Simulating the Impact of Gene Regulatory Network Mutations on Cancerous Cell Behavior Using a Virtual Cell Model

Research Problem:

Cancer remains one of the most urgent and complex challenges in modern medicine. At the core of many cancers are disruptions in gene regulatory networks, systems of genes, proteins, and regulatory interactions that maintain normal cellular function. When mutations occur in these networks, they can drive abnormal behaviors such as uncontrolled proliferation, resistance to apoptosis, and metabolic reprogramming. These disruptions backup many of the hallmarks of cancer.

​

Traditional wet-lab methods for studying such mutations are often time-consuming, costly, and limited in scope. Computational modeling, however, provides a powerful and accessible alternative that allows researchers to simulate the dynamics of GRNs and observe how specific mutations might influence cellular behavior. This project seeks to explore that potential by simulating the impact of cancer-associated mutations on virtual cells and comparing the outcomes to real-world gene expression data. By doing so, it aims to evaluate the accuracy of computational modeling as a predictive tool in cancer biology.

 

Existing Literature:

There is a rich body of research linking mutations in specific genes, such as TP53, MYC, and RB1, to cancerous transformations. Public databases such as The Cancer Genome Atlas and the Gene Expression Omnibus provide extensive data on gene expression in both healthy and tumor tissues. In parallel, platforms like Cell Collective, COPASI, and GeneNetWeaver have been used in previous studies to simulate regulatory networks in various biological contexts.

​

However, few existing studies directly compare virtual cell behavior under mutational stress to real-world cancer data. This proposal aims to bridge that gap by conducting a comparative analysis, which can help determine the accuracy of simulated predictions and potentially validate computational modeling as a tool for identifying early mutational drivers of cancer.

 

Research Question:

How do specific mutations in a simulated gene regulatory network influence cancerous behavior in a virtual cell?

 

This research question is precise and measurable, focusing on the causal relationship between mutations and observable phenotypic changes in a controlled virtual environment.

Methodology:​ 

To begin, a focused literature review will identify critical genes and interactions frequently implicated in cancer. Based on this, a virtual gene regulatory network will be constructed using a platform such as Cell Collective or COPASI, which allows for the simulation of complex molecular dynamics. The model will represent a healthy cell’s regulatory framework, which can then be systematically modified.

 

Specific mutations, such as the deletion of tumor suppressor genes or the overexpression of oncogenes, will be introduced into the model one at a time and in combinations. Simulations will observe changes in gene expression, protein interactions, and cell-level outcomes such as proliferation or apoptosis resistance. These behaviors will be compared to known hallmarks of cancer to determine whether the simulated cell adopts a cancer-like phenotype.

 

To validate the virtual model, its outputs will be compared to real-world gene expression data from cancer databases. Metrics such as pathway activation, expression pattern similarity, and classification accuracy (e.g., normal vs. cancerous) will be used to evaluate alignment between simulated and biological data.

 

Research Topic Justification:

This topic is both innovative and urgent. It uses computational modeling, a rising field in systems biology, to explore one of the most fundamental mechanisms driving cancer. The project also promotes cross-disciplinary skill development by combining molecular biology, computer science, and data analysis. Its applications are far-reaching, including virtual drug testing, personalized medicine, and efficient preclinical cancer research.

 

Next Steps:

The next steps involve moving into the implementation phase of the project. This includes conducting a comprehensive literature review to finalize the key genes and regulatory interactions to be modeled. I will then select and begin working with a virtual cell modeling platform to construct the baseline gene regulatory network. Once the model is built, I will introduce specific mutations associated with cancer and run simulations to observe resulting changes in cellular behavior. The simulation outputs will be compared with real-world gene expression data from databases such as TCGA or GEO to evaluate accuracy. As the project progresses, I will analyze the data, refine the model as needed, and draw conclusions about how these mutations influence cancer-like behaviors in cells. This hands-on phase will transform the proposal into a completed research study with important and meaningful findings.

​

References :

Helikar, T., Kowal, B., McClenathan, S., Bruckner, M., Rowley, T., Madrahimov, A., Wicks, B., Shrestha, M., Limbu, K., & Rogers, J. A. (2012). The Cell Collective: Toward an open and collaborative approach to systems biology. BMC Systems Biology, 6, 96. https://doi.org/10.1186/1752-0509-6-96

 

Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., & Kummer, U. (2006). COPASI—a COmplex PAthway SImulator. Bioinformatics, 22(24), 3067–3074. https://doi.org/10.1093/bioinformatics/btl485

 

The Cancer Genome Atlas Research Network. (2013). Comprehensive molecular portraits of human breast tumours. Nature, 490, 61–70. https://doi.org/10.1038/nature11412

 

Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., ... & Edgar, R. (2013). NCBI GEO: Archive for functional genomics data sets—update. Nucleic Acids Research, 41(D1), D991–D995. https://doi.org/10.1093/nar/gks1193

 

Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5), 646–674. https://doi.org/10.1016/j.cell.2011.02.013