text: user734: I am not sure how to tackle this one, so I’ll explain the general idea I have in mind. 
  Given results of a CRISPR knockout screening experiment (like in this paper) with two types of assays: control vs. treated with 5 time points per each of the assays. 
  Is there a way to use the screening data to perform systems biology network analysis? Something which is related to signal transduction, transcriptional regulatory, or metabolic networks? 
  Maybe something which uses Bayesian inference? Or something similar to differentially expressed representation of the CRISPR screening? Alternatively, a GWAS analysis similar to this paper?
  
  user6509: What do you call screening results?
  Do you have a kind of array with protein or RNA data?
  If so, you can use several open-source tools available on the internet, like Enrichr or network analyst to better understand the pathways affected by your treatment.
  
  user3119: To investigate genes through a network based approach you can use several tools. I don't know which level of programming you have or which tool you prefer but here is a brief overview of the possibilities you have.
  You can look into established databases containing information about interactions like, for instance, STRING. Or, you can build networks using the wgcna method with R.
  Then, once you have your network you can analyse its properties and here you also have several tools available such as standalone, java-base software like cytoscape, R libraries such as igraph, python libraries such as networkX online tools like centiserver...
  Also you can perform gene set enrichment analsysis, which means adding a further layer of biological information to you set of genes like metabolic pathways (from KEGG, for example) and molecular functions (from GeneOntology, for instance) (gsea) as here, here, and here.
  Here there is also a nice tutorial about network analysis with R.
  
  
original_text: 734: <p>I am not sure how to tackle this one, so I’ll explain the general idea I have in mind. </p>
  
  <p>Given results of a CRISPR knockout screening experiment <a href="https://www.nature.com/articles/s41467-018-06676-2" rel="nofollow noreferrer">(like in this paper)</a> with two types of assays: control vs. treated with 5 time points per each of the assays. </p>
  
  <p>Is there a way to use the screening data to perform systems biology <a href="https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data-introduction/network-analysis-biology-0" rel="nofollow noreferrer">network analysis</a>? Something which is related to signal transduction, transcriptional regulatory, or metabolic networks? </p>
  
  <p>Maybe something which uses Bayesian inference? Or something similar to <a href="https://www.mathworks.com/help/bioinfo/ug/identifying-differentially-expressed-genes-from-rna-seq-data.html" rel="nofollow noreferrer">differentially expressed representation</a> of the CRISPR screening? Alternatively, a GWAS analysis similar to <a href="https://www.nature.com/articles/s41467-019-13585-5" rel="nofollow noreferrer">this paper</a>?</p>
   6509: <p>What do you call screening results?
  Do you have a kind of array with protein or RNA data?
  If so, you can use several open-source tools available on the internet, like <a href="https://amp.pharm.mssm.edu/Enrichr" rel="nofollow noreferrer">Enrichr</a> or <a href="https://www.networkanalyst.ca" rel="nofollow noreferrer">network analyst</a> to better understand the pathways affected by your treatment.</p>
   3119: <p>To investigate genes through a network based approach you can use several tools. I don't know which level of programming you have or which tool you prefer but here is a brief overview of the possibilities you have.</p>
  <p>You can look into established databases containing information about interactions like, for instance, <a href="https://string-db.org" rel="nofollow noreferrer">STRING</a>. Or, you can build networks using the <a href="https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/" rel="nofollow noreferrer">wgcna</a> method with R.</p>
  <p>Then, once you have your network you can analyse its properties and here you also have several tools available such as standalone, java-base software like <a href="https://cytoscape.org/" rel="nofollow noreferrer">cytoscape</a>, R libraries such as <a href="https://igraph.org/r/" rel="nofollow noreferrer">igraph</a>, python libraries such as <a href="https://networkx.github.io/" rel="nofollow noreferrer">networkX</a> online tools like <a href="http://www.centiserver.org/" rel="nofollow noreferrer">centiserver</a>...</p>
  <p>Also you can perform gene set enrichment analsysis, which means adding a further layer of biological information to you set of genes like metabolic pathways (from KEGG, for example) and molecular functions (from GeneOntology, for instance) (gsea) as <a href="http://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results" rel="nofollow noreferrer">here</a>, <a href="http://www.bioconductor.org/packages/release/bioc/vignettes/fgsea/inst/doc/fgsea-tutorial.html" rel="nofollow noreferrer">here</a>, and <a href="https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html#gsea-analysis" rel="nofollow noreferrer">here</a>.</p>
  <p>Here there is also a nice <a href="http://kateto.net/networks-r-igraph" rel="nofollow noreferrer">tutorial</a> about network analysis with R.</p>
  <hr />
  