Combining biological networks to predict genetic interactions
AUTOR(ES)
Wong, Sharyl L.
FONTE
National Academy of Sciences
RESUMO
Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=524818Documentos Relacionados
- Biological networks: from physical principles to biological insights
- Descending interactions with spinal cord networks: a time to build?
- Identifying genetic networks underlying myometrial transition to labor
- Genetic Networks
- Minimizing inter-genotypic competition effects to predict genetic values and selection in forestry genetic tests