Topics in Computational Biology (Selected Topics in Machine Learning and Modelling in Biology)
Module MA5607
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2018
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |
|---|---|
| SS 2019 | SS 2018 |
Basic Information
MA5607 is a semester module in English language at Master’s level which is offered irregular.
This Module is included in the following catalogues within the study programs in physics.
- Elective Modules Natural Sciences in the Master Program Matter to Life
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 180 h | 60 h | 6 CP |
Content, Learning Outcome and Preconditions
Content
In all fields of life sciences, ranging from yeast strain optimization for brewing (→ bioprocess engineering) over stem cell research (→ basic biology) to the treatment of disease (→ medicine), machine learning and modelling methods are employed to deepen our understanding of the respective biological processes and systems. As the range of biological
questions approached using computational biology is rather broad, the number of different machine learning, biostatistics and modelling methods applied in this field is tremendous. Commonly used tools include gene sequence analysis, image analysis, statistical network modeling and dynamic pathway modelling. All of these tools span one or more fields of
mathematics, e.g., statistics, differential equations and optimization.
This lecture series aims at providing the participants with an overview about different fields of computational biology and the methods / algorithms used in this field. To complement the theoretical part, concrete application and ongoing research projects will be presented.
Topics includes:
- Statistical inference for dynamical biological systems
- Models of Stem Cell Decision Making
- Quantitative models of transcriptional gene regulation
- Hidden Markov Models for the analysis of epigenomics data
- Polygenic Risk Analysis
- Imputing single-cell gene expression
questions approached using computational biology is rather broad, the number of different machine learning, biostatistics and modelling methods applied in this field is tremendous. Commonly used tools include gene sequence analysis, image analysis, statistical network modeling and dynamic pathway modelling. All of these tools span one or more fields of
mathematics, e.g., statistics, differential equations and optimization.
This lecture series aims at providing the participants with an overview about different fields of computational biology and the methods / algorithms used in this field. To complement the theoretical part, concrete application and ongoing research projects will be presented.
Topics includes:
- Statistical inference for dynamical biological systems
- Models of Stem Cell Decision Making
- Quantitative models of transcriptional gene regulation
- Hidden Markov Models for the analysis of epigenomics data
- Polygenic Risk Analysis
- Imputing single-cell gene expression
Learning Outcome
After the successful completion of the module, the participants are able to
- explore a selection of machine learning & modelling methods used in computational biology and biomedicine
- understand advantages and limitations of each method / algorithm
- to select suitable computational biology / systems biology approaches for a given biological / biomedical problem.
Furthermore, they have an overview in computational biology.
- explore a selection of machine learning & modelling methods used in computational biology and biomedicine
- understand advantages and limitations of each method / algorithm
- to select suitable computational biology / systems biology approaches for a given biological / biomedical problem.
Furthermore, they have an overview in computational biology.
Preconditions
Bachelor in mathematics, bioinformatics, statistics or related fields.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
| Type | SWS | Title | Lecturer(s) | Dates | Links |
|---|---|---|---|---|---|
| VO | 2 | Selected Topics in Machine Learning & Modelling in Biology [MA5607] | Marr, C. Peng, T. Theis, F. |
Wed, 14:15–15:45, MI 03.06.011 |
eLearning |
| UE | 2 | Selected Topics in Machine Learning & Modelling in Biology (Exercise Session) [MA5607] | Marr, C. Peng, T. Theis, F. |
Wed, 16:00–18:00, MI 02.06.011 |
Learning and Teaching Methods
The module consists in multiple lectures in which different research topics and methods in computational biology are introduced, followed by a parallel exercise course that gives students hands-on experience. The exercise course also allows students to interact with lecturers, discussing possible research projects in their research group.
- The weekly lecture includes one introductory lecture and 12 lectures introducing specific research questions and computational approaches, given by group leaders from the ICB (see http://icb.helmholtz-muenchen.de for an overview).
- In parallel, each lecture will be accompanied by an exercise course that gives students hands-on experience of the research topics addressed in the lecture. Participation to the exercise
course is compulsory and should be registered to the exercise group via Moodle and TUMonline. All participants should bring their own laptop for the exercises and should install the latest version of the Jupyter Notebook with Python kernel. The lecturer and teaching assistant are present during each exercise course to give professional advices.
The individual lectures will be taught by researchers from the: - M12 Biomathematics, Center of Mathematical Sciences, TUM - Institute of Computational Biology, Helmholtz Center Munich
- The weekly lecture includes one introductory lecture and 12 lectures introducing specific research questions and computational approaches, given by group leaders from the ICB (see http://icb.helmholtz-muenchen.de for an overview).
- In parallel, each lecture will be accompanied by an exercise course that gives students hands-on experience of the research topics addressed in the lecture. Participation to the exercise
course is compulsory and should be registered to the exercise group via Moodle and TUMonline. All participants should bring their own laptop for the exercises and should install the latest version of the Jupyter Notebook with Python kernel. The lecturer and teaching assistant are present during each exercise course to give professional advices.
The individual lectures will be taught by researchers from the: - M12 Biomathematics, Center of Mathematical Sciences, TUM - Institute of Computational Biology, Helmholtz Center Munich
Media
slides, blackboard
Literature
H. Kitano (2002) Computational systems biology. Nature 420 (6912): 206-210.
F. Markowetz (2017) All biology is computational biology. PLOS Biology.
F. Markowetz (2017) All biology is computational biology. PLOS Biology.
Module Exam
Description of exams and course work
The exam will be in written form (90 minutes). Students demonstrate that they have gained sufficient knowledge of commonly used tools in computational biology, including gene sequence analysis, image computing, statistical network approaches and dynamic pathway modeling presented in the course and their applicability in data analysis. The students are expected to be able to derive the methods, to explain their properties, and to apply them to specific examples. In particular, some problems from weekly exercise could reappear on the exam with small modifications. Students are also allowed to take one page of course notes during the examination.
Exam Repetition
The exam may be repeated at the end of the semester.