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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Contribution of individual pathways to the total apoptosis level (in percent). “Combined”: all three pathways are activated; “Extrinsic”: only extrinsic pathway is active; “Intrinsic”: only intrinsic pathway is active; “Perforin”: only perforin pathway is active.
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fig4: Contribution of individual pathways to the total apoptosis level (in percent). “Combined”: all three pathways are activated; “Extrinsic”: only extrinsic pathway is active; “Intrinsic”: only intrinsic pathway is active; “Perforin”: only perforin pathway is active.

Mentions: One advantage of having a computer simulated model is that we could measure biological system properties that are hard to quantify in laboratory experiments. One example is quantifying the contribution of each pathway in inducing apoptosis. For this purpose only, on those proteins described in Table 4 as random between [0,1], we intentionally set them equal to 1, while the others stay at 0. This removes the random effect from the initial conditions. The apoptosis (Apop) value obtained from computing all biochemical kinetics equations in Table 2 gives the total apoptosis level from these three pathways combined. To measure the contribution of an individual pathway, we set the other two pathways inactive by assigning their proteins' initial values to 0. For instance, by setting the initial values of FasL, Casp8, granB, and Casp10 to 0 and computing only those equations in blocks B and D of Table 2, we turn off the extrinsic and perforin pathways and hence obtain the apoptosis level contributed by the intrinsic pathway only. In a similar manner, one can measure the apoptosis level produced by extrinsic and perforin pathways separately. Figure 4 shows the percent contribution of each signaling pathway under different secretome concentration for short term (48 hours) and long term (800 hours) treatment.


A Computational Model for Investigating Tumor Apoptosis Induced by Mesenchymal Stem Cell-Derived Secretome
Contribution of individual pathways to the total apoptosis level (in percent). “Combined”: all three pathways are activated; “Extrinsic”: only extrinsic pathway is active; “Intrinsic”: only intrinsic pathway is active; “Perforin”: only perforin pathway is active.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120213&req=5

fig4: Contribution of individual pathways to the total apoptosis level (in percent). “Combined”: all three pathways are activated; “Extrinsic”: only extrinsic pathway is active; “Intrinsic”: only intrinsic pathway is active; “Perforin”: only perforin pathway is active.
Mentions: One advantage of having a computer simulated model is that we could measure biological system properties that are hard to quantify in laboratory experiments. One example is quantifying the contribution of each pathway in inducing apoptosis. For this purpose only, on those proteins described in Table 4 as random between [0,1], we intentionally set them equal to 1, while the others stay at 0. This removes the random effect from the initial conditions. The apoptosis (Apop) value obtained from computing all biochemical kinetics equations in Table 2 gives the total apoptosis level from these three pathways combined. To measure the contribution of an individual pathway, we set the other two pathways inactive by assigning their proteins' initial values to 0. For instance, by setting the initial values of FasL, Casp8, granB, and Casp10 to 0 and computing only those equations in blocks B and D of Table 2, we turn off the extrinsic and perforin pathways and hence obtain the apoptosis level contributed by the intrinsic pathway only. In a similar manner, one can measure the apoptosis level produced by extrinsic and perforin pathways separately. Figure 4 shows the percent contribution of each signaling pathway under different secretome concentration for short term (48 hours) and long term (800 hours) treatment.

View Article: PubMed Central - PubMed

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.

No MeSH data available.