2024 Faculty Courses School of Computing Department of Mathematical and Computing Science Graduate major in Artificial Intelligence
Design Theory in Biological Systems
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Masayuki Yamamura / Masakazu Sekijima / Masahiro Takinoue / Shogo Hamada
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Mon / 5-6 Thu
- Class
- -
- Course Code
- ART.T546
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
Students will gain an understanding of the fundamentals of design principles of biological systems from a systems-theoretic perspective, learn a broad range of relevant computational methods, and gain a broad understanding of the applied fields of bioinformatics, including synthetic biology, systems biology, bioinformatics, molecular computing, and molecular robotics.
Course description and aims
The mathematical basics are provided in the linear/non-linear differential equation systems, statistical physics, thermodynamic systems, automata, and stochastic processes as the design principles of life. The following topics are also provided: modeling and simulation on biological networks including genetic circuits and neural circuits, design and implementation methods of novel device systems inspired by life systems and computational science methods including bioinformatics and biological molecular simulation. Students will be able to select and explain how such modeling and simulation technique is used and to design novel molecular information devices, molecular computers, etc. using biomolecules.
Keywords
Synthetic biology, Systems biology, Evolutionary computation, Molecular simulation, Biophysics, Molecular computing, Molecular robotics, Bioinformatics
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
In every class, instructors lecture independent topics with original handouts. Every class includes simple exercises to be solved by individual students or by groups. These exercises help understand the principle and will be used as materials for final evaluation.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Mathematical Basics 1: modeling and simulation with linear/non-linear differential equations |
solving linear / non-linear differential equations |
| Class 2 | Mathematical Basics 2: modeling and simulation with automata and stochastic process |
solving stochastic processes |
| Class 3 | Systems Biology / Synthetic Biology 1: Biological Information Flow and metabolism in cells |
understanding the central dogma |
| Class 4 | Systems Biology / Synthetic Biology 2: Design and analysis of Artificial Genetic Circuits |
understanding the concept of genetic circuit |
| Class 5 | Systems Biology / Synthetic Biology 3: Evolution in Artificial Life |
implementation of evolutionary computation |
| Class 6 | Molecular Simulation / Bioinformatics 1:bioinformaticss |
understanding bioinformatics basics |
| Class 7 | Molecular Simulation / Bioinformatics 2:molecular dynamics simulation |
understanding formulas on molecular dynamics |
| Class 8 | Molecular Simulation / Bioinformatics 3:docking simulation |
understanding geometric simulation basics |
| Class 9 | Molecular Computing 1: Basics of molecular computing |
understanding the basic concept of molecular computers |
| Class 10 | Molecular Computing 1: Thermodynamics of DNA/RNA, DNA/RNA secondary structure prediction and sequence design (free energy and Hamming distance) |
understanding of thermodynamics and free energy, secondary structure of biopolymers and its prediction methods |
| Class 11 | Molecular Computing 3: DNA computing and chemical reaction models |
understanding chemical reaction equations |
| Class 12 | Molecular Robotics 1: DNA nanotechnology and DNA nanostructures |
Understanding the basics of DNA nanotechnology including DNA nanostructures |
| Class 13 | Molecular Robotics 2: Molecular devices |
Understanding biomolecular devices (sensors, processors, and actuators) |
| Class 14 | Molecular Robotics 3: Integration examples and their applications |
Understanding the integration of biomolecular devices as systems |
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)
Unspecified.
Reference books, course materials, etc.
For every class, teachers lecture independent topics with original handouts.
Evaluation methods and criteria
Every class also includes simple exercises by individual students or by groups. These exercises help to understand the principle and also become materials for final evaluation.
Related courses
- CSC.T353 ： Biological Data Analysis
- ART.T543 ： Bioinformatics
- ART.T545 ： Molecular Simulation
- ART.T456 ： Non-linear Dynamical Systems
Prerequisites
none
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
e-mail : my[at]c.titech.ac.jp, tel. : 045-924-5212
Office hours
Contact in advance
Other
none