diff --git a/README.md b/README.md
index bb839c5d157b3804a129010152cd99674c3a8aa4..28cd6b49334bf6f3dcf82ee5b7896c89af05167d 100644
--- a/README.md
+++ b/README.md
@@ -58,17 +58,14 @@ We have developed [Docker images](https://hub.docker.com/repository/docker/ruana
 
 ## HTRfit simulation workflow
 
+In the realm of RNAseq analysis, various key experimental parameters play a crucial role in influencing the statistical power to detect expression changes. Parameters such as sequencing depth, the number of replicates, and more have a significant impact. To navigate the selection of optimal values for these experimental parameters, we introduce a comprehensive statistical framework known as **HTRfit**, underpinned by computational simulation. Moreover, **HTRfit** offers seamless compatibility with DESeq2 outputs, facilitating a comprehensive evaluation of RNAseq analysis. 
+
+
 <div id="bg"  align="center">
   <img src="./vignettes/figs/htrfit_workflow.png" width="500" height="300">
 </div> 
 
 
-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