2020 Bioinformatics(LST)

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Undergraduate major in Life Science and Technology
Yamada Takuji  Kitao Akio 
Course component(s)
Mode of instruction
Day/Period(Room No.)
Mon7-8(H121)  Thr7-8(H121)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

This course focuses on the basics of bioinformatics, which is an integrated field of life science, information science, and statistical mechanics.
Yamada: Understanding basics of bioinformatics for genome and metagenome analyses (6 lectures)
Kitao: Learning statistical mechanics to connect biological microscopic states and macroscopic states (8 lectures)

Student learning outcomes

Yamada: Understanding basics of bioinformatics
Kitao: Understanding the relation between statistical mechanics and biological phenomena


Bioinformatics, database, sequence analysis, phylogenetic analysis
Physical Chemitry, thermodynancs, statistical mechanics

Competencies that will be developed

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

Class flow

Lecture is given for each topic, followed by some practices when necessary.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Intoduction to bioinformatics (Yamada 1) Tools, databases and their significance
Class 2 Genome and gene (Yamada 2) Concept of gene, gene structure annotation, gene function annotation
Class 3 Large-scale experiments for bioinformatics (Yamada 3) DNA sequencer and its principle
Class 4 Metagenome analysis: shotgun and 16S (Yamada 4) Concept of metagenome, understanding of phylogenetics and its assignement, gene function annotation
Class 5 Statistical ananysls in genome/metagenome analysis (Yamada 5) Statistics, concept of statistical test, comparison between groups
Class 6 Machine learning in bioinformatics (Yamada 6) Applications of machine learning in life science
Class 7 Microscopic states and probability (Kitao 1) Understand probability and microscopic states in biological systems
Class 8 Introduction to statisitical ensembles 1 (Kitao 2) Understanding microcanonical ensemble
Class 9 Introduction to statisitical ensembles 2 (Kitao 3) Understanding canonical and other ensembles
Class 10 Applications of statisitical ensembles 1 (Kitao 4) Understanding of some applications of statistical ensembles including two-state model
Class 11 Applications of statisitical ensembles 2 (Kitao 5) Understanding of some applications of statistical ensembles including harmonic oscillator
Class 12 Free energy change (Kitao 6) Understanding of thermodynamic cycle, free energy perturbation and umbrella sampling
Class 13 Correlation and spectum (Kitao 7) Understanding Fourier transform, autocorrelation function, crosscorrelation function and spectra
Class 14 Brownian motion and fluctuation-dissipation theorem (Kitao 8) Understanding Browninan motion, Langevin equation and fluctuation-dissipation theorem

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.



Reference books, course materials, etc.

Japanese Society of Bioinformatics. Bioinformatics Nyumon (Japanese), ISBN-13: 978-4766422511
David Mount. Bioinformatics: Sequence and Genome Analysis 2nd Edition, ISBN-13: 978-0879697129
Donald A. McQuarrie, Statistical Mechanics ISBN-13: 978-8130918938

Assessment criteria and methods

Evaluation of assignments imposed during the lecture and those submitted after the lecture.

Related courses

  • LST.A241 : Biostatistics
  • LST.A351 : Genome Informatics
  • LST.A201 : Physical Chemistry I
  • LST.A206 : Physical Chemistry II
  • LST.A211 : Physical Chemistry III

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


Page Top