SpaceScanner: COPASI wrapper for automated management of global stochastic optimisation experiments

Atis Elsts, Agris Pentjuss, Egils Stalidzans

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
144 Downloads (Pure)


Motivation: Due to their universal applicability, global stochastic optimization methods are popular for designing improvements of biochemical networks. The drawbacks of global stochastic optimization methods are: (i) no guarantee of finding global optima, (ii) no clear optimization run termination criteria and (iii) no criteria to detect stagnation of an optimization run. The impact of these drawbacks can be partly compensated by manual work that becomes inefficient when the solution space is large due to combinatorial explosion of adjustable parameters or for other reasons.

Results: SpaceScanner uses parallel optimization runs for automatic termination of optimization tasks in case of consensus and consecutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in the currently used method. Automatic scan of adjustable parameter combination subsets for best objective function values is possible with a summary file of ranked solutions.

Availability and implementation:
Original languageEnglish
Early online date29 Jul 2017
Publication statusE-pub ahead of print - 29 Jul 2017


Dive into the research topics of 'SpaceScanner: COPASI wrapper for automated management of global stochastic optimisation experiments'. Together they form a unique fingerprint.

Cite this