# HG changeset patch
# User luca_milaz
# Date 1726661800 0
# Node ID 7c01dab3e961cb5be19f47308023e9a46d54688a
# Parent  5a69dd9062561eff743423bdf7443a03aa0d96af
Uploaded

diff -r 5a69dd906256 -r 7c01dab3e961 COBRAxy/flux_simulation.xml
--- a/COBRAxy/flux_simulation.xml	Wed Sep 18 12:10:55 2024 +0000
+++ b/COBRAxy/flux_simulation.xml	Wed Sep 18 12:16:40 2024 +0000
@@ -100,24 +100,26 @@
 -------------
 
 This tool generates flux samples starting from a model in JSON or XML format by using CBS (Corner-based sampling) or OPTGP (mproved Artificial Centering Hit-and-Run sampler) sampling algorithms.
+
 It can return sampled fluxes by appliying summary statistics: 
    - mean
    - median
    - quantiles (0.25, 0.50, 0.75). 
-Moreover, flux analysis can be perfomed over the metabolic model:
+
+Flux analysis can be perfomed over the metabolic model:
    - parsimoniuos-FBA (optimized by Biomass)
    - FVA
    - Biomass sensitivity analysis (single reaction knock-out). It is the ratio between the optimal of the Biomass reaction computed by FBA after knocking-out a reaction and the same over the complete model.
 
 Accepted files:
-   - A model: JSON or XML file reporting reactions and rules contained in the model. It can be a single model, multiple models or a collection of models. 
-   - Cell-specific bounds: generated by RAS to Bounds tool.
+   - A model: JSON or XML file reporting reactions and rules contained in the model. It can be ENGRO2 or a custom model. 
+   - Context-specific bounds: generated by RAS to Bounds tool. This can be a collection of bounds too (one bounds file per context).
 
 Output:
 -------------
 
 The tool generates:
-   - Samples: reporting the sampled fluxes for each reaction. Format: csv.
+   - Samples: reporting the sampled fluxes for each reaction (reaction names on the rows and sample names on the columns). Format: csv.
    - a log file (.txt).
 
 **TIP**: The Batches parameter is useful to mantain in memory just a batch of samples at time. For example, if you wish to sample 10.000 points, than it is suggested to select n_samples = 1.000 and n_batches=10.