NPJ Systems Biology and Applications最新文献_第4页

A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence. 布尔模型解释了肝癌干细胞出现的表型可塑性变化。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-09-02 DOI: 10.1038/s41540-024-00422-9

Alexis Hernández-Magaña, Antonio Bensussen, Juan Carlos Martínez-García, Elena R Álvarez-Buylla

{"title":"A Boolean model explains phenotypic plasticity changes underlying hepatic cancer stem cells emergence.","authors":"Alexis Hernández-Magaña, Antonio Bensussen, Juan Carlos Martínez-García, Elena R Álvarez-Buylla","doi":"10.1038/s41540-024-00422-9","DOIUrl":"10.1038/s41540-024-00422-9","url":null,"abstract":"In several carcinomas, including hepatocellular carcinoma, it has been demonstrated that cancer stem cells (CSCs) have enhanced invasiveness and therapy resistance compared to differentiated cancer cells. Mathematical-computational tools could be valuable for integrating experimental results and understanding the phenotypic plasticity mechanisms for CSCs emergence. Based on the literature review, we constructed a Boolean model that recovers eight stable states (attractors) corresponding to the gene expression profile of hepatocytes and mesenchymal cells in senescent, quiescent, proliferative, and stem-like states. The epigenetic landscape associated with the regulatory network was analyzed. We observed that the loss of p53, p16, RB, or the constitutive activation of β-catenin and YAP1 increases the robustness of the proliferative stem-like phenotypes. Additionally, we found that p53 inactivation facilitates the transition of proliferative hepatocytes into stem-like mesenchymal phenotype. Thus, phenotypic plasticity may be altered, and stem-like phenotypes related to CSCs may be easier to attain following the mutation acquisition.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11369243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120344","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

Network topology and interaction logic determine states it supports. 网络拓扑和交互逻辑决定了它所支持的状态。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-28 DOI: 10.1038/s41540-024-00423-8

Tomáš Gedeon

引用次数: 0

Recovering biomolecular network dynamics from single-cell omics data requires three time points. 从单细胞奥米克斯数据中恢复生物分子网络动态需要三个时间点。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-27 DOI: 10.1038/s41540-024-00424-7

Shu Wang, Muhammad Ali Al-Radhawi, Douglas A Lauffenburger, Eduardo D Sontag

{"title":"Recovering biomolecular network dynamics from single-cell omics data requires three time points.","authors":"Shu Wang, Muhammad Ali Al-Radhawi, Douglas A Lauffenburger, Eduardo D Sontag","doi":"10.1038/s41540-024-00424-7","DOIUrl":"10.1038/s41540-024-00424-7","url":null,"abstract":"Single-cell omics technologies can measure millions of cells for up to thousands of biomolecular features, enabling data-driven studies of complex biological networks. However, these high-throughput experimental techniques often cannot track individual cells over time, thus complicating the understanding of dynamics such as time trajectories of cell states. These \"dynamical phenotypes\" are key to understanding biological phenomena such as differentiation fates. We show by mathematical analysis that, in spite of high dimensionality and lack of individual cell traces, three time-points of single-cell omics data are theoretically necessary and sufficient to uniquely determine the network interaction matrix and associated dynamics. Moreover, we show through numerical simulations that an interaction matrix can be accurately determined with three or more time-points even in the presence of sampling and measurement noise typical of single-cell omics. Our results can guide the design of single-cell omics time-course experiments, and provide a tool for data-driven phase-space analysis.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11350189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142081061","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

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer. 基于约束的建模可预测低度和高度浆液性卵巢癌的代谢特征。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-24 DOI: 10.1038/s41540-024-00418-5

Kate E Meeson, Jean-Marc Schwartz

{"title":"Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer.","authors":"Kate E Meeson, Jean-Marc Schwartz","doi":"10.1038/s41540-024-00418-5","DOIUrl":"10.1038/s41540-024-00418-5","url":null,"abstract":"Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056191","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

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks. SignalingProfiler 2.0 是一种基于网络的方法，可将多组学数据与表型特征联系起来。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-23 DOI: 10.1038/s41540-024-00417-6

Veronica Venafra, Francesca Sacco, Livia Perfetto

{"title":"SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks.","authors":"Veronica Venafra, Francesca Sacco, Livia Perfetto","doi":"10.1038/s41540-024-00417-6","DOIUrl":"10.1038/s41540-024-00417-6","url":null,"abstract":"Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046959","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

Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis. 利用可解释深度网络对婴儿支气管炎病理生物学进行综合组学分析

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-22 DOI: 10.1038/s41540-024-00420-x

Tadao Ooka, Naoto Usuyama, Ryohei Shibata, Michihito Kyo, Jonathan M Mansbach, Zhaozhong Zhu, Carlos A Camargo, Kohei Hasegawa

{"title":"Integrated-omics analysis with explainable deep networks on pathobiology of infant bronchiolitis.","authors":"Tadao Ooka, Naoto Usuyama, Ryohei Shibata, Michihito Kyo, Jonathan M Mansbach, Zhaozhong Zhu, Carlos A Camargo, Kohei Hasegawa","doi":"10.1038/s41540-024-00420-x","DOIUrl":"10.1038/s41540-024-00420-x","url":null,"abstract":"Bronchiolitis is the leading cause of infant hospitalization. However, the molecular networks driving bronchiolitis pathobiology remain unknown. Integrative molecular networks, including the transcriptome and metabolome, can identify functional and regulatory pathways contributing to disease severity. Here, we integrated nasopharyngeal transcriptome and metabolome data of 397 infants hospitalized with bronchiolitis in a 17-center prospective cohort study. Using an explainable deep network model, we identified an omics-cluster comprising 401 transcripts and 38 metabolites that distinguishes bronchiolitis severity (test-set AUC, 0.828). This omics-cluster derived a molecular network, where innate immunity-related metabolites (e.g., ceramides) centralized and were characterized by toll-like receptor (TLR) and NF-κB signaling pathways (both FDR < 0.001). The network analyses identified eight modules and 50 existing drug candidates for repurposing, including prostaglandin I2 analogs (e.g., iloprost), which promote anti-inflammatory effects through TLR signaling. Our approach facilitates not only the identification of molecular networks underlying infant bronchiolitis but the development of pioneering treatment strategies.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036502","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

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality. 评估不同数据质量下代谢途径中生化调控网络结构的不确定性。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-22 DOI: 10.1038/s41540-024-00412-x

Yue Han, Mark P Styczynski

{"title":"Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality.","authors":"Yue Han, Mark P Styczynski","doi":"10.1038/s41540-024-00412-x","DOIUrl":"10.1038/s41540-024-00412-x","url":null,"abstract":"Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341918/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036501","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

Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails. 使用 DeepSignalingFlow 挖掘信号流，解释鸡尾酒的协同作用机制。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-21 DOI: 10.1038/s41540-024-00421-w

Heming Zhang, Yixin Chen, Philip Payne, Fuhai Li

{"title":"Using DeepSignalingFlow to mine signaling flows interpreting mechanism of synergy of cocktails.","authors":"Heming Zhang, Yixin Chen, Philip Payne, Fuhai Li","doi":"10.1038/s41540-024-00421-w","DOIUrl":"10.1038/s41540-024-00421-w","url":null,"abstract":"Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142018176","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

Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies. 空间计算模型揭示了肿瘤微环境在利用免疫疗法治疗胶质母细胞瘤中的作用。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-18 DOI: 10.1038/s41540-024-00419-4

Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi, Benoit Fiset, Daniela F Quail, Logan A Walsh, Adrianne L Jenner, Morgan Craig

{"title":"Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies.","authors":"Blanche Mongeon, Julien Hébert-Doutreloux, Anudeep Surendran, Elham Karimi, Benoit Fiset, Daniela F Quail, Logan A Walsh, Adrianne L Jenner, Morgan Craig","doi":"10.1038/s41540-024-00419-4","DOIUrl":"10.1038/s41540-024-00419-4","url":null,"abstract":"Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under the current standard of care. Immunotherapies like immune checkpoint inhibitors and oncolytic viruses have been extensively studied to improve this endpoint. However, most thus far have failed. To improve the efficacy of immunotherapies to treat glioblastoma, new single-cell imaging modalities like imaging mass cytometry can be leveraged and integrated with computational models. This enables a better understanding of the tumour microenvironment and its role in treatment success or failure in this hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions of combination chemotherapy, oncolytic virus, and immune checkpoint inhibitors against glioblastoma. We initialised our model with patient imaging mass cytometry data to predict patient-specific responses and found that oncolytic viruses drive combination treatment responses determined by intratumoral cell density. We found that tumours with higher tumour cell density responded better to treatment. When fixing the number of cancer cells, treatment efficacy was shown to be a function of CD4 + T cell and, to a lesser extent, of macrophage counts. Critically, our simulations show that care must be put into the integration of spatial data and agent-based models to effectively capture intratumoral dynamics. Together, this study emphasizes the use of predictive spatial modelling to better understand cancer immunotherapy treatment dynamics, while highlighting key factors to consider during model design and implementation.","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000470","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

Insights gained from computational modeling of YAP/TAZ signaling for cellular mechanotransduction. 从细胞机械传导的 YAP/TAZ 信号的计算建模中获得的启示。

IF 3.5 2区生物学

NPJ Systems Biology and Applications Pub Date : 2024-08-15 DOI: 10.1038/s41540-024-00414-9

Hamidreza Jafarinia, Ali Khalilimeybodi, Jorge Barrasa-Fano, Stephanie I Fraley, Padmini Rangamani, Aurélie Carlier

引用次数: 0