Natural computation meta-heuristics for the in silico optimization of microbial strains

doi:10.1186/1471-2105-9-499

BMC Bioinformatics

Volume 9

Viewing options:

Abstract
Full text
PDF (4.7MB)
Additional files

Associated material:

Related literature:

Articles citing this article
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Rocha M
Maia P
Mendes R
Pinto JP
Ferreira EC
Nielsen J
Patil KR
Rocha I

on PubMed

Rocha M
Maia P
Mendes R
Pinto JP
Ferreira EC
Nielsen J
Patil KR
Rocha I
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Research article

Natural computation meta-heuristics for the in silico optimization of microbial strains

Miguel Rocha¹ , Paulo Maia¹ , Rui Mendes¹ , José P Pinto¹ , Eugénio C Ferreira² , Jens Nielsen⁴ , Kiran Raosaheb Patil³ and Isabel Rocha²

¹Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal

²IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, Universidade do Minho, 4710-057 Campus de Gualtar, Braga, Portugal

³Center for Microbial Biotechnology, Department of Systems Biology, Building 223, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark

⁴Systems Biology, Dept. Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden

author email corresponding author email

BMC Bioinformatics 2008, 9:499doi:10.1186/1471-2105-9-499


Published:	27 November 2008

Additional files

Additional file 1:

The complete results of the SEA and SA for the four case studies. For each case study the details on the organism, the target product and the conditions are given in the header. In each table, the algorithm (SA or SEA) and the maximum number of allowed knockouts k (VS stands for variable size) are given. For each configuration the mean, the confidence interval and the best value of the objective function (BPCY) obtained over the 30 runs are provided. Furthermore, the number of runs where the best solution was reached is also shown.

Format: XLS Size: 17KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2:

The list of the best solutions found by each algorithm and configuration. For each case study, algorithm (SA/SEA) and configuration (value of k) the fitness of the best solution and the corresponding list of knockouts is given. Alternative optimum solutions are provided when applicable.

Format: XLS Size: 65KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 3:

The complete results of the gene frequencies analysis. For each case study, algorithm (SA/SEA) and configuration (value of k) the frequency of the presence of each particular gene knockout within the set of near-optimal is given. The set of solutions used in this case is built from the set with the best solutions from each run, keeping the ones that are within 1% of the best overall solution (over the 30 runs). A global frequency for all values of k is calculated.

Format: XLS Size: 74KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 4:

The results of EA with only mutation operators. For each case study and algorithm (SA/SEA) the results are shown in a way similar to the ones in additional file 1.

Format: XLS Size: 18KB Download file

This file can be viewed with: Microsoft Excel Viewer