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

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...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2017-09, Vol.33 (18), p.2966-2967
Main Authors: Elsts, Atis, Pentjuss, Agris, Stalidzans, Egils
Format: Article
Language:English
Subjects:
Computational Biology - methods
Models, Biological
Software
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Summary: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. 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. https://github.com/atiselsts/spacescanner . egils.stalidzans@lu.lv. Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btx363