Design of perturbations is the key to inference of tumour specific gene regulation
2010 (English)Other (Other academic)
Tumour development requires alteration of the normal gene regulation of involved cell types. Mapping of these alterations and inference of the resulting local disease network is therefore crucial to improve our understanding of tumour progression and develop novel cures. Based on the number of known alterations and subtypes of each form of cancer, we assume that the network inference needs to be based on subtype and cell specific expression data to obtain the necessary specific knowledge. We have identified design of perturbations as the key to successful inference of such locally altered gene regulatory networks. Analysis of published gene expression data sets reveal that the variation in expression is concentrated to significantly fewer “characteristic modes” (Holter et al. 2000) or “eigengenes” (Alter et al. 2000) than both the number of recorded assays and the number of measured genes. In other words, the responses obtained in standard experiments are typically concentrated to a subset of the gene space. This is an advantage when considering modelling for predicting gene responses to external perturbations, since the model only needs to capture the characteristic modes correctly for this purpose. However, it seriously hampers network inference, since it implies that models with widely different network structure are practically indistinguishable based on standard response data. To infer the structure we need to design specific perturbations that yield a sufficiently strong signal also for perturbations that are attenuated by the system, i.e., excite the weak modes of the network. The perturbations needed depend on the unknown system and we have therefore developed an iterative design, which we here demonstrate on two published gene expression data sets (Lorenz et al. 2009, Gardner et al. 2003). Alter O, Brown PO, Botstein D. Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A. 2000 Aug 29; 97(18): 10101-6. Gardner TS, di Bernardo D, Lorenz D, Collins JJ, Inferring genetic networks and identifying compound mode of action via expression profiling. Science. 2003 Jul 4; 301(5629): 102-5. Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci U S A. 2000 Jul 18; 97(15): 8409-14. Lorenz DR, Cantor CR, Collins JJ. A network biology approach to aging in yeast. Proc Natl Acad Sci U S A. 2009 Jan 27; 106(4): 1145-50.
Place, publisher, year, edition, pages
MGH-KI-Cell press Days of Molecular Medicine, Stockholm (Sweden) , 2010.
Cancer, Experiment design, Interampatteness, Network inference, System identification, Systems Biology
Bioinformatics and Systems Biology Control Engineering
IdentifiersURN: urn:nbn:se:kth:diva-80757OAI: oai:DiVA.org:kth-80757DiVA: diva2:496755
NV 201504272012-02-102012-02-102015-04-27Bibliographically approved