b) log( ) i=a, j=b p ij (a,b) p i (a) p j (b) Context-dependent Complexity

10. 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 DataMMS Fitness Data  biological statements  Drees, et al.0.57520.2728 Segré, et al.0.52470.3219 St Onge, et al.--0.1610 Maximum  0.79720.6232

11. Complexity and Biological Information Number of biological statements is correlated with  115k possible MMS fitness networks, r = 0.80

12. Genetic Interaction Networks Maximally complex MMS fitness network RuleFrequencyInequalities Classical Interpretation (Drees et al. 2005) 1120 P AB = P A < P B < P WT epistatic 255 P AB < P A = P B < P WT additive 392 P AB < P A < P B < P WT additive 430 P AB = P A = P B < P WT P AB = P A < P B = P WT asynthetic non-interactive 526 P AB < P A = P B = P WT P A < P AB = P B < P WT P AB = P A = P B = P WT P AB < P A < P B = P WT P A < P AB < P B < P WT synthetic epistatic non-interactive conditional single-nonmonotonic

13. geneinteracts viawith genesP SGS1Rule 5error-free DNA repair0.00014 SWC5Rule 2error-free DNA repair0.00056 CSM2Rule 4error-free DNA repair0.0026 SHU2Rule 4error-free DNA repair0.0030 SHU1Rule 4error-free DNA repair0.0065 Genetic Interaction Networks Biological statements from the maximally complex MMS fitness network geneinteracts viawith genesP PSY3Rule 1meiotic recombination0.0011 St Onge, et al. Figure 5d

14. 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

15. 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 Thanks to