A growing body of biomedical literature suggests a bidirectional regulatory relationship between cardiac calcium (Ca²⁺)-regulating proteins and reactive oxygen species (ROS) that is integral to the pathogenesis of various cardiac disorders via oxidative stress (OS) signaling. To address the challenge of finding hidden connections within the growing volume of biomedical research, we developed a data science pipeline for efficient data extraction, transformation, and loading. Employing the CaseOLAP (Context-Aware Semantic Analytic Processing) algorithm, our pipeline quantifies interactions between 128 human cardiomyocyte Ca²⁺-regulating proteins and eight cardiovascular disease (CVD) categories. Our machine-learning analysis of CaseOLAP scores reveals that the molecular interfaces of Ca²⁺-regulating proteins uniquely associate with cardiac arrhythmias and diseases of the cardiac conduction system, distinguishing them from other CVDs. Additionally, a knowledge graph analysis identified 59 of the 128 Ca²⁺-regulating proteins as involved in OS-related cardiac diseases, with cardiomyopathy emerging as the predominant category. By leveraging a link prediction algorithm, our research illuminates the interactions between Ca²⁺-regulating proteins, OS, and CVDs. The insights gained from our study provide a deeper understanding of the molecular interplay between cardiac ROS and Ca²⁺-regulating proteins in the context of CVDs. Such an understanding is essential for the innovation and development of targeted therapeutic strategies.

A growing body of biomedical literature suggests a bidirectional regulatory relationship between cardiac calcium (Ca ²⁺ )-regulating proteins and reactive oxygen species (ROS), which is integral to the pathogenesis of various cardiac disorders via oxidative stress signaling. To address the challenge of finding hidden connections within the growing volume of biomedical research, we developed a data science pipeline for efficient data extraction, transformation, and loading. Employing the CaseOLAP (Context-Aware Semantic Analytic Processing) algorithm, our pipeline quantifies interactions between 128 human cardiomyocyte Ca ²⁺ -regulating proteins and eight cardiovascular disease (CVD) categories. Our machine learning analysis of CaseOLAP scores reveals that the molecular interfaces of Ca ²⁺ -regulating proteins uniquely associate with cardiac arrhythmias and diseases of the cardiac conduction system, distinguishing them from other CVDs. Additionally, a knowledge graph analysis identified 59 of the 128 Ca ²⁺ -regulating proteins as involved in OS-related cardiac diseases, with cardiomyopathy emerging as the predominant category. By leveraging a link prediction algorithm, our research illuminates interactions between Ca ²⁺ -regulating proteins, OS, and CVDs. The insights gained from our study provide a deeper understanding of the molecular interplay between cardiac ROS and Ca ²⁺ -regulating proteins in the context of CVDs. Such understanding is essential for the innovation and development of targeted therapeutic strategies.

We present a physics-inspired classical digital twin of the cell: a graph neural network constrained to a compartmental, multi-clock port-Hamiltonian form, with parameters learned from multi-omic measurements. The port-Hamiltonian structure is a modelling choice — it buys conservation, passivity and a clean separation of storage, routing and dissipation — not a claim about what a cell is. The state pairs each species’ abundance deviation with a phase coordinate, assigned only where a per-clock rhythmicity gate certifies periodicity. Stored energy decomposes over five functional compartments, so stability is verified compartment by compartment. Two distinct clocks are included — the 24-hour transcription–translation loop and the 20-hour transcription-independent redox oscillator — coupled through a zero-net-power link, with the central-dogma correspondence hard-wired and moiety pools exact invariants. On a real mouse-liver three-omic dataset the verdict is mixed. Across ten seeds the trained twin is thermodynamically stable (no violations at any sampled state) and forecasts held-out segments (root-mean-square error 0.325 ± 0.002). Its central prediction — cross-omic phase lag equals arctan of clock frequency over degradation rate — matches the aggregate transcript-to-protein lag (5.74 ± 0.03 versus 4.90 hours), but the per-gene correlation is indistinguishable from zero ( r = 0.06 ± 0.27, sign unstable across seeds), so the law is supported in aggregate and unresolved per species. Recovery of withheld interaction edges is at chance (AUROC 0.50 ± 0.13, nine of ten seeds scoreable): 24 timepoints do not identify network topology, which we report as a bound on what this data volume supports rather than as a property of the framework. Because the port-Hamiltonian form is imposed by construction, edits to the twin preserve it, so specialisation and disease can be expressed as structured perturbations of this reference twin rather than as separate models.

Integrating multi-omics data—transcriptomics, proteomics, metabolomics, single-cell—remains a fundamental challenge in systems biology. We present BioPhasor, a framework that encodes each measurement as a complex phasor z = e ^iϕ on the compact N -torus T ^N , modelling the cell as phase-coupled oscillatory programs whose dissipative dynamics generate limit cycles and an attractor landscape. From this geometry we derive the Cell State Tensor (CST), a rank-3 tensor whose axes we root in measured multi-omics quantities: a pathway/module atlas on the regulatory axis and a directional central-dogma modality axis. Across nine scenarios on open public data (GEO, CPTAC), loaded through one unmodified data layer, we report verdicts honestly: four reproduce, three are partial, two do not. A data-driven cell-cycle axis lifts agreement with a reference method from 0.34 to 0.69; an explicit circadian origin cuts peak-time error from 10.6 to 1.4 h; and central-dogma coupling—mRNA phase organising protein amplitude—clears a surrogate null and is tumour-specific. Grounding the quantum-ready claim, the CST maps to a density-matrix formalism whose coherence and entropy match quantum-information counterparts, and the phasor circuit transpiles gate-for-gate to a variational quantum circuit, though no empirical advantage emerges. A single loader regenerates every reported number, and the code is released.