Digital Twin Modeling of MYC-Dependent Transcriptional Condensates for Personalized Cancer Therapeutics
Keywords:
digital twin, MYC, transcriptional condensates, phase separation, personalized medicine, systems biology, artificial intelligence, clinical governance, multi-omics integrationAbstract
The emergence of digital twin technology offers a transformative paradigm for modeling complex biological systems at the interface of molecular dynamics, large-scale data integration, and personalized medicine. This paper presents a conceptual and architectural framework for a digital twin system that captures the behavior of MYC-dependent transcriptional condensates, which are phase-separated assemblies that modulate gene expression in cancer. The proposed system integrates multi-omics data, real-time patient monitoring, biophysical simulations, and machine learning to create a dynamic, patient-specific replica of the transcriptional condensate network. Emphasis is placed on system-level design choices, structural trade-offs between model fidelity and computational tractability, governance of data and algorithmic fairness, and sustainability of deployment across clinical infrastructures. The analysis draws on recent experimental evidence that MYC undergoes phase separation to selectively influence the transcriptome [6], linking this discovery to scalable digital twin architectures. Cross-domain comparisons with digital twin applications in aerospace and manufacturing are used to highlight unique challenges in biological systems, such as stochasticity, multiscale coupling, and ethical constraints. The paper also addresses policy implications regarding data ownership, algorithmic transparency, and equitable access to personalized cancer therapeutics. By situating the digital twin as a socio-technical infrastructure, this work provides a roadmap for translational research that balances innovation with responsible stewardship.
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