636-0007-00L  Computational Systems Biology

SemesterAutumn Semester 2016
LecturersJ. Stelling
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
636-0007-00 VComputational Systems Biology3 hrs
Wed14:15-17:00HG D 3.2 »
J. Stelling
636-0007-00 UComputational Systems Biology2 hrs
Fri10:15-12:00CAB G 11 »
J. Stelling

Catalogue data

AbstractStudy of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).
ObjectiveThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
ContentBiology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.
Lecture notesLink
LiteratureU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2006.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersJ. Stelling
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Written aidskeine
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkLecture Material
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Biology MasterElective Compulsory Master Courses I: ComputationWInformation
Biology MasterElective Compulsory Master CoursesWInformation
Certificate of Advanced Studies in Computer ScienceFocus Courses and ElectivesWInformation
Chemical and Bioengineering MasterElectivesWInformation
Computational Biology and Bioinformatics MasterCore CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Computer Science MasterFocus Core Courses Computational ScienceWInformation
Computational Science and Engineering BachelorBiologyWInformation
Computational Science and Engineering MasterBiologyWInformation
Robotics, Systems and Control MasterCore CoursesWInformation