update fenced code part in README.md

README.md

@@ -11,14 +11,14 @@ This ia a porting of the respective MATLAB code, mainly the functions
 CNAreduceMFNetwork, CNAcompressMFNetwork, to Python with following 
 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)
 - use of open software: Python, COBRApy, GNU glpk (CPLEX also supported)
 - modifications can be implemented easier in Python compared to 
   MATLAB (fewer people program in MATLAB)
 - direct interface to SBML coded models (import from SBML / export to SBML)
 - 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 
   with external databases easier.
 - protected reactions and metabolites can be configured using Python set or 
@@ -29,7 +29,7 @@ enhancements:
 Speed-up improvements mainly du to faster implementation of flux variability 
 function calls on COBRApy. Additional speed improvements achieved by 
 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
 
@@ -41,27 +41,26 @@ $ pip3 install  networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net
 ## Small Python example
 
 ```python
->>> import networkred
->>> 
->>> # file names
->>> original_sbml = 'sample_data/SBML_models/Deniz_model_fba.xml'
->>> reduced_sbml = 'sample_data/SBML_models/Deniz_model_fba_reduced.xml'
->>> protected_parts = 'sample_data/data/Deniz_model_fba_nrp.xlsx'
+import networkred
+# file names
+original_sbml = 'sample_data/SBML_models/Deniz_model_fba.xml'
+reduced_sbml = 'sample_data/SBML_models/Deniz_model_fba_reduced.xml'
+protected_parts = 'sample_data/data/Deniz_model_fba_nrp.xlsx'
     
->>> # load the original model
->>> red_model = networkred.ReduceModel(original_sbml)
-
->>> # load and configure protected parts for network reduction
->>> nrp = networkred.load_raw_protected_data(protected_parts)
->>> red_model.set_reduction_params(protect_rids=nrp['reactions'],
-                                   protect_mids=nrp['metabolites'],
-                                   protect_funcs=nrp['functions'],
-                                   temp_fbc_bounds=nrp['bounds'])
->>>  # reduce the network
->>> red_model.reduce()
->>> 
->>> # export reduced model to sbml
->>> red_model.write_sbml(reduced_sbml)
+# load the original model
+red_model = networkred.ReduceModel(original_sbml)
+
+# load and configure protected parts for network reduction
+nrp = networkred.load_raw_protected_data(protected_parts)
+red_model.set_reduction_params(protect_rids=nrp['reactions'],
+                               protect_mids=nrp['metabolites'],
+                               protect_funcs=nrp['functions'],
+                               temp_fbc_bounds=nrp['bounds'])
+# reduce the network
+red_model.reduce()
+ 
+# export reduced model to sbml
+red_model.write_sbml(reduced_sbml)
 ```
 
 
@@ -73,7 +72,6 @@ $ pip3 install  networkred@git+https://gitlab.cs.uni-duesseldorf.de/schubert/net
 Peter Schubert, October 2020
 
 ### References
-
 [1]: Kamp A, Thiele S, Haedicke O, Klamt S. (2017) Use of CellNetAnalyzer
 in biotechnology and metabolic engineering. Journal of Biotechnolgy 261: 221-228.