2017 Computational Biology

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Academic unit or major
Graduate major in Life Science and Technology
Instructor(s)
Sakurai Minoru  Itoh Takehiko  Yamada Takuji  Kotera Masaaki  Kitao Akio 
Course component(s)
Lecture
Day/Period(Room No.)
Mon1-2(J221,W831)  Thr1-2(J221,W831)  
Group
-
Course number
LST.A408
Credits
2
Academic year
2017
Offered quarter
3Q
Syllabus updated
2017/10/26
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

How deep knowledge or useful information can we retrieve from diverse and enormous data obtain from multi-omics analysis? This course forcuses on Bioinformatics. Topics includes molecular evolution, sequence analysis, comparative genomics, multi-omics analysis, algorithms for bioinformatics, molecular or metabolic network analysis, and data mining methods. By combining lectures and exercises, the course enables students to understand and acquire the fundamentals of bioinformatics widely applicable to biological research. Bioinformatic approaches taught in this course are not only useful in analyzing multi-omics data, but are applicable to various other types of biological problem.

Student learning outcomes

By the end of this course, students will be able to:
1) Understand principles and methods of sequence analysis based on molecular evolution
2) Understand the knowledge obtained by comparing the gene sequences and genomic sequences
3) Understand computer algorithms in bioinformatic analyses
4) Understand the fundamentals and applications of multi- omics analysis

Keywords

Bioinformatics

Competencies that will be developed

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

Class flow

Required learning should be completed outside of the classroom for preparation and review purposes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to computational biology Understand the outline of bioinformatics
Class 2 Introduction of molecular evolution Understand fundamentals of molecular evolution
Class 3 Fundamentals of molecular evolution for amino acids Understand fundamentals of amino acid sequence analysis method
Class 4 Fundamentals of molecular evolution for nucleic acids Understand fundamentals of nucleic acid sequence analysis method
Class 5 Fundamentals of sequence analysis and genome analysis Understand fundamentals of genome analysis
Class 6 Comparative genomics Understand fundamentals of comparative genomics
Class 7 Multi-omics analysis Understand fundamentalsl of multi-omics analysis
Class 8 Overview of classical biomolecular simulation Understanding of overview of classical biomolecular simulation
Class 9 Model building of biomolecules (molecular mechanics, etc) Understanding of molecular mechanics
Class 10 Classical biomolecular simulation Understanding of molecular dynamics, normal mode analysis, and Monte Carlo
Class 11 Advanced simulation methods Understanding of free energy calculation and efficient sampling methods
Class 12 Computer modeling of biomolecules 1 Understanding of the the application of all-atom MD methods to protein chemistry
Class 13 Computer modeling of biomolecules 2 Understanding of the the application of QM/MM methods to protein chemistry
Class 14 Computer modeling of biomolecules 3 Understanding of the the application of coarse-grained MD methods to protein chemistry

Textbook(s)

None

Reference books, course materials, etc.

Neil C. Jones and Pavel A. Pevzner. An Introduction to Bioinformatics Algorithms. ISBN-13: 978-0262101066
Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. ISBN-13: 978-0195135855

Assessment criteria and methods

By written reports for each class.

Related courses

  • None

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

None

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