Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/18142
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
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摘要: 
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。

關鍵字:基因調控網路、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

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.
URI: http://hdl.handle.net/11455/18142
其他識別: U0005-2506200916515100
Appears in Collections:應用數學系所

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