BMB547: Molecular Data Science

students taking the course are expected to:

• Have knowledge of mathematics corresponding to the A-level in the Danish high school system.

The teaching in the course will be largely problem/case based for example based on results from relevant published papers. This will  ensure that the topics are introduced in the relevant framework of molecular biology/medicine. Furthermore, when possible the course will take relevant examples from other ongoing courses. 

• Knowledge and skills in applications of basic mathematical functions relevant to life sciences. 
• Understanding of and practical skills in application of basic statistics on relevant data sets. 
• Basic skills in analysing and visualizing relevant data sets from real experimental data.
• Understanding and practical applications of calculus and differential equations used to model molecular biology related examples.  
• Skills in linear algebra to support basic programming skills, numerical modelling and examples from systems biology
• Acquire theoretical background in Harmonic functions and Fourier transforms to understand the basic concepts of Fourier transform mass spectrometry
• Learn basic programming skills to be able to manipulate, analyse and visualize data from different molecular biology related examples
• Be able to successfully perform simple computational biology related data science projects.

Examples of applications can include: 

• Poiseuille’s Law: Blood flow
• Enzyme kinetics
• Chemical reactions
• Tumor growth (Cancer Therapy)
• Bacterial Growth
• Cancer-Immune Interaction
• Spread of Disease
• modeling biological systems/networks
• DNA/RNA/protein sequence analysis
• Image analysis 
• Ion trap analyzer (Fourier transformation)
• DNA/RNA/protein sequence analysis.
• Image analysis
• Systems biology

Students participating in the course will gain the following competences: 

Mathematics / Statistics 

• Calculations and analysis of mathematical functions relevant to life sciences. 
• Probability 
• Statistics (descriptive statistics, elements of probability, hypothesis testing, nonparametric methods, correlation analysis, and linear regression. Emphasis will be on how to choose appropriate statistical tests and how to assess statistical significance.)
• Linear algebra: linear equations, eigenvalue problems, linear differential equations, principal component analysis,
• Mathematical biology (modeling)
• Discrete mathematics (combinations, graphs, and logical statements)
• Harmonic functions, Fourier transform
• Differential equations
• Calculus
• Numerical methods

• Basic programming (most likely R)
• Data structures
• Create Basic Visualizations of data
• Apply Ordinary Least Squares method to Create Linear Regressions
• Assess R-Squared for all types of models
• Assess the Adjusted R-Squared for all types of models
• Create a Simple Linear Regression (SLR)
• Create a Multiple Linear Regression (MLR)
• Interpret coefficients of an MLR
• Successfully perform all steps in a Data Science project
• Analysis of large data sets using linear equations, eigenvalue problems, linear differential equations, principal component analysis.
• Introduction to Data Mining 

Planned lessons:
Total number of planned lessons: 104

Hereof:
Common lessons in classroom/auditorium: 32
Team lessons in classroom: 40
Team lessons in laboratory: 32

The common lessons consists of lectures which provide an introduction to the course and the course material. Students are expected to independently read prescribed text (the text book) to achieve the expected competencies and necessary overview.

The team lessons deals with the central parts of the course using theoretical and computer based exercises. The tutorials are based on prior independent work or, if wanted, self-organized group work.
The team lessons also includes Computer based lab exercises in which students work together in groups.

Other planned teaching activities:

• Preparation for the common and team lessons.
• Preparation of laboratory reports.
• Exam preparation (repetition).