Methodology of scientific research

Module code

mlsScienceMethod-01a

Abbreviated title

ScienceMethod

Module components

Lecture Medical statistic, tutorial Medical statistics, lecture Systems biology, tutorial Systems biology

When

Semester 1 (summer semester)

Module coordinator/

Organiser

IMIS, IEM

Lecturers

S. Freitag-Wolf (IMIS), C. Kaleta (IEM)

Contact hours

Lecture  Med. Statistics 2 CH            Tutorial Med. Statistics 1 CH

Lecture Systems biology 2 CH           Tutorial Systems biology 1 CH

Workload

Total: 180 h

Lectures: 120 h
Attendance time 46 h, preparation 30 h, revision 44 h
Tutorials: 60 h
Attendance time 24 h, preparation 16 h, revision 20 h

Credit points

6 (lecture Med. statistics 2 CP, tutorial Med. Statistics 2 CP, lecture Systems biology 1 CP, tutorial Systems biology 1 CP)

Requirements

-

Expected outcome

Knowledge: Students

- understand the theoretical and methodological foundations of evidence-based medicine

- know the different designs of medico-scientific research approaches including their respective advantages and disadvantages

- are familiar with common statistical methods

- are familiar with the software R and know basic applications

- are familiar with the ethical principles of Good Clinical Practice

- have a thorough understanding of the concepts of systems biology

- have a sound grasp of bioinformatical workflows used in systems biology
- know the most important data processing techniques for interpretation of large data sets.

Skills: Students

- can apply common statistical methods

- can code simple commands in R and apply them for statistical analysis

- can formulate potential hypotheses based on bioinformatical data analysis results 
- can discuss topics in systems biology adequately with informatics fellow students (terminology and argumentation in discussions subject related and relevant)

- can use their knowledge to work with modeling approaches in future research projects.

Competences: Students

- are able to assess the appropriateness of scientific statements and can evaluate them

- have understood the concept of the computer language R and when and to what aim it is used

- can select a suitable approach to execute a statistical analysis of a given data set, including the use of R.

Content

Lecture Medical statistics: Basics of statistics with regard to their application in medicine: descriptive statistics, probability theory, estimation theory, epidemiology, diagnostic testing, statistical testing, regression and correlation, statistical models.

Tutorial medical statistics: Application and consolidation of knowledge and skills taught in the lecture via exercises using the Software R.

Systems biology lecture and tutorial:

Basic concepts, aims and uses of modeling biological systems; modeling paradigms, modeling metabolic networks, modeling for integration and interpretation of large-scale experimental data sets.

Module evaluation/

exam

Ungraded

Medical statistics:

Written exam (multiple choice)

Systems biology:

Written assignments during semester [compound exam]

Media used

Formula development in class, lecture notes

Literature

Medical statistics

Kirkwood BR, Essential Medical Statistics (Wiley-Blackwell, 2^nd edition, 2003) [still valid]

Bland M, An Introduction to Medical Statistics (OUP 4^th revised edition, 2015)

Pezzullo J, Biostatistics for Dummies (For Dummies, 2013)

Systems biology

Klipp E, Liebermeister W, Wierling C, Kowald A, Systems biology: A Textbook (Wiley 2^nd edition, 2016)