Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model

Abstract

Motivation: The growth and survival of myeloma cells are greatly affected by their surrounding microenvironment. To understand the molecular mechanism and the impact of stiffness on the fate of myeloma-initiating cells (MICs), we develop a systems biological model to reveal the dynamic regulations by integrating reverse-phase protein array data and the stiffness-associated pathway.

Results: We not only develop a stiffness-associated signaling pathway to describe the dynamic regulations of the MICs, but also clearly identify three critical proteins governing the MIC proliferation and death, including FAK, mTORC1 and NFκB, which are validated to be related with multiple myeloma by our immunohistochemistry experiment, computation and manually reviewed evidences. Moreover, we demonstrate that the systematic model performs better than widely used parameter estimation algorithms for the complicated signaling pathway.

Availability and implementation: We can not only use the systems biological model to infer the stiffness-associated genetic signaling pathway and locate the critical proteins, but also investigate the important pathways, proteins or genes for other type of the cancer. Thus, it holds universal scientific significance.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

The workflow of the systematic procedure. (A) Model development (left panel shows Markov chain detailed in Figure S4A of Supplementary Material S3). (B) Model training (right panel shows model testing results detailed in Figure S1 of Supplementary Material S6). (C) Model testing (left panel shows the sensitivity analysis result detailed in Figure S2 of Supplementary Material S7)

Fig. 2.

The structure of the genetic BMSC’s stiffness-associated signaling pathway

Fig. 3.

The algorithm comparison among PSO, GA and MCMFG

Fig. 4.

The variation analysis results. The x- and y-axis are parameter variation and the name of parameters (Table S1 of Supplementary Material S9), respectively

Fig. 5.

Immunohistochemistry experiment. NFκB staining for the (A) patient (20%), (B) healthy individual (5%). It is noted that the percentage of positively stained cells (subscript of A and B) is determined by two independent pathologists

Fig. 6.

Impact of the crucial proteins on cell phenotype. (+) and (−) denote increasing and decreasing the initial concentration of corresponding protein concentration. Red and green indicate strong and weak concentration, respectively (Color version of this figure is available at Bioinformatics online.)