Introductory course in mathematical, statistical and computational biology for experimental biologists [1 credit (year-round)]
Basic Course in Mathematical, Statistical and Computational Biology for Experimental Biologists.
Name of person in charge:
Kenta Terai (Graduate School of Life Sciences)
Naoki Honda (Graduate School of Life Sciences)
Tadashi Imayoshi (Graduate School of Life Sciences)
Yusuke Suzuki (Institute for Frontier Life and Medical Sciences)
Takeshi Hirashima (Graduate School of Medicine)
Dividend grade: Ph.D., Master's
Number of credits: 1
Opening period: All year
Day time: 5th month (16: 30-18: 00)
Venue: Seminar Room, 1st Floor, F Building, School of Medicine Campus
Class format: Lectures / practices
Course requirements: Being able to bring a laptop
Class outline / purpose:
It provides an introduction to mathematics, statistics, and computational biology, which are the basic knowledge necessary for interdisciplinary fusion research in life science these days.
The target is graduate students who mainly belong to the experimental life science laboratory and are interested in mathematical, statistical, and computational biology.
The purpose is to understand this knowledge and apply it to my research.
Attainment target:
Based on the above knowledge, it will be possible to describe various life phenomena with mathematical models, verify operating principles and extract working hypotheses through computer simulations.
Lesson plan: See itinerary below
---- 2019 Class Schedule ----
April 23: Explanation of class outline, basics and solution of differential equations May 27: Mathematical modeling and numerical analysis by intracellular signal transmission system / ordinary differential equations Introduction to MATLAB June 24: ODE solver / nerve firing Mathematical model Nullcline July 22: Partial differential equation (reaction diffusion / flow)
August 26: Cell motility, morphogenesis, epithelial dynamics September 30 : Statistical basis (probability distribution, stochastic process, etc.)
October 28: Application to machine learning and time series analysis November 25: Bioinformatics, statistical advance, principal component analysis December 23: Multivariate analysis January 27: Image processing basics for bioimaging data Hen February 24: Image processing application for bioinformatics data ―― 1
March 23: Image processing application for bioimaging data ―― 2
* Attendance as the lesson plan and contents will be explained in the lecture on April 23.
Grade evaluation method / viewpoint and achievement level:
【Evaluation method】
Evaluate by a small report submitted to each instructor. Small reports are evaluated based on the degree of achievement of the achievement goal.
Details will be explained at the start of the course.
【Evaluation criteria】
Emphasis is placed on attendance and, in principle, submission of reports to all instructors. After each lecture, you will be asked to decide whether you understand the content of the lecture, so submit it by the next time.