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Topology.html

@@ -87,7 +87,7 @@
 <li class="toctree-l1"><a class="reference internal" href="Using.html">Using</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Visualization.html">Visualization examples</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Topology analysis</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#definations-and-examples">Definations and examples</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#definitions-and-examples">Definitions and examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#code-availability">Code availability</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#using">Using</a></li>
 </ul>
@@ -160,8 +160,8 @@
 
   <div class="section" id="topology-analysis">
 <span id="topology"></span><h1>Topology analysis<a class="headerlink" href="#topology-analysis" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="definations-and-examples">
-<h2>Definations and examples<a class="headerlink" href="#definations-and-examples" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="definitions-and-examples">
+<h2>Definitions and examples<a class="headerlink" href="#definitions-and-examples" title="Permalink to this headline">¶</a></h2>
 <p>From the perspective of topology, we first compared the distribution of reaction degree of species,
 defined as the number of reactions one species belonged to. We also observed the distribution of
 reaction distance between a random pair of species, defined as the minimum number of reactions
@@ -182,27 +182,27 @@ <h2>Using<a class="headerlink" href="#using" title="Permalink to this headline">
 <ol class="arabic simple">
 <li><p><strong>To use the Python script rxn_degree_dist.py to generate reaction degree distributions.</strong></p></li>
 </ol>
-<p>For example, to analysis a set of SBML files of signaling networks downloaded from BioModels
+<p>For example, to analyze a set of SBML files of signaling networks downloaded from the BioModels
 Database.</p>
 <ul class="simple">
 <li><p>First of all, create a folder Biomodels as on GitHub (<a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/Biomodels">https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/Biomodels</a>).</p></li>
 <li><p>Then, put the <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/degree_dist/Biomodels/Biomodels.zip">zip file</a> of the SBML files into the folder.</p></li>
 <li><p>Finally, run the Python script <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/degree_dist/Biomodels/rxn_degree_dist.py">rxn_degree_dist.py</a>.</p></li>
-<li><p>The generated results include the degree distribution data saved as degree_dist.txt, the plot of the degree distribution saved as Degree_Distribution.pdf, and the zip file would also get upziped as a folder BioModels.</p></li>
+<li><p>The generated results include the degree distribution data saved as degree_dist.txt, the plot of the degree distribution saved as Degree_Distribution.pdf, and the zip file would also get unzipped as a folder BioModels.</p></li>
 </ul>
-<p>Users can use similar procedure to generate reaction degree distributions for synthetic signaling
+<p>Users can use a similar procedure to generate reaction degree distributions for synthetic signaling
 networks referring to the example on GitHub (<a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/synthetic_networks">https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/synthetic_networks</a>).</p>
 <ol class="arabic simple" start="2">
 <li><p><strong>To use the Python script rxn_distance_dist.py to generate reaction distance distributions.</strong></p></li>
 </ol>
-<p>For example, to analysis a set of SBML files of signaling networks downloaded from BioModels Database.</p>
+<p>For example, to analyze a set of SBML files of signaling networks downloaded from BioModels Database.</p>
 <ul class="simple">
 <li><p>First of all, create a folder Biomodels as on GitHub (<a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/Biomodels">https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/Biomodels</a>).</p></li>
-<li><p>Then, put the <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/distance_dist/Biomodels/Biomodels.zip">zip file</a> of the SBML files into the folder.</p></li>
+<li><p>Then, put the <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/degree_dist/Biomodels/Biomodels.zip">zip file</a> of the SBML files into the folder.</p></li>
 <li><p>Finally, run the Python script <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/distance_dist/Biomodels/rxn_distance_dist.py">rxn_distance_dist.py</a>.</p></li>
-<li><p>The generated results include the distance distribution data saved as distance_dist.txt, the plot of the distance distribution saved as Distance_Distribution.pdf, and the zip file would also get upziped as a folder BioModels.</p></li>
+<li><p>The generated results include the distance distribution data saved as distance_dist.txt, the plot of the distance distribution saved as Distance_Distribution.pdf, and the zip file would also get unzipped as a folder BioModels.</p></li>
 </ul>
-<p>Users can use similar procedure to generate reaction distance distributions for synthetic signaling
+<p>Users can use a similar procedure to generate reaction distance distributions for synthetic signaling
 networks referring to the example on GitHub (<a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/synthetic_networks">https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/synthetic_networks</a>).</p>
 </div>
 </div>

Using.html

@@ -182,7 +182,7 @@ <h2>Using Julia script to generate synthetic signaling networks<a class="headerl
 <p>4. Finally, download the Julia script from
 <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/Ground_truth_generation.jl">GitHub</a>
 into one folder. At the Julia console type: include(“pathto/Ground_truth_generation.jl”) to run
-the main script. Note that “pathto” was the path where you saved the network generation script to.</p>
+the main script. Note that “pathto” was the path to which you saved the network generation script to.</p>
 <p>5. A random signaling network would be generated in SBML format called sampleNetwork0.xml before the
 steady state and sampleNetwork.xml after the steady state.</p>
 <p>6. The Julia script was implemented in Julia 1.6 on Windows 10 with RoadRunner.jl version 0.1.2 for
@@ -201,15 +201,15 @@ <h2>Adjustable parameters for biological applications<a class="headerlink" href=
 <li><p>The randomly assigned ranges for species concentrations by “rnd_species_initial” and “rnd_species_range”, for species concentrations in the range of [rnd_species_initial, rnd_species_initial + rnd_species_range);</p></li>
 <li><p>The randomly assigned ranges for rate constants by “rnd_parameter_initial” and “rnd_parameter_range”, for rate constants in the range of [rnd_parameter_initial, rnd_parameter_initial + rnd_parameter_range);</p></li>
 <li><p>“concentration_perturb” could be used to set the factor that perturbs the concentration at the input species;</p></li>
-<li><p>“RXN_MECH_WEIGHT_VALUE” could be used to set the probability of the reaction motifs in the order of  [“UNICAT”, “UNIBI”, “BIUNI”, “BIBI”, “CIRCLE”, “DBCIRCLE”] with the sum as 1.</p></li>
+<li><p>“RXN_MECH_WEIGHT_VALUE” could be used to set the probability of the reaction motifs in the order of uni-uni, uni-bi, bi-uni, bi-bi, single phosphorylation dephosphorylation circle, and dual phosphorylation dephosphorylation cycle ([“UNICAT”, “UNIBI”, “BIUNI”, “BIBI”, “CIRCLE”, “DBCIRCLE”]) with the sum as 1.</p></li>
 <li><p>The number of random networks to generate could be set by changing the variable “sampleSize”.</p></li>
 </ul>
 </div>
 <div class="section" id="default-parameters-setup">
 <h2>Default parameters setup<a class="headerlink" href="#default-parameters-setup" title="Permalink to this headline">¶</a></h2>
 <p>The Julia script on <a class="reference external" href="https://github.com/sys-bio/artificial_random_signaling_network/blob/master/Ground_truth_generation.jl">GitHub</a>
-is a concrete example with specific parameter values shown as below. Users can refer to it by assigning
-their own values with their certain demands or based on future biological data in hand.</p>
+is a concrete example with specific parameter values shown below. Users can refer to it by assigning
+their values to their certain demands or based on future biological data in hand.</p>
 <ul class="simple">
 <li><p>“nSpeces” = 15, assigning the number of species as 15;</p></li>
 <li><p>“nRxns” = 22, assigning the number of reactions as 22;</p></li>
@@ -218,7 +218,7 @@ <h2>Default parameters setup<a class="headerlink" href="#default-parameters-setu
 <li><p>“rxn_parameter_initial” = 0, and “rnd_parameter” = 1, meaning random generation for rate constants in the range of [0, 1);</p></li>
 <li><p>“RXN_MECH_WEIGHT_VALUE” set as [0.2, 0.2, 0.2, 0.2, 0.1, 0.1] for the probabilities of reaction motifs in the order of  [“UNICAT”, “UNIBI”, “BIUNI”, “BIBI”, “CIRCLE”, “DBCIRCLE”] with the sum as 1.</p></li>
 <li><p>“concentration_perturb” = 2, meaning doubling the concentration at the input species.</p></li>
-<li><p>“sampleSize” = 1, meaning generating one sample of synthetic randon signaling network.</p></li>
+<li><p>“sampleSize” = 1, meaning generating one sample of the synthetic random signaling network.</p></li>
 </ul>
 </div>
 </div>

Visualization.html

@@ -177,7 +177,7 @@ <h2>Reaction network motifs<a class="headerlink" href="#reaction-network-motifs"
 <h2>Artificial random signaling network examples<a class="headerlink" href="#artificial-random-signaling-network-examples" title="Permalink to this headline">¶</a></h2>
 <p><strong>1) Reaction networks with 15 species and 11 reactions.</strong></p>
 <p>The figure below illustrates two examples of randomly generated signaling networks. Figure A
-included four types of chemical reaction motifs. Species were labelled S1 to S15. The reactions
+included four types of chemical reaction motifs. Species were labeled S1 to S15. The reactions
 included four uni-uni processes: S6 → S7 catalyzed by S5, S8 → S6 catalyzed by S10, S9 → S5
 catalyzed by S15, and S11 → S9 catalyzed by S15; one bi-bi reactions: S_in + S14 → S10 + S13;
 one single phosphorylationdephosphorylation cycle: S12 ⇋ S_out catalyzed by S7 and S1; and one
@@ -188,7 +188,7 @@ <h2>Artificial random signaling network examples<a class="headerlink" href="#art
 <a class="reference internal image-reference" href="_images/15-11.png"><img alt="_images/15-11.png" src="_images/15-11.png" style="width: 700px;" /></a>
 <p><strong>2) Reaction networks with 15 species and 22 reactions.</strong></p>
 <p>The figure below illustrates two examples of randomly generated signaling networks. Figure A
-included five types of chemical reaction motifes. Species were labelled S1 to S15. These include
+included five types of chemical reaction motifs. Species were labeled S1 to S15. These include
 three uni-uni processes: S2→S9 catalyzed by S7, S14 → S5 catalyzed by S2, and S10 → S7 catalyzed
 by S11; one bi-uni reaction: S14 + S12 → S12; two bi-bi reactions: S11 + S_in → S15 + S13 and
 S12 + S15 → S12 + S14; two single phosphorylation-dephosphorylation cycles: S1 ⇋ S_out catalyzed

_sources/Topology.rst.txt

@@ -6,7 +6,7 @@ Topology analysis
 
 
 -------------------------
-Definations and examples
+Definitions and examples
 -------------------------
 
 From the perspective of topology, we first compared the distribution of reaction degree of species, 
@@ -35,7 +35,7 @@ Using
 
 1. **To use the Python script rxn_degree_dist.py to generate reaction degree distributions.**
 
-For example, to analysis a set of SBML files of signaling networks downloaded from BioModels 
+For example, to analyze a set of SBML files of signaling networks downloaded from the BioModels 
 Database.
 
 - First of all, create a folder Biomodels as on GitHub (https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/Biomodels).
@@ -44,25 +44,25 @@ Database.
 
 - Finally, run the Python script `rxn_degree_dist.py <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/degree_dist/Biomodels/rxn_degree_dist.py>`_.
 
-- The generated results include the degree distribution data saved as degree_dist.txt, the plot of the degree distribution saved as Degree_Distribution.pdf, and the zip file would also get upziped as a folder BioModels.
+- The generated results include the degree distribution data saved as degree_dist.txt, the plot of the degree distribution saved as Degree_Distribution.pdf, and the zip file would also get unzipped as a folder BioModels.
 
-Users can use similar procedure to generate reaction degree distributions for synthetic signaling 
+Users can use a similar procedure to generate reaction degree distributions for synthetic signaling 
 networks referring to the example on GitHub (https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/degree_dist/synthetic_networks).
 
 
 2. **To use the Python script rxn_distance_dist.py to generate reaction distance distributions.**
 
-For example, to analysis a set of SBML files of signaling networks downloaded from BioModels Database.
+For example, to analyze a set of SBML files of signaling networks downloaded from BioModels Database.
 
 - First of all, create a folder Biomodels as on GitHub (https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/Biomodels).
 
-- Then, put the `zip file <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/distance_dist/Biomodels/Biomodels.zip>`_ of the SBML files into the folder.
+- Then, put the `zip file <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/degree_dist/Biomodels/Biomodels.zip>`_ of the SBML files into the folder.
 
 - Finally, run the Python script `rxn_distance_dist.py <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/topology_analysis/distance_dist/Biomodels/rxn_distance_dist.py>`_.
 
-- The generated results include the distance distribution data saved as distance_dist.txt, the plot of the distance distribution saved as Distance_Distribution.pdf, and the zip file would also get upziped as a folder BioModels.
+- The generated results include the distance distribution data saved as distance_dist.txt, the plot of the distance distribution saved as Distance_Distribution.pdf, and the zip file would also get unzipped as a folder BioModels.
 
-Users can use similar procedure to generate reaction distance distributions for synthetic signaling 
+Users can use a similar procedure to generate reaction distance distributions for synthetic signaling 
 networks referring to the example on GitHub (https://github.com/sys-bio/artificial_random_signaling_network/tree/master/topology_analysis/distance_dist/synthetic_networks).
 
 

_sources/Using.rst.txt

@@ -32,7 +32,7 @@ installed already.
 4. Finally, download the Julia script from 
 `GitHub <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/Ground_truth_generation.jl>`_
 into one folder. At the Julia console type: include("pathto/Ground_truth_generation.jl") to run 
-the main script. Note that "pathto" was the path where you saved the network generation script to. 
+the main script. Note that "pathto" was the path to which you saved the network generation script to. 
 
 5. A random signaling network would be generated in SBML format called sampleNetwork0.xml before the 
 steady state and sampleNetwork.xml after the steady state. 
@@ -62,7 +62,7 @@ be adjusted for biological applications including:
 
 * "concentration_perturb" could be used to set the factor that perturbs the concentration at the input species;
 
-* "RXN_MECH_WEIGHT_VALUE" could be used to set the probability of the reaction motifs in the order of  ["UNICAT", "UNIBI", "BIUNI", "BIBI", "CIRCLE", "DBCIRCLE"] with the sum as 1.
+* "RXN_MECH_WEIGHT_VALUE" could be used to set the probability of the reaction motifs in the order of uni-uni, uni-bi, bi-uni, bi-bi, single phosphorylation dephosphorylation circle, and dual phosphorylation dephosphorylation cycle (["UNICAT", "UNIBI", "BIUNI", "BIBI", "CIRCLE", "DBCIRCLE"]) with the sum as 1.
 
 * The number of random networks to generate could be set by changing the variable "sampleSize".
 
@@ -71,8 +71,8 @@ Default parameters setup
 --------------------------
 
 The Julia script on `GitHub <https://github.com/sys-bio/artificial_random_signaling_network/blob/master/Ground_truth_generation.jl>`_
-is a concrete example with specific parameter values shown as below. Users can refer to it by assigning
-their own values with their certain demands or based on future biological data in hand.
+is a concrete example with specific parameter values shown below. Users can refer to it by assigning
+their values to their certain demands or based on future biological data in hand.
 
 * "nSpeces" = 15, assigning the number of species as 15;
 
@@ -88,7 +88,7 @@ their own values with their certain demands or based on future biological data i
 
 * "concentration_perturb" = 2, meaning doubling the concentration at the input species.
 
-* "sampleSize" = 1, meaning generating one sample of synthetic randon signaling network.
+* "sampleSize" = 1, meaning generating one sample of the synthetic random signaling network.
 
 
 

_sources/Visualization.rst.txt

@@ -29,7 +29,7 @@ Artificial random signaling network examples
 **1) Reaction networks with 15 species and 11 reactions.**
 
 The figure below illustrates two examples of randomly generated signaling networks. Figure A 
-included four types of chemical reaction motifs. Species were labelled S1 to S15. The reactions 
+included four types of chemical reaction motifs. Species were labeled S1 to S15. The reactions 
 included four uni-uni processes: S6 → S7 catalyzed by S5, S8 → S6 catalyzed by S10, S9 → S5 
 catalyzed by S15, and S11 → S9 catalyzed by S15; one bi-bi reactions: S_in + S14 → S10 + S13; 
 one single phosphorylationdephosphorylation cycle: S12 ⇋ S_out catalyzed by S7 and S1; and one 
@@ -44,7 +44,7 @@ networks had 15 species in addition to input and output species, with 11 reactio
 **2) Reaction networks with 15 species and 22 reactions.**
 
 The figure below illustrates two examples of randomly generated signaling networks. Figure A 
-included five types of chemical reaction motifes. Species were labelled S1 to S15. These include 
+included five types of chemical reaction motifs. Species were labeled S1 to S15. These include 
 three uni-uni processes: S2→S9 catalyzed by S7, S14 → S5 catalyzed by S2, and S10 → S7 catalyzed 
 by S11; one bi-uni reaction: S14 + S12 → S12; two bi-bi reactions: S11 + S_in → S15 + S13 and 
 S12 + S15 → S12 + S14; two single phosphorylation-dephosphorylation cycles: S1 ⇋ S_out catalyzed