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Toxicological Tipping Points: Learning Boolean Networks from High-Content Imaging Data. (BOSC meeting)
Citation:
Shah, I. AND T. Antonijevic. Toxicological Tipping Points: Learning Boolean Networks from High-Content Imaging Data. (BOSC meeting). Presented at CSS BOSC meeting, RTP, NC, November 16 - 18, 2016. https://doi.org/10.23645/epacomptox.5181145
Impact/Purpose:
Poster presentation at the CSS BOSC meeting. In the poster, HepG2 cells showed three phenotypes in response to chemical treatments: no effect, adaptation and injury. We inferred the minimal number of BNs that could explain these phenotypes. We are in the process of identifying the critical network perturbations involved in the transition from adaptation to injury. Understanding these critical network features may enable the prediction of system tipping points with limited time-course data.
Description:
The objective of this work is to elucidate biological networks underlying cellular tipping points using time-course data. We discretized the high-content imaging (HCI) data and inferred Boolean networks (BNs) that could accurately predict dynamic cellular trajectories. We found three main classes of BNs including: cell recovery, adaptation, and injury. We believe biological network analysis can predict critical chemical exposures and mechanisms underlying cellular tipping points.
URLs/Downloads:
DOI: Toxicological Tipping Points: Learning Boolean Networks from High-Content Imaging Data. (BOSC meeting)
BOSC-2016-V3.PDF (PDF, NA pp, 1895.215 KB, about PDF)