2024 Faculty Courses School of Life Science and Technology Department of Life Science and Technology Graduate major in Life Science and Technology
Computational Biology
- Academic unit or major
- Graduate major in Life Science and Technology
- Instructor(s)
- Kengo Sato / Takehiko Itoh / Takuji Yamada / Akio Kitao / Koichiro Uriu
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Mon / 1-2 Thu
- Class
- -
- Course Code
- LST.A408
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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 problems. Group work will also be conducted for better understanding.
Course description and aims
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
- 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.
This class will be conducted by using Zoom system to reduce the burden caused by travel for students enrolled at both Ookayama and Suzukakedai campuses when taking classes and doing group work.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Dynamics of gene regulatory networks (1): simple transcriptional regulation and negative feedback loop | Understanding of basic mathematical modeling of gene regulation |
| Class 2 | Dynamics of gene regulatory networks (2): positive feedback and feedforward loops | Understanding of basic dynamical systems theory |
| Class 3 | Mathematical analysis of gene expression rhythms (1): circadian rhythms | Understanding of computer modeling of biomolecules using molecular simulation |
| Class 4 | Overview of genome information analysis using NGS and principles of NGS | Understanding the background of genomic information analysis using NGS |
| Class 5 | Mapping-based NGS analysis and its algorithms | Understanding the basic algorithms used in mapping-based NGS analysis |
| Class 6 | Algorithms in genome assembly, RNA-seq analysis, and ChIP-seq analysis | Understanding the basic algorithms used in genome assembly, RNA-seq analysis, and ChIP-seq analysis |
| Class 7 | Mathematical analysis of gene expression rhythms (2): ultradian rhythms | Understanding of mechanisms for gene expression rhythms |
| Class 8 | Basics of omics data analysis | Understanding of omics data analysis |
| Class 9 | Metagenomics for microbiome | Understanding of metagenomics |
| Class 10 | Combinatorial optimization for bioinformatics | Understanding techniques for formulating and solving various bioinformatics problems as combinatorial optimization. |
| Class 11 | Machine learning for bioinformatics | Understanding techniques for formulating and solving various bioinformatics problems as machine learning. |
| Class 12 | Overview of classical biomolecular simulation | Understanding of overview of classical biomolecular simulation |
| Class 13 | Model building of biomolecules (molecular mechanics, etc) | Understanding of molecular mechanics |
| Class 14 | Classical biomolecular simulation | Understanding of molecular dynamics simulation |
Study advice (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
Evaluation methods and criteria
By written reports for each class.
Related courses
- None
Prerequisites
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