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Academic Courses

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
Post-Transcriptional 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
       ● Non-parametric 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
       ● Object-oriented programming: classes
       ● Comparative study
       ● Data analysis toolbox
       ● Web programming


Core Courses II (Mandatory):
B2. Big Data in Bioinformatics - From Data-Driven 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
          ● Protein-protein 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
        ● High-throughput 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., RNA-Seq and ChIP-Seq), maximum likelihood estimation, the EM algorithm, Bayesian inference, Monte Carlo methods, Resampling (Bootstrap & permutation test), Human Genetics, clustering and classification, dimension-reduction 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 (3 Credits)
          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
          ● Michaelis-Menten kinetics
          ● Appendices A and B (Alon)
          ● Bifurcation analysis
          ● Two-dimensional flows
          ● Oscillations in Biology
          ● Noises in biology-introduction


Computational Biology (3 Credits, Offered at NTHU, Spring Semesters only)

Conferring the basic knowledge of how math and physics have been helping solve biological problems

         Biological sequences

         Probability & Statistics I
         ● Protein Structure Comparison and Prediction
         ● Protein Dynamics
         ● Molecular Dynamics Simulations

         Normal Mode Analysis

         Protein-Nucleotides and Protein


Any other courses approved by the Curriculum Committee of BP.