NPJ Systems Biology and Applications最新文献_第6页

Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer. 定量癌症免疫周期模型预测晚期转移性结直肠癌的疾病进展。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-04-13 DOI: 10.1038/s41540-025-00513-1

Chenghang Li, Yongchang Wei, Jinzhi Lei

{"title":"Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer.","authors":"Chenghang Li, Yongchang Wei, Jinzhi Lei","doi":"10.1038/s41540-025-00513-1","DOIUrl":"https://doi.org/10.1038/s41540-025-00513-1","url":null,"abstract":"Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"33"},"PeriodicalIF":3.5,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11993626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020721","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}

引用次数: 0

A mechanism for the emergence of low-dimensional structures in brain dynamics. 脑动力学中低维结构出现的机制。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-04-10 DOI: 10.1038/s41540-025-00499-w

Claudio Runfola, Spase Petkoski, Hiba Sheheitli, Christophe Bernard, Anthony R McIntosh, Viktor Jirsa

{"title":"A mechanism for the emergence of low-dimensional structures in brain dynamics.","authors":"Claudio Runfola, Spase Petkoski, Hiba Sheheitli, Christophe Bernard, Anthony R McIntosh, Viktor Jirsa","doi":"10.1038/s41540-025-00499-w","DOIUrl":"https://doi.org/10.1038/s41540-025-00499-w","url":null,"abstract":"Recent neuroimaging advancements have led to datasets characterized by an overwhelming number of features. Different dimensionality reduction techniques have been employed to uncover low-dimensional manifold representations underlying cognitive functions, while maintaining the fundamental characteristics of the data. These range from linear algorithms to more intricate non-linear methods for manifold extraction. However, the mechanisms responsible for the emergence of these simplified architectures remain a topic of debate. Motivated by concepts from dynamical systems theory, such as averaging and time-scale separation, our study introduces a novel mechanism for the collapse of high dimension brain dynamics onto lower dimensional manifolds. In our framework, fast neuronal activity oscillations average out over time, leading to the resulting dynamics approximating task-related processes occurring at slower time scales. This leads to the emergence of low-dimensional solutions as complex dynamics collapse into slow invariant manifolds. We test this assumption via neural simulations using a simplified model and then enhance the complexity of our simulations by incorporating a large-scale brain network model to mimic realistic neuroimaging signals. We observe in the different cases the convergence of fast oscillatory fluctuations of neuronal activity across time scales that correspond to simulated behavioral configurations. Specifically, by employing various dimensionality reduction techniques and manifold extraction schemes, we observe the reduction of high-dimensional dynamics onto lower-dimensional spaces, revealing emergent low-dimensional solutions. Our findings shed light on the role of frequency and time-scale separation in neuronal activity, proposing and testing a novel theoretical framework for understanding the inner mechanisms governing low-dimensional pattern formation in brain dynamics.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"32"},"PeriodicalIF":3.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11985988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036807","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}

引用次数: 0

Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity. EMT动力学建模揭示了与泛癌中间状态和可塑性相关的基因。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-04-10 DOI: 10.1038/s41540-025-00512-2

MeiLu McDermott, Riddhee Mehta, Evanthia T Roussos Torres, Adam L MacLean

{"title":"Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.","authors":"MeiLu McDermott, Riddhee Mehta, Evanthia T Roussos Torres, Adam L MacLean","doi":"10.1038/s41540-025-00512-2","DOIUrl":"https://doi.org/10.1038/s41540-025-00512-2","url":null,"abstract":"Epithelial-mesenchymal transition (EMT) is a cell state transition co-opted by cancer that drives metastasis via stable intermediate states. Here we study EMT dynamics to identify marker genes of highly metastatic intermediate cells via mathematical modeling with single-cell RNA sequencing (scRNA-seq) data. Across multiple tumor types and stimuli, we identified genes consistently upregulated in EMT intermediate states, many previously unrecognized as EMT markers. Bayesian parameter inference of a simple EMT mathematical model revealed tumor-specific transition rates, providing a framework to quantify EMT progression. Consensus analysis of differential expression, RNA velocity, and model-derived dynamics highlighted SFN and NRG1 as key regulators of intermediate EMT. Independent validation confirmed SFN as an intermediate state marker. Our approach integrates modeling and inference to identify genes associated with EMT dynamics, offering biomarkers and therapeutic targets to modulate tumor-promoting cell state transitions driven by EMT.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"31"},"PeriodicalIF":3.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11986130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021011","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}

引用次数: 0

Understanding therapeutic tolerance through a mathematical model of drug-induced resistance. 通过药物诱导耐药的数学模型了解治疗耐受性。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-04-10 DOI: 10.1038/s41540-025-00511-3

Jana L Gevertz, James M Greene, Samantha Prosperi, Natacha Comandante-Lou, Eduardo D Sontag

{"title":"Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.","authors":"Jana L Gevertz, James M Greene, Samantha Prosperi, Natacha Comandante-Lou, Eduardo D Sontag","doi":"10.1038/s41540-025-00511-3","DOIUrl":"https://doi.org/10.1038/s41540-025-00511-3","url":null,"abstract":"There is growing recognition that phenotypic plasticity enables cancer cells to adapt to various environmental conditions. An example of this adaptability is the ability of an initially sensitive population of cancer cells to acquire resistance and persist in the presence of therapeutic agents. Understanding the implications of this drug-induced resistance is essential for predicting transient and long-term tumor dynamics subject to treatment. This paper introduces a mathematical model of drug-induced resistance which provides excellent fits to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose. The predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"30"},"PeriodicalIF":3.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11982405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003027","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}

引用次数: 0

Exploring cell-to-cell variability and functional insights through differentially variable gene analysis. 通过差异变量基因分析探索细胞间的可变性和功能见解。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-20 DOI: 10.1038/s41540-025-00507-z

Victoria Gatlin, Shreyan Gupta, Selim Romero, Robert S Chapkin, James J Cai

{"title":"Exploring cell-to-cell variability and functional insights through differentially variable gene analysis.","authors":"Victoria Gatlin, Shreyan Gupta, Selim Romero, Robert S Chapkin, James J Cai","doi":"10.1038/s41540-025-00507-z","DOIUrl":"10.1038/s41540-025-00507-z","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular variability by capturing gene expression profiles of individual cells. The importance of cell-to-cell variability in determining and shaping cell function has been widely appreciated. Nevertheless, differential expression (DE) analysis remains a cornerstone method in analytical practice. Current computational analyses overlook the rich information encoded by variability within the single-cell gene expression data by focusing exclusively on mean expression. To offer a deeper understanding of cellular systems, there is a need for approaches to assess data variability rather than just the mean. Here we present spline-DV, a statistical framework for differential variability (DV) analysis using scRNA-seq data. The spline-DV method identifies genes exhibiting significantly increased or decreased expression variability among cells derived from two experimental conditions. Case studies show that DV genes identified using spline-DV are representative and functionally relevant to tested cellular conditions, including obesity, fibrosis, and cancer.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"29"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926233/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670326","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}

引用次数: 0

Plant cold acclimation and its impact on sensitivity of carbohydrate metabolism. 植物冷驯化及其对碳水化合物代谢敏感性的影响。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-19 DOI: 10.1038/s41540-025-00505-1

Stephan O Adler, Anastasia Kitashova, Ana Bulović, Thomas Nägele, Edda Klipp

引用次数: 0

A molecular systems architecture of neuromuscular junction in amyotrophic lateral sclerosis. 肌萎缩性侧索硬化症神经肌肉连接处的分子系统结构。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-17 DOI: 10.1038/s41540-025-00501-5

V A Shiva Ayyadurai, Prabhakar Deonikar, Roger D Kamm

引用次数: 0

A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers. 解码妇科癌症和乳腺癌代谢调控的单细胞网络方法。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-13 DOI: 10.1038/s41540-025-00506-0

Akansha Srivastava, P K Vinod

{"title":"A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers.","authors":"Akansha Srivastava, P K Vinod","doi":"10.1038/s41540-025-00506-0","DOIUrl":"10.1038/s41540-025-00506-0","url":null,"abstract":"Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"26"},"PeriodicalIF":3.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11906788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625532","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}

引用次数: 0

Enhancing yeast cell tracking with a time-symmetric deep learning approach. 用时间对称深度学习方法增强酵母细胞跟踪。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-13 DOI: 10.1038/s41540-024-00466-x

Gergely Szabó, Paolo Bonaiuti, Andrea Ciliberto, András Horváth

引用次数: 0

An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition. 上皮-间质转化群体动力学的综合表型结构偏微分方程模型。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-06 DOI: 10.1038/s41540-025-00502-4

Jules Guilberteau, Paras Jain, Mohit Kumar Jolly, Camille Pouchol, Nastassia Pouradier Duteil

{"title":"An integrative phenotype-structured partial differential equation model for the population dynamics of epithelial-mesenchymal transition.","authors":"Jules Guilberteau, Paras Jain, Mohit Kumar Jolly, Camille Pouchol, Nastassia Pouradier Duteil","doi":"10.1038/s41540-025-00502-4","DOIUrl":"10.1038/s41540-025-00502-4","url":null,"abstract":"Phenotypic heterogeneity along the epithelial-mesenchymal (E-M) axis contributes to cancer metastasis and drug resistance. Recent experimental efforts have collated detailed time-course data on the emergence and dynamics of E-M heterogeneity in a cell population. However, it remains unclear how different intra- and inter-cellular processes shape the dynamics of E-M heterogeneity. Here, using Cell Population Balance model, we capture the dynamics of cell density along E-M phenotypic axis resulting from interplay between-(a) intracellular regulatory interaction among biomolecules, (b) cell division and death and (c) stochastic cell-state transition. We find that while the existence of E-M heterogeneity depends on intracellular regulation, heterogeneity gets enhanced with stochastic cell-state transitions and diminished by growth rate differences. Further, resource competition among E-M cells can lead to both bi-phasic growth of the total population and/or bi-stability in the phenotypic composition. Overall, our model highlights complex interplay between cellular processes shaping dynamic patterns of E-M heterogeneity.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"24"},"PeriodicalIF":3.5,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11885588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573355","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}

引用次数: 0