MLU
INF.06126.02 - Computational Biodiversity Lab (Complete module description)
Original version English
INF.06126.02 5 CP
Module label Computational Biodiversity Lab
Module code INF.06126.02
Semester of first implementation
Faculty/Institute Institut für Informatik
Module used in courses of study / semesters
  • Bioinformatik (MA120 LP) (Master) > Bioinformatik BioinformatikMA120, Version of accreditation (WS 2009/10 - SS 2016) > Bioinformatik
  • Bioinformatik (MA120 LP) (Master) > Bioinformatik BioinformatikMA120, Version of accreditation (WS 2016/17 - WS 2022/23) > Bioinformatik (HI)
  • Informatik (MA120 LP) (Master) > Informatik InformatikMA120, Version of accreditation (WS 2013/14 - SS 2016) > Vertiefende Module der Vertiefungsrichtung `Bioinformatik`
  • Informatik (MA120 LP) (Master) > Informatik InformatikMA120, Version of accreditation (WS 2016/17 - WS 2022/23) > Vertiefende Module der Vertiefungsrichtung `Bioinformatik`
Responsible person for this module
Further responsible persons
Prof. Jonathan Chase, Dr. Annabell Berger
Prerequisites
Skills to be acquired in this module
In the last years computer science developed more and more to a field bridging the gap between its theory and practice. Nowadays, algorithms have to solve special problems with underlying data sets and not only well-defined, idealized problems. These new conditions lead to a more comprehensive thinking style in computer science connecting many fields like algorithm theory, network analysis, data mining, software engineering and theoretical informatics. But very often computer scientists are focused on very technical problems. The new pioneering challenge is to discover the living world which is mostly much more complex.
Biodiversity science is experiencing a 'Renaissance' and is poised to address some of the most critical problems facing humanity in an increasingly human dominated world. Its theory is based on a set of mathematical-inspired foundational principles, including evolutionary process, thermodynamics and stoichiometry, birth-death processes, network theory and probability. The next generation of biodiversity scientists will need be adept at a diversity of complex quantitative approaches.
In this seminar we want to connect computer science and biodiversity research starting with some foundations of every field like dynamical systems (Lotka,Volterra), algorithmic graph theory, network analysis and probability (birth-death processes). We want to point out basic principles and methods and then connect them very fast to concrete biodiversity problems. This course offers lectures about fundamentals, practical work on scientific papers, work on concrete problems and exercises. We offer discussions and want to talk with you. This course teaches the basics of the new arising field of computational biodiversity sciences.
Module contents
  • Biodiversity
  • Dynamical Systems
  • Network Analysis
  • Algorithmic Graph Theory
  • Data analysis
  • Stability
  • Probability
  • Birth-Death Processes
  • Case Studies
Forms of instruction Seminar (4 SWS)
Course
Course
Languages of instruction German, English
Duration (semesters) 1 Semester Semester
Module frequency nicht festlegbar
Module capacity unlimited
Time of examination
Credit points 5 CP
Share on module final degree Course 1: %; Course 2: %; Course 3: %.
Share of module grade on the course of study's final grade 1
Module course label Course type Course title SWS Workload of compulsory attendance Workload of preparation / homework etc Workload of independent learning Workload (examination and preparation) Sum workload
Course 1 Seminar Seminar 4 0
Course 2 Course Selbststudium 0
Course 3 Course Exam preparation 0
Workload by module 150 150
Total module workload 150
Examination Exam prerequisites Type of examination
Course 1
Course 2
Course 3
Final exam of module
Regelmäßige Mitarbeit und aktive Beteiligung bei den Übungsteilen der Veranstaltung;, Präsentation von erfolgreich gelösten Hausaufgaben
mündliche Prüfung oder Klausur
Exam repetition information
Prerequisites and conditions Prerequisites Frequency Compulsory attendance Share on module grade in percent
Course 1 Winter semester No %
Course 2 Winter semester No %
Course 3 Winter semester No %