Reorganize repository structure

autopacmen/AutoPACMEN_Model_Calibrator_MATLAB/README_Model_Calibrator_MATLAB.txt → AutoPACMEN_Model_Calibrator_MATLAB/README.md

@@ -5,5 +5,5 @@ for an example of the Model Calibrator's kcat optimization method usage.
 NOTE: You have to change the paths to the iJO1366*C model (see Supplementary File 1) to its location on your
 system in order to rerun "kcat_optimization_run_fmincon_ec_model_2019_06_25.m".
 
-MATLAB scripts starting with "p_" are considered "private" scripts which are not intended for a direct
+MATLAB scripts starting with "p_" are considered "private" scripts which are not intended for direct
 usage by the user and are therefore not documented in the manual.

README.md

@@ -6,67 +6,64 @@
 ## General Description
 
 AutoPACMEN allows one to apply the sMOMENT method of automatically expanding a stoichiometric metabolic
-model with protein allocation constraints ([Bekiaris & Klamt, 2020](#autopacmens-publication)).
-AutoPACMEN consists of 2 parts:
-1) A Python 3 Model Generator which primarily uses [cobrapy](https://github.com/opencobra/cobrapy) and which applies the sMOMENT
-   method on a stoichiometric metabolic model.
-2) An optional mixed Python 3/MATLAB Model Calibrator whose MATLAB parts primarily use [CellNetAnalyzer](https://www2.mpi-magdeburg.mpg.de/projects/cna/cna.html)
-   and which allows one to optimize given protein allocation constraints in order to get a better fit
-   with <i>in vivo</i> data.
+model with protein allocation constraints (as described in further detail in [Bekiaris & Klamt, 2020](#autopacmens-publication)).
 
+This repository of AutoPACMEN consists of three source code parts:
+
+1) The AutoPACMEN Model Generator, implemented as the Python module "autopacmen-Paulocracy" module, which provides a mostly automated way to generate enzyme-constraint-enhanced stoichiometric metabolic models, including an automatic retrieval of k<sub>cat</sub> values. It is primarily dependent on [cobrapy](https://github.com/opencobra/cobrapy).
+<br>→This module can be found in the "autopacmen" subfolder.
+
+2) An optional mixed Python 3/MATLAB AutoPACMEN Model Calibrator whose MATLAB parts primarily use [CellNetAnalyzer](https://www2.mpi-magdeburg.mpg.de/projects/cna/cna.html) and which allows one to optimize given protein allocation constraints in order to get a better fit with <i>in vivo</i> data.
+<br>→The Python parts can be found in the "autopacmen" subfolder as described in AutoPACMEN's manual,the MATLAB parts can be found in the subfolder "AutoPACMEN_Model_Calibrator_MATLAB".
+
+3) The exemplary usage of autopacmen-Paulocracy and the Model Calibrator resulting in the enzyme-constraint-enhanced model iJO1366*, as described in Supplementary File 1 of AutoPACMEN's publication. The final iJO1366* model is stored in a ready-to-use SBML form as "./iJO1366star/ec_model_2019_06_25_output_optimization/iJO1366star.xml".
+<br>→The relevant scripts and data can be found in the "iJO1366" subfolder. <b>Note:</b> As it has a huge file size, the obligatory downloaded complete BRENDA text file brenda_downloads.txt (as described in AutoPACMEN's manual) is not included here. Instead, it can be downloaded from BRENDA's web site.
 
 ## Documentation
 
-AutoPACMEN's manual can be found as "manual.pdf" or "manual.odt" in the "docs" subfolder of the "autopacmen" folder.
+The combined manual for autopacmen-Paulocracy and the Model Calibrator can be found as "manual.pdf" or LibreOffice-compatible "manual.odt" in the "docs" subfolder folder.
 It explains the manual installation process (without pip, see next chapter for the installation with pip) and usage of AutoPACMEN in detail.
 
-An HTML documentation of the source code of AutoPACMEN's Python modules can be found under the main folder's subfolder "html".
+An additional HTML documentation of the source code of AutoPACMEN's Python modules can be found under the "autopacmen" folder's subfolder "html".
 This HTML documentation was automatically generated using [pdoc3](https://pdoc3.github.io/pdoc/) (link accessed on Oct 30, 2019).
-The HTML documentation's starting point is "index.html" in the "./html/autopacmen" subfolder.
-
-All scripts starting with "ec_model_2019_06_25" show the exemplary usage of AutoPACMEN for the generation of iJO1366*. All these scripts are intended to be run from the "autopacmen" subfolder of this repository. AutoPACMEN's exemplary usage is explained in AutoPACMEN's publication Supplementary File 1.
-
-<b>Note:</b> As it has a huge file size, the downloaded complete BRENDA text file brenda_downloads.txt (as described in
-AutoPACMEN's manual) is not included here.
+The HTML documentation's starting point is "index.html" in the "./autopacmen/html/autopacmen" subfolder.
 
 If you are particularily interested in the generation of k<sub>cat</sub> databases from BRENDA and SABIO-RK, look up
 the scripts "data_parse_brenda_textfile.py", "data_parse_sabio_rk_for_model.py" as well as the combining
 script "data_create_combined_kcat_database.py". These scripts create JSON files with the k<sub>cat</sub> data from these
 databases with EC number, organism and substrate information. An exemplary created database with BRENDA is
-"kcat_database_brenda" in the main folder's  subfolder "ec_model_2019_06_25_output", a database with SABIO-RK is
+"kcat_database_brenda" in the main folder's  subfolder "./iJO1366star/ec_model_2019_06_25_output", a database with SABIO-RK is
 "kcat_database_sabio_rk" in the same subfolder.
 
-AutoPACMEN requires Python >=3.7 for its Python parts, and MATLAB >=2017a for its optional Model Calibrator
-MATLAB scripts.
 
+## Installation of autopacmen-Paulocracy using pip
 
-
-## Installation of Python parts using pip
-
-You can install the Python parts of AutoPACMEN's latest release [from PyPI](https://pypi.org/project/autopacmen-Paulocracy/) using pip as follows:
+You can install autopacmen-Paulocracy [from PyPI](https://pypi.org/project/autopacmen-Paulocracy/) using pip as follows:
 <pre>
 pip install autopacmen-Paulocracy
 </pre>
 
+autopacmen-Paulocracy requires Python >=3.7 for its Python parts, and MATLAB >=2017a for its optional Model Calibrator
+MATLAB scripts.
+
 
 ## Structure of AutoPACMEN's source code
 
-<b>All relevant scripts are in the "autopacmen" main folder.</b>
+All relevant scripts of autopacmen-Paulocracy are in the "autopacmen" main folder.
 
 In this main folder, the scripts which start with "analysis_", "data_" and "modeling_" are command-line interfaces (CLI)
 for AutoPACMEN's Python modules. These Python modules can be found the main folder's "submodules" subfolder.
 
-The main folder's subfolder "AutoPACMEN_Model_Calibrator_MATLAB" contains the Model Calibrator's MATLAB parts.
+The subfolder "AutoPACMEN_Model_Calibrator_MATLAB" contains the Model Calibrator's MATLAB parts.
 
-All scripts and folders starting with "ec_model_2019_06_25" are part of the generation and analysis of iJO1366* (see AutoPACMEN's publication for more on it). From these scripts, these ones
-starting with "ec_model_2019_06_25_figure" create either a full figure or data for a figure used in AutoPACMEN's publication.
+All scripts and folders within "iJO1366star" are part of the generation and analysis of iJO1366* (see AutoPACMEN's publication for more about it). From these scripts, these ones
+starting with "./iJO1366star/ec_model_2019_06_25_figure" create either a full figure or data for a figure used in AutoPACMEN's publication.
 
-The main script for the generation of the uncalibrated iJO1366* model is "ec_model_2019_06_25_sMOMENT_iJO_CREATION.py" in the main folder. This
+The main script for the generation of the uncalibrated iJO1366* model is "./iJO1366star/ec_model_2019_06_25_sMOMENT_iJO_CREATION.py" in the "iJO1366" subfolder. This
 main script uses AutoPACMEN's functionalities as Python modules. The commented steps in this script correspond to the steps described in supplementary
 File 1 of (Bekiaris & Klamt, in submission).
 
-
-The main folder's subfolder "iJOstar_MCS_analysis_scripts" contains the scripts used for the computation and the analysis of the published Minimal Cut Set enumeration
+The "iJO1366star" folder's subfolder "iJOstar_MCS_analysis_scripts" contains the scripts used for the computation and the analysis of the published Minimal Cut Set enumeration
 with iJO1366 and iJO1366*.
 
 AutoPACMEN creates a cache of SABIO-RK, NCBI TAXONOMY and UniProt data in the "_cache" main folder's subfolder.  In the current state, the cache is the one

autopacmen/html/autopacmen/analysis_fva_comparison.html

@@ -56,19 +56,21 @@ <h1 class="title">Module <code>autopacmen.analysis_fva_comparison</code></h1>
 # External module for command-line interfaces
 import click
 # Internal module, contains the actual FBA comparison function
-from submodules.fva_comparison import fva_comparison_with_sbml
+from .submodules.fva_comparison import fva_comparison_with_sbml
 
 
 # Set-up command-line parameters using click decorators
 @click.command()
 @click.option(&#34;--sbml_original_path&#34;,
               required=True,
-              type=click.Path(exists=True, file_okay=True, dir_okay=True, readable=True),
+              type=click.Path(exists=True, file_okay=True,
+                              dir_okay=True, readable=True),
               prompt=&#34;Original SBML path&#34;,
               help=&#34;Full SBML path of original model without protein allocation constraints&#34;)
 @click.option(&#34;--sbml_protein_constrained_path&#34;,
               required=True,
-              type=click.Path(exists=True, file_okay=True, dir_okay=True, readable=True),
+              type=click.Path(exists=True, file_okay=True,
+                              dir_okay=True, readable=True),
               prompt=&#34;SBML path of sMOMENT-enhanced model&#34;,
               help=&#34;Full SBML path of sMOMENT-enhanced model.&#34;)
 @click.option(&#34;--objective&#34;,
@@ -86,7 +88,8 @@ <h1 class="title">Module <code>autopacmen.analysis_fva_comparison</code></h1>
     Example: Print a comparative FVA for the non-constrained model &#39;C:\\original.xml&#39; and the sMOMENT-model &#39;C:\\pac.xml&#39; and the objective &#39;ACALD&#39;:
     python analysis_fva_comparison.py --sbml_original_path C:\\original.xml --sbml_protein_constrained_path C:\\pac.xml --objective ACALD
     &#34;&#34;&#34;
-    fva_comparison_with_sbml(sbml_original_path, sbml_protein_constrained_path, objective)
+    fva_comparison_with_sbml(
+        sbml_original_path, sbml_protein_constrained_path, objective)
 
 
 # Start-up routine if script is called

autopacmen/html/autopacmen/analysis_fva_prot_pool.html

@@ -53,7 +53,7 @@ <h1 class="title">Module <code>autopacmen.analysis_fva_prot_pool</code></h1>
 import click
 from typing import List
 # Internal modules
-from submodules.fva_prot_pool import fva_prot_pool_with_sbml
+from .submodules.fva_prot_pool import fva_prot_pool_with_sbml
 
 
 # Set-up command-line parameters using click decorators

autopacmen/html/autopacmen/data_create_combined_kcat_database.html

@@ -50,7 +50,7 @@ <h1 class="title">Module <code>autopacmen.data_create_combined_kcat_database</co
 # External modules
 import click
 # Internal modules
-from submodules.create_combined_kcat_database import create_combined_kcat_database
+from .submodules.create_combined_kcat_database import create_combined_kcat_database
 
 
 # Set-up command-line parameters using click decorators
@@ -68,7 +68,7 @@ <h1 class="title">Module <code>autopacmen.data_create_combined_kcat_database</co
 @click.option(&#34;--output_path&#34;,
               required=True,
               type=click.Path(exists=True, file_okay=True, dir_okay=True),
-              prompt=&#34;BRENDA JSON path&#34;,
+              prompt=&#34;Output path&#34;,
               help=&#34;Full path to the newly created combined JSON&#34;)
 def parse_create_combined_kcat_database(sabio_rk_kcat_database_path: str, brenda_kcat_database_path: str, output_path: str) -&gt; None:
     &#34;&#34;&#34;Combines the BRENDA and SABIO-RK JSONs into one big JSON which can be used by modeling_get_reactions_kcat_mapping.py

autopacmen/html/autopacmen/data_parse_bigg_metabolites_file.html

@@ -52,7 +52,7 @@ <h1 class="title">Module <code>autopacmen.data_parse_bigg_metabolites_file</code
 # External modules
 import click
 # Internal modules
-from submodules.parse_bigg_metabolites_file import parse_bigg_metabolites_file
+from .submodules.parse_bigg_metabolites_file import parse_bigg_metabolites_file
 
 
 # Set-up command-line parameters using click decorators

autopacmen/html/autopacmen/data_parse_brenda_json_for_model.html

@@ -52,19 +52,21 @@ <h1 class="title">Module <code>autopacmen.data_parse_brenda_json_for_model</code
 # External modules
 import click
 # Internal modules
-from submodules.parse_brenda_json_for_model import parse_brenda_json_for_model
+from .submodules.parse_brenda_json_for_model import parse_brenda_json_for_model
 
 
 # Set-up command-line parameters using click decorators
 @click.command()
 @click.option(&#34;--sbml_path&#34;,
               required=True,
-              type=click.Path(exists=True, file_okay=True, dir_okay=True, readable=True),
+              type=click.Path(exists=True, file_okay=True,
+                              dir_okay=True, readable=True),
               prompt=&#34;Path to SBML model&#34;,
               help=&#34;Full path to the SBML with the model of which the BRENDA JSON will be derived.&#34;)
 @click.option(&#34;--brenda_json_path&#34;,
               required=True,
-              type=click.Path(exists=True, file_okay=True, dir_okay=True, readable=True),
+              type=click.Path(exists=True, file_okay=True,
+                              dir_okay=True, readable=True),
               prompt=&#34;BRENDA JSON path&#34;,
               help=&#34;Full path to the BRENDA JSON created with data_parse_brenda_textfile.py&#34;)
 @click.option(&#34;--json_output_path&#34;,