Systematic quantitative characterization of cellular responses induced by multiple signals

Ibrahim Al-Shyoukh, Fuqu Yu, Jiaying Feng, Karen Yan, Steven Dubinett, Chih Ming Ho, Jeff S. Shamma, Ren Sun*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Background: Cells constantly sense many internal and environmental signals and respond through their complex signaling network, leading to particular biological outcomes. However, a systematic characterization and optimization of multi-signal responses remains a pressing challenge to traditional experimental approaches due to the arising complexity associated with the increasing number of signals and their intensities.Results: We established and validated a data-driven mathematical approach to systematically characterize signal-response relationships. Our results demonstrate how mathematical learning algorithms can enable systematic characterization of multi-signal induced biological activities. The proposed approach enables identification of input combinations that can result in desired biological responses. In retrospect, the results show that, unlike a single drug, a properly chosen combination of drugs can lead to a significant difference in the responses of different cell types, increasing the differential targeting of certain combinations. The successful validation of identified combinations demonstrates the power of this approach. Moreover, the approach enables examining the efficacy of all lower order mixtures of the tested signals. The approach also enables identification of system-level signaling interactions between the applied signals. Many of the signaling interactions identified were consistent with the literature, and other unknown interactions emerged.Conclusions: This approach can facilitate development of systems biology and optimal drug combination therapies for cancer and other diseases and for understanding key interactions within the cellular network upon treatment with multiple signals.

Original languageEnglish (US)
Article number1752
JournalBMC SYSTEMS BIOLOGY
Volume5
DOIs
StatePublished - May 30 2011
Externally publishedYes

Bibliographical note

Funding Information:
We thank Shiva Portonovo and Drs. Christopher Denny, Desmond Smith, Helen Brown, Elliot Landaw, and Henry Huang for critical reading of the manuscript; all Sun lab members for helpful discussions. This study is supported by NIH (CA091791 and CA127042), the Stop Cancer Foundation, Burroughs Wellcome Fund, NSF grant (ECS-0501394) and NIH Roadmap for Medical Research (PN2 EY018228).

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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