05 Februari 2024 - 11 April 2024
On Monday 13:15-17:00; Tuesday and Thursday
10:00-17:00.
The course will be held in various lecture rooms in the Uithof. (schedule)
(please check "mytimetable" for room updates)
Target:
This course is for bachelors and master students, as well as PhD
students. Please consult Prof.
dr.
P. Hogeweg
Admission:
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
Purpose:
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.
Contents:
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:
Evolutionary dynamics
eco-evolutionary dynamics and spatial pattern
formation
host-pathogen co-evolution
genome evolution, e.g. interaction between gene regulation
and evolution;
evolution of complexity, robustness and evolvability
Developmental dynamics (from genes to organisms) (plant and animal models will be used)
pattern formation
cell differentiation
morphogenesis and mechanical interactions between cells
EVO-DEVO (evolution of development).
Network dynamics
gene regulation and metabolic networks
RNA interference
Behaviour
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
and MAPs)
Reaction Diffusion Systems (PDE)
Cellular automata machines
Event based models
Individually oriented models
Evolutionary computation
Hybrid models using several combinatiions of the above formalisms
Analysis tools include
bifurcation analysis, sensitivity analysis, and various pattern
analysis techniques.
Form:
The course is given on tuesdays and thursday. A typical day
starts with lectures from 10:00 to 13:00, followed by computational
modeling excercises
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.
Evaluation:
The student's final mark is primarily based on the exam;
with a miniproject and literature seminar for rounding the grade