Abstract
Constructing genetic regulatory networks from expression profiles is one of the most important issues in systems biology research. To automate the procedure of network construction, this work presents an integrated approach for network inference, in which the parameter identification and parameter optimization techniques are developed to deal with the scalability and network robustness problems, respectively. To validate the proposed approach, experiments have been conducted on several artificial and real datasets. The results show that our approach can be used to infer robust gene networks with desired system behaviors successfully.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alon, U.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall (2006)
Sîrbu, A., Ruskin, H.J., Crane, M.: Comparison of Evolutionary Algorithms in Gene Regulatory Network Model Inference. BMC Bioinformatics 11, 59 (2010)
Lee, W.P., Hsiao, Y.T.: Inferring Gene Regulatory Networks Using A Hybrid GA-PSO Approach With Numerical Constraints and Network Decomposition. Information Sciences 188, 80–99 (2012)
Mussel, C., Hopfensitz, M., Kestler, H.A.: BoolNet-an R Package for Generation, Reconstruction and Analysis of Boolean networks. Bioinformatics 26, 1378 (2010)
Hsiao, Y.-T., Lee, W.-P.: Evolving Gene Regulatory Networks: A Sensitivity-Based Approach. In: Huang, D.-S., Gan, Y., Premaratne, P., Han, K. (eds.) ICIC 2011. LNCS (LNBI), vol. 6840, pp. 508–513. Springer, Heidelberg (2012)
Cho, K., Shin, S., Kolch, W., Wolkenhauer, O.: Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using A Monte Carlo Method: A Case Study for The TNFα-mediated NF-kB Signal Transduction Pathway. Simulation 79, 726–729 (2003)
Cao, H., Kang, L., Chen, Y.: Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming. Genetic Programming and Evolvable Machines 1, 309–337 (2000)
Ronen, M., Rosenberg, R., Shraiman, B.I., Alon, U.: Assigning Numbers to The Arrows: Parameterizing A Gene Regulation Network by Using Accurate Expression Kinetics. PNAS 99, 10555–10560 (2002)
Bansal, M., Gatta, G.D., di Bernardo, D.: Inference of Gene Regulatory Networks and Compound Mode of Action from Time Course Gene Expression Profiles. Bioinformatics 22, 815–822 (2006)
Kimura, S., Sonoda, K., Yamane, S., Maeda, H., Matsumura, K., Hatakeyama, M.: Function Approximation Approach to The Inference of Reduced NGnet Models of Genetic Networks. BMC Bioinformatics 9, 23 (2008)
Kabir, M., Noman, N., Iba, H.: Reversely Engineering Gene Regulatory Network from Microarray Data Using Linear Time-variant Model. BMC Bioinformatics 11, S56 (2010)
Bazil, J.N., Qi, F., Beard, D.A.: A parallel algorithm for reverse engineering of biological networks. Integrative Biology 3, 1145–1145 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hsiao, YT., Lee, WP. (2013). An Effective Parameter Estimation Approach for the Inference of Gene Networks. In: Huang, DS., Gupta, P., Wang, L., Gromiha, M. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2013. Communications in Computer and Information Science, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39678-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-39678-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39677-9
Online ISBN: 978-3-642-39678-6
eBook Packages: Computer ScienceComputer Science (R0)