<p>The approach we would like to pursue is based on the central idea of constructing a gene/protein regulatory network and eventually perform stochastic simulations to map cellular microenvironmental inputs to cellular phenotypes. The final structure of the internship will be determined based on the number of interns, whether one or two.<br />First step is the study of an approach already found in literature for prostate cancer [1-3] but here using a dataset on cirrhotic and hepatocellular carcinoma (HCC) patients. The underlying pipeline consists of several stages:</p>
<ul>
<li>collect patient data within the cohort and publicly available data &amp; information</li>
<li>analysis of transcriptomics dataset with common tools</li>
<li>building the regulatory network based both on analyzed data and literature information</li>
<li>benchmarking of results and eventual refinement of the signaling network</li>
<li>optional: individuate strategy to apply personalization of the signaling network for individual patients data</li>
<li>optional: simulation of cell phenotypes from the initial microenvironment (stochastic Boolean/ODEs)<br /><br /></li>
</ul>
<p>Bibliography<br />[1] Montagud A. eLife (2022)<br />[2] Ponce-de-Leon A. NPJ Syst Biol Appl. (2023)<br />[3] Ruscone M. PLoS Comput Biol. (2025)</p>