NPJ Systems Biology and Applications最新文献

{"title":"Patient-specific modeling of G2/M cell cycle dynamics reveals prognostic dynamical regimes driven by copy number alterations.","authors":"Andrea Tonina, Alessandro Romanel","doi":"10.1038/s41540-026-00766-4","DOIUrl":"10.1038/s41540-026-00766-4","url":null,"abstract":"<p><p>Cell cycle dysregulation is a hallmark of cancer and a major source of tumor heterogeneity. While large-scale cancer genomics studies have characterized recurrent copy number alterations affecting cell cycle regulators, how these alterations translate into patient-specific dynamical dysregulation remains poorly understood. Here, we develop a stochastic, human-specific model of the G2/M cell cycle transition and integrate somatic copy number alterations to generate patient-specific perturbations of cell cycle dynamics. Copy number profiles from breast cancer patients are quantitatively mapped to model components, enabling simulation of individualized G2/M transition behavior. Model simulations reveal that copy number alterations induce distinct dynamical regimes characterized by systematic shifts in transition timing and switching commitment. Importantly, model-derived dynamical metrics stratify patients into groups with significantly different overall survival, independently of age and molecular subtype. Patients exhibiting delayed or impaired G2/M transitions show improved survival probability, consistent with reduced proliferative capacity. These results demonstrate that mechanistic, stochastic modeling can transform static genomic alterations into clinically relevant dynamical phenotypes, highlighting the potential of systems-level approaches to bridge cancer genomics and functional tumor behavior.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13578713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148205580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction variability in physiologically based pharmacokinetic modeling of tissue disposition under deep uncertainty.","authors":"Mustafa Farahat, D T Flaherty, Zachary R Fox, Belinda S Akpa","doi":"10.1038/s41540-026-00739-7","DOIUrl":"10.1038/s41540-026-00739-7","url":null,"abstract":"<p><p>Physiologically based pharmacokinetic (PBPK) models are increasingly invoked in virtual screening workflows for therapeutics. These mechanistic models project pharmacokinetic (PK) outcomes from molecular properties, with data-driven models acting as intermediaries to map molecular structure to PBPK input parameters. Errors in predicted parameters and unvalidated assumptions within PBPK models expose PK predictions to deep uncertainty. Herein, we examine how these uncertainties affect the prediction variability of dynamic, tissue-specific exposure. We validated four PBPK models against 1854 experimental datapoints-to establish their predictive fidelity before introducing parameter uncertainty typical of property-prediction models. Depending on molecule properties and model choice, the coefficient of variation under parameter uncertainty ranged from 10^-6 to 31 for predicted PK statistics. Further, we identified notable model disagreement for a subset of drug-like chemical space characterized by lipophilic, protonated molecules. Uncertainty quantification revealed biophysicochemical properties and parameter interactions that drove disagreement and highlighted model assumptions that exacerbated prediction variance. Our findings delineate the challenges presented by deep epistemic uncertainty in PBPK modeling.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148194918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing genomics-aware clinical agents in precision oncology.","authors":"Rangan Das, K Maheswari, Shaheen Siddiqui, Jeet Nanshi, Nikita Arora, Ankush Paul, Pavan Kumar, Varun Udbalkar, Apoorva Sarvade, Harsha Chaturvedi, Tammy Shvartsman, Shet Masih, Krishna Prasad, Vindhya Vasini Andra, Ravi Thippeswamy, Shekar Patil, S S Nirni, Ujjwal Maulik, Sanghamitra Bandyopadhyay, Brian Garsson, Mohammed Farooq, Debarka Sengupta","doi":"10.1038/s41540-026-00753-9","DOIUrl":"https://doi.org/10.1038/s41540-026-00753-9","url":null,"abstract":"<p><p>Agentic systems are proposed for clinical decision support, yet unrestricted tool access can undermine accountability and safety in precision oncology, where recommendations must be grounded in evolving guidelines, regulatory labels, and patient-specific molecular evidence. We compare four architectures: (i) a standalone LLM, (ii) an unconstrained function-calling agent with over 200 biomedical tools, (iii) a ReAct agent over the same broad tool surface, and (iv) gSage, a workflow-orchestrated agent restricted to 20 curated domain tools backed by a versioned knowledge stack. We evaluate 375 clinician-authored questions across 41 cases spanning breast, non-small cell lung, prostate, ovarian, and colorectal/GI tract cancers using multiple frontier LLM backbones. A two-pillar evaluation, combining LLM-based rubric scoring with expert adjudication and an independent embedding-based content concordance analysis, yields convergent results. The workflow-orchestrated agent outperforms all alternatives on five rubric axes (guideline concordance, correctness, patient centricity, reasoning transparency, and safety) and achieves the highest embedding recall under both a biomedical (MedCPT, R = 0.877) and general-purpose (Snowflake Arctic, R = 0.803) model, significantly exceeding all full-sample arms (p < 0.05, Bonferroni-corrected). These findings show that principled workflow orchestration with constrained, curated tools, rather than maximal tool breadth, is what drives reliability and safety in genomics-aware oncology decision support.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2026-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148176390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fundamental trade-off among noise suppression, response sensitivity and speed in biological feedback networks.","authors":"Ka Kit Kong, Feng Liu","doi":"10.1038/s41540-026-00755-7","DOIUrl":"10.1038/s41540-026-00755-7","url":null,"abstract":"<p><p>Biological regulatory networks rely on feedback control to suppress intrinsic noise while remaining sensitive and responsive to external signals, yet whether these objectives can be achieved simultaneously remains unclear. Here, we show that biological feedback networks face an unavoidable constraint: intrinsic fluctuations cannot be arbitrarily suppressed without sacrificing response sensitivity or slowing response speed via feedback control. Using a general framework for stochastic feedback dynamics, we derive a fundamental trade-off that limits how these three performance objectives can be jointly optimized. Theoretical results and numerical simulations demonstrate that this constraint persists across high-dimensional systems. We further show that nonequilibrium, non-gradient dynamics, which are prevalent in biological regulation, can partially relax but never eliminate this limitation, reducing the minimal cost of noise suppression by at most a factor of two. We validate our theory using a biologically motivated activator-inhibitor feedback motif. Together, our results elucidate a fundamental limitation of feedback control in enhancing the information transmission capacity of biological regulatory networks.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13538697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148157473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale hyperbolic embedding reveals hierarchical structure in complex biological systems.","authors":"Mingchen Yao, Anoop Praturu, Tatyana O Sharpee","doi":"10.1038/s41540-026-00725-z","DOIUrl":"10.1038/s41540-026-00725-z","url":null,"abstract":"<p><p>The rapid expansion of biological and computational datasets demands scalable methods that support both visualization and quantitative interpretation. Hyperbolic embeddings are well-suited to represent hierarchical structure, but existing approaches are limited by fixed curvature assumptions or poor scalability to large datasets. We introduce MuH-MDS, a multiscale hyperbolic multidimensional scaling algorithm that employs an adiabatic optimization strategy: local positions are iteratively refined while cluster centroids are temporarily fixed. This strategy accelerates computation by 10³ and enables scaling to datasets with over 80,000 samples. Applied to diverse benchmarks, including C. elegans embryogenesis scRNA-seq data, MuH-MDS uncovers intrinsic hierarchical organization and improves both pseudotime inference and lineage reconstruction relative to UMAP and other standard methods. In contrast to UMAP and t-SNE, which prioritize local neighborhoods at the expense of global coherence and metric fidelity, MuH-MDS preserves both local detail and global hierarchy, providing a metrically faithful framework for multiscale analysis of complex biological systems.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13530193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148150474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic proteomic analysis of neuroimaging metrics identifies therapeutic targets for pituitary neuroendocrine.","authors":"Xiao Wu, Haoyang Peng, Peng Wang, Zeyu Zhang, Yi Huang, Chaoyang Zhou, Han Ding","doi":"10.1038/s41540-026-00754-8","DOIUrl":"10.1038/s41540-026-00754-8","url":null,"abstract":"<p><p>The relationship between proteomics and neuroimaging metrics (NIMs) is still not fully understood. By examining the specific proteins expressed in different NIMs, researchers can gain insights into how these NIMs contribute to pituitary neuroendocrine tumors (PitNETs), ultimately enabling the development of targeted interventions and treatments. We identified 15 significantly NIMs and 18 proteins that exhibit a noteworthy causal relationship with the risk of developing PitNETs by forward MR analysis. Additionally, 10 proteins and one distinct neuroimaging metric with PitNETs. Then, the MR results indicated the identification of 33 significant relationships, which connect proteins with NIMs across five distinct categories. Then, we discovered that NIMs are capable of mediating 63% of the inverse relationship observed between WNT3 and the phenotypic characteristics of PitNETs. The results also suggested that WNT3 was associated with hypothalamic function, pituitary function, thyroid function, adrenal function, and gonadal function. Additionally, WNT3 expression was higher in PitNETs and was verified in multiple datasets. This research effectively connects the roles of protein markers with the structures of the brain and the PitNETs that can affect it. By establishing this link, the study provides valuable insights that can help in understanding the complex mechanisms that contribute to PitNETs.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148150511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.","authors":"Jun Allard","doi":"10.1038/s41540-026-00741-z","DOIUrl":"10.1038/s41540-026-00741-z","url":null,"abstract":"<p><p>Efforts to develop quantitative models must face a trade-off between interpretability and quantitative accuracy, which often disfavors interpretability. Here we adopt an operational definition of interpretability, specifically that a model is described by an arrow diagram wherein each arrow corresponds to a positive effect or negative effect of one component upon a process, and fewer arrows is more interpretable than more arrows. We then develop a method to add flexibility-and thus accuracy in fitting data-to mathematical models by relaxing functional form assumptions, while constrained by the same arrow diagram and thus the same interpretability. We apply this method to the T cell, where quantitative models are particularly needed, in part because of ongoing efforts to engineer T cells as therapeutics. One avenue of experiments exposes T cells to pulsatile inputs and measures their frequency response, finding several nonlinear frequency responses: high-pass, low-pass, band-pass, and band-stop. Using our modeling approach with enhanced flexibility, we show that a simple signaling model quantitatively captures the frequency response of CD69 surface expression, one of the correlates of T cells activation, with accuracy within the experimental inter-replicate standard error of the mean. Specific qualitative behaviors map to specific parts of the arrow diagram: Band-pass behavior can be explained by refractory de-sensitizing circuit (we refer to this as \"first-aid icing a wound\"). Band-stop behavior can be explained by removal-inhibition (we refer to this as \"roommate interrupts my studying\"). Apparent low-pass emerges naturally when total stimulation time is constant. We test the model on independent experimental datasets from multiple labs. Taken together, our results demonstrate the ability to achieve both quantitative prediction and interpretability in understanding cellular dynamics. Simple models may at first appear incapable of explaining complex data, but might indeed be able to by adding this modest flexibility.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13526819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148150466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.","authors":"Hoyeon Jeong, Junghan Oh, Yoon-La Choi","doi":"10.1038/s41540-026-00735-x","DOIUrl":"10.1038/s41540-026-00735-x","url":null,"abstract":"<p><p>Immune checkpoint inhibitors (ICIs), which reactivate T-cell responses against tumours, show limited clinical efficacy due to low response rates and the lack of robust predictive biomarkers. Cell-cell interactions within the tumour microenvironment influence therapeutic response and can be analysed at single-cell resolution using imaging-based spatial transcriptomics. We present GraphTME, a spatially informed and biologically interpretable framework that predicts anti-PD-1 response by modelling pathway-specific ligand-receptor signalling as a multi-relational directed graph with edge weights inversely scaled by spatial distance. Using CD8⁺ T cells from single-cell RNA sequencing data of ICI-treated non-small cell lung cancer (NSCLC) patients, we trained a model to infer immune responsiveness. GraphTME achieved an F₁ score exceeding 0.83 in predicting ICI response and was examined using MERFISH data from NSCLC patients with clinical responses. In these patients, CD8⁺ T cells predicted as responders exhibited higher abundance and prominent directional signalling towards tumour cells. These cells also expressed genes associated with antitumour activity. GraphTME is among the first frameworks to quantitatively capture single-cell-level interactions within spatial tumour architecture and leverage them for ICI response prediction. It offers a spatially resolved, biologically grounded biomarker for immunotherapy and a tool for dissecting immune dynamics in situ.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13503875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148056102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing kinetic versus stoichiometric priorities in hybrid models of CHO metabolism.","authors":"Pratik Khare, Nelson Ndahiro, Stephanie Klaubert, Edward Ma, Tom S Bertalan, Yannis Kevrekidis, Sarah W Harcum, Michael J Betenbaugh","doi":"10.1038/s41540-026-00703-5","DOIUrl":"10.1038/s41540-026-00703-5","url":null,"abstract":"<p><p>Understanding Chinese hamster ovary (CHO) cell metabolism through mathematical models is essential for optimizing culture media and biomanufacturing processes. Stoichiometric models estimate intracellular fluxes under steady state, while kinetic models capture dynamic behavior but often for a limited number of reactions; dynamic metabolic flux analysis (dMFA) combines both approaches in a hybrid framework, though challenges remain in applying such hybrid frameworks to bioprocesses. Here, we enhanced an existing CHO-metabolism model by incorporating ¹³C-labeled data to refine kinetic expressions and stoichiometric constraints in key pathways, like the asparagine-aspartate network and serine biosynthesis. A novel kinetic-oriented modeling framework (KOM) which emphasizes kinetic expressions, was introduced and compared against the stoichiometric-oriented model (SOM) which prioritizes the pseudo steady state assumption (PSSA). Across two industrially relevant fed-batch CHO culture conditions with varying initial concentrations of nutrients, the KOM outperformed the SOM in predicting viable cell density, antibody production, and multiple amino acids and metabolites. Indeed, the KOM was able to predict production-to-consumption shifts in lactate and alanine, fluctuations in ammonia, and dynamics of amino acids like asparagine and the serine-glycine pool. The KOM also showed better performance under lactate supplementation, with slight parameter adjustments helping to improve model fidelity, likely due to the impact of high lactate on antibody and VCD. Our findings demonstrate that hybrid models emphasizing empirical kinetics over strict pseudo-steady-state constraints capture biologically realistic dynamics, and also produce parameters useful across varying conditions, making them a practical and powerful tool for characterizing CHO cell culture performance in the future.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148056049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping gene expression dynamics to developmental phenotypes with information entropy analysis.","authors":"Ben Ansbacher, Malachy Guzman, Jordi Garcia-Ojalvo, Arjendu K Pattanayak","doi":"10.1038/s41540-026-00748-6","DOIUrl":"10.1038/s41540-026-00748-6","url":null,"abstract":"<p><p>The development of multicellular organisms entails a deep connection between time-dependent biochemical processes taking place at the subcellular level and the resulting macroscopic phenotypes that arise in populations of up to trillions of cells. Constructing a statistical mechanics of developmental processes would help to understand how microscopic genotypes map onto macroscopic phenotypes, a general goal across biology. Here, we present an attempt in this direction in the context of the fruit fly, Drosophila melanogaster. Applying a variety of information-theoretic measures to public transcriptomics datasets of whole fly embryos during development, we show that the global temporal dynamics of gene expression can be understood as a process that probabilistically guides embryonic dynamics across macroscopic phenotypic stages. In particular, our results suggest signatures of irreversibility in the information complexity of transcriptomic dynamics, as measured mainly by the permutation entropy of indexed ensembles (PI entropy). We also show that the dynamics of PI entropy correlate strongly with developmental stages. Overall, this is a test case in applying information complexity analysis to relate the statistical mechanics of biomarkers to macroscopic developmental dynamics.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13500645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148056056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}