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2014 – Advanced computational modeling

Period 2, 2014-2015


NEW: The course booklet, containing the reports from all teams, is now available here.


This course builds onto the introductory Computational modeling techniques course. It aims to provide a deeper view into several computational modeling techniques. We discuss in the course modeling with stochastic processes, with Petri nets and with rule-based systems. We will demonstrate 2-3 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, applications in economy, chemistry, and social sciences.

The course consists of 9 lectures and of team projects. The course evaluation is done on the basis of the projects and of the project presentations. In particular, there is no final exam.

During the course the students will develop a computational model on one of the case-studies of the course and will write an article-like report on it. Students will form 2-person teams and each team will select one case-study. Each of the two members of a team will choose his/her own modeling software, different from that of their team partner. The choices will be made so that there is no duplicate of a combination case-study–modeling software. Each student’s task will be:

  1. to  write an essay (2-5 pages) on the case-study of their team; this will be jointly done with their team partner;
  2. to implement the case-study in their modeling software of choice and to write a report (5-10 pages) on it;
  3. to write a blind referee report (1-2 pages) on another student’s project;
  4. to finalize their report based on the referee report they received;
  5. to present their final results.

At the end of the course we will collect all reports in a course volume, that will be published on the course website. The volume will consists of a number of articles (written in an academic style), one for each team. Each article will consists of four parts: (i) the essay on the chosen case-study; (ii) first modeling implementation; (iii) second modeling implementation; (iv) conclusions.

Reporting on the project: The reports should be written using the LNCS style. The reports should be written in Latex, strictly adhering to the LNCS format style. The style of the report should be academic, similar to what one would normally submit to a scientific workshop/conference. The report should include a carefully compiled list of references. We stress a very strict policy against plagiariasm of any kind; we will run all articles through a dedicated plagiarism-detection software.

Important dates

  • October 31: form the team
  • November 3: assignment of case studies
  • November 10: submit the essay on your case study
  • November 24, 26: group presentations on their case studies
  • December 1: submit the report on the modeling project, including the models themselves
  • December 3: get the project on which you should act as a referee
  • December 7: submit your referee report
  • December 8-10: project presentations
  • December 12: submit the final version of your project report
  • December 17: publish the booklet with the project reports

This course is primarily meant for Master and PhD students in computer science, computer engineering, information systems, and applied mathematics.


  • Modeling with continuous-time Markov chains
  • Gillespie’s algorithm
  • Tools for ODE models: sensitivity analysis, elementary fluxes, parameter estimation, flux-balance analysis
  • ODE vs CTMC
  • Biomodeling laws
  • Petri net-based modeling in biology
  • Rule-based modeling in biology
  • Tool demo: Copasi
  • Tool demo: Snoopy
  • Tool demo: Prism

Credits: 5 study points.

Components: 18h lectures, projects. 

Time schedule: October 27 – December 17, 2014. Lectures will be given every Monday 13-15 and Wednesday 10-12.

Lecture room: Catbert (B3028), ICT.

Prerequisites: Computational modeling techniques.

Lecturer: Ion Petre, Department of IT, Åbo Akademi University, ipetre’AT’

Course assistants: Cristi Gratie, Bogdan Iancu, Sepinoud Azimi, Diana Gratie, Department of IT, Åbo Akademi University

Course webpage:

Lecture slides (to be uploaded throughout the course):

Last updated: November 24, 2014