title: Probabilistic reconstruction of the tumor progression process in gene regulatory networks in the presence of uncertainty.
abstract: BACKGROUND
  Accumulation of gene mutations in cells is known to be responsible for tumor progression, driving it from benign states to malignant states. However, previous studies have shown that the detailed sequence of gene mutations, or the steps in tumor progression, may vary from tumor to tumor, making it difficult to infer the exact path that a given type of tumor may have taken.
  
  
  RESULTS
  In this paper, we propose an effective probabilistic algorithm for reconstructing the tumor progression process based on partial knowledge of the underlying gene regulatory network and the steady state distribution of the gene expression values in a given tumor. We take the BNp (Boolean networks with pertubation) framework to model the gene regulatory networks. We assume that the true network is not exactly known but we are given an uncertainty class of networks that contains the true network. This network uncertainty class arises from our partial knowledge of the true network, typically represented as a set of local pathways that are embedded in the global network. Given the SSD of the cancerous network, we aim to simultaneously identify the true normal (healthy) network and the set of gene mutations that drove the network into the cancerous state. This is achieved by analyzing the effect of gene mutation on the SSD of a gene regulatory network. At each step, the proposed algorithm reduces the uncertainty class by keeping only those networks whose SSDs get close enough to the cancerous SSD as a result of additional gene mutation. These steps are repeated until we can find the best candidate for the true network and the most probable path of tumor progression.
  
  
  CONCLUSIONS
  Simulation results based on both synthetic networks and networks constructed from actual pathway knowledge show that the proposed algorithm can identify the normal network and the actual path of tumor progression with high probability. The algorithm is also robust to model mismatch and allows us to control the trade-off between efficiency and accuracy.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA. m.shahrokh@tamu.edu
authors.forename: Mohammad Shahrokh
authors.initials: MS
authors.lastname: Esfahani
authors.forename: Byung-Jun
authors.initials: BJ
authors.lastname: Yoon
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D051736:Proto-Oncogene Proteins c-mdm2 / Q000235:genetics; D015398:Signal Transduction; D016159:Tumor Suppressor Protein p53 / Q000235:genetics; D035501:Uncertainty
chemical_list: C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; C497206:MDM2 protein, human; D051736:Proto-Oncogene Proteins c-mdm2