Published tools/packages that work in concert with CellNetAnalyzer


9) Characterizing and Ranking Computed Metabolic Engineering Strategies.
Schneider P and Klamt S (2019), Bioinformatics, 35:3063–3072.

Here you can download MATLAB scripts for reproducing the results of this paper. Please read first README.txt.



8) Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms
von Kamp A, Klamt S (2017) Nature Communications 8: 15956.

Here you can download related code:
Script to calculate cMCS inducing growth-coupled overproduction



7) EColiCore2: a reference model of the central metabolism of Escherichia coli and the relationships to its genome-scale parent model.
Haedicke O, Klamt S (2017) Scientific Reports 7:39647.

Here you can download the (corrected) SBML model files:
ECC2comp
ECC2
ECGS



6) Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process.
Koch S, Benndorf D, Fronk K, Reichl U, Klamt S (2015) Biotechnology for Biofuels 9:17.

Here you can download MATLAB scripts for reproducing the results and Figures presented in the paper. Please read the provided README.txt.


5) An algorithm for the reduction of genome-scale metabolic network models to meaningful core models.
Erdrich P, Steuer R, Klamt S (2015) BMC Systems Biology, 9:48.

Here you can download MATLAB scripts for reproducing the results presented in the paper. Please read the provided 00README.txt.



4) Genome-scale strain designs based on regulatory minimal cut sets.
Mahadevan R, von Kamp A, Klamt S (2015) Bioinformatics 31: 2844-2851.

Here you can download MATLAB scripts for reproducing the results presented in the paper. Please read the provided README.txt.



3) Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks.
A. von Kamp and S. Klamt. PLOS Computational Biology (2014), 10:e1003378.

Here you can download MATLAB scripts for reproducing the results presented in the paper. These files use the new MCSEnumerator functionality of CellNetAnalyzer. Please read the provided README.txt.



2) Detecting and Removing Inconsistencies Between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs
I.N. Melas, R. Samaga, L.G. Alexopoulos, S. Klamt. PLOS Computational Biology (2013) 9: e1003204.

Here you can download SigNetTrainer for GUROBI (includes a manual).
Here you can download SigNetTrainer for MATLAB/CPLEX and MATLAB/GLPKMEX (includes a manual). Whenever possible, we strongly recommend to use it with CPLEX (instead of GLPKMEX).



1) An effective framework for reconstructing gene regulatory networks from genetical genomics data.
R.J. Flassig, S. Heise, K. Sundmacher, S. Klamt. Bioinformatics (2013) 29: 246-254.

MATLAB script for the reconstruction framework: ReconstructionGRNfromGeneticalGenomicsData.m
(For documentation and help type 'help ReconstructionGRNfromGeneticalGenomicsData' in MATLAB)

Important: The final step of the reconstruction (TRANSWESD) requires CellNetAnalyzer to be properly installed which you can download here. Alternatively, you may set the parameter TRANSWESD to 0, which would skip this last operation (yielding graph G4; see reference above).