Journal: Cell systems

Article Title: Inferring leading interactions in the p53/Mdm2/Mdmx circuit through live-cell imaging and modeling

doi: 10.1016/j.cels.2019.10.010

Figure Lengend Snippet: Key Resource Table

Article Snippet: ​ table ft1 table-wrap mode="anchored" t5 caption a7 REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Mdm2 (SMP 14 ) Santa Cruz Cat. No. sc-965 P53 (FL-393) Santa Cruz Cat. No. sc-6243 Goat anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor Plus 488 ThermoFisher Scientific Cat. No. A32723 Donkey anti-Rabbit IgG (H+L) Highly Cross-Ad-sorbed Secondary Antibody, Alexa Fluor Plus 647 ThermoFisher Scientific Cat. No. A32795 DharmaFect I Dharmacon Cat. No. T-2001 Chemicals DAPI Sigma D9542 DABCO 33-LV Sigma Cat. No. 290734 Experimental Models: Cell Lines MCF7+p53shRNA+p53-mCerulean ( Gaglia et al., 2013 ) N/A Oligonucleotides MDMX siRNA: AGCCCTCTCTATGATATGCTA Qiagen Cat. No. 1027417 MDMX siRNA: GACCACGAGACGGGAACATTA Qiagen Cat. No. 1027417 AllStars Negative Control siRNA Qiagen Cat. No. 1027280 Software and Algorithms MATLAB 2019b The MathWorks, Inc. https://www.math-works.com P53 Cinema Single Cell Tracking Software ( Reyes et al., 2018 ) https://github.com/balvahal/p53Cine-maManual Custom Matlab script- model This work https://github.com/Mathiasheltberg/In-teractionsP53Net-work Open in a separate window Key Resource Table Impact factors in systems biology - Using models to rule out and validate hypotheses Mathematical models are routinely built to represent known biological interactions and recapitulate the behaviors they generate.

Techniques: Negative Control, Software, Single Cell Tracking