ゲノム情報科学研究教育機構  アブストラクト
Date 2:00pm Mar 30, 2021
Speaker Mariko Okada
Professor
Institute for Protein Research
Osaka University
Title Mathematical modeling of cancer signaling networks for patient classification
Abstract Signal transduction pathways are biochemical networks that connect environmental factors to genetic factors in the nucleus for cell determination. Mathematical models of signaling pathways have been studied to facilitate a logical understanding of the molecular mechanisms embedded in cell fate regulation. Currently, hundreds of ordinary differentiation equation (ODE) models of signaling network are stored in public databases. At the same time, a huge amount of clinical omics data has become available. The next challenge is to use these resources to comprehensively and logically understand the mechanism of disease onset. To make this possible, we have developed BioMASS (Modeling and Analysis of Signaling Systems), a Python-based modeling platform, for building patient-specific models based on transcriptome data. In this talk, I will introduce a study on the heterogeneity of signal transduction responses in breast cancer patients and their potential application for personalized medicine.

References

  1. Imoto H, Zhang S, Okada M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. Cancers 12 (10), 2878, 2020.
  2. Nakakuki T, Birtwistle MR, Saeki Y, Yumoto N, Ide K, Nagashima T, Brusch L, Ogunnaike BA, Okada-Hatakeyama M, Kholodenko BN. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Cell 141(5): 884-896, 2010.
  3. Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analysis. Mol. Syst. Biol. 3:144, 2007.
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