diff --git a/README.md b/README.md
index b0d56f2093d1a5ba7b6788e5f843b43925cc0f1a..8de4d9cf7d941f68684952058069d09693e21af3 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,16 @@
-# HTRfit
+# High-Throughput RNA-seq model fit
+
+- [Installation](#installation)
+- [CRAN packages dependencies](#cran-packages-dependencies)
+- [Docker](#docker)
+- [HTRfit simulation workflow](#htrfit-simulation-workflow)
+- [Getting started](#getting-started)
+
 
 ## Installation
 
-* method A:
+* method A:  
+
 To install the latest version of HTRfit, run the following in your R console :
 ```
 if (!requireNamespace("remotes", quietly = TRUE))
@@ -12,12 +20,12 @@ remotes::install_git("https://gitbio.ens-lyon.fr/aduvermy/HTRfit")
 
 * method B:
 
-Alternatively, you can download a release on https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/releases. 
-Then, run the following in your R console:
+You also have the option to download a release directly from the [HTRfit release page](https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/releases). Once you've downloaded the release, simply untar the archive. After that, open your R console and execute the following command, where HTRfit-v1.0.0 should be replaced with the path to the untarred folder:
 
 ```
-## -- example with release HTRfit-v1.0.0
+## -- Example using the HTRfit-v1.0.0 release
 install.packages('/HTRfit-v1.0.0', repos = NULL, type='source')
+
 ```
 
 When dependencies are met, installation should take a few minutes.
@@ -25,25 +33,70 @@ When dependencies are met, installation should take a few minutes.
 
 ## CRAN packages dependencies
 
-The following depandencies are mandatory:
+The following depandencies are required:
 
 ```
+## -- required
 install.packages(c('car', 'parallel', 'data.table', 'ggplot2', 'gridExtra', 'glmmTMB', 'magrittr', 'MASS', 'plotROC', 'reshape2', 'rlang', 'stats', 'utils', 'BiocManager'))
 BiocManager::install('S4Vectors', update = FALSE)
 ## -- optional 
 BiocManager::install('DESeq2', update = FALSE)
 ```
 
-### System requirements
+## Docker
+
+We have developed [Docker images](https://hub.docker.com/repository/docker/ruanad/htrfit/general) to simplify the package's utilization. For an optimal development and coding experience with the Docker container, we recommend using Visual Studio Code (VSCode) along with the DevContainer extension. This setup provides a convenient and isolated environment for development and testing.
+
+1. Install VSCode
+2. Install Docker on your system and on VSCode
+3. Launch the HTRfit container directly from VSCode
+4. Install the DevContainer extension for VSCode.
+5. Launch a remote window connected to the running Docker container.
+6. Enjoy HTRfit !
+
 
+## HTRfit simulation workflow
 
+<div id="bg"  align="center">
+  <img src="vignettes\figs\htrfit_workflow.png" width="500" height="300">
+</div> 
 
-### Getting started
 
-You can access the package's main vignette from your R console with
+In this modeling framework, counts denoted as $K_{ij}$ for gene i and sample j are generated using a negative binomial distribution. The negative binomial distribution considers a fitted mean $\mu_{ij}$ and a gene-specific dispersion parameter $\alpha_i$.
+
+The fitted mean $\mu_{ij}$ is determined by a parameter, qij, which is proportionally related to the sum of all effects specified using `init_variable()` or `add_interaction()`. If basal gene expressions are provided, the $\mu_{ij}$ values are scaled accordingly using the gene-specific basal expression value ($bexpr_i$).
+
+Furthermore, the coefficients $\beta_i$ represent the natural logarithm fold changes for gene i across each column of the model matrix X. The dispersion parameter $\alpha_i$ plays a crucial role in defining the relationship between the variance of observed counts and their mean value. In simpler terms, it quantifies how far we expect observed counts to deviate from the mean value.
+
+
+
+## Getting started
 
 ```
-library(HTRfit)
-vignette("HTRfit")
-vignette("HTRfit-pdf")
+## -- init a design 
+input_var_list <- init_variable( name = "varA", mu = 0, sd = 0.29, level = 60) %>%
+                  init_variable( name = "varB", mu = 0.27, sd = 0.6, level = 2) %>%
+                    add_interaction( between_var = c("varA", "varB"), mu = 0.44, sd = 0.89)
+## -- simulate RNAseq data 
+mock_data <- mock_rnaseq(input_var_list, 
+                         n_genes = 30,
+                         min_replicates  = 10,
+                         max_replicates = 10, 
+                         basal_expression = 5 )
+## -- prepare data & fit a model with mixed effect
+data2fit = prepareData2fit(countMatrix = mock_data$counts, 
+                           metadata =  mock_data$metadata, 
+                           normalization = F)
+l_tmb <- fitModelParallel(formula = kij ~ varB + (varB | varA),
+                          data = data2fit, 
+                          group_by = "geneID",
+                          family = glmmTMB::nbinom2(link = "log"), 
+                          log_file = "log.txt",
+                          n.cores = 1)
+## -- evaluation
+resSimu <- simulationReport(mock_data, 
+                            list_tmb = l_tmb,
+                            coeff_threshold = 0.27, 
+                            alt_hypothesis = "greater")
+
 ```
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