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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:
Naoki Honda (Graduate School of Life Sciences, Kyoto University)
Tadashi Imayoshi (Graduate School of Life Sciences, Kyoto University)
Shigeyuki Ohba (Graduate School of Informatics, Kyoto University)
Yusuke Suzuki (Graduate School of Medicine, Kyoto University)
Takeshi Hirashima (Graduate School of Medicine, Kyoto University)

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

  ---- 2018 Class Schedule​  ----

April 23: Explanation of class outline, basics and solution of differential equations May 28: Mathematical modeling and numerical analysis by intracellular signal transmission system / ordinary differential equations Introduction to MATLAB June 25: ODE solver / nerve firing Mathematical model Nullcline July 23: Partial differential equation (reaction diffusion / flow)
August 27: Cell motility, morphogenesis, epithelial mechanics (automaton, spring model, etc.)
September 25: Statistical basis (probability distribution, stochastic process, etc.)
November 5: Application to machine learning and time series analysis November 26: Bioinformatics, statistical advance, principal component analysis December 25: Multivariate analysis January 28: Image processing basics for bioimaging data Hen February 25: Image processing application for bioinformatics data ―― 1
March 25: Image Processing for Bioimaging Data Applications-- 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.

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