1
|
Phenix H, Perkins T, Kærn M. Identifiability and inference of pathway motifs by epistasis analysis. CHAOS (WOODBURY, N.Y.) 2013; 23:025103. [PMID: 23822501 DOI: 10.1063/1.4807483] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The accuracy of genetic network inference is limited by the assumptions used to determine if one hypothetical model is better than another in explaining experimental observations. Most previous work on epistasis analysis-in which one attempts to infer pathway relationships by determining equivalences among traits following mutations-has been based on Boolean or linear models. Here, we delineate the ultimate limits of epistasis-based inference by systematically surveying all two-gene network motifs and use symbolic algebra with arbitrary regulation functions to examine trait equivalences. Our analysis divides the motifs into equivalence classes, where different genetic perturbations result in indistinguishable experimental outcomes. We demonstrate that this partitioning can reveal important information about network architecture, and show, using simulated data, that it greatly improves the accuracy of genetic network inference methods. Because of the minimal assumptions involved, equivalence partitioning has broad applicability for gene network inference.
Collapse
Affiliation(s)
- Hilary Phenix
- Ottawa Institute of Systems Biology and Graduate Program in Cellular & Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, Ontario K1H 8M5, Canada.
| | | | | |
Collapse
|
2
|
Michaut M, Bader GD. Multiple genetic interaction experiments provide complementary information useful for gene function prediction. PLoS Comput Biol 2012; 8:e1002559. [PMID: 22737063 PMCID: PMC3380825 DOI: 10.1371/journal.pcbi.1002559] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Accepted: 05/01/2012] [Indexed: 11/19/2022] Open
Abstract
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
Collapse
Affiliation(s)
- Magali Michaut
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| |
Collapse
|
3
|
Phenix H, Morin K, Batenchuk C, Parker J, Abedi V, Yang L, Tepliakova L, Perkins TJ, Kærn M. Quantitative epistasis analysis and pathway inference from genetic interaction data. PLoS Comput Biol 2011; 7:e1002048. [PMID: 21589890 PMCID: PMC3093353 DOI: 10.1371/journal.pcbi.1002048] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2010] [Accepted: 03/28/2011] [Indexed: 11/20/2022] Open
Abstract
Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.
Collapse
Affiliation(s)
- Hilary Phenix
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Katy Morin
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Biochemistry, Immunology and Microbiology, University of Ottawa, Ottawa, Ontario, Canada
| | - Cory Batenchuk
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Jacob Parker
- Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Vida Abedi
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Liu Yang
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Lioudmila Tepliakova
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Theodore J. Perkins
- Department of Biochemistry, Immunology and Microbiology, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Mads Kærn
- Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Ontario, Canada
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Physics, University of Ottawa, Ottawa, Ontario, Canada
| |
Collapse
|