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

Atis Elsts, Agris Pentjuss, Egils Stalidzans (Corresponding Author)

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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: https://github.com/atiselsts/spacescanner.

Original languageEnglish
Pages (from-to)2966-2967
Number of pages2
JournalBioinformatics
Volume33
Issue number18
DOIs
Publication statusPublished - 15 Sept 2017
Externally publishedYes

Field of Science*

  • 1.1 Mathematics
  • 1.6 Biological sciences
  • 1.4 Chemical sciences
  • 1.2 Computer and information sciences

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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