| Brenda Andrews Department of Medical Genetics & Microbiology, Faculty of Medicine, University of Toronto Banting and Best Department of Medical Research (BBDMR), Faculty of Medicine, University of Toronto Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto www.utoronto.ca/andrewslab/ | Saccharomyces cerevisiae | genetic interactions / discovery / techniques / SGA genetic interactions / discovery / techniques / SDL |
| Anders Blomberg Goteborg University | genetic interactions | |
| Jef D. Boeke The John Hopkins University School of Medicine, The John Hopkins University www.bs.jhmi.edu/MBG/boekelab/ | genetic interactions / discovery / techniques / SLAM genetic interactions / discovery / techniques / dSLAM | |
| Charlie Boone Department of Medical Genetics & Microbiology, Faculty of Medicine, University of Toronto Banting and Best Department of Medical Research (BBDMR), Faculty of Medicine, University of Toronto Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto www.utoronto.ca/boonelab/ Development of the synthetic genetic array (SGA) technique. | Saccharomyces cerevisiae | genetic interactions / discovery / techniques / SGA |
| Howard Bussey Department of Biology, McGill University biology.mcgill.ca/faculty/bussey/ | Saccharomyces cerevisiae | genetic interactions |
| Andrew Fraser www.sanger.ac.uk/Teams/Team37/ Construction of the first systematic genetic interaction map for any animal. Prediction of genetic interactions through data integration. Automated quantitative analysis of RNAi phenotypes. | Caenorhabditis elegans | genetic interactions / discovery / techniques / RNAi |
| John L. Hartman Department of Genetics, The University of Alabama at Birmingham openwetware.org/wiki/Hartman_Lab Discover how the arrangement of gene circuitry provides robustness, i. e. phenotypic stability over perturbing genetic and environmental inputs. Use cell proliferation as phenotypic readout to quantify the interaction between the perturbation and a deletion at each yeast locus. Vary the type and intensity of perturbation to determine selectivity and strength of interaction and reveal the relative buffering specificity of each gene. | Saccharomyces cerevisiae | genetic interactions |
| Trey G. Ideker Department of Bioengineering, University of California, San Diego chianti.ucsd.edu/idekerlab/ | genetic interactions / interpretation & analysis | |
| Nevan J. Krogan Department of Cellular and Molecular Pharmacology, University of California, San Francisco kroganlab.ucsf.edu | Saccharomyces cerevisiae, Schizosaccharomyces pombe | genetic interactions / discovery / techniques / SGA / E-MAP genetic interactions / discovery / techniques / pombe epistasis mapper (PEM) |
| Chad Myers Department of Computer Science and Engineering, University of Minnesota www-users.cs.umn.edu/~cmyers/ | genetic interactions / interpretation & analysis | |
| Balázs Papp Biological Research Center http://www.brc.hu/sysbiol/ | Saccharomyces cerevisiae | genetic interactions |
| Fritz Roth llama.med.harvard.edu | genetic interactions / interpretation & analysis | |
| Eytan Ruppin www.cns.tau.ac.il Interested in constructing a causal interpretation of biological data via a multi-perturbation (knockout) approach; applies the multi-perturbation Shapley value analysis (MSA) and the functional influence network (FIN) approaches to gene knockouts. | genetic interactions / interpretation & analysis | |
| Grace S. Shieh Institute of Statistical Science, Academia Sinica http://www.stat.sinica.edu.tw/~gshieh/ | genetic interactions / discovery / techniques / computational prediction | |
| Michael Wade Department of Biology, Indiana University www.bio.indiana.edu/facultyresearch/faculty/Wade.html | genetic interactions / applications / population genetics & evolution | |
| Jonathan Weissman Department of Cellular and Molecular Pharmacology, University of California, San Francisco weissmanlab.ucsf.edu | genetic interactions / discovery / techniques / SGA / E-MAP genetic interactions / discovery / techniques / SGA / computational colony size measurement genetic interactions / interpretation & analysis |