Commit bd7ac2ce authored by Peter Schubert's avatar Peter Schubert
Browse files

update fenced code part in README.md

parent dd4082eb
...@@ -11,14 +11,14 @@ This ia a porting of the respective MATLAB code, mainly the functions ...@@ -11,14 +11,14 @@ This ia a porting of the respective MATLAB code, mainly the functions
CNAreduceMFNetwork, CNAcompressMFNetwork, to Python with following CNAreduceMFNetwork, CNAcompressMFNetwork, to Python with following
enhancements: enhancements:
- significant speedup (e.g. network reduction of genome-scale E. coli network - significant speedup (network reduction of genome-scale E. coli network
30 h -> 1 h on a laptop) 30 h -> 1 h on a laptop)
- use of open software: Python, COBRApy, GNU glpk (CPLEX also supported) - use of open software: Python, COBRApy, GNU glpk (CPLEX also supported)
- modifications can be implemented easier in Python compared to - modifications can be implemented easier in Python compared to
MATLAB (fewer people program in MATLAB) MATLAB (fewer people program in MATLAB)
- direct interface to SBML coded models (import from SBML / export to SBML) - direct interface to SBML coded models (import from SBML / export to SBML)
- due to COBRA integration, SBML identifiers, annotations, - due to COBRA integration, SBML identifiers, annotations,
gene-product-associations, genes and groups are taken over transparently gene-product-associations, genes and groups taken over transparently
from original network to the reduced network, making integration from original network to the reduced network, making integration
with external databases easier. with external databases easier.
- protected reactions and metabolites can be configured using Python set or - protected reactions and metabolites can be configured using Python set or
...@@ -29,7 +29,7 @@ enhancements: ...@@ -29,7 +29,7 @@ enhancements:
Speed-up improvements mainly du to faster implementation of flux variability Speed-up improvements mainly du to faster implementation of flux variability
function calls on COBRApy. Additional speed improvements achieved by function calls on COBRApy. Additional speed improvements achieved by
continually removing reactions and blocked metabolites during the network continually removing reactions and blocked metabolites during the network
reduction process. Essential reactions are identified earlier. reduction process. Essential reactions identified earlier.
Peter Schubert, Computational Cell Biology, HHU Duesseldorf, November 2021 Peter Schubert, Computational Cell Biology, HHU Duesseldorf, November 2021
...@@ -41,27 +41,26 @@ $ pip3 install networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net ...@@ -41,27 +41,26 @@ $ pip3 install networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net
## Small Python example ## Small Python example
```python ```python
>>> import networkred import networkred
>>> # file names
>>> # file names original_sbml = 'sample_data/SBML_models/Deniz_model_fba.xml'
>>> original_sbml = 'sample_data/SBML_models/Deniz_model_fba.xml' reduced_sbml = 'sample_data/SBML_models/Deniz_model_fba_reduced.xml'
>>> reduced_sbml = 'sample_data/SBML_models/Deniz_model_fba_reduced.xml' protected_parts = 'sample_data/data/Deniz_model_fba_nrp.xlsx'
>>> protected_parts = 'sample_data/data/Deniz_model_fba_nrp.xlsx'
>>> # load the original model # load the original model
>>> red_model = networkred.ReduceModel(original_sbml) red_model = networkred.ReduceModel(original_sbml)
>>> # load and configure protected parts for network reduction # load and configure protected parts for network reduction
>>> nrp = networkred.load_raw_protected_data(protected_parts) nrp = networkred.load_raw_protected_data(protected_parts)
>>> red_model.set_reduction_params(protect_rids=nrp['reactions'], red_model.set_reduction_params(protect_rids=nrp['reactions'],
protect_mids=nrp['metabolites'], protect_mids=nrp['metabolites'],
protect_funcs=nrp['functions'], protect_funcs=nrp['functions'],
temp_fbc_bounds=nrp['bounds']) temp_fbc_bounds=nrp['bounds'])
>>> # reduce the network # reduce the network
>>> red_model.reduce() red_model.reduce()
>>>
>>> # export reduced model to sbml # export reduced model to sbml
>>> red_model.write_sbml(reduced_sbml) red_model.write_sbml(reduced_sbml)
``` ```
...@@ -73,7 +72,6 @@ $ pip3 install networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net ...@@ -73,7 +72,6 @@ $ pip3 install networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net
Peter Schubert, October 2020 Peter Schubert, October 2020
### References ### References
[1]: Kamp A, Thiele S, Haedicke O, Klamt S. (2017) Use of CellNetAnalyzer [1]: Kamp A, Thiele S, Haedicke O, Klamt S. (2017) Use of CellNetAnalyzer
in biotechnology and metabolic engineering. Journal of Biotechnolgy 261: 221-228. in biotechnology and metabolic engineering. Journal of Biotechnolgy 261: 221-228.
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