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)
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
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Lecture is given for each topic, followed by some practices when necessary.
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 |
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.
None.
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
Evaluation of assignments imposed during the lecture and those submitted after the lecture.
None.