Pub Date : 2024-08-22DOI: 10.1038/s41540-024-00420-x
Tadao Ooka, Naoto Usuyama, Ryohei Shibata, Michihito Kyo, Jonathan M Mansbach, Zhaozhong Zhu, Carlos A Camargo, Kohei Hasegawa
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.
{"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":"<p><p>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 I<sub>2</sub> 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.</p>","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}
Pub Date : 2024-08-22DOI: 10.1038/s41540-024-00412-x
Yue Han, Mark P Styczynski
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2024-08-21DOI: 10.1038/s41540-024-00421-w
Heming Zhang, Yixin Chen, Philip Payne, Fuhai Li
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.
{"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":"<p><p>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.</p>","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}
Pub Date : 2024-08-18DOI: 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
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.
胶质母细胞瘤是成人中最常见、最致命的脑肿瘤,在目前的治疗标准下,中位生存期为 15 个月。为了改善这一终点,人们对免疫检查点抑制剂和溶瘤病毒等免疫疗法进行了广泛研究。然而,迄今为止,大多数研究都以失败告终。为了提高免疫疗法治疗胶质母细胞瘤的疗效,可以利用成像质控细胞仪等新型单细胞成像模式,并将其与计算模型相结合。这样就能更好地了解肿瘤微环境及其对这种难以治疗的肿瘤的治疗成败所起的作用。在这里,我们实施了一个基于代理的模型,该模型可对联合化疗、溶瘤病毒和免疫检查点抑制剂治疗胶质母细胞瘤进行空间预测。我们利用患者成像质谱数据对模型进行了初始化,以预测患者的特异性反应,并发现溶瘤病毒会驱动由瘤内细胞密度决定的联合治疗反应。我们发现,肿瘤细胞密度较高的肿瘤对治疗的反应更好。在固定癌细胞数量的情况下,治疗效果与 CD4 + T 细胞有关,其次与巨噬细胞数量有关。重要的是,我们的模拟结果表明,必须小心整合空间数据和基于代理的模型,才能有效捕捉肿瘤内的动态变化。总之,这项研究强调了使用预测性空间建模来更好地理解癌症免疫疗法的治疗动态,同时突出了在模型设计和实施过程中需要考虑的关键因素。
{"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":"<p><p>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.</p>","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}
Pub Date : 2024-08-15DOI: 10.1038/s41540-024-00414-9
Hamidreza Jafarinia, Ali Khalilimeybodi, Jorge Barrasa-Fano, Stephanie I Fraley, Padmini Rangamani, Aurélie Carlier
YAP/TAZ signaling pathway is regulated by a multiplicity of feedback loops, crosstalk with other pathways, and both mechanical and biochemical stimuli. Computational modeling serves as a powerful tool to unravel how these different factors can regulate YAP/TAZ, emphasizing biophysical modeling as an indispensable tool for deciphering mechanotransduction and its regulation of cell fate. We provide a critical review of the current state-of-the-art of computational models focused on YAP/TAZ signaling.
{"title":"Insights gained from computational modeling of YAP/TAZ signaling for cellular mechanotransduction.","authors":"Hamidreza Jafarinia, Ali Khalilimeybodi, Jorge Barrasa-Fano, Stephanie I Fraley, Padmini Rangamani, Aurélie Carlier","doi":"10.1038/s41540-024-00414-9","DOIUrl":"10.1038/s41540-024-00414-9","url":null,"abstract":"<p><p>YAP/TAZ signaling pathway is regulated by a multiplicity of feedback loops, crosstalk with other pathways, and both mechanical and biochemical stimuli. Computational modeling serves as a powerful tool to unravel how these different factors can regulate YAP/TAZ, emphasizing biophysical modeling as an indispensable tool for deciphering mechanotransduction and its regulation of cell fate. We provide a critical review of the current state-of-the-art of computational models focused on YAP/TAZ signaling.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988476","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}
Pub Date : 2024-08-14DOI: 10.1038/s41540-024-00409-6
Jamie Porthiyas, Daniel Nussey, Catherine A A Beauchemin, Donald C Warren, Christian Quirouette, Kathleen P Wilkie
Mechanistic mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the most likely parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.
机理数学模型(MMs)是帮助我们理解和预测各种条件下肿瘤生长动态的有力工具。在这项工作中,我们使用了参数数量不断增加的 5 个 MM,来探讨从肿瘤生长实验数据中估算参数时的某些(通常被忽视的)决定是如何影响分析结果的。特别是,我们提出了一个框架,用于将通常被剔除的、超出检测上下限的肿瘤体积测量数据包括在内。我们展示了排除删减数据如何导致高估首次测量前的初始肿瘤体积和 MM 预测肿瘤体积,以及低估承载能力和超过最新可测量时间点的 MM 预测肿瘤体积。我们展示了 MM 参数先验值的选择会以何种方式影响后验分布,并说明报告最可能的参数及其 95% 可信区间可能会导致混乱或误导性解释。我们希望这项工作能鼓励其他人仔细考虑参数估计中的选择,并采用我们在此提出的方法。
{"title":"Practical parameter identifiability and handling of censored data with Bayesian inference in mathematical tumour models.","authors":"Jamie Porthiyas, Daniel Nussey, Catherine A A Beauchemin, Donald C Warren, Christian Quirouette, Kathleen P Wilkie","doi":"10.1038/s41540-024-00409-6","DOIUrl":"10.1038/s41540-024-00409-6","url":null,"abstract":"<p><p>Mechanistic mathematical models (MMs) are a powerful tool to help us understand and predict the dynamics of tumour growth under various conditions. In this work, we use 5 MMs with an increasing number of parameters to explore how certain (often overlooked) decisions in estimating parameters from data of experimental tumour growth affect the outcome of the analysis. In particular, we propose a framework for including tumour volume measurements that fall outside the upper and lower limits of detection, which are normally discarded. We demonstrate how excluding censored data results in an overestimation of the initial tumour volume and the MM-predicted tumour volumes prior to the first measurements, and an underestimation of the carrying capacity and the MM-predicted tumour volumes beyond the latest measurable time points. We show in which way the choice of prior for the MM parameters can impact the posterior distributions, and illustrate that reporting the most likely parameters and their 95% credible interval can lead to confusing or misleading interpretations. We hope this work will encourage others to carefully consider choices made in parameter estimation and to adopt the approaches we put forward herein.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982878","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}
Pub Date : 2024-08-14DOI: 10.1038/s41540-024-00415-8
Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang
We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.
{"title":"Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.","authors":"Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang","doi":"10.1038/s41540-024-00415-8","DOIUrl":"10.1038/s41540-024-00415-8","url":null,"abstract":"<p><p>We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982877","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}
Pub Date : 2024-08-12DOI: 10.1038/s41540-024-00411-y
Yusuke Tokuhara, Tatsuya Akutsu, Jean-Marc Schwartz, Jose C. Nacher
Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.
{"title":"A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks","authors":"Yusuke Tokuhara, Tatsuya Akutsu, Jean-Marc Schwartz, Jose C. Nacher","doi":"10.1038/s41540-024-00411-y","DOIUrl":"https://doi.org/10.1038/s41540-024-00411-y","url":null,"abstract":"<p>Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968650","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}
Pub Date : 2024-08-11DOI: 10.1038/s41540-024-00413-w
Candela L Szischik, Juliana Reves Szemere, Rocío Balderrama, Constanza Sánchez de la Vega, Alejandra C Ventura
Ligand-receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component's timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand-receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked.
{"title":"Transient frequency preference responses in cell signaling systems.","authors":"Candela L Szischik, Juliana Reves Szemere, Rocío Balderrama, Constanza Sánchez de la Vega, Alejandra C Ventura","doi":"10.1038/s41540-024-00413-w","DOIUrl":"10.1038/s41540-024-00413-w","url":null,"abstract":"<p><p>Ligand-receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component's timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand-receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11317535/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917152","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}
Pub Date : 2024-08-10DOI: 10.1038/s41540-024-00410-z
S Mukundan, Girish Deshpande, M S Madhusudhan
The strength of molecular interactions is characterized by their dissociation constants (KD). Only high-affinity interactions (KD ≤ 10-8 M) are extensively investigated and support binary on/off switches. However, such analyses have discounted the presence of low-affinity binders (KD > 10-5 M) in the cellular environment. We assess the potential influence of low-affinity binders on high-affinity interactions. By employing Gillespie stochastic simulations and continuous methods, we demonstrate that the presence of low-affinity binders can alter the kinetics and the steady state of high-affinity interactions. We refer to this effect as 'herd regulation' and have evaluated its possible impact in two different contexts including sex determination in Drosophila melanogaster and in signalling systems that employ molecular thresholds. We have also suggested experiments to validate herd regulation in vitro. We speculate that low-affinity binders are prevalent in biological contexts where the outcomes depend on molecular thresholds impacting homoeostatic regulation.
{"title":"High-affinity biomolecular interactions are modulated by low-affinity binders.","authors":"S Mukundan, Girish Deshpande, M S Madhusudhan","doi":"10.1038/s41540-024-00410-z","DOIUrl":"10.1038/s41540-024-00410-z","url":null,"abstract":"<p><p>The strength of molecular interactions is characterized by their dissociation constants (K<sub>D</sub>). Only high-affinity interactions (K<sub>D</sub> ≤ 10<sup>-8</sup> M) are extensively investigated and support binary on/off switches. However, such analyses have discounted the presence of low-affinity binders (K<sub>D</sub> > 10<sup>-5</sup> M) in the cellular environment. We assess the potential influence of low-affinity binders on high-affinity interactions. By employing Gillespie stochastic simulations and continuous methods, we demonstrate that the presence of low-affinity binders can alter the kinetics and the steady state of high-affinity interactions. We refer to this effect as 'herd regulation' and have evaluated its possible impact in two different contexts including sex determination in Drosophila melanogaster and in signalling systems that employ molecular thresholds. We have also suggested experiments to validate herd regulation in vitro. We speculate that low-affinity binders are prevalent in biological contexts where the outcomes depend on molecular thresholds impacting homoeostatic regulation.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141913464","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}