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Title: Systems Biology for Organotypic Cell Cultures

Abstract

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss thismore » ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [4];  [1];  [7];  [4];  [1];  [1];  [1];  [8];  [9];  [10];  [4];  [11];  [12];  [4] more »;  [1];  [13] « less
  1. RTI International, Research Triangle Park, NC (United States)
  2. Texas A & M Univ., College Station, TX (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States)
  5. GlaxoSmithKline, Research Triangle Park, NC (United States)
  6. Translational Genomics Research Inst., Phoenix, AZ (United States)
  7. HemoShear Therapeutics, Charlottesville, VA (United States)
  8. Johns Hopkins Univ., Baltimore, MD (United States). Center for Alternatives to Animal Testing
  9. IPQ Analytics, Kennett Square, PA (United States)
  10. Phillip Morris International, Neuchatel (Switzerland)
  11. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  12. Defense Threat Reduction Agency, Ft. Belvoir, VA (United States)
  13. The Hamner Inst. for Health Sciences, Research Triangle Park, NC (United States); ScitoVation, Research Triangle Park, NC (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
National Inst. of Environmental Health Sciences, Research Triangle Park, NC (United States); Johns Hopkins Univ., Baltimore, MD (United States); RTI International, Research Triangle Park, NC (United States); Texas A & M Univ., College Station, TX (United States)
OSTI Identifier:
1313549
Report Number(s):
LLNL-TR-699637
DOE Contract Number:  
AC52-07NA27344; AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., and Yoon, Miyoung. Systems Biology for Organotypic Cell Cultures. United States: N. p., 2016. Web. doi:10.2172/1313549.
Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., & Yoon, Miyoung. Systems Biology for Organotypic Cell Cultures. United States. doi:10.2172/1313549.
Grego, Sonia, Dougherty, Edward R., Alexander, Francis J., Auerbach, Scott S., Berridge, Brian R., Bittner, Michael L., Casey, Warren, Cooley, Philip C., Dash, Ajit, Ferguson, Stephen S., Fennell, Timothy R., Hawkins, Brian T., Hickey, Anthony J., Kleensang, Andre, Liebman, Michael N., Martin, Florian, Maull, Elizabeth A., Paragas, Jason, Qiao, Guilin, Ramaiahgari, Sreenivasa, Sumner, Susan J., and Yoon, Miyoung. Thu . "Systems Biology for Organotypic Cell Cultures". United States. doi:10.2172/1313549. https://www.osti.gov/servlets/purl/1313549.
@article{osti_1313549,
title = {Systems Biology for Organotypic Cell Cultures},
author = {Grego, Sonia and Dougherty, Edward R. and Alexander, Francis J. and Auerbach, Scott S. and Berridge, Brian R. and Bittner, Michael L. and Casey, Warren and Cooley, Philip C. and Dash, Ajit and Ferguson, Stephen S. and Fennell, Timothy R. and Hawkins, Brian T. and Hickey, Anthony J. and Kleensang, Andre and Liebman, Michael N. and Martin, Florian and Maull, Elizabeth A. and Paragas, Jason and Qiao, Guilin and Ramaiahgari, Sreenivasa and Sumner, Susan J. and Yoon, Miyoung},
abstractNote = {Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, “organotypic” cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data. This consensus report summarizes the discussions held.},
doi = {10.2172/1313549},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Aug 04 00:00:00 EDT 2016},
month = {Thu Aug 04 00:00:00 EDT 2016}
}

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