2020 Computational Biology

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Life Science and Technology
Instructor(s)
Itoh Takehiko  Yamada Takuji  Kitao Akio 
Course component(s)
Lecture
Mode of instruction
ZOOM
Day/Period(Room No.)
Mon1-2(Zoom)  Thr1-2(Zoom)  
Group
-
Course number
LST.A408
Credits
2
Academic year
2020
Offered quarter
3Q
Syllabus updated
2020/9/18
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
5) Understanding of basics and applications of molecular dynamics simulation

Keywords

Bioinformatics

Competencies that will be developed

Specialist skills Intercultural skills Communication 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 Overview of classical biomolecular simulation Understanding of overview of classical biomolecular simulation
Class 2 Model building of biomolecules (molecular mechanics, etc) Understanding of molecular mechanics
Class 3 Classical biomolecular simulation Understanding of molecular dynamics simulation
Class 4 Applications of simulation and analysis Understanding of applications of simulation and analysis of the obtained results.
Class 5 Computer modeling of biomolecules Understanding of computer modeling of biomolecules using molecular simulation
Class 6 Overview of fundamental bioinformatics Understanding of overview of fundamental bioinformatics
Class 7 Basics of omics data analysis Understanding of omics data analysis
Class 8 Metagenomics for microbiome Understanding of metagenomics
Class 9 Applications of metagenomics for human gut microbiome Understanding of applications of metagenomics
Class 10 Basics of machine learning for omics data Understanding of machine learning for omics data
Class 11 Fundamentals of Next Generation Sequencers Understand the fundamentals of Next Generation Sequencers
Class 12 Application of Next Generation Sequencers 1 Understand the application of Next Generation Sequencers 1
Class 13 Application of Next Generation Sequencers 2 Understand the application of Next Generation Sequencers 2
Class 14 Application of Next Generation Sequencers 3 Understand the application of Next Generation Sequencers 3

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

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.)

Basic level of physical chemistry (quantum chemistry and classical mechanics)
Basic level of mathematics (calculus and linear algebra)
Basic level of statistical physics
Basic level of genomics

Page Top