Reversing cancer cell behavior using AI-guided CRISPR and quantum nanobiology: a systems-based approach to epigenetic...

Area of Science:

• Biophysics
• Computational Biology
• Nanomedicine

Background:

• Cancer treatment is limited by tumor genetic complexity and phenotypic plasticity.
• CRISPR-Cas gene editing faces challenges in specificity, off-target effects, and adapting to tumor heterogeneity.

Purpose of the Study:

• To investigate integrating quantum biological processes, artificial intelligence (AI), and nanomaterials for advanced CRISPR gene editing.
• To improve the precision and efficacy of gene editing in modulating or reversing malignant cancer phenotypes.

Main Methods:

• Utilizing quantum machine learning (QML) to simulate quantum biological processes in DNA repair and enzyme activity.
• Employing AI-powered optimization to guide CRISPR editing within oncogenic signaling networks for targeted phenotypic reversal.
• Leveraging nanomaterials (graphene, gold nanoparticles, lipid vectors) for biocompatible delivery of CRISPR systems to the tumor microenvironment.

Main Results:

• Hypothesized partial restoration of homeostatic gene-expression states in experimental cancer models.
• Demonstrated a potential integrated strategy combining quantum-informed AI, CRISPR, and nanomaterial delivery.

Conclusions:

• The proposed integrated strategy offers a novel approach beyond tumor suppression towards controlled modulation of malignant cell states.
• Substantial preclinical and clinical validation is required to translate this approach into future cancer therapies.