Pancreatic ductal adenocarcinoma (PDAC) develops chemoresistance through dynamic cell-state transitions, yet the regulatory network architecture governing drug-tolerant states remains poorly characterised, because static expression snapshots cannot capture how interaction topology evolves along treatment-induced trajectories. We established a 3D PANC-1 tissue model on decellularised porcine intestinal matrix, which reaches patient-like proliferation indices and tissue-niche-specific drug responses, and performed single-cell RNA sequencing across four conditions: control, gemcitabine (GEM), TGF-β1, and their combination. Coupling pseudotime trajectory inference to dynamic protein–protein interaction network analysis, we tracked betweenness centrality continuously across cell-state transitions rather than comparing discrete states. GEM survivors engaged a proliferative programme (E2F1, CDK1, AURKA, TPX2, TOP2A, BIRC5), whereas TGF-β1 induced an independent EMT attractor (SPOCK1, MBOAT2, COL5A1, THBS1, FN1). Where the two converged, an emergent bottleneck appeared: CDK1 betweenness centrality spiked selectively, restrained by CDKN1A (p21) and WEE1, defining an S-phase-persistence state enriched for GEM survivors. Cross-examination in a PDAC atlas of 726,107 cells from 231 patients showed 8.7-fold metastatic enrichment of triple-positive cells. This framework, linking controlled 3D tissue experimentation, single-cell systems analysis and clinical-scale patient data, is applicable to drug resistance across cancer types.
Mechanical measurements of the nucleus remain fragmented across modalities and are often reduced to scalar “stiffness” proxies. We define the Genuine Nuclear Mechanome as a regime-aware, physically interpretable representation of intrinsic nuclear mechanical state. Readouts from non-equivalent techniques are converted into model-dependent descriptors and mapped onto experimentally supported chromatin contexts. A living-HeLa reconstruction illustrates the framework, which requires state preservation, identifiability, uncertainty propagation and cross-regime validation for multimodal systems biology.
How cells generate discrete signaling outputs from continuous biochemical reactions is a fundamental question in cell biology. Using a rule-based model of Ras activation, we demonstrate that SOS processivity—the ability of a single membrane-bound SOS molecule to catalyze the activation of many Ras molecules before dissociating—produces quantized Ras activation in confined membrane corrals without requiring bistability or positive feedback. Distinct SOS occupancy states each generate a characteristic RasGTP level, producing multimodal distributions whose peaks are predicted by a minimal activator–target framework. Four dimensionless conditions determine where peaks appear: low activator number, resolvable peak spacing, timescale separation, and unsaturated catalysis. This framework describes any system in which processive enzymes act on confined target pools. Positive feedback modulates peak weights without shifting positions, while eliminating processivity abolishes quantization and collapses distributions to mean-field behavior. These results establish processivity-driven quantization as a general mechanism for discrete signaling in localized enzymatic systems.
Neurotrauma remains clinically challenging due to heterogeneous biology and evolving injury cascades. Traditional severity-based classifications fail to capture underlying molecular complexity. Neuromics combines genomic, transcriptomics, proteomic, metabolomic, lipidomics, EV-omics, and single-cell technologies into systems framework to enable endotype discovery and AI-driven stratification. We address modelling challenges, promote agent-driven multi-paradigm methods that go beyond steady-state assumptions, and advocate for targeted therapeutics, repositioning neurotrauma care as predictive, mechanism-informed, and personalised clinical management.
