The Pan-Omics Computational Engine (PCE): An Entropy-Driven Framework for Self-Evolving Digital Twins in Systems Biology

Current approaches in computational biology pre-dominantly rely on static analyses of fragmented multi-omicsdata, often neglecting the dynamic, self-organizing properties in-trinsic to living systems. We present the Pan-Omics ComputationalEngine (PCE), a unified, entropy-driven framework designed tofunction as a self-evolving digital twin of biological organization.The PCE introduces a Bio-Quantum Entropy Functional (BQEF)that minimizes system entropy through a generative simulationprocess rather than a purely predictive one. The architectureintegrates five primary modules: (1) the Multi-Omics GraphIntegration Layer (MOGIL) for cross-modal data harmoniza-tion, (2) the Quantum-Latent Entropy Minimizer (Q-LEM) forentropy-aware optimization across latent representations, (3) theEntropic Evolutionary Dynamics Engine (E3DE) for adaptivemodel evolution, (4) the Hierarchical Digital Twin Simulator(HDTS) for real-time biological state reconstruction, and (5)the Cognitive-Integration Substrate (CIS) that enables high-order correlation discovery. We demonstrate the PCE\u2019s abilityto detect non-equilibrium system states via quantitative entropydifferentials across synthetic pan-omics datasets. This work estab-lishes a methodological foundation for transitioning from biologyas data to biology as a self-organizing computational process,enabling applications in systems biology, synthetic life design,and precision medicine. Finally, we discuss ethical and biosecurityimplications of self-optimizing biological simulations and proposeinitial governance protocols to ensure safe development anddeployment.