Frontiers in systems biology最新文献

{"title":"Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity.","authors":"Patrick C Kinnunen, Kenneth K Y Ho, Siddhartha Srivastava, Chengyang Huang, Wanggang Shen, Krishna Garikipati, Gary D Luker, Nikola Banovic, Xun Huan, Jennifer J Linderman, Kathryn E Luker","doi":"10.3389/fsysb.2024.1333760","DOIUrl":"10.3389/fsysb.2024.1333760","url":null,"abstract":"<p><p>Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1333760"},"PeriodicalIF":2.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing precision medicine therapeutics for Parkinson's utilizing a shared quantitative systems pharmacology model and framework.","authors":"Christopher Denaro, Diane Stephenson, Martijn L T M Müller, Benedetto Piccoli, Karim Azer","doi":"10.3389/fsysb.2024.1351555","DOIUrl":"10.3389/fsysb.2024.1351555","url":null,"abstract":"<p><p>A rich pipeline of therapeutic candidates is advancing for Parkinson's disease, many of which are targeting the underlying pathophysiology of disease. Emerging evidence grounded in novel genetics and biomarker discoveries is illuminating the true promise of precision medicine-based therapeutic strategies for PD. There has been a growing effort to investigate disease-modifying therapies by designing clinical trials for genetic forms of PD - providing a clearer link to underlying pathophysiology. Leading candidate genes based on human genetic findings that are under active investigation in an array of basic and translational models include SNCA, LRRK2, and GBA. Broad investigations across mechanistic models show that these genes signal through common molecular pathways, namely, autosomal lysosomal pathways, inflammation and mitochondrial function. Therapeutic clinical trials to date based on genetically defined targets have not yet achieved approvals; however, much is to be learned from such pioneering trials. Fundamental principles of drug development that include proof of pharmacology in target tissue are critical to have confidence in advancing such precision-based therapies. There is a clear need for downstream biomarkers of leading candidate therapies to demonstrate proof of mechanism. The current regulatory landscape is poised and primed to support translational modeling strategies for the effective advancement of PD disease-modifying therapeutic candidates. A convergence of rich complex data that is available, the regulatory framework of model informed drug development (MIDD), and the new biological integrated staging frameworks when combined are collectively setting the stage for advancing new approaches in PD to accelerate progress. This perspective review highlights the potential of quantitative systems pharmacology (QSP) modeling in contributing to the field and hastening the pace of progress in advancing collaborative approaches for urgently needed PD disease-modifying treatments.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1351555"},"PeriodicalIF":2.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning.","authors":"Emily Nieves, Raj Dandekar, Chris Rackauckas","doi":"10.3389/fsysb.2024.1338518","DOIUrl":"10.3389/fsysb.2024.1338518","url":null,"abstract":"<p><p>The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1338518"},"PeriodicalIF":2.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding our thought horizons in systems biology and medicine","authors":"Jennifer C. Lovejoy","doi":"10.3389/fsysb.2024.1385458","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1385458","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing electrogenetic activation via a network model of biological signal propagation","authors":"Kayla Chun, Eric VanArsdale, Elebeoba May, Gregory F. Payne, William E. Bentley","doi":"10.3389/fsysb.2024.1291293","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1291293","url":null,"abstract":"Introduction: Molecular communication is the transfer of information encoded by molecular structure and activity. We examine molecular communication within bacterial consortia as cells with diverse biosynthetic capabilities can be assembled for enhanced function. Their coordination, both in terms of engineered genetic circuits within individual cells as well as their population-scale functions, is needed to ensure robust performance. We have suggested that “electrogenetics,” the use of electronics to activate specific genetic circuits, is a means by which electronic devices can mediate molecular communication, ultimately enabling programmable control.Methods: Here, we have developed a graphical network model for dynamically assessing electronic and molecular signal propagation schemes wherein nodes represent individual cells, and their edges represent communication channels by which signaling molecules are transferred. We utilize graph properties such as edge dynamics and graph topology to interrogate the signaling dynamics of specific engineered bacterial consortia.Results: We were able to recapitulate previous experimental systems with our model. In addition, we found that networks with more distinct subpopulations (high network modularity) propagated signals more slowly than randomized networks, while strategic arrangement of subpopulations with respect to the inducer source (an electrode) can increase signal output and outperform otherwise homogeneous networks.Discussion: We developed this model to better understand our previous experimental results, but also to enable future designs wherein subpopulation composition, genetic circuits, and spatial configurations can be varied to tune performance. We suggest that this work may provide insight into the signaling which occurs in synthetically assembled systems as well as native microbial communities.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"120 41","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pareto task inference analysis reveals cellular trade-offs in diffuse large B-Cell lymphoma transcriptomic data","authors":"Jonatan Blais, Julie Jeukens","doi":"10.3389/fsysb.2024.1346076","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1346076","url":null,"abstract":"One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"25 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BioModels’ Model of the Year 2023","authors":"Rahuman S. Malik Sheriff, Hiroki Asari, Henning Hermjakob, Wolfgang Huber, Thomas Quail, Silvia D. M. Santos, Amber M. Smith, Virginie Uhlmann","doi":"10.3389/fsysb.2024.1363884","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1363884","url":null,"abstract":"Mathematical modeling is a pivotal tool for deciphering the complexities of biological systems and their control mechanisms, providing substantial benefits for industrial applications and answering relevant biological questions. BioModels’ Model of the Year 2023 competition was established to recognize and highlight exciting modeling-based research in the life sciences, particularly by non-independent early-career researchers. It further aims to endorse reproducibility and FAIR principles of model sharing among these researchers. We here delineate the competition’s criteria for participation and selection, introduce the award recipients, and provide an overview of their contributions. Their models provide crucial insights into cell division regulation, protein stability, and cell fate determination, illustrating the role of mathematical modeling in advancing biological research.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"3 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational inference of chemokine-mediated roles for the vagus nerve in modulating intra- and inter-tissue inflammation","authors":"Ashti M. Shah, R. Zamora, Derek A. Barclay, Jinling Yin, Fayten el-Dehaibi, M. Addorisio, T. Tsaava, A. Tynan, Kevin Tracey, Sangeeta Chavan, Y. Vodovotz","doi":"10.3389/fsysb.2024.1266279","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1266279","url":null,"abstract":"Introduction: The vagus nerve innervates multiple organs, but its role in regulating cross-tissue spread of inflammation is as yet unclear. We hypothesized that the vagus nerve may regulate cross-tissue inflammation via modulation of the putatively neurally regulated chemokine IP-10/CXCL10.Methods: Rate-of-change analysis, dynamic network analysis, and dynamic hypergraphs were used to model intra- and inter-tissue trends, respectively, in inflammatory mediators from mice that underwent either vagotomy or sham surgery.Results: This analysis suggested that vagotomy primarily disrupts the cross-tissue attenuation of inflammatory networks involving IP-10 as well as the chemokines MIG/CXCL9 and CCL2/MCP-1 along with the cytokines IFN-γ and IL-6. Computational analysis also suggested that the vagus-dependent rate of expression of IP-10 and MIG/CXCL9 in the spleen impacts the trajectory of chemokine expression in other tissues. Perturbation of this complex system with bacterial lipopolysaccharide (LPS) revealed a vagally regulated role for MIG in the heart. Further, LPS-stimulated expression of IP-10 was inferred to be vagus-independent across all tissues examined while reducing connectivity to IL-6 and MCP-1, a hypothesis supported by Boolean network modeling.Discussion: Together, these studies define novel spatiotemporal dimensions of vagus-regulated acute inflammation.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"103 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational inference of chemokine-mediated roles for the vagus nerve in modulating intra- and inter-tissue inflammation","authors":"Ashti M. Shah, R. Zamora, Derek A. Barclay, Jinling Yin, Fayten el-Dehaibi, M. Addorisio, T. Tsaava, A. Tynan, Kevin Tracey, Sangeeta Chavan, Y. Vodovotz","doi":"10.3389/fsysb.2024.1266279","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1266279","url":null,"abstract":"Introduction: The vagus nerve innervates multiple organs, but its role in regulating cross-tissue spread of inflammation is as yet unclear. We hypothesized that the vagus nerve may regulate cross-tissue inflammation via modulation of the putatively neurally regulated chemokine IP-10/CXCL10.Methods: Rate-of-change analysis, dynamic network analysis, and dynamic hypergraphs were used to model intra- and inter-tissue trends, respectively, in inflammatory mediators from mice that underwent either vagotomy or sham surgery.Results: This analysis suggested that vagotomy primarily disrupts the cross-tissue attenuation of inflammatory networks involving IP-10 as well as the chemokines MIG/CXCL9 and CCL2/MCP-1 along with the cytokines IFN-γ and IL-6. Computational analysis also suggested that the vagus-dependent rate of expression of IP-10 and MIG/CXCL9 in the spleen impacts the trajectory of chemokine expression in other tissues. Perturbation of this complex system with bacterial lipopolysaccharide (LPS) revealed a vagally regulated role for MIG in the heart. Further, LPS-stimulated expression of IP-10 was inferred to be vagus-independent across all tissues examined while reducing connectivity to IL-6 and MCP-1, a hypothesis supported by Boolean network modeling.Discussion: Together, these studies define novel spatiotemporal dimensions of vagus-regulated acute inflammation.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Combining mechanistic modeling with machine learning to study multiscale biological processes","authors":"S. Peirce-Cottler, Y. Vodovotz","doi":"10.3389/fsysb.2024.1367549","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1367549","url":null,"abstract":"","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"49 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139810381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}