09 November 2020- 04 Februari 2021
On Monday 13:15-17:00; Tuesday and Thursday
The course is planned in hybrid format: lectures partly on campus and partly online
(but may be moved entirely online when further lockdown is imposed).
FROM 15 DECEMBER 2020 TILL 19 JANUARI 2021 NO LECTURES ON CAMPUS!
Lectures Mondays 13:15-15:00, Tuesdays and Thursdays 10:00-12:45.
Online, in Teams
and 10 persons can attend live (Kruyt O622) after registration on Teams.
Computational assignments will be done online in working groups:
Tuesdays and Thursdays 13:15-17:00
For details check the Teams group of the course
Computational Biology 2020-2021
This course is for bachelors and master students, as well as PhD
students. Please consult Prof.
Anybody who has successfully completed the Biological modeling
course can participate. The 3rd year
bachelors students who have completed the quantitative biology course
can also participate. Others please consult Prof.
dr. P. Hogeweg
Computational Biology uses computer modeling to investigate
biological problems. The course teaches a variety of modeling
techniques and techniques to analyse the model behaviour. Moreover,
biological theory obtained by computational modeling is examined.
During the course, the emphasis will be on composing exact models,
based on specific hypotheses. The models are analyzed, the results
yielding insights in the original biological system. The models that
are studied address fundamental questions from a variety of biological
fields, among which:
eco-evolutionary dynamics and spatial pattern
genome evolution, e.g. interaction between gene regulation
evolution of complexity, robustness and evolvability
Developmental dynamics (from genes to organisms) (plant and animal models will be used)
morphogenesis and mechanical interactions between cells
EVO-DEVO (evolution of development).
gene regulation and metabolic networks
behavioral self-structuring through local interactions
interface between learning and evolution
(Spatial) pattern formation and emergent properties are
common themes emphasised in all these areas
and the related general theory is introduced as a separate module.
A number of different model formalisms are used, namely:
Non-linear differential/difference equations (ODE
Reaction Diffusion Systems (PDE)
Cellular automata machines
Event based models
Individually oriented models
Hybrid models using several combinatiions of the above formalisms
Analysis tools include
bifurcation analysis, sensitivity analysis, and various pattern
The course is given on tuesdays and thursday. A typical day
starts with lectures from 10:00 to 13:00, followed by computational
until about 17:00. Literature will be handed out related to the
computer excercises , and at the end of the course, literature
seminars are given by the students.
The student's final mark is primarily based on the exam;
with a miniproject and literature seminar for rounding the grade