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<div style="margin-top: 0pt; margin-bottom: 0pt; line-height: 1.38;" class=""><span style="font-size: 10pt; color: rgb(67, 67, 67); line-height: normal;" class="">ISCB Academy is part of the International Society for Computational Biology (ISCB), which aims
to </span><span style="font-size: 10pt; color: rgb(67, 67, 67); background-color: rgb(255, 255, 255); line-height: normal;" class="">provide training and workshops worldwide. We</span><span style="font-size: 10pt; color: rgb(67, 67, 67); line-height: normal;" class=""> provide
hands-on experience accessing and using newly developed bioinformatics tools while ensuring best practices for rigour and reproducibility. The next tutorial is under the title </span><span style="font-size: 10pt; color: rgb(67, 67, 67); font-weight: 700; line-height: normal;" class="">“Integrating
gene expression and biological knowledge for drug discovery and repurposing”</span><span style="font-size: 10pt; color: rgb(67, 67, 67); line-height: normal;" class="">, which will be held on </span><span style="font-size: 10pt; color: rgb(67, 67, 67); font-weight: 700; line-height: normal;" class="">the
22nd of April at 8:00am EDT.</span><span style="font-size: 10pt; color: rgb(67, 67, 67); line-height: normal;" class=""> </span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); font-weight: 700; line-height: normal;" class="">Description of the Tutorial: </span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); line-height: normal;" class="">Screening for potential cancer therapies using existing large datasets of drug perturbations requires expertise and resources not available to all. This is often a barrier
for lab scientists to tap into these valuable resources. To address these issues, one can take advantage of prior knowledge especially those coded in standard formats such as causal biological networks (CBN). Large datasets can be converted into appropriate
structures, analyzed once and the results made freely available in easy-to-use formats. </span><span style="font-size: 10pt; color: rgb(51, 51, 51); font-weight: 700; line-height: normal;" class="">In these three parts tutorial, we will give a full description
of one large scale analysis of using this approach, one case study of building a network of metastasis suppressors from scratch, and a walkthrough example code to perform and adapt these tools for different use cases.</span></p>
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<div style="margin-top: 0pt; margin-bottom: 0pt; line-height: 1.62;" class=""><span style="font-weight: 700; font-size: 10pt; line-height: normal;" class="">Part One:</span><span style="font-size: 10pt; line-height: normal;" class=""> a talk describing the
construction of a database for cancer-cell-specific perturbations of biological networks (LINPS). We pre-computed cancer-cell-specific perturbation amplitudes of several biological networks and made the output available in a database with an interactive web
interface.</span></div>
</li><li dir="ltr" style="font-size: 10pt; color: rgb(51, 51, 51);" class="">
<div style="margin-top: 0pt; margin-bottom: 0pt; line-height: 1.62;" class=""><span style="font-weight: 700; font-size: 10pt; line-height: normal;" class="">Part Two:</span><span style="font-size: 10pt; line-height: normal;" class=""> a talk describing the
building of a functional network model of the metastasis suppressor RKIP and its regulators in breast cancer cells. In this case study, we applied text mining and a manual literature search to extract known interactions between several metastasis suppressors
and their regulators. Then we adopted a reverse causal reasoning approach to evaluate and prioritize pathways that are most consistent and responsive to drugs that inhibit cell growth. We further validated some of the predicted regulatory links in the breast
cancer cell line MCF7 experimentally and highlighted the points of uncertainty in our model.</span></div>
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<span style="font-weight: 700; font-size: 10pt; line-height: normal;" class="">Part Three:</span><span style="font-size: 10pt; line-height: normal;" class=""> a code walkthrough encoding directed interactions into the biological expression language (BEL), computing
the network perturbation amplitudes (NPA), and interpreting the output.</span></p>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); font-weight: 700; line-height: normal;" class="">Pre-requisites:</span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); line-height: normal;" class="">● Knowledge of the mechanisms of transcriptional regulation</span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); line-height: normal;" class="">● Familiarity with high-throughput gene expression data</span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); line-height: normal;" class="">● Basic knowledge of R and Bioconductor</span></div>
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<span style="font-size: 10pt; color: rgb(51, 51, 51); line-height: normal;" class="">● Familiarity with Docker and Git (This is only required to execute the code on a local machine)</span></div>
<span style="font-size: 10pt;" class="">Register </span><span style="font-size: 10pt; color: rgb(138, 180, 248); text-decoration: underline;" class="">👉 </span><a href="https://www.iscb.org/iscbacademy" class=""><span style="font-size: 10pt; color: rgb(17, 85, 204); font-weight: 700;" class="">https://www.iscb.org/iscbacademy</span></a><span style="font-size: 10pt; color: rgb(67, 67, 67); font-weight: 700;" class=""> </span><br class="">
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—<br class="">
Dr. Venkata Satagopam<br class="">
Bioinformatics Core<br class="">
Luxembourg Centre For Systems Biomedicine (LCSB)<br class="">
University of Luxembourg<br class="">
Campus Belval, House of Biomedicine II<br class="">
6, avenue du Swing<br class="">
L-4367 Belvaux<br class="">
<br class="">
T +352-466-644-6421<br class="">
F +352-466-644-36421<br class="">
<a href="mailto:venkata.satagopam@uni.lu" class="">venkata.satagopam@uni.lu</a> <br class="">
<a href="http://lcsb.uni.lu" class="">http://lcsb.uni.lu</a><br class="">
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