NPJ Systems Biology and Applications最新文献_第10页

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

Gene regulatory network inference during cell fate decisions by perturbation strategies. 微扰策略下细胞命运决定过程中的基因调控网络推断。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-04 DOI: 10.1038/s41540-025-00504-2

Qing Hu, Xiaoqi Lu, Zhuozhen Xue, Ruiqi Wang

{"title":"Gene regulatory network inference during cell fate decisions by perturbation strategies.","authors":"Qing Hu, Xiaoqi Lu, Zhuozhen Xue, Ruiqi Wang","doi":"10.1038/s41540-025-00504-2","DOIUrl":"10.1038/s41540-025-00504-2","url":null,"abstract":"With rapid advances in biological technology and computational approaches, inferring specific gene regulatory networks from data alone during cell fate decisions, including determining direct regulations and their intensities between biomolecules, remains one of the most significant challenges. In this study, we propose a general computational approach based on systematic perturbation, statistical, and differential analyses to infer network topologies and identify network differences during cell fate decisions. For each cell fate state, we first theoretically show how to calculate local response matrices based on perturbation data under systematic perturbation analysis, and we also derive the wild-type (WT) local response matrix for specific ordinary differential equations. To make the inferred network more accurate and eliminate the impact of perturbation degrees, the confidence interval (CI) of local response matrices under multiple perturbations is applied, and the redefined local response matrix is proposed in statistical analysis to determine network topologies across all cell fates. Then in differential analysis, we introduce the concept of relative local response matrix, which enables us to identify critical regulations governing each cell state and dominant cell states associated with specific regulations. The epithelial to mesenchymal transition (EMT) network is chosen as an illustrative example to verify the feasibility of the approach. Largely consistent with experimental observations, the differences of inferred networks at the three cell states can be quantitatively identified. The approach presented here can be also applied to infer other regulatory networks related to cell fate decisions.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"23"},"PeriodicalIF":3.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542828","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

Loss formulations for assumption-free neural inference of SDE coefficient functions. SDE系数函数无假设神经推理的损失公式。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-03-01 DOI: 10.1038/s41540-025-00500-6

Marc Vaisband, Valentin von Bornhaupt, Nina Schmid, Izdar Abulizi, Jan Hasenauer

引用次数: 0

Integrative systems biology framework discovers common gene regulatory signatures in mechanistically distinct inflammatory skin diseases. 综合系统生物学框架发现在机制上不同的炎症性皮肤病中共同的基因调控特征。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-27 DOI: 10.1038/s41540-025-00498-x

Bharat Mishra, Yifei Gou, Zhengzhi Tan, Yiqing Wang, Getian Hu, Mohammad Athar, M Shahid Mukhtar

{"title":"Integrative systems biology framework discovers common gene regulatory signatures in mechanistically distinct inflammatory skin diseases.","authors":"Bharat Mishra, Yifei Gou, Zhengzhi Tan, Yiqing Wang, Getian Hu, Mohammad Athar, M Shahid Mukhtar","doi":"10.1038/s41540-025-00498-x","DOIUrl":"10.1038/s41540-025-00498-x","url":null,"abstract":"More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. This study aims to identify common regulatory pathways and master regulators that regulate the molecular pathogenesis of inflammatory skin diseases. We designed an integrative systems biology framework to identify the significant regulators across several diseases. Network analytics unraveled 55 high-value proteins as significant regulators in molecular pathogenesis which can serve as putative drug targets for more effective treatments. We identified IKZF1 as a shared master regulator in hidradenitis suppurativa, atopic dermatitis, and rosacea with known disease-derived molecules for developing efficacious combinatorial treatments for these diseases. The proposed framework is very modular and indicates a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"21"},"PeriodicalIF":3.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524016","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

Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity. 多细胞肿瘤球体的数学建模量化了患者间和肿瘤内的异质性。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-15 DOI: 10.1038/s41540-025-00492-3

Adam A Malik, Kyle C Nguyen, John T Nardini, Cecilia C Krona, Kevin B Flores, Sven Nelander

引用次数: 0

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series. 从螺旋图时间序列中检测和早期预测慢性阻塞性肺疾病的深度学习。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-15 DOI: 10.1038/s41540-025-00489-y

Shuhao Mei, Xin Li, Yuxi Zhou, Jiahao Xu, Yong Zhang, Yuxuan Wan, Shan Cao, Qinghao Zhao, Shijia Geng, Junqing Xie, Shengyong Chen, Shenda Hong

{"title":"Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.","authors":"Shuhao Mei, Xin Li, Yuxi Zhou, Jiahao Xu, Yong Zhang, Yuxuan Wan, Shan Cao, Qinghao Zhao, Shijia Geng, Junqing Xie, Shengyong Chen, Shenda Hong","doi":"10.1038/s41540-025-00489-y","DOIUrl":"10.1038/s41540-025-00489-y","url":null,"abstract":"Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1-5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"18"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425862","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

Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection. 跨物种转录组学翻译揭示了未折叠蛋白在结核分枝杆菌感染中的作用。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-15 DOI: 10.1038/s41540-024-00487-6

Krista M Pullen, Ryan Finethy, Seung-Hyun B Ko, Charlotte J Reames, Christopher M Sassetti, Douglas A Lauffenburger

{"title":"Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection.","authors":"Krista M Pullen, Ryan Finethy, Seung-Hyun B Ko, Charlotte J Reames, Christopher M Sassetti, Douglas A Lauffenburger","doi":"10.1038/s41540-024-00487-6","DOIUrl":"10.1038/s41540-024-00487-6","url":null,"abstract":"Numerous studies have identified similarities in blood transcriptomic signatures of tuberculosis (TB) phenotypes between mice and humans, including type 1 interferon production and innate immune cell activation. However, murine infection pathophysiology is distinct from human disease. We hypothesized that this is partly due to differences in the relative importance of biological pathways across species. To address this animal-to-human gap, we applied a systems modeling framework, Translatable Components Regression, to identify the axes of variation in the preclinical data most relevant to human TB disease state. Among the pathways our cross-species model pinpointed as highly predictive of human TB phenotype was the infection-induced unfolded protein response. To validate this mechanism, we confirmed that this cellular stress pathway modulates immune functions in Mycobacterium tuberculosis-infected mouse macrophages. Our work demonstrates how systems-level computational models enhance the value of animal studies for elucidating complex human pathophysiology.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"19"},"PeriodicalIF":3.5,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11830044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425860","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

Overall biomass yield on multiple nutrient sources. 多种营养源的总生物量产量。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-10 DOI: 10.1038/s41540-025-00497-y

Ohad Golan, Olivia Gampp, Lina Eckert, Uwe Sauer

{"title":"Overall biomass yield on multiple nutrient sources.","authors":"Ohad Golan, Olivia Gampp, Lina Eckert, Uwe Sauer","doi":"10.1038/s41540-025-00497-y","DOIUrl":"10.1038/s41540-025-00497-y","url":null,"abstract":"Microorganisms primarily utilize nutrients to generate biomass and replicate. When a single nutrient source is available, the produced biomass typically increases linearly with the initial amount of that nutrient. This linear trend can be accurately predicted by \"black box models\", which conceptualize growth as a single chemical reaction, treating nutrients as substrates and biomass as a product. However, natural environments usually present multiple nutrient sources, prompting us to extend the black box framework to incorporate catabolism, anabolism, and biosynthesis of biomass precursors. This modification allows for the quantification of co-utilization effects among multiple nutrients on microbial biomass production. The extended model differentiates between different types of nutrients: non-degradable nutrients, which can only serve as a biomass precursor, and degradable nutrients, which can also be used as an energy source. We experimentally demonstrated using Escherichia coli that, in contrast to initial model predictions, different nutrients affect each other's utilization in a mutually dependent manner; i.e., for some combinations, the produced biomass was no longer proportional to the initial amounts of nutrients present. To account for these mutual effects within a black box framework, we phenomenologically introduced an interaction between the metabolic processes involved in utilizing the nutrient sources. This phenomenological model qualitatively captures the experimental observations and, unexpectedly, predicts that the total produced biomass is influenced not only by the combination of nutrient sources but also by their relative initial amounts - a prediction we subsequently validated experimentally. Moreover, the model identifies which metabolic processes - catabolism, anabolism, or precursor biosynthesis-is affected in each specific nutrient combination, offering insights into microbial metabolic coordination.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"17"},"PeriodicalIF":3.5,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391364","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 critical dosing strategies for stromal-induced resistance to cancer therapy. 模拟基质诱导的癌症治疗耐药的关键给药策略。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2025-02-06 DOI: 10.1038/s41540-025-00495-0

Anna K Kraut, Colleen M Garvey, Carly Strelez, Shannon M Mumenthaler, Jasmine Foo

{"title":"Modeling critical dosing strategies for stromal-induced resistance to cancer therapy.","authors":"Anna K Kraut, Colleen M Garvey, Carly Strelez, Shannon M Mumenthaler, Jasmine Foo","doi":"10.1038/s41540-025-00495-0","DOIUrl":"10.1038/s41540-025-00495-0","url":null,"abstract":"Complex interactions between stromal cells, tumor cells and therapies can influence environmental factors that in turn impact anticancer treatment efficacy. Disentangling these phenomena is critical for understanding treatment response and designing effective dosing strategies. We propose a mathematical model for a common tumor-stromal interaction motif where stromal cells secrete factors that promote drug resistance. We demonstrate that the presence of this interaction modulates the therapeutic dose window of efficacy and can lead to nonmonotonic treatment response. We consider combination strategies that target stromal cells and their secretome, and identify strategies that constrain drug concentrations within the efficacious window for long-term response. We explore an experimental dataset from colorectal cancer cells treated with anti-EGFR targeting therapy, cetuximab, where cancer-associated fibroblasts increase epidermal growth factor secretion under treatment. We apply our general approach to identify a critical drug concentration threshold and study effective dosing regimens for single-drug and combination therapies.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"11 1","pages":"16"},"PeriodicalIF":3.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365062","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