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, Article 47. DOI: 10.2202/1544-6115.1727 ©2011 De Gruyter. All rights reserved. Fitting Boolean Networks from Steady State Perturbation Data Anthony Almudevar, Matthew N. McCall, Helene McMurray, and Hartmut Land Abstract Gene perturbation experiments are commonly used for the reconstruction of gene regulatory networks. Typical experimental methodology imposes persistent changes on the network. The resulting data must therefore be interpreted as a steady state from an altered gene regulatory network, rather than a direct observation of the original network. In this

) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high- dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show

, owing to the reason that integrating the information from different types of networks may lead to the notion of functional networks and functional modules, to find these modules, we should consider the general question of the potential effect of individual genes on the global dynamical network behavior both from the view of random gene perturbation as well as intervention. It should be emphasized that although computational tools and resources can be used to identify putative drug targets, validating targets is still a process that requires understanding the

]. Since most biological processes arise from integrated activities among many genes, interpreting the consequences on a pathway level contributes to understanding how gene perturbations account for disease [ 8 ]. Thus, the characterization of pathway changes is imperative for understanding the molecular mechanisms of SCD. Existing pathway algorithms have been classified into three categories: over-representation analysis, functional class scoring and pathway topology-based approach [ 9 ]. However most existing pathway techniques mainly concern the identification of

.2202/1544-6115.1189 The purpose of the present report is to describe and characterize a new phenomenon revealed in microarray gene expression data. The presence of this phenomenon has been corroborated by similar analyses of several smaller data sets. A molecular mechanism underlying Type A stochastic dependence has yet to be deciphered. Specially designed experiments could shed light on finer details of the DR - MOD relationships and the putative mechanism by which the cell distributes various tasks among genes. In particular, gene perturbation experiments could be designed to see how

phospholipid-binding proteins Biochim. Biophys. Acta 1197 63 93 Reiske, H., Sui, B., Ung-Medoff, H., Donahue, R., Li, W.B., Goldblatt, M., Li, L., and Kinch, M.S. (2010). Identification of annexin A13 as a regulator of chemotherapy resistance using random homozygous gene perturbation. Anal. Quant. Cytol. Histol. 32 , 61–69. 20701074 Reiske H. Sui B. Ung-Medoff H. Donahue R. Li W.B. Goldblatt M. Li L. Kinch M.S. 2010 Identification of annexin A13 as a regulator of chemotherapy resistance using random homozygous gene perturbation Anal. Quant. Cytol. Histol. 32 61 69

, 5, e12475, doi: 10.1371/journal.pone.0012475. 10.1371/journal.pone.0012475 Davidich, M. I. and S. Bornholdt (2008): “Boolean network model predicts cell cycle sequence of fission yeast,” PLoS One, 3, e1672, doi: 10.1371/journal.pone.0001672. 10.1371/journal.pone.0001672 18301750 Garg, A., I. Xenarios, L. Mendoza and G. DeMicheli (2007): “Efficient methods for dynamic analysis of genetic networks and in silico gene perturbation experiments,” Lect. Notes Bioinform., 4453, 62–76. Goelzer, A., F. B. Brikci, I. M. Verstraete and P. e. a. Noirot (2008): “Reconstruction

distribu- tion) of seeing a given number or more genes in an event by chance, and significance was expressed by a p-value. Onto-Tool analysis calculates a perturbation factor for each input gene, which reflects the relative importance of each dif- ferentially regulated gene. Significance is presented as an impact factor of the entire pathway and takes into consideration the proportion of differentially regulated genes in the pathway and gene perturbation factors of all genes in the pathway. Immunohistochemical validation Paraffin blocks were sectioned 4 mm thick, mounted

.J. Jennings J.R. Jenkin J.C. Alvarado A.S. 2005a SMEDWI-2 is a PIWI-like protein that regulates planarian stem cells Science 310 1327 1330 Reddien, P.W., Bermange, A.L., Murfitt, K.J., Jennings, J.R., and Alvarado, A.S. (2005b). Identification of genes needed for regeneration, stem cell function, and tissue homeostasis by systematic gene perturbation in planaria. Dev. Cell 8 , 635–649. Reddien P.W. Bermange A.L. Murfitt K.J. Jennings J.R. Alvarado A.S. 2005b Identification of genes needed for regeneration, stem cell function, and tissue homeostasis by systematic gene

DBN learning framework that incorporates gene perturbation data. They used the BDe score and with an exact search method for inferring small scale networks. Lähdesmäki and Shmulevich (2008) use a combination of steady state measurements and time series data to learn a DBN structure. They use the BIC score with MCMC sampling. Methods based on local search can deal with scalability issues, for example DELDBN ( Li et al. 2011 ) implements a local causality discovery algorithm by identifying the Markov blanket of the transcription rate using constraint based methods