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

Abstract

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.

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

“…We also expect ASURAT to improve scRNA-seq data-driven mathematical modeling for patient classification 39 , which includes parameter estimations of dynamical systems of gene regulatory network. Since ASURAT detects significant biological functions (e.g., biological process, pathway activity, and chemical reaction) for cell clustering, one can obtain promising candidates for a core regulatory network, which may greatly reduce the numbers of parameters.…”

Mathematical Modeling and Transcriptome Profiling of Breast Cancer Cells During Tamoxifen Treatment Reveal Multiple Trajectories for Resistant Subtypes

“…We obtained 20 model parameter sets, which could reproduce two experimental time-course datasets, total cell growth rate in the presence of TAM (Figure 1b) and rate of cell subpopulation (Figure 3e), simultaneously (Fig. 4b and c and Supplementary Figure 6a) using the BioMASS computational framework 26 .…”

“…Next, any bias due to differences in the cell cycle stage was removed using the function CellCycleScoring and cell cycle gene set, and the effect of cell cycle phases on gene expression data was regressed. The data were imported into the Monocle 3 software 26 , and the data dimensions were reduced to three with UMAP. Then, cells were categorized into multiple classes.…”

“…Details of the equations are summarized in Supplementary Table 1. Mathematical simulation and parameter search were performed using the BioMASS platform 26 . During the parameter search process, we attempted to minimize the weighted sum of squared percentage errors (wSSPE):…”

“…The single parameter sensitivity of each reaction is de ned as follows: where v i is the i th reaction; v is a reaction vector (v = v 1 , v 2 , …); and q(v) is a target function (e.g., the integral of growth rate in the current study). The sensitivity of each reaction was calculated with 1% increase in the reaction rate using the BioMASS platform 26 .…”