A Computational Model for Investigating Tumor Apoptosis Induced by Mesenchymal Stem Cell-Derived Secretome

Abstract

Apoptosis is a programmed cell death that occurs naturally in physiological and pathological conditions. Defective apoptosis can trigger the development and progression of cancer. Experiments suggest the ability of secretome derived from mesenchymal stem cells (MSC) to induce apoptosis in cancer cells. We develop a hybrid discrete-continuous multiscale model to further investigate the effect of MSC-derived secretome in tumor growth. The model encompasses three biological scales. At the molecular scale, a system of ordinary differential equations regulate the expression of proteins involved in apoptosis signaling pathways. At the cellular scale, discrete equations control cellular migration, phenotypic switching, and proliferation. At the extracellular scale, a system of partial differential equations are employed to describe the dynamics of microenvironmental chemicals concentrations. The simulation is able to produce both avascular tumor growth rate and phenotypic patterns as observed in the experiments. In addition, we obtain good quantitative agreements with the experimental data on the apoptosis of HeLa cancer cells treated with MSC-derived secretome. We use this model to predict the growth of avascular tumor under various secretome concentrations over time.

Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors

“…One application of our work is that the model can also be linked with computational models that predict events on the cellular scale. Our model culminates with ERK activation, complementing published models that substantially simplify the intracellular signaling and focus on specific cellular behavior, such as proliferation [35], the probability of sprout formation and the speed of vessel growth [36], or tumor growth [37]. However, these models reduced the intracellular signaling network such that the output signal is simply linearly proportional to the fraction of bound receptors.…”

“…In comparison, our mechanistic model considers intracellular signaling and quantitatively analyzes pERK response, which could be a better indicator for these cellular behaviors. For example, Hendrata and Sudiono constructed a computational model that includes molecular, cellular, and extracellular scales to study tumor apoptosis [37]. Our model can be utilized in combination with such models to more accurately predict cellular behavior.…”

Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity

“…Another potential application is using the data-driven model inside of an agent-based model (ABM). While some ABMs do use simple ODE models as a way of making cellular decisions ( Hendrata and Sudiono, 2016 ; Wang et al, 2007 ; Zhang et al, 2009 ), most use discrete or probabilistic rules to govern how each cell behaves, as that is much more computationally efficient. Using a data-driven model, ODE networks could potentially be simplified, allowing ABMs to become more biologically detailed without a significant increase in computational cost.…”

The Secretome Analysis of Activated Human Renal Fibroblasts Revealed Beneficial Effect of the Modulation of the Secreted Peptidyl-Prolyl Cis-Trans Isomerase A in Kidney Fibrosis

“…The cell secretome consists of proteins that are secreted or shed from the cell surface, as well as intracellular proteins released into the extracellular environment due to vesiculation, cell lysis, apoptosis, and/or necrosis. The secretome can accurately reflect the functional state of secreting cells at specific time points [ 3 ] and is associated with broad cellular processes, including homeostasis, developmental regulation, proteolysis, immune defense, development of the extracellular matrix (ECM), signal transduction, and cell adhesion [ 4 , 5 , 6 , 7 , 8 , 9 ].…”