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Issue No.02 - April-June (2008 vol.5)
pp: 262-274
ABSTRACT
Recently, the concept of mutual information has been proposed for infering the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.
INDEX TERMS
Biology and genetics, information theory, genetic regulatory network, DNA microarray
CITATION
Wentao Zhao, Erchin Serpedin, Edward R. Dougherty, "Inferring Connectivity of Genetic Regulatory Networks Using Information-Theoretic Criteria", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, no. 2, pp. 262-274, April-June 2008, doi:10.1109/TCBB.2007.1067
REFERENCES
 [1] J.D. Watson et al., Molecular Biology of the Gene, fifth ed. Benjamin Cummings, 2004. [2] S. Liang, S. Fuhrman, and R. Somogyi, “REVEAL, a General Reverse-Engineering Algorithm for Inference of Genetic Network Architectures,” Proc. Pacific Symp. Biocomputing (PSB '98), pp. 18-29, 1998. [3] I. Tabus and C.D. Giurcaneanu, “Genetic Networks Inferred from Time Series of Gene Expression Data,” Proc. First Int'l Symp. Control, Comm. and Signal Processing (ISCCSP '04), pp. 755-758, Mar. 2004. [4] I. Huang and Y. Wang, “Bayesian Inference of Cell Cycle Regulatory Networks,” Proc. IEEE Int'l Workshop Genomic Signal Processing and Statistics (GENSIPS '05), May 2005. [5] K. Missal, M.A. Cross, and D. Drasdo, “Gene Network Inference from Incomplete Expression Data: Transcriptional Control of Hematopoietic Commitment,” Bioinformatics, vol. 22, pp. 731-738, 2006. [6] W. Zhao, E. Serpedin, and E.R. Dougherty, “Inferring Gene Regulatory Networks from Time Series Data Using the Minimum Description Length Principle,” Bioinformatics, vol. 22, pp. 2129-2135, 2006. [7] T. Dean and K. Kanazawa, “A Model for Reasoning About Persistence and Causation,” Computational Intelligence, vol. 5, pp.142-150, 1989. [8] H. Lahdesmaki et al., “Relationships between Probabilistic Boolean Networks and Dynamic Bayesian Networks as Models of Gene Regulatory Networks,” Signal Processing, vol. 86, pp. 814-834, 2006. [9] K. Murphy and S. Mia, “Modeling Gene Expression Data Using Dynamic Bayesian Networks,” technical report, Berkeley Univ., 1999. [10] I. Simon et al., “Combined Static and Dynamic Analysis for Determining the Quality of Time-Series Expression Profiles,” Nature Biotechnology, vol. 23, pp. 1503-1508, 2005. [11] I. Shmulevich and O. Yli-Harja, “Inference of Genetic Regulatory Networks under the Best-Fit Extension Paradigm,” Proc. Fifth IEEE-EURASIP Workshop Nonlinear Signal and Image Processing (NSIP '01), June 2001. [12] S.A. Kauffman, “Metabolic Stability and Epigenesist in Randomly Constructed Genetic Nets,” J. Theoretical Biology, vol. 22, pp. 437-467, 1969. [13] X. Zhou, X. Wang, and E.R. Dougherty, “Construction of Genomic Networks Using Mutual-Information Clustering and Reversible-Jump Markov-Chain-Monte-Carlo Predictor Design,” Signal Processing, vol. 83, pp. 745-761, 2003. [14] I. Shmulevich et al., “Probabilistic Boolean Networks: A Rule-Based Uncertainty Model for Gene Regulatory Networks,” Bioinformatics, vol. 18, pp. 261-274, 2002. [15] W. Friedman, M. Linial, I. Nachman, and D. Peer, “Using Bayesian Network to Analyze Expression Data,” J. Computational Biology, vol. 7, pp. 601-620, 2000. [16] J. Yu et al., “Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks,” Proc. Third Int'l Conf. Systems Biology (ICSB '02), Dec. 2002. [17] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988. [18] A.J. Butte and I.S. Kohane, “Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements,” Proc. Pacific Symp. Biocomputing (PSB '00), pp. 418-429, Jan. 2000. [19] G.F. Cooper, “A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships,” Data Mining and Knowledge Discovery, vol. 1, pp. 203-224, 1997. [20] A. Wagner, “Reconstructing Pathways in Large Genetic Networks from Genetic Perturbations,” J. Computational Biology, vol. 11, pp.53-60, 2004. [21] A.A. Margolin et al., “ARACNE: An Algorithm for Reconstruction of Genetic Networks in a Mammalian Cellular Context,” BMC Bioinformatics, vol. 7, p. S7, 2006. [22] T.M. Cover and J.A. Thomas, Elements of Information Theory. Wiley-Interscience, 1991. [23] P. Brazhnic, A. de la Fuente, and P. Mendes, “Gene Networks: How to Put the Function in Genomics,” Trends in Biotechnology, vol. 20, pp. 467-472, 2002. [24] M. Gustafsson, M. Hornquist, and A. Lombardi, “Constructing and Analyzing a Large-Scale Gene-to-Gene Regulatory Network: Lasso-Constrained Inference and Biological Validation,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 2, no. 3, July-Sept. 2005. [25] D.W. Scott, Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons, 1992. [26] A. Ihler, “Kernel Density Estimation Software,” http://www. ics.uci.edu/~ihlercode/, 2006. [27] J. Beirlant, E. Dudewicz, L. Gyorfi, and E. van der Meulen, “Nonparametric Entropy Estimation: An Overview,” Int'l J. Math. and Statistical Sciences, vol. 6, pp. 17-39, 1997. [28] L. Paninski, “Estimation of Entropy and Mutual Information,” Neural Computing, vol. 15, pp. 1191-1253, 2003. [29] L. Paninski, “Estimating Entropy on $m$ Bins Given Fewer than $m$ Samples,” IEEE Trans. Information Theory, vol. 50, pp. 2200-2203, 2004. [30] A. Treves and S. Panzeri, “The Upward Bias in Measures of Information Derived from Limited Data Samples,” Neural Computing, vol. 7, pp. 399-407, 1995. [31] D. Wolpert and D. Wolf, “Estimating Functions of Probability Distributions from a Finite Set of Samples,” Physical Rev. E, vol. 52, pp. 6841-6854, 1995. [32] N. Guelzim et al., “Topological and Causal Structure of the Yeast Transcriptional Regulatory Network,” Nature Genetics, vol. 31, pp.60-63, 2002. [33] L. Giot et al., “A Protein Interaction Map of Drosophila melanogaster,” Science, vol. 302, pp. 1727-1736, 2003. [34] P. Mendes, “Artificial Genetic Networks,” http://mendes.vbi. vt.edu/AGN/Centuryindex.html , 2006. [35] T.V.D. Bulcke et al., “SynTReN: A Generator of Synthetic Gene Expression Data for Design and Analysis of Structure Learning Algorithms,” BMC Bioinformatics, vol. 7, p. 43, 2006. [36] M Bittner et al., “Molecular Classification of Cutaneous Malignant Melanoma by Gene Expression Profiling,” Nature, vol. 406, pp.536-540, 2000. [37] A.T. Weeraratna et al., “Wnt5a Signaling Directly Affects Cell Motility and Invasion of Metastatic Melanoma,” Cancer Cell, vol. 3, pp. 279-288, 2002. [38] S.M. Pulukuri et al., “RNA Interference-Directed Knockdown of Urokinase Plasminogen Activator and Urokinase Plasminogen Activator Receptor Inhibits Prostate Cancer Cell Invasion, Survival, and Tumorigenicity In Vivo,” J. Biological Chemistry, vol. 280, pp. 36529-36540, 2005. [39] F. Al-Ejeh, D. Croucher, and M. Ranson, “Kinetic Analysis of Plasminogen Activator Inhibitor Type-2: Urokinase Complex Formation and Subsequent Internalization by Carcinoma Cell Lines,” Experimental Cell Research, vol. 297, pp. 259-271, 2004. [40] E.T. Ifon et al., “U94 Alters FN1 and ANGPTL4 Gene Expression and Inhibits Tumorigenesis of Prostate Cancer Cell Line PC3,” Cancer Cell Int'l, vol. 22, pp. 5-19, 2005. [41] R.S. Watnick et al., “Ras Modulates Myc Activity to Repress Thrombospondin-1 Expression and Increase Tumor Angiogenesis; Ras Modulates Myc Activity to Repress Thrombospondin-1 Expression and Increase Tumor Angiogenesis,” Cancer Cell, vol. 3, pp. 219-231, 2003. [42] H.R. Abeysinghe et al., “THY1 Expression Is Associated with Tumor Suppression of Human Ovarian Cancer,” Cancer Genetics and Cytogenetics, vol. 143, pp. 125-132, 2003. [43] B.C. Fuchs and J.C. Perez et al., “Inducible Antisense RNA Targeting Amino Acid Transporter ATB0/ASCT2 Elicits Apoptosis in Human Hepatoma Cells,” Am. J. Physiology—, Gastrointestinal and Liver Physiology, vol. 286, pp. G467-G478, 2003. [44] Q.X. Li, N. Ke, R. Sundaram, and F. Wong-Staal, “NR4A1, 2, 3-An Orphan Nuclear Hormone Receptor Family Involved in Cell Apoptosis and Carcinogenesis,” Histology and Histopathology, vol. 21, pp. 533-540, 2006. [45] A.J. Hartemink, “Reverse Engineering Gene Regulatory Networks,” Nature Biotechnology, vol. 23, pp. 554-555, 2005.