Frontiers in systems biology最新文献

{"title":"Digital patient modeling identifies predictive biomarkers of regorafenib response in elderly metastatic colorectal cancer.","authors":"Juan Manuel García-Illarramendi, Pedro Matos-Filipe, Jose Manuel Mas, Judith Farrés, Xavier Daura","doi":"10.3389/fsysb.2025.1648559","DOIUrl":"10.3389/fsysb.2025.1648559","url":null,"abstract":"<p><p><i>In</i> <i>silico</i> clinical trials that simulate individualized mechanisms of action offer a powerful approach to assess drug efficacy across large and diverse patient populations, while also enabling the identification of predictive biomarkers. In this study, we conducted an <i>in silico</i> clinical trial of first-line, single-agent regorafenib in 399 elderly patients with metastatic colorectal cancer (mCRC). Individualized network-based models were constructed using patient-specific differential transcriptomic profiles and employed to simulate the target-specific effects of regorafenib. From this analysis, we identified both predictive and mechanistic biomarkers of treatment response. Notably, four proteins-MARK3, RBCK1, LHCGR, and HSF1-emerged as dual biomarkers, showing associations with both response mechanisms and predictive potential. Three of these (MARK3, RBCK1, and HSF1) were validated in an independent cohort of mCRC patients and were also found to be targets of previously reported regorafenib-predictive miRNAs. This study demonstrates a novel systems biology strategy for evaluating drug response <i>in silico</i>, leveraging transcriptomic data to simulate individual treatment outcomes and uncover clinically relevant biomarkers. Our findings suggest that such approaches may serve as valuable complements to traditional clinical trials for assessing drug efficacy and guiding precision oncology.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1648559"},"PeriodicalIF":2.3,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202191","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":"Bridging academia and industry: advancing systems biology and QSP education through AstraZeneca's collaborative partnerships.","authors":"Cesar Pichardo-Almarza, Holly Kimko","doi":"10.3389/fsysb.2025.1627214","DOIUrl":"10.3389/fsysb.2025.1627214","url":null,"abstract":"<p><p>Collaborations between industry leaders and academia are crucial for advancing systems biology education and training. This article explores opportunities for partnerships to enhance the educational landscape and develop a workforce skilled in systems modelling, particularly for quantitative systems pharmacology (QSP) in drug development. Companies with a strong focus on innovation frequently explore collaborative ventures involving joint research, co-designed curricula, and specialized training programs. These partnerships provide students and researchers with insights into real-world applications of systems biology and QSP. We explicitly review success criteria for collaboration at MSc and PhD levels, discuss earlier pipeline considerations, and carefully balance the roles, incentives, and challenges for both academia and industry in collaborative ventures. Challenges in aligning academic and industry objectives exist, including resource allocation and intellectual property concerns. However, the importance of training skilled systems biologists for advancing drug discovery and development outweighs these challenges. The article concludes by highlighting successful industry-academia partnerships and offering recommendations for optimizing collaborations to meet the evolving needs of systems biology education and drive innovation in pharmaceutical research.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1627214"},"PeriodicalIF":2.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12463889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187652","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":"MicrobiomeKG: bridging microbiome research and host health through knowledge graphs.","authors":"Skye L Goetz, Amy K Glen, Gwênlyn Glusman","doi":"10.3389/fsysb.2025.1544432","DOIUrl":"10.3389/fsysb.2025.1544432","url":null,"abstract":"<p><p>The microbiome represents a complex community of trillions of microorganisms residing in various body parts and plays critical roles in maintaining host health and wellbeing. Understanding the interactions between microbiota and their host offers valuable insights into potential strategies for promoting health, including microbiome-targeted interventions. We have created MicrobiomeKG, a knowledge graph for microbiome research, that bridges various taxa and microbial pathways with host health. This novel knowledge graph derives algorithmically generated knowledge assertions from the supplementary tables that support published microbiome papers. By identifying knowledge assertions from supplementary tables and expressing them as knowledge graphs, we are casting this valuable content into a format that is ideal for hypothesis generation. To address the high heterogeneity of study contexts, methodologies, and reporting standards, we leveraged neural networks to implement a standardized edge scoring system, which we use to perform centrality analyses. We present three example use cases: linking helminth infections with non-alcoholic fatty-liver disease via microbial taxa, exploring connections between the <i>Alistipes</i> genus and inflammation, and identifying the <i>Bifidobacterium</i> genus as the most central connection with attention deficit hyperactivity disorder. MicrobiomeKG is deployed for integrative analysis and hypothesis generation, both programmatically and via the Biomedical Data Translator ecosystem. By bridging data gaps and facilitating the discovery of new biological relationships, MicrobiomeKG will help advance personalized medicine through a deeper understanding of the microbial contributions to human health and disease mechanisms.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1544432"},"PeriodicalIF":2.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066488","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":"A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools.","authors":"Francesco Canonaco, Joverlyn Gaudillo, Nicole Astrologo, Fabio Stella, Enzo Acerbi","doi":"10.3389/fsysb.2025.1631901","DOIUrl":"10.3389/fsysb.2025.1631901","url":null,"abstract":"<p><p>A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, referred to as structural learning and parameter learning, are actively investigated by the research community, with several algorithms proposed and no single method having established itself as standard. A wide range of software, tools, and packages have been developed for BNs analysis and made available to academic researchers and industry practitioners. As a consequence of having no one-size-fits-all solution, moving the first practical steps and getting oriented into this field is proving to be challenging to outsiders and beginners. In this paper, we review the most relevant tools and software for BNs structural and parameter learning to date, with a focus on causal discovery tools, providing our subjective recommendations directed to an audience of beginners. In addition, we provide an extensive easy-to-consult overview table summarizing all software packages and their main features. By improving the reader's understanding of which available software might best suit their needs, we improve accessibility to the field and make it easier for beginners to take their first step into it.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1631901"},"PeriodicalIF":2.3,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12415694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145031220","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":"From flux analysis to self contained cellular models.","authors":"Andreas Kremling","doi":"10.3389/fsysb.2025.1546072","DOIUrl":"10.3389/fsysb.2025.1546072","url":null,"abstract":"<p><p>Mathematical models for cellular systems have become more and more important for understanding the complex interplay between metabolism, signalling, and gene expression.In this manuscript, starting from the well-known flux balance analysis, tools and methods are summarised and illustrated by various examples that describe the way to models with kinetics for individual reactions steps that are finally self-contained. While flux analysis requires known (measured) input fluxes, self-contained (or self-sustained) models only get information on concentrations of environmental components. Kinetic reaction laws, feedback structures, and protein allocation then determine the temporal output of all intracellular metabolites and macromolecules. Emphasis is placed on (i) mass conservation, a crucial system property frequently overlooked in models incorporating cellular structures like macromolecular structures like proteins, RNA, and DNA, and (ii) thermodynamic constraints which further limit the solution space. Matlab Live Scripts are provided for all simulation studies shown and additional reading material is given in the appendix.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1546072"},"PeriodicalIF":2.3,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016767","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":"A model-based design strategy to engineer miRNA-regulated detection systems.","authors":"Renske J Verkuijlen, Robert W Smith","doi":"10.3389/fsysb.2025.1601854","DOIUrl":"10.3389/fsysb.2025.1601854","url":null,"abstract":"<p><p>miRNAs are promising diagnostic biomarkers. These small RNA molecules are always present in the human body but become dysregulated when a person develops certain diseases. Although the detection of these biomarkers in cell-free tests is ongoing work, current systems often focus solely on detecting the presence or absence of a specific miRNA, rather than the miRNAs concentration. Thus, these tests may miss relative changes in miRNA concentration when disease-induced dysregulation occurs. This work, part of the WUR iGEM 2024 project (miRADAR), aimed to address this gap by incorporating an miRNA concentration-dependent threshold mechanism in a cell-free diagnostic test. In this system, continuous miRNA input concentrations need to be converted into a binary output signal, classifying the miRNA concentration as healthy (no output signal) or indicative of disease (strong output signal). To aid the experimental engineering of the test, here we use mathematical models to evaluate and assess different candidate networks. We apply a previously published multi-objective optimisation strategy to obtain designs that satisfy relevant constraints, such as low basal expression, high readout levels, and steep switching behaviour between low and high input miRNA concentrations. Models for three different biological mechanisms were compared based on their ability to generate the desired binary output signal. One approach used three-node protein networks (such as feed-forward loops), while the other two utilised RNA-based toehold systems. Overall, the toehold-mediated strand displacement systems demonstrated the most potential for experimental implementation. These systems are believed to be less burdensome in a cell-free environment, can be more readily engineered for new miRNA sequences, and showed high detection accuracy. Based on our results, we discuss how the inclusion of sequence-specific parameters could expand the design space of our mathematical models and how careful engineering of optimisation criteria is required to evaluate designs. Ultimately, our model-based study highlights that toehold-mediated strand displacement networks have the potential to be efficient miRNA detection systems for biosensing tools in the future.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1601854"},"PeriodicalIF":2.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981069","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":"Unraveling the role of mobile genetic elements in antibiotic resistance transmission and defense strategies in bacteria.","authors":"Ranjith Kumavath, Puja Gupta, Eswar Rao Tatta, Mahima S Mohan, Simi Asma Salim, Siddhardha Busi","doi":"10.3389/fsysb.2025.1557413","DOIUrl":"10.3389/fsysb.2025.1557413","url":null,"abstract":"<p><p>Irrational antibiotic use contributes to the development of antibiotic resistance in bacteria, which is a major cause of healthcare-associated infections globally. Molecular research has shown that multiple resistance frequently develops from the uptake of pre-existing resistance genes, which are subsequently intensified under selective pressures. Resistant genes spread and are acquired through mobile genetic elements which are essential for facilitating horizontal gene transfer. MGEs have been identified as carriers of genetic material and are a significant player in evolutionary processes. These include insertion sequences, transposons, integrative and conjugative elements, plasmids, and genomic islands, all of which can transfer between and within DNA molecules. With an emphasis on pathogenic bacteria, this review highlights the salient features of the MGEs that contribute to the development and spread of antibiotic resistance. MGEs carry non-essential genes, including AMR and virulence genes, which can enhance the adaptability and fitness of their bacterial hosts. These elements employ evolutionary strategies to facilitate their replication and dissemination, thus enabling survival without positive selection for the harboring of beneficial genes.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1557413"},"PeriodicalIF":2.3,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850021","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":"A <i>Pseudomonas fluorescens</i> AND-gate biosensor for protein expression at plant root proximity.","authors":"Nico van Donk, Antoine Raynal, Enrique Asin-Garcia","doi":"10.3389/fsysb.2025.1620608","DOIUrl":"10.3389/fsysb.2025.1620608","url":null,"abstract":"<p><p>By 2050, global population growth will significantly increase food demand, placing additional pressure on agriculture, a sector already vulnerable to climate change. Traditional approaches like fertilizers and pesticides have helped boost yields but are increasingly seen as unsustainable. As bioengineering becomes more accessible, engineered soil microorganisms are emerging as promising alternatives. However, their application in the rhizosphere is often limited by poor survivability and the high metabolic cost of expressing heterologous genes without appropriate regulation. To address this, we developed a microbial whole-cell biosensor that activates gene expression only under favorable conditions: in close proximity to plant roots and at high bacterial population densities. We engineered the pSal/nahR system in our host <i>Pseudomonas fluorescens</i> SBW25 to respond to salicylic acid, a key root exudate. In parallel, we implemented a quorum sensing system based on LuxI and the luxpR/LuxR pair to monitor cell density. Both inputs were integrated using a toehold switch-based AND gate, triggering expression only when both conditions were met. This strategy minimizes metabolic burden and offers a tightly controlled system for expression at target locations. While further validation in rhizosphere-like conditions is required, our results provide a foundation for safer open-environment applications of microorganisms, making this biosensor a versatile tool for future agricultural biotechnology.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1620608"},"PeriodicalIF":2.3,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850001","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":"Inflammation mediated brain damage and cytokine expression in a maternally derived murine model for preterm hypoxic-ischemic encephalopathy.","authors":"Tyler C Hillman, Braeden Jacobson, Kiara Piaggio Hurtado De Medoza, Marlene Lopez, Nicholas Iwakoshi, Christopher G Wilson","doi":"10.3389/fsysb.2025.1517712","DOIUrl":"10.3389/fsysb.2025.1517712","url":null,"abstract":"<p><strong>Introduction: </strong>Preterm hypoxic-ischemic encephalopathy (pHIE) is a complex brain injury that contributes to chronic neural inflammation and neurological disorders. The signs and symptoms of in utero pHIE can often be overlooked, untreated or lumped into more generic conditions such as encephalopathy of prematurity (EOP). Clinical interventions like hypothermia and erythropoietin do not improve pHIE. We characterized a murine model for pHIE, which includes hypoxia and maternal factors as a cost-effective alternative to large animal models of HIE.</p><p><strong>Methods: </strong>We injected pregnant mouse dams with LPS to stimulate an inflammatory response on embryonic days 15-16 (E15-E16), and whole cage hypoxia exposures occurred from postnatal days 3 to 9. To quantify the development of inflammation in the pHIE model, we used immunohistochemistry to stain for Caspase-9 in the cortex (20 μm per slice) and then counted Caspase-9 positive cells using unbiased stereology. We stained brain tissue with MAP2 to quantify neuronal intermediate filament expression and staining using a machine-learning based image analysis approach. We quantified cytokines (IL-1β, IL-6, IL-10, IL-18 and TNF-α) using RT-qPCR and (IL-18) ELISA to characterize differential expression in all treatment groups. The pHIE animals were compared with controls (LPS-Normoxia, Saline-Hypoxia, Saline-Normoxia, and Naïve) and with a model of only hypoxia (10% O₂) exposure in mouse pups.</p><p><strong>Results: </strong>The pHIE pups showed significantly higher expression of Caspase-9 throughout the cortex compared to Naïve pup brains (p < 0.05). MAP2 expression was significantly decreased (p < 0.05) between 1.5-6.0 mm of the brain compared to Saline-Hypoxia and Naïve animals. Both IL-1β and IL-10 expression in LPS-Hypoxia animals was significantly higher (p < 0.05) than in Saline-Hypoxia and Naive animals. TNF-α expression was not significantly different between LPS-Hypoxia and Saline-Hypoxia animals. However, both showed significantly different transcription, compared to Naive animals.</p><p><strong>Discussion: </strong>The model we describe here shows cortical damage similar to that seen in human HIE.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1517712"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342018/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850005","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":"Enzyme-displaying spores as a novel strategy for mixed fiber textile recycling.","authors":"Matti Lehmann, Max Herrmann","doi":"10.3389/fsysb.2025.1603731","DOIUrl":"10.3389/fsysb.2025.1603731","url":null,"abstract":"<p><p>Global textile manufacturing practices are responsible for an increasing amount of textile waste that pollutes our planet. Mixed fiber blends pose a recycling challenge due to their heterogeneous structure. Current mechanical, chemical, thermochemical and enzymatic strategies suffer from several limitations such as high energy costs, extensive pre-treatment requirements and enzyme instability. This mini-review aims to present recent developments in the research field and to introduce Spore Surface Display (SSD) technology as a new biological approach for mixed textile degradation. SSD allows enzymes to be anchored on the robust bacterial spore surface, immobilizing multiple enzymes required for simultaneous cotton-polyester degradation into their respective monomers. The mini-review also includes an initial proposal for a process design suitable for a full mixed textile degradation process using this synthetic biology approach.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"5 ","pages":"1603731"},"PeriodicalIF":2.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12342026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850003","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}