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標題: 以由常微分方程式導出的狀態空間模型從時間序列基因表現資料對基因調控網路的推論
Genetic regulatory networks inferences from time series gene expression data with state-space models based on ordinary differential equations
作者: 吳佩儒
Wu, Pei-Ju
關鍵字: genetic regulatory network;基因調控網路;identification;ODE;nonlinear state-space model;time series gene expression data;辨識;常微分方程式;非線性狀態空間模型;時間序列基因表現資料
出版社: 應用數學系所
引用: [1] Davidson, E.H., et al., A genomic regulatory network for development. Science, 2002. 295(5560): p. 1669-78. [2] Gardner, T.S., et al., Inferring genetic networks and identifying compound mode of action via expression profiling. Science, 2003. 301(5629): p. 102-5. [3] Lee, T.I., et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804. [4] McAdams, H.H. and L. Shapiro, Circuit simulation of genetic networks. Science, 1995. 269(5224): p. 650-6. [5] de Jong, H., Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol, 2002. 9(1): p. 67-103. [6] Bansal, M., G.D. Gatta, and D. di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, 2006. 22(7): p. 815-22. [7] Guthke, R., et al., Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics, 2005. 21(8): p. 1626-34. [8] Nam, D., S.H. Yoon, and J.F. Kim, Ensemble learning of genetic networks from time-series expression data. Bioinformatics, 2007. 23(23): p. 3225-31. [9] van Someren, E.P., L.F. Wessels, and M.J. Reinders, Linear modeling of genetic networks from experimental data. Proc Int Conf Intell Syst Mol Biol, 2000. 8: p. 355-66. [10] Mandic, D.P. and J.A. Chambers, Recurrent neural networks for prediction. 2001, New York: John Wiley & Sons, Ltd. [11] Hu, X., A. Maglia, and D.C. Wunsch, A General Recurrent Neural Network Approach to Model Genetic Regulatory Networks. Engineering in Medicine and Biology Society, EEE-EMBS 2005, 2005. 27th Annual International Conference of the 2005: p. 4735 - 4738 [12] Rui, X., W. Donald, II, and F. Ronald, Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2007. 4(4): p. 681-692. [13] Vohradsky, J., Neural network model of gene expression. Faseb J, 2001. 15(3): p. 846-54. [14] Vu, T.T. and J. Vohradsky, Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae. Nucleic Acids Res, 2007. 35(1): p. 279-87. [15] Weaver, D., C. Workman, and G. Stormo, Modeling regulatory networks with weight matrices. Pacific Symposium on Biocomputing, 1999. 4: p. 112 - 123. [16] Vohradsky, J., Neural Model of the Genetic Network. J. Biol. Chem., 2001. 276(39): p. 36168-36173. 17. Kalman Filtering and Neural Networks. Adaptive and learning systems for signal processing, communications, and control, ed. S. Haykin. 2001: Wiley. 304. [18] Beal, M.J., et al., A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 2005. 21: p. 349 - 356. [19] Dojer, N., et al., Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinformatics, 2006. 7(1): p. 249. [20] Husmeier, D., Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 2003. 19(17): p. 2271 - 2282. [21] Kim, C., Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks. BMC Bioinformatics, 2007. 8(1): p. 251. [22] Li, Z., et al., Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics, 2006. 22(6): p. 747-754. [23] Nachman, I., A. Regev, and N. Friedman, Inferring quantitative models of regulatory networks from expression data. Bioinformatics, 2004. 20 Suppl 1: p. i248-56. [24] Pe''er, D., et al., Inferring subnetworks from perturbed expression profiles. Bioinformatics, 2001. 17(Suppl 1): p. 215 - 224. [25] Perrin, B.E., et al., Gene networks inference using dynamic Bayesian networks. Bioinformatics, 2003. 19(Suppl 2): p. II138 - II148. [26] Xiong, H. and Y. Choe, Structural systems identification of genetic regulatory networks. Bioinformatics, 2008. 24(4): p. 553-60. [27] Quach, M., N. Brunel, and F. d''Alche-Buc, Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference. Bioinformatics, 2007. 23(23): p. 3209-3216. [28] Mendes, P., W. Sha, and K. Ye, Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics, 2003. 19 Suppl 2: p. ii122-9. [29] Ronen, M., et al., Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. Proceedings of the National Academy of Sciences of the United States of America, 2002. 99(16): p. 10555-10560. [30] Vohradsky, J., Neural model of the genetic network. J Biol Chem, 2001. 276(39): p. 36168-73. [31] Julier, S.J. and J.K. Uhlmann, A New Extension of the Kalman Filter to Nonlinear Systems in In Proc. of AeroSense: The 11th Int. Symp. on Aerospace/Defence Sensing, Simulation and Controls. 1997. [32] Nocedal, J. and S.J. Wright, Numerical Optimization. 2nd ed. 2006, New York:: Springer-Verlag [33] Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer series in statistics 2001, New Your: Springer-Verlag. [34] Barabasi, A.-L. and R. Albert, Emergence of scaling in random networks. Science, 1999. 286: p. 509-512.
基因以基因表現的方式和其他基因交互作用以執行細胞功能來形成基因調控網路(genetic regulatory network (GRN))。基因表現資料日益增加的可取得性已經讓我們更可能的明瞭基因調控的行為。在這份報告中,我們由描述核糖核酸(RNA)、蛋白質(protein)及代謝分子(metabolite)在基因表現中動態(dynamics)的常微分方程式(ordinary differential equation (ODE))導出基因調控網路的非線性狀態空間模型(nonlinear state-space model (SSM))。我們使用非察覺型卡爾曼濾波器(unscented Kalman filte(UKF))與最小平方估計法由時間序列基因表現資料(time series gene expression data)學習非線性狀態空間模型。我們從估計出的非線性狀態空間模型((nonlinear SSM))中核醣核酸的表現量在核醣核酸轉錄速率造成的影響預測基因和基因調控的關係。非線性狀態空間模型在電腦模擬和實際的基因表現資料測試結果顯示,此種新的方法比現有的方法對基因調控網路的結構有更高的辨識力。


Genes interacting with one another in the gene expression to carry out cellular functions form the genetic regulatory network (GRN). The increasing availability of gene expression data has made understanding genetic regulatory activities possible. We propose an ordinary differential equation (ODE) system that describes the dynamics of RAN and protein in the gene expression, and develop a nonlinear state-space model (SSM) based on the ODE model for computations. An iterative procedure of parameter-latent state re-estimation using unscented Kalman filter (UKF) and least square optimization is applied to learn the nonlinear SSM from time series gene expression data. We predict gene-gene regulatory interactions by the effects of RNA levels on RNA transcriptional rates estimated by the nonlinear SSM. Test results of the nonlinear SSM method on both computer synthesized and real gene expression show that the new method can be more efficient than methods based on popular linear models for identifying GRN structures.
其他識別: U0005-2506200916515100
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