應用基因演算法設計多聚合酵素鏈鎖反應的引子

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研究生：	黃熙淵 Hsi-Yuan Huang
論文名稱：	應用基因演算法設計多聚合酵素鏈鎖反應的引子 Primer Design for Multiplex PCR Using a Genetic Algorithm
指導教授：	洪炯宗 Jorng-Tzong Horng 黃憲達 Hsien-Da Huang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
畢業學年度：	92
語文別：	英文
論文頁數：	40
中文關鍵詞：	多聚合酵素鏈鎖反應、基因演算法、引子設計
外文關鍵詞：	primer design, genetic algorithm, multiplex PCR
相關次數：	點閱：19 下載：0
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多聚合酵素鏈鎖反應為多個引子在同一聚合酵素鏈鎖反應中反應。由於可同時放大多個目標去氧核醣核酸片段，因此可節省實驗的時間與花費。一個成功的反應有賴於引子的設計，但此引子受限於解鏈溫度，嘌呤嘧啶組成比例，長度及與目標序列的互補狀況，因此為一個相當繁雜的任務。設計引子的方法於這幾十年蓬勃發展，但大多數都不支援多聚合酵素鏈鎖反應。系統主要運用遺傳演算法，模擬自然界的演化方式，對既定問題求最佳解。目前的實驗結果顯示，其可很快的找出目標區域的引子，並將其分組，使它們不只是符合引子設計的原則，並可於同一聚合酵素鏈鎖反應中反應。

Multiplex PCR is the term used when more than one pair of primers is used in a polymerase chain reaction. The goal of multiplex PCR is to amplify several segments of target DNA simultaneously and thereby to conserve template DNA, save time, and minimize expense. The success of above-mentioned methods is dependent on primer design. However, this is a tedious task as too many constrains such as melting temperatures, primer length, GC content and complementarity to optimize the PCR product needs to be satisfied. Various kinds of approaches for designing a primer have been proposed in the last few decades, but most of them don’t satisfy the multiplex PCR. The system draws on genetic algorithm which imitates nature’s process of evolution and genetic operations on chromosomes in order to achieve the optimal solutions. Presently experimental results indicate that the proposed algorithm can find some group of primer pairs that not only obey the design properties but also work at same tube.

Chapter 1 Introduction	1
1 Background	1
2 Motivation	3
3 Goal	4
Chapter 2 Related Works	5
1 Constraints of PCR	5
2 Constraints of multiplex PCR	6
2 Literature reviews	6
Chapter 3 Methods	9
1 Unique regions searching	9
2 Multiplex PCR primer design using a GA	11
2.1 Chromosome	11
2.2 GA process flow	12
2.3 Fitness	15
2.4 Crossover	15
2.5 Mutation	17
3 Result verification	20
4 Parameter determination	20
Chapter 4 Results	23
Chapter 5 Case Studies	25
1 Case Study I	25
2 Case Study II	26
Chapter 6 Discussions and Conclusions	28
1 Characteristics of our tool	28
2 Limitations and Future Works	28
3 Conclusions	29
References	30

                                

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