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Title: Prediction of Optimal Drug Schedules for Controlling Autophagy

Abstract

Here, the effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy.

Authors:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [1]
  1. Univ. of New Mexico, Albuquerque, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1495159
Report Number(s):
LA-UR-18-28998
Journal ID: ISSN 2045-2322
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Biological Science

Citation Formats

Shirin, Afroza, Klickstein, Isaac S., Feng, Song, Lin, Yen Ting, Hlavacek, William S., and Sorrentino, Francesco. Prediction of Optimal Drug Schedules for Controlling Autophagy. United States: N. p., 2019. Web. doi:10.1038/s41598-019-38763-9.
Shirin, Afroza, Klickstein, Isaac S., Feng, Song, Lin, Yen Ting, Hlavacek, William S., & Sorrentino, Francesco. Prediction of Optimal Drug Schedules for Controlling Autophagy. United States. doi:10.1038/s41598-019-38763-9.
Shirin, Afroza, Klickstein, Isaac S., Feng, Song, Lin, Yen Ting, Hlavacek, William S., and Sorrentino, Francesco. Tue . "Prediction of Optimal Drug Schedules for Controlling Autophagy". United States. doi:10.1038/s41598-019-38763-9. https://www.osti.gov/servlets/purl/1495159.
@article{osti_1495159,
title = {Prediction of Optimal Drug Schedules for Controlling Autophagy},
author = {Shirin, Afroza and Klickstein, Isaac S. and Feng, Song and Lin, Yen Ting and Hlavacek, William S. and Sorrentino, Francesco},
abstractNote = {Here, the effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy.},
doi = {10.1038/s41598-019-38763-9},
journal = {Scientific Reports},
number = 1,
volume = 9,
place = {United States},
year = {2019},
month = {2}
}

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