Uusi opinto-opas (sisältäen myös opetusohjelmat) lukuvuodelle 2018-2019 sijaitsee osoitteessa https://opas.peppi.utu.fi . Tältä sivustolta löytyvät enää vanhat opinto-oppaat ja opetusohjelmat.

The new study guide (incl. teaching schedules) for academic year 2018-2019 can be found at https://studyguide.utu.fi. This site contains only previous years' guides.

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Archived Curricula Guide 2014–2016
Curricula Guide is archieved. Please refer to current Curricula Guides
ÅA456402 Computational Modeling Techniques 5 ECTS
Person in charge
Ion Petre

General description

This course is arranged by Åbo Akademi University. See http://tucs.fi/education/courses/

Learning outcomes

This course aims to provide an introduction to the entire computational modeling process, from the formulation of a qualitative model, to its quantitative formulation, to model fitting and validation, model analysis, and model predictions. We focus on the various computational methods that can be employed for modeling and especially on the advantages (and disadvantages) of each approach. We discuss in the course modeling with differential equations, with stochastic processes, with Petri nets and with Bayesian networks. We also cover some numerical methods for such models and will demonstrate two computer-based environments for modeling. The examples we follow throughout the course are mainly from biology and ecology, but the applicability of the methods covered in the course is very broad and it includes dependability issues in complex systems, resource availability, but also applications in economy, chemistry, and social sciences.


-Computational modeling: genralities
-Basic modeling techniques: modeling change,proportionality and geometric similarity
-Model fiting
-Modeling with ordinary differential equations
-Numerical integration methods for ODEs
-Basic numerical techniques for ODE-based models: steady state analysis, sensitivity analysis, flux control analysis
-Parameter estimation methods
-Tool demonstration: COPASI
-Modeling with stochastic processes
-Gillespie's algorithm
-Tool demonstration: PRISM
-Biomodeling laws
-Modeling with Petri nets and with Bayesian nets
-Case-study: modeling the heat shock response

Teaching language


Modes of study

Lectures, exam


Numeric 0-5.

Belongs to following study modules

Department of Future Technologies
Department of Future Technologies
Department of Biochemistry
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Implementation details are unavailable.
Department of Future Technologies
DP in Computer Science
DP in Computer Science
DP Bachelor of Science in Techn.(Communication St)
DP in Information and Communication Technology
Finnish Study Modules