How can we kill cancer cells: Insights from the computational models of apoptosis
1Subhadip Raychaudhuri, Department of Biomedical Engineering, Biophysics Graduate Group, Graduate Group in Immunology, and Graduate Group in Applied Mathematics, University of California, Davis, CA 95616, United States.
Abstract:
Cancer cells are widely known to be protected from apoptosis, a phenomenon that is a major hurdle to successful anticancer therapy. Over-expression of several anti-apoptotic proteins, or mutations in pro-apoptotic factors, has been recognized to confer such resistance. Development of new experimental strategies, such as in silico modeling of biological pathways, can increase our understanding of how abnormal regulation of apoptotic pathway in cancer cells can lead to tumour chemoresistance. Monte Carlo simulations are in particular well suited to study inherent variability, such as spatial heterogeneity and cell-to-cell variations in signaling reactions. Using this approach, often in combination with experimental validation of the computational model, we observed that large cell-to-cell variability could explain the kinetics of apoptosis, which depends on the type of pathway and the strength of stress stimuli. Most importantly, Monte Carlo simulations of apoptotic signaling provides unexpected insights into the mechanisms of fractional cell killing induced by apoptosis-inducing agents, showing that not only variation in protein levels, but also inherent stochastic variability in signaling reactions, can lead to survival of a fraction of treated cancer cells.
Insights
Cancer cells resist apoptosis due to protein variations. Monte Carlo simulations reveal that inherent signaling variability, not just protein levels, causes fractional cell killing and treatment resistance in tumors.
Area of Science:
- Computational Biology
- Cancer Research
- Biophysics
Background:
- Cancer cells exhibit resistance to apoptosis, a key challenge in chemotherapy.
- Overexpression of anti-apoptotic proteins or mutations in pro-apoptotic factors contribute to this resistance.
- Understanding abnormal apoptosis regulation is crucial for overcoming tumor chemoresistance.
Purpose of the Study:
- To investigate the role of cell-to-cell variability in apoptotic signaling in cancer cells.
- To explore how stochasticity in signaling reactions impacts cancer cell survival.
- To gain insights into the mechanisms of fractional cell killing by apoptosis-inducing agents.
Main Methods:
- Utilized in silico modeling, specifically Monte Carlo simulations, to analyze biological pathways.
- Studied inherent variability, including spatial heterogeneity and cell-to-cell variations in signaling.
- Combined computational modeling with experimental validation.
Main Results:
- Observed that significant cell-to-cell variability explains apoptosis kinetics, influenced by pathway type and stimulus strength.
- Demonstrated that stochastic variability in signaling reactions, alongside protein level variations, contributes to cancer cell survival.
- Provided unexpected insights into fractional cell killing mechanisms.
Conclusions:
- In silico modeling, particularly Monte Carlo simulations, enhances understanding of apoptosis regulation in cancer.
- Stochastic variability in signaling pathways is a critical factor in cancer cell resistance to apoptosis-inducing agents.
- Computational approaches offer novel perspectives on tumor chemoresistance and therapeutic strategies.
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