Required Courses: 
Seminar (1 credit unit, a total of 6 semesters are
required) *Not considered as graduation credits 
Lab Rotation
(0 credit unit/semester, a total of 2 semesters are
required)
● Working in a lab of the student's choice for one semester
● This courses can be taken more than once, but not with the same lab. 
Student
Presentation
(1 credit unit, a total of 4 credit units；4 semesters
within the first 3 years, ) 
TA (0 credit unit, a minimum of 1 semester is required)
*Student may have to take more than 1 term as a course TA 

Core courses I (Required): 
B1. Basic molecular biology for bioinformatics (3 credit units)
●
Molecules, Cells, and Evolution
● Chemical Foundations
●
Protein Structure and Function
● Basic Molecular Genetic
Mechanisms
● Molecular Genetic Techniques
● Genes, Genomics, and
Chromosomes
● Transcriptional Control of
Gene Expression
●
PostTranscriptional Gene Control
●
Cellular Energetics
● Signal Transduction and G
Protein–Coupled Receptors
● Signaling Pathways That
Control Gene Expression
●
The Eukaryotic Cell Cycle
● Cancer

C1. Biological computing I (3 credit units)
This
course is aimed at students without prior knowledge of
computer science who have
desire to apply computational approaches to biological
problem solving.
The goal is to
provide students with a brief introduction to several topics
related to basic computer science and biological
computing. Students are expected to make productive use of
computational techniques after they take this course.
We cover the following topics in this course:
● Data
Structure
● Algorithmic techniques
● Analysis of algorithms
● Computational algorithms
● Bioinformatics algorithms

S1.
Fundamental Statistical Methods in Bioinformatics
(3 credit units)
[Previously
C2]
This course covers the fundamentals of
statistics and basic tools for bioinformatics analysis. In
the first part students will learn basic statistical
concepts and methods, including probability, random
variables and distributions, parameter estimation,
hypothesis testing, regression analysis, and categorical
data analysis. In the second part several commonly used
methods in bioinformatics will be introduced, including the
following topics:
● Descriptive statistics
● Probability
● Discrete distribution
● Continuous distribution
● Parameter estimation, confidence interval
● Hypothesis testing
● Comparative study
● Analysis of categorical data
● Correlation, regression, and analysis of
variance
● Nonparametric analysis
● Clustering
● Classification
● Survival data analysis


P1. Programming Language  Python (2 credit units)

This
course introduces basic aspects of programming language and
its application in bioinformatics. First, fundamental
programming techniques in Python are introduced. After that,
this course focuses on the practical implementation of
programs to analyze various biological data. The use of
existing available resources from the Internet is also
incorporated. Finally, the students implement bioinformatics
projects (i.e., motif finding, pattern matching, sequence
alignment, biomedical database analysis, etc.)
●
Basic Elements of Python
● Basic statements I: branching programs and inputs
● Modules, Files, and Structured Types
● Exception handling
● Introduction to Biopython
● Objectoriented programming: classes
● Comparative study
● Data analysis toolbox
● Web programming

Core Courses II
(Mandatory): 
B2.
Big Data in Bioinformatics  From DataDriven Analysis to
Knowledge (3 credit units)
● Genome sequence acquisition & analysis
● The human genome project
● Genomic variations
● Genomics Databases & Bioinformatics Applications (I)
● Genomics Databases & Bioinformatics Applications (II)
● Introduction to statistical genetics
● Introduction to evolutionary genomics
● DNA Microarrays: principles and applications (I)
● DNA Microarrays: principles and applications (II)
● Transcriptome  related bioinformatics databases & applications
● Protein informatics
● Structural proteomics & drug design
● Proteinprotein interaction network and databases
● Databases of biochemical pathways 
C2. Advanced Algorithms in Computational Biology (3 credit units)
[Previously C3]
In this course we cover the following but not
limit to:
● Sequence analysis algorithms
● Machine
Learning
● Highthroughput
Data Analysis

S2.
Fundamental Statistical Methods in Bioinformatics
(3 credit units)
[Previously
C4]
Introduction to very useful and advanced statistical methods
in computational biology. The topics include: Analysis of
next generation sequencing (NGS) Data (e.g., RNASeq and
ChIPSeq), maximum likelihood estimation, the EM algorithm,
Bayesian inference, Monte Carlo methods, Resampling
(Bootstrap & permutation test), Human Genetics, clustering
and classification, dimensionreduction and missing data.
● Advanced analysis
of omics data
● Advanced analysis of
sequencing data
● Maximum likelihood
estimates and EM algorithm
● Bayesian methods with
Monte Carlo Markov Chains
● Advanced regression and
dimension reduction
● Resampling procedures
and permutation tests
● Advanced clustering,
classification and data visualization
● Biomedical image
analysis
● Statistics in human
genetics
● Biosystem network
analysis

Elective Course:
Dynamics in Systems
The vast advancement in technology and
accumulation of information
nowadays has enabled us to study
biology with great details in time and space resolution, and
in the molecular level. Understanding of biology at the
systems level has become possible in many cases.
The
dynamical aspect of these studies often include a
mathematical model to describe and to predict the behavior
of the system. The construction, evolution and prediction of
these biological models are closely related to a branch in
mathematics – nonlinear dynamics. In this course we cover
the following topics:
●
Central dogma in
molecular biology
●
MichaelisMenten
kinetics
●
Appendices A and B (Alon)
●
Bifurcation analysis
●
Twodimensional
flows
●
Oscillations in
Biology
●
Noises in
biologyintroduction
Any other courses approved by the
Curriculum Committee of BP.

