Protein Networks as Logic Functions in Development and Cancer
By Janusz Dutkowski, Trey Ideker. 2011
109 citations recorded by the source corpus.
Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.
Subfields: Computational Theory and Mathematics, Molecular Biology
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In Computational Theory and Mathematics
In Molecular Biology
Citations in the corpus
- EgoNet: identification of human disease ego-network modules (2014, background): “[20] proposed Network-Guided Forests (NGF) which integrates the key ideas of Random Forests (RF) into the selection of disease modules.”
- Network-based stratification of tumor mutations (2013, background): “An increasing number of studies have successfully integrated these network databases with tumor molecular profiles to map the molecular pathways of cancer 23–27 .”