自從高通量科技(high-throughput technologies)的發展之後，我們可以從 DNA微陣列晶片中獲得大量基因作用的時間序列數據，因此有一些科技的方法被提出來模擬基因調控網路。而基因調控網路主要用來表達基因之間的作用關係，但卻只能單純的表達基因之間的作用關係，並沒有辦法清楚的呈現基因之間的調控是如何的運作。在模擬基因調控的方法中，利用數學的方法是較常被使用的，然而在數學方法當中，非線性微分方程式中的S-system 是最被廣泛使用的。
利用數學方法模擬基因調控網路時，最主要有二個觀點，(1)決定模型的結構、(2)估計參數值，然而利用S-system模擬基因調控網路時，我們只能夠知道其基因的表達波形，並沒有辦法知道基因之間的調控關係，但是要了解基因之間的作用關係，就要清楚的了解基因之間是如何運作的。因此我們提出利用對調控參數編碼的方式，來推導基因之間的調控參數。
我們提出對調控參數編碼的方法，並且利用六個人工基因資料，以及假設調控參數100%已知、90%已知、70%已知、50%已知、30%已知、10%已知的六種比例。實驗結果呈現出，除了有很高的比例能夠調控出無調控、正調控與副調控三種關係之外，更能夠調控出精確的調控數值，更能夠清楚的了解基因之間的調控關係。

Since the development of high-throughput technologies, we can capture large quantities of gene’s expression data from DNA microarray data, so there are some technologies have been proposed to model gene regulatory networks. Gene regulatory networks is mainly used to express the relationship between the genes, but only can express a simple relationship, and can’t clearly show how the operation between genes regulatory. In the simulation method of gene regulation, the mathematical methods are more often used. In the mathematical methods, S-system is the most widely used in non-linear differential equations.
When the use of mathematical simulation of gene regulatory networks, there are mainly two aspects：(1) deciding on the model structure and (2) estimating the involved parameter values. However, when using S-system simulated the gene regulatory networks, we can only know the gene profiles, and there is no way to know the regulatory relationships between genes, but in order to understand the relationship between genes, we must clearly understand how genes work. Therefore, we propose to encode parameter values to infer the regulatory parameter values between genes.
We propose the method of encoding parameter values, and using six artificial genetic datasets, and assuming 100% parameter values are known, 90% known, 70% known, 50% known, 30% known, 10% known. The experimental results show, besides it can infer a high proportion of non-regulation, positive regulation and negative regulation, also can infer more precise parameter values, and also has a clear understanding of the regulatory relationship between genes.

1. 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 問題描述 4
1.4 論文架構 5
2. 文獻探討 6
2.1 模擬基因調控網路的方法 6
2.1.1 布林網路(Boolean Networks) 6
2.1.2 貝氏網路(Bayesian Networks) 7
2.1.3 微分方程式(Differential Equations) 9
2.2 方程式最佳化及評估方式 10
2.2.1 粒子群優化演算法(Particle Swarm Optimization) 10
2.2.2 參數值評估方式 12
3. 研究方法與架構 14
3.1 研究方法 14
3.1.1 參數未知比例與基因資料 14
3.1.2 編碼方式 15
3.1.3 適合度值(Fitness Value)與懲罰方式 17
3.2 實驗流程 18
4. 實驗結果與討論 21
4.1 實驗資料 21
4.2 三尺度編碼方式 23
4.2.1 4個基因數資料 23
4.2.2 第一筆5個基因數資料 25
4.2.3 第二筆5個基因數資料 27
4.2.4 第三筆5個基因數資料 29
4.2.5 第四筆5個基因數資料 31
4.2.6 10個基因數資料 33
4.3 七尺度編碼 35
4.3.1 4個基因數資料 35
4.3.2 第一筆5個基因數資料 37
4.3.3 第二筆5個基因數資料 39
4.3.4 第三筆5個基因數資料 41
4.3.5 第四筆5個基因數資料 43
4.3.6 10個基因數資料 45
4.4 實驗結果與討論 47
4.4.1 實驗一 47
4.4.2 實驗二 51
4.4.3 實驗三 54
4.4.4 實驗四 58
4.4.5 實驗五 61
4.4.6 實驗六 65
4.5 討論 68
5. 結論與未來研究 71
6. 參考文獻 73

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