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Issue No.01 - January-March (2009 vol.6)

pp: 158-170

Sridharakumar Narasimhan , ITT Madras, Chennai

Raghunathan Rengaswamy , Clarkson University, Potsdam

Rajanikanth Vadigepalli , Thomas Jefferson University, Philadelphia

ABSTRACT

The study of gene regulatory networks is a significant problem in systems biology. Of particular interest is the problem of determining the unknown or hidden higher level regulatory signals by using gene expression data from DNA microarray experiments. Several studies in this area have demonstrated the critical aspect of the network structure in tackling the network modelling problem. Structural analysis of systems has proved useful in a number of contexts, viz., observability, controllability, fault diagnosis, sparse matrix computations etc. In this contribution, we formally define structural properties that are relevant to Gene Regulatory Networks. We explore the structural implications of certain quantitative methods and explain completely the connections between the identifiability conditions and structural criteria of observability and distinguishability. We illustrate these concepts in case studies using representative biologically motivated network examples. The present work bridges the quantitative modelling methods with those based on the structural analysis.

INDEX TERMS

Biology and genetics, Graph Theory, Network Models, System Identification, Data Analysis

CITATION

Sridharakumar Narasimhan, Raghunathan Rengaswamy, Rajanikanth Vadigepalli, "Structural Properties of Gene Regulatory Networks: Definitions and Connections",

*IEEE/ACM Transactions on Computational Biology and Bioinformatics*, vol.6, no. 1, pp. 158-170, January-March 2009, doi:10.1109/TCBB.2007.70231REFERENCES

- [1] O. Wolkenhauer, “Mathematical Modelling in the Post-Genome Era: Understanding Genome Expression and Regulation—A System Theoretic Approach,”
BioSystems, vol. 65, pp. 1-18, 2002.- [3] P. Uetz, L. Giot, G. Cagney, T. Mansfield, R. Judson, J. Knight, D. Lockshon, V. Narayan, M. Srinivasan, P. Pochart, A. Qureshi-Emili, Y. Li, B. Godwin, D. Conover, T. Kalbfleisch, G. Vijayadamodar, M. Yang, M. Johnston, S. Fields, and J. Rothberg, “A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces Cerevisiae,”
Nature, vol. 403, no. 6770, pp. 623-627, Feb. 2000.- [10] H. Jeong, S. Mason, A. Barabsi, and Z. Oltvai, “Lethality and Centrality in Protein Networks,”
Nature, vol. 411, no. 6833, pp. 41-42, May 2001.- [14] S. Raychaudhuri, J.M. Stuart, and R.S. Altman, “Principal Components Analysis to Summarize Microarray Experiments: Application to Sporulation Time Series,”
Proc. Pacific Symp. Biocomputing (PSB '00), vol. 5, pp. 455-456, 2000.- [17] H. Bussemaker, H. Li, and E. Siggia, “Regulatory Element Detection Using Correlation with Expression,”
Nature Genetics, vol. 27, pp. 167-171, 2001.- [18] M.K. Yeung, J. Tegnér, and J.J. Collins, “Reverse Engineering Gene Networks Using Singular Value Decomposition and Robust Regression,”
Proc. Nat'l Academy of Sciences, vol. 99, pp. 6163-6168, 2002.- [24] C.-T. Lin, “Structural Controllability,”
IEEE Trans. Automatic Control, vol. 19, pp. 201-208, 1974.- [28] L. Ljung,
System Identification, Theory for the User, second ed. Prentice Hall, 1999.- [36] R. Shamir, A. Maron-Katz, A. Tanay, C. Linhart, I. Steinfeld, R. Sharan, Y. Shiloh, and R. Elkon, “Expander—An Integrative Program Suite for Microarray Data Analysis,”
BMC Bioinformatics, vol. 6, p. 232, 2005.- [37] C.H. Papadimitriou and K. Steiglitz,
Combinatorial Optimization: Algorithms and Complexity. Dover, 1998.- [38] A. Georgiou and C.A. Floudas, “An Optimization Model for Generic Rank Determination of Structural Matrices,”
Int'l J. Control, vol. 49, pp. 1633-1644, 1989. |