||2:00pm Mar 30, 2021
Institute for Protein Research
Mathematical modeling of cancer signaling networks for patient classification
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.
- 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.
- 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.
- 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,