This course provides an introduction to the design principle of life in the viewpoint of system theory, through lecturing topics on related computational methodology.
The mathematical basics are provided in the linear/non-linear differential equation systems, statistical physics, thermodynamic systems, automata and stochastic process as the design principle 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 system 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.
synthetic biology, systems biology, evolutionary computation, computational neuroscience, molecular simulation, molecular robotics
|✔ Specialist skills||Intercultural skills||Communication skills||Critical thinking skills||Practical and/or problem-solving skills|
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||Required learning|
|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||Computational Neuro Science 1 : What is Computational Neuro-Science||understanding neuro-science basics|
|Class 7||Computational Neuro Science 2 : Deriving equivalent circuit models for cellular membrane and Hodgkin-Huxley model||solving equivalent circuits|
|Class 8||Computational Neuro Science 3: FitzHugh-Nagumo model and bifurcation theory||understanding bifurcation theory|
|Class 9||Molecular Simulation / Bioinformatics 1:bioinformaticss||understanding bioinformatics basics|
|Class 10||Molecular Simulation / Bioinformatics 2:molecular dynamics simulation||understanding formulas on molecular dynamics|
|Class 11||Molecular Simulation / Bioinformatics 3:docking simulation||understanding geometric simulation basics|
|Class 12||Molecular Robotics 1: DNA sequence design (free energy and Hamming distance)||understanding the concept of free energy|
|Class 13||Molecular Robotics 2: Calculation of DNA/RNA secondary structure and its application to DNA nanotechnology||understanding secondary structure formation|
|Class 14||Molecular Robotics 3: DNA computing and chemical reaction models||understanding chemical reaction equations|
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.
For every class, teachers lecture independent topics with original handouts.
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.
e-mail : my[at]c.titech.ac.jp, tel. : 045-924-5212
Suzukake-dai campus J2 build, rm1706, Mon&Thu 17:00—18:00