Bioinformatics Vol. 16 no. 8 2000
Pages 727-734
© 2000 Oxford University Press
Computing with Genetic Networks |
Inferring qualitative relations in genetic networks and metabolic pathways
1 Human Genome Center, Institute of Medical
Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo
108-8639, Japan
2 Graduate School of Genetic Resources
Technology, Kyushu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka
812-8581, Japan
Received on February 22, 1999
; revised on April 18, 2000
; accepted on April 18, 2000
Motivation: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network.
Results: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.
Contact: takutsu{at}ims.u-tokyo.ac.jp
To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. Kimura, S. Nakayama, and M. Hatakeyama Genetic network inference as a series of discrimination tasks Bioinformatics, April 1, 2009; 25(7): 918 - 925. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Martin, Z. Zhang, A. Martino, and J.-L. Faulon Boolean dynamics of genetic regulatory networks inferred from microarray time series data Bioinformatics, April 1, 2007; 23(7): 866 - 874. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Tian, S. Xu, J. Gao, and K. Burrage Simulated maximum likelihood method for estimating kinetic rates in gene expression Bioinformatics, January 1, 2007; 23(1): 84 - 91. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Wang, T. Joshi, X.-S. Zhang, D. Xu, and L. Chen Inferring gene regulatory networks from multiple microarray datasets Bioinformatics, October 1, 2006; 22(19): 2413 - 2420. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Tian and K. Burrage Stochastic models for regulatory networks of the genetic toggle switch PNAS, May 30, 2006; 103(22): 8372 - 8377. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. V. HarshaRani, S. J. Vayttaden, and U. S. Bhalla Electronic Data Sources for Kinetic Models of Cell Signaling J. Biochem., June 1, 2005; 137(6): 653 - 657. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Bulashevska and R. Eils Inferring genetic regulatory logic from expression data Bioinformatics, June 1, 2005; 21(11): 2706 - 2713. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. D. King, S. M. Garrett, and G. M. Coghill On the use of qualitative reasoning to simulate and identify metabolic pathways Bioinformatics, May 1, 2005; 21(9): 2017 - 2026. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Kimura, K. Ide, A. Kashihara, M. Kano, M. Hatakeyama, R. Masui, N. Nakagawa, S. Yokoyama, S. Kuramitsu, and A. Konagaya Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm Bioinformatics, April 1, 2005; 21(7): 1154 - 1163. [Abstract] [Full Text] [PDF]
|
|