Next Article in Journal
Main Strategies for the Identification of Neoantigens
Next Article in Special Issue
An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling
Previous Article in Journal
Regulation of NFκB Signalling by Ubiquitination: A Potential Therapeutic Target in Head and Neck Squamous Cell Carcinoma?
Previous Article in Special Issue
Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling
Open AccessArticle

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway

Institute for Protein Research, Osaka University, Suita, Osaka 565-0871; Japan
Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki; Osaka 567-0085, Japan
Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
Author to whom correspondence should be addressed.
Cancers 2020, 12(10), 2878;
Received: 28 August 2020 / Revised: 24 September 2020 / Accepted: 24 September 2020 / Published: 7 October 2020
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.
A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate. View Full-Text
Keywords: mathematical modeling; parameter estimation; ErbB signaling pathway; breast cancer mathematical modeling; parameter estimation; ErbB signaling pathway; breast cancer
Show Figures

Graphical abstract

MDPI and ACS Style

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 2020, 12, 2878.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop