Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential Equations

Curating models from BioModels: Developing a workflow for creating OMEX files

The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the validation procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.

Mathematical modeling predicts pathways to successful implementation of combination TRAIL-producing oncolytic virus and PAC-1 to treat granulosa cell tumors of the ovary

The development of new cancer therapies requires multiple rounds of validation from in vitro and in vivo experiments before they can be considered for clinical trials. Mathematical models assist in this preclinical phase by combining experimental data with human parameters to provide guidance about potential therapeutic regimens to bring forward into trials. However, granulosa cell tumors of the ovary lack a relevant mouse model, complexifying preclinical drug development for this rare tumor. To bridge this gap, we established a mathematical model as a framework to explore the potential of using a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-producing oncolytic vaccinia virus in combination with the chemotherapeutic agent first procaspase activating compound (PAC-1). We have previously shown that TRAIL and PAC-1 act synergistically on granulosa tumor cells. In line with our previous results, our current model predicts that, although it is unable to stop the tumor from growing in its current form, combination oral PAC-1 with oncolytic virus (OV) provides the best result compared to monotherapies. Encouragingly, our results suggest that increases to the OV infection rate can lead to the success of this combination therapy within a year. The model developed here can continue to be improved as more data become available, allowing for regimen-tailoring via virtual clinical trials, ultimately shepherding effective regimens into trials.

Global Dynamics of a Breast Cancer Competition Model

In this paper, we present a system of five ordinary differential equations which consider population dynamics among cancer stem cells, tumor cells, and healthy cells. Additionally, we consider the effects of excess estrogen and the body's natural immune response on the aforementioned cell populations. Employing a variety of analytical methods, we study the global dynamics of the full system, along with various submodels. We find sufficient conditions on parameter values to ensure cancer persistence in the absence of immune cells, and cancer eradication when an immune response is included. We conclude with a discussion on the biological implications of the resulting global dynamics.

Numb Gene Enhances Radiation Sensitivity of Nonsmall Cell Lung Cancer Stem Cells

OBJECTIVE

To study the effects of Numb gene expression on radiation sensitivity of nonsmall cell lung cancer (NSCLC) stem cells.

MATERIALS AND METHODS

The side population (SP) cells A549-SP were transfected with pcDNA3.1 (pcDNA3.1 group), pcDNA-Numb (pcDNA-Numb group) and shRNA-Numb (shRNA-Numb group). Real-time quantitative polymerase chain reaction and Western blot were performed to determine Numb expression; MTT method was used to measure the proliferation activity change of the NSCLC stem cells both before and after irradiation with different doses of 60Coγ ray; Hoechst staining and Annexin V-FITC/PI were used to detect the apoptosis of the NSCLC stem cells; and colony-forming assay was used to determine the effect of Numb expression on radiation sensitivity of the NSCLC stem cells.

RESULTS

Increased mRNA and protein expressions of the A549-SP cells were found in the pcDNA-Numb group, and decreased mRNA and protein expressions were found in the shRNA-Numb group. The optical density value of the cells decreased in the pcDNA-Numb group but increased in the shRNA-Numb group. The cells with over-expressed Numb showed obvious nuclear condensation and fragmentation; the apoptosis rate increased significantly. The cells with knockdown Numb showed less nuclear damage; the apoptosis rate significantly decreased. After irradiation, the cells in the pcDNA-Numb group showed decreased survival rate, clonality, and the values of D0, Dq, N, and SF2; whereas the cells in the shRNA-Numb group showed the opposite trend.

CONCLUSIONS

Radiation sensitivity of NSCLC stem cells was enhanced with the increase of Numb expression. Determination of Numb expression helped to evaluate the response of lung cancer to radiotherapy, which was important for guiding tumor treatment clinically.