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Understanding the mTOR signaling pathway via mathematical modeling.
Sulaimanov, Nurgazy; Klose, Martin; Busch, Hauke; Boerries, Melanie.
Afiliação
  • Sulaimanov N; Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Darmstadt, Germany.
  • Klose M; Department of Biology, Technische Universitat Darmstadt, Darmstadt, Germany.
  • Busch H; Systems Biology of the Cellular Microenvironment at the DKFZ Partner Site Freiburg - Member of the German Cancer Consortium, Institute of Molecular Medicine and Cell Research, Albert-Ludwigs-University Freiburg, Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Boerries M; Systems Biology of the Cellular Microenvironment at the DKFZ Partner Site Freiburg - Member of the German Cancer Consortium, Institute of Molecular Medicine and Cell Research, Albert-Ludwigs-University Freiburg, Freiburg, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany.
Article em En | MEDLINE | ID: mdl-28186392
ABSTRACT
The mechanistic target of rapamycin (mTOR) is a central regulatory pathway that integrates a variety of environmental cues to control cellular growth and homeostasis by intricate molecular feedbacks. In spite of extensive knowledge about its components, the molecular understanding of how these function together in space and time remains poor and there is a need for Systems Biology approaches to perform systematic analyses. In this work, we review the recent progress how the combined efforts of mathematical models and quantitative experiments shed new light on our understanding of the mTOR signaling pathway. In particular, we discuss the modeling concepts applied in mTOR signaling, the role of multiple feedbacks and the crosstalk mechanisms of mTOR with other signaling pathways. We also discuss the contribution of principles from information and network theory that have been successfully applied in dissecting design principles of the mTOR signaling network. We finally propose to classify the mTOR models in terms of the time scale and network complexity, and outline the importance of the classification toward the development of highly comprehensive and predictive models. WIREs Syst Biol Med 2017, 9e1379. doi 10.1002/wsbm.1379 For further resources related to this article, please visit the WIREs website.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transdução de Sinais / Serina-Treonina Quinases TOR / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transdução de Sinais / Serina-Treonina Quinases TOR / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article