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The interplay between DNA damage and autophagy in lung cancer: A mathematical study

Dipanka Tanu Sarmah1, Nandadulal Bairagi2, Samrat Chatterjee1

  • 1Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India.

Bio Systems
|May 21, 2021
PubMed

Insights

This study models the interplay between p53, DNA damage, and autophagy in lung cancer. Modulating beclin1 levels via AMPK and BCL2 may offer a strategy to combat lung cancer progression.

Area of Science:

  • Oncology
  • Molecular Biology
  • Systems Biology

Background:

  • Lung cancer mortality remains high, with existing treatments facing limitations.
  • DNA damage, caused by various genotoxic agents, is an early event in cancer development.
  • The tumor suppressor protein p53 is crucial for DNA repair and influences cancer progression and autophagy.

Purpose of the Study:

  • To develop a mathematical model elucidating the relationship between p53, DNA damage, and autophagy in lung cancer.
  • To investigate the dynamic interactions within this complex biological system.
  • To identify potential therapeutic strategies for lung cancer based on modulating key regulatory pathways.

Main Methods:

  • Formulation of a seven-dimensional mathematical model integrating p53, DNA damage, and autophagy.
  • Implementation of local and global sensitivity analyses to understand system dynamics.
  • Parameter recalibration analysis to refine model predictions.

Main Results:

  • The model successfully captures the intricate connections between p53, DNA damage, and autophagy.
  • Sensitivity analyses revealed critical parameters influencing the system's behavior.
  • The study highlights the dual role of autophagy regulation by p53, involving AMPK and BCL2.

Conclusions:

  • Modulating beclin1 levels, through the regulation of AMPK and BCL2, is proposed as a potential strategy to mitigate lung cancer progression.
  • Understanding the p53-autophagy axis is vital for developing novel lung cancer therapies.
  • Mathematical modeling provides valuable insights into complex cancer biology and therapeutic interventions.
Keywords:
AutophagyDNA damageLung cancerMathematical modelSensitivity analysis

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