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A novel network control model for identifying personalized driver genes in cancer

Fig 1

Overview of personalized network control model (PNC) for identifying personalized driver genes.

(a) The motivation of PNC. By integrating sample specific network theory and structure based network control theory, a small number of personalized driver genes that are altered in response to oncogene activations is detected for triggering the state transition of an individual patient from the healthy state to the disease state at expression level. The main consideration of PNC is to design methods for i) constructing personalized state transition networks to capture the phenotypic transitions between healthy and disease states / attractors, and ii) developing network control method on personalized state transition networks where we identify personalized driver genes for driving individual system in cancer from normal attractor to disease attractor through oncogene activation. (b) The main workflow of PNC. Our PNC consists of two main parts. One is a paired single sample network construction method (Paired-SSN), which we used to construct personalized state transition networks. In these networks, edges denote the significant difference of gene interactions between normal sample and tumor sample of an individual patient. For the second part of PNC, we designed a novel nonlinear structural control method (namely, NCUA) for identifying personalized driver genes. This was done to ensure that the state in the personalized state transition network would asymptotically be changed from normal state to disease state through oncogene activations.

Fig 1

doi: https://doi.org/10.1371/journal.pcbi.1007520.g001