2017 Bioinformatics

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Academic unit or major
Graduate major in Artificial Intelligence
Akiyama Yutaka  Konagaya Akihiko  Ishida Takashi 
Class Format
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(W833,G311)  Fri5-6(W833,G311)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

This course provides a comprehensive overview of bioinformatics where living matters are modeled and analyzed as information systems. The fundamental notions and methods in genome sequence analysis, protein structural bioinformatics, and system biology are introduced with illustrative examples of recent research.
This course is aiming to show students live instances of computing technology application in our society, especially via combination of various mathematical methods in order to extract meanings from vast and vague real-world data.

Student learning outcomes

By the end of this course, students will be able to:
1) Explain fundamental knowledge on bioinformatics.
2) Explain novel mathematical methods to extract meanings from various data
3) Explain instances of computing technology application in society


Genome sequence analysis, protein structural bioinformatics, system biology, computational biology

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Each class starts with explanation of a new topic. In the class occasionally, students are given exercise problems to solve. Students are asked to submit final reports.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Sequence bioinformatics overview Genome sequence, sequence analysis, global/local alignment
Class 2 Multiple alignment and sequence motifs Approximation methods for multiple alignment, regular expression/profile matrix/HMM representation
Class 3 Homology search from databases E-value, FASTA, BLAST, PSI-BLAST, BLAT
Class 4 Phylogenetic tree estimation Distance matrix methods, character state methods, bootstrapping
Class 5 Coding region prediction Gene coding region prediction by Markov model, etc.
Class 6 Structure bioinformatics overview Protein structure, structure-function relationship, structure comparison, structure classification
Class 7 Protein secondary structure prediction Protein structure prediction based on machine learning methods
Class 8 Protein tertiary structure prediction Comparative modeling, de novo prediction
Class 9 Protein docking Protein-protein docking, protein-ligand docking, virtual screening
Class 10 Molecular simulation Molecular dynamics, Molecular orbital method
Class 11 System biology overview Biological system, complex network, network biology
Class 12 Gene expression regulation network analysis Gene expression, transcription factor, Boolean network, Bayesian network
Class 13 Metabolic pathway network analysis Metabolites, metabolic pathway, flux analysis
Class 14 Phamacokinetic modeling and analysis Drugs, phamacokinetics, compartment model, PBPK model


Original class slides are provided.

Reference books, course materials, etc.

Mount, David. Bioinformatics: Sequence and Genome Analysis (2nd edition). Cold Spring Harbor Laboratory Press; ISBN-13: 978-087969712-9

Assessment criteria and methods

Students' knowledge and their ability to apply them to solving problems will be assessed with final report.

Related courses

  • CSC.T242 : Probability Theory and Statistics
  • CSC.T353 : Biological Data Analysis
  • CSC.T272 : Artificial Intelligence
  • CSC.T352 : Pattern Recognition

Prerequisites (i.e., required knowledge, skills, courses, etc.)


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