1、本課程之上課方式，首先由實際的分子生物學問題出發，並據以樹立定義明確的問題(well-defined problem)，然後建立解決該問題的模型(modeling)，提出計算方法(演算法)，並分析計算複雜度(Computational Complexity)，最後依計算結果，評估對原分子生物學問題的解答程度。
2、尋求有效率的計算方法，一直是生物資訊學所面臨的挑戰。因此第一週說明何謂生物資訊學後，將先簡介計算複雜度與 NP-completeness 問題。
3、第二週將簡介分子生物學。
4、前二週為學生加退選課程時間，為使較晚選修此課程的學生較無學習落差，因此第三週才會開始本課程主談的生物資訊主題。
5、50年來，計算機科學家發現，即使各領域解決的問題不同，但所提出的各種演算法，有雷同的原理，亦即基本的技巧並不多。本課程的另一主軸，為介紹各種基本的演算法。
6、授課大綱所列教學主題較多，應無法於一個學期內講授完畢。將視學生學習狀況調整。1. The teaching method of this course starts from the actual molecular biology problem, and then establishes a well-defined problem (well-defined problem), and then establishes a model to solve the problem (modeling) and proposes a calculation method (algorithm). ), and analyze the computational complexity (Computational Complexity), and finally evaluate the degree of solution to the original molecular biology problem based on the calculation results.
2. Finding efficient calculation methods has always been a challenge faced by bioinformatics. Therefore, after explaining what bioinformatics is in the first week, we will first introduce the problem of computational complexity and NP-completeness.
3. The second week will introduce molecular biology.
4. The first two weeks provide additional time for students to withdraw from the course. In order to ensure that students who choose this course later will have less learning gaps, the biological information topics discussed in this course will not be started until the third week.
5. Over the past 50 years, computer scientists have discovered that even though the problems solved in each field are different, the various algorithms proposed have similar principles, that is, there are not many basic skills. Another main focus of this course is the introduction of various basic algorithms.
6. There are many teaching topics listed in the syllabus and it should not be possible to complete them in one semester. Adjustments will be made based on student learning conditions.
何謂 “計算生物學” (或稱生物資訊學)? DNA由a,t,c,g 4個字母組合而成，如下例即為一串DNA序列(sequence)： atgcactctt caatagtttt ggccaccgtg ctctttgtag cgattgcttc agcatcaaaa acgcgagagc tatgcatgaa atcgctcgag catgccaagg ttggcaccag caaggaggcg (習慣上，每10個字母寫成一小串，小串間以一“空白”隔開。此例計有120個字母，我們稱其長度為120) 人類DNA總長為30億，這30億個字母決定了一個人。1988年開始的人類基因計劃的主要目的，就是將這30億個字母寫出來。而這些字母是如何運作的，則有待進一步了解。這些隱藏於字母中的生命秘密，我們稱之為生物資訊(Biological information)。 DNA會製造出蛋白質，以營造活生生的生命。蛋白質由20個英文字母 (各代表一種氨基酸)組合而成，長度從數十至數百都有，如下例即為一條蛋白質序列： mhssivlatv lfvaiasask trelcmksle hakvgtskea kqdgidlykh mfehypamkk yfkhrenytp advqkdpffi kqgqnillac hvlcatyddr etfdayvgel marherdhvk 人類約有2萬條不同的蛋白質。這些蛋白質如何營造出生命，有待進一步了解。這些隱藏於字母中的秘密，也是所謂的生物資訊(Biological information)。 研究DNA如何運作及蛋白質如何營造生命, 也就是研究生物資訊(Biological information)，是今日蓬勃發展的“生命科學”之目的。 所謂“生物序列”(Biological sequence)，指的是DNA序列或蛋白質序列。 提出有效的生物序列分析方法(演算法或模型)，以計算機為工具，挖掘隱藏在大量字母裡的生物資訊，我們稱之為“計算生物學”(Computational Biology)，或稱之為“生物資訊學”(Bioinformatics)。
What is "computational biology" (or bioinformatics)? DNA is composed of 4 letters a, t, c, g. The following example is a sequence of DNA: atgcactctt caatagtttt ggccaccgtg ctctttgtag cgattgcttc agcatcaaaa acgcgagagc tatgcatgaa atcgctcgag catgccaagg ttggcaccag caaggaggcg (Customally, every 10 letters are written in a small string, separated by a "blank". In this example, there are 120 letters, we call the length 120) The total length of human DNA is 3 billion, these 30 Billions of letters determine a person. The main purpose of the Human Genome Project started in 1988 is to write these 3 billion letters. How these letters work remains to be understood. These secrets of life hidden in letters are called biological information. DNA makes proteins to create living life. Proteins are composed of 20 English letters (each representing an amino acid), with lengths ranging from tens to hundreds. The following example is a protein sequence: mhssivlatv lfvaiasask trelcmksle hakvgtskea kqdgidlykh mfehypamkk yfkhrenytp advqkdpffi kqgqnillac hvlcatyddr etfdayvgel marherdhvk Humans have about 2 Ten thousand different proteins. How these proteins create life remains to be understood. These secrets hidden in letters are also so-called biological information. Studying how DNA works and how proteins create life, that is, studying biological information, is the purpose of today's booming "life sciences." The so-called "biological sequence" refers to a DNA sequence or a protein sequence. Propose effective biological sequence analysis methods (algorithms or models), use computers as tools to mine biological information hidden in a large number of letters, we call it "Computational Biology" (Computational Biology), or "Biological Information" "Bioinformatics".
Textbook:
An Introduction to Bioinformatics Algorithms
Neil C. Jones and Pavel A. Pevzner
2004, MIT
References:
1. Algorithms on Strings, Trees, and Sequences
── Computer Science and Computational Biology
Dan Gusfield
1997, Cambridge
2. Biological Sequence Analysis
R. Durbin etc.
1998 Cambridge
3. Computers and Intractability
Michael R. Garey and David S. Johnson
1979 W.H. Freeman and company
4. Bioinformatics for Biologists
Pavel A. Pevzner etc.
2011 Cambridge
5. Combinatorics of Genome Rearrangements
Guillaume Fertin etc.
2009 MIT
Textbook:
An Introduction to Bioinformatics Algorithms
Neil C. Jones and Pavel A. Pevzner
2004, MIT
References:
1. Algorithms on Strings, Trees, and Sequences
── Computer Science and Computational Biology
Dan Gusfield
1997, Cambridge
2. Biological Sequence Analysis
R. Durbin etc.
1998 Cambridge
3. Computers and Intractability
Michael R. Garey and David S. Johnson
1979 W.H. Freeman and company
4. Bioinformatics for Biologists
Pavel A. Pevzner etc.
2011 Cambridge
5. Combinatorics of Genome Rearrangements
Guillaume Fertin etc.
2009 MIT
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|
