Frontiers in systems biology最新文献

{"title":"Context-aware knowledge selection and reliable model recommendation with ACCORDION.","authors":"Yasmine Ahmed, Cheryl A Telmer, Gaoxiang Zhou, Natasa Miskov-Zivanov","doi":"10.3389/fsysb.2024.1308292","DOIUrl":"10.3389/fsysb.2024.1308292","url":null,"abstract":"<p><p>New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (<b>ACC</b>elerating and <b>O</b>ptimizing model <b>R</b>ecommen<b>D</b>at<b>ION</b>s), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: <i>comprehensive</i>, retrieving relevant knowledge from a range of literature sources through machine reading engines; very <i>effective</i>, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; <i>selective</i>, recommending only the most relevant, context-specific, and useful subset (15%-20%) of candidate knowledge in literature; <i>diverse</i>, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.</p>","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"4 ","pages":"1308292"},"PeriodicalIF":2.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12341976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849987","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 review of mathematical modeling of bone remodeling from a systems biology perspective","authors":"Carley V. Cook, Ariel M. Lighty, Brenda J. Smith, Ashlee N. Ford Versypt","doi":"10.3389/fsysb.2024.1368555","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1368555","url":null,"abstract":"Bone remodeling is an essential, delicately balanced physiological process of coordinated activity of bone cells that remove and deposit new bone tissue in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important aspects frequently found in mathematical models for bone remodeling and discuss how and why these aspects are included when considering the physiology of the bone basic multicellular unit, which is the term used for the collection of cells responsible for bone remodeling. The review also emphasizes the view of bone remodeling from a systems biology perspective. Understanding the systemic mechanisms involved in remodeling will help provide information on bone pathology associated with aging, endocrine disorders, cancers, and inflammatory conditions and enhance systems pharmacology. Furthermore, some features of the bone remodeling cycle and interactions with other organ systems that have not yet been modeled mathematically are discussed as promising future directions in the field.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"13 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722853","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":"Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression","authors":"Samuel King, Xinyi E. Chen, Sarah W. S. Ng, Kimia Rostin, Samuel V. Hahn, Tylo Roberts, Janella C. Schwab, Parneet Sekhon, Madina Kagieva, Taylor Reilly, Ruo Chen Qi, Paarsa Salman, Ryan J. Hong, Eric J. Ma, Steven J. Hallam","doi":"10.3389/fsysb.2024.1284668","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1284668","url":null,"abstract":"The emergence of SARS-CoV-2 variants during the COVID-19 pandemic caused frequent global outbreaks that confounded public health efforts across many jurisdictions, highlighting the need for better understanding and prediction of viral evolution. Predictive models have been shown to support disease prevention efforts, such as with the seasonal influenza vaccine, but they require abundant data. For emerging viruses of concern, such models should ideally function with relatively sparse data typically encountered at the early stages of a viral outbreak. Conventional discrete approaches have proven difficult to develop due to the spurious and reversible nature of amino acid mutations and the overwhelming number of possible protein sequences adding computational complexity. We hypothesized that these challenges could be addressed by encoding discrete protein sequences into continuous numbers, effectively reducing the data size while enhancing the resolution of evolutionarily relevant differences. To this end, we developed a viral protein evolution prediction model (VPRE), which reduces amino acid sequences into continuous numbers by using an artificial neural network called a variational autoencoder (VAE) and models their most statistically likely evolutionary trajectories over time using Gaussian process (GP) regression. To demonstrate VPRE, we used a small amount of early SARS-CoV-2 spike protein sequences. We show that the VAE can be trained on a synthetic dataset based on this data. To recapitulate evolution along a phylogenetic path, we used only 104 spike protein sequences and trained the GP regression with the numerical variables to project evolution up to 5 months into the future. Our predictions contained novel variants and the most frequent prediction mapped primarily to a sequence that differed by only a single amino acid from the most reported spike protein within the prediction timeframe. Novel variants in the spike receptor binding domain (RBD) were capable of binding human angiotensin-converting enzyme 2 (ACE2) in silico, with comparable or better binding than previously resolved RBD-ACE2 complexes. Together, these results indicate the utility and tractability of combining deep learning and regression to model viral protein evolution with relatively sparse datasets, toward developing more effective medical interventions.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140723569","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":"Is there room in epilepsy for the claustrum?","authors":"Glenn D. R. Watson, Stefano Meletti, Anil K. Mahavadi, Pierre Besson, S. Bandt, Jared B. Smith","doi":"10.3389/fsysb.2024.1385112","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1385112","url":null,"abstract":"The function of the claustrum and its role in neurological disorders remains a subject of interest in the field of neurology. Given the claustrum’s susceptibility to seizure-induced damage, there is speculation that it could serve as a node in a dysfunctional epileptic network. This perspective article aims to address a pivotal question: Does the claustrum play a role in epilepsy? Building upon existing literature, we propose the following hypotheses for the involvement of the claustrum in epilepsy: (1) Bilateral T2/FLAIR magnetic resonance imaging (MRI) hyperintensity of the claustrum after status epilepticus represents a radiological phenomenon that signifies inflammation-related epileptogenesis; (2) The ventral claustrum is synonymous with a brain area known as ‘area tempestas,’ an established epileptogenic center; (3) The ventral subsector of the claustrum facilitates seizure generalization/propagation through its connections with limbic and motor-related brain structures; (4) Disruption of claustrum connections during seizures might contribute to the loss of consciousness observed in impaired awareness seizures; (5) Targeting the claustrum therapeutically could be advantageous in seizures that arise from limbic foci. Together, evidence from both clinical case reports and animal studies identify a significant role for the ventral claustrum in the generation, propagation, and intractable nature of seizures in a subset of epilepsy syndromes.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"237 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748799","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":"Intein-mediated thyroid hormone biosensors: towards controlled delivery of hormone therapy","authors":"Quim Martí-Baena, Andreu Pascuet-Fontanet, Tomas Berjaga-Buisan, Miriam Caravaca-Rodríguez, Jaume Puig-Costa-Jussà, A. Sanchez-Mejias, Dimitrije Ivančić, Sira Mogas-Díez, Marc Güell, Javier Macia","doi":"10.3389/fsysb.2024.1270071","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1270071","url":null,"abstract":"Although blood sampling and medical imaging are well-established techniques in clinical diagnostics, they often require long post-processing procedures. Fast and simple quantification of signaling molecules can enable efficient health monitoring and improve diagnoses. Thyroid hormones (THs) treatment relies on trial-and-error dose adjustments, and requires constant tracking via blood tests. Thus, a fast and reliable method that can constantly track THs levels could substantially improve patient quality of life by reducing their visits to doctors. Synthetic biosensors have shown to be inexpensive and easy tools for sensing molecules, with their use in healthcare increasing over time. This study describes the construction of an engineered THs bacterial biosensor, consisting of a split-intein-based TH receptor ligand binding domain (LBD) biosensor that reconstitutes green fluorescence protein (GFP) after binding to TH. This biosensor could quantitatively measure THs concentrations by evaluating fluorescence intensity. In vitro sensing using Escherichia coli produced GFP over a wide dynamic range. The biosensor was further optimized by adding a double LBD, which enhanced its dynamic range until it reached healthy physiological conditions. Moreover, a mathematical model was developed to assess the dynamic properties of the biosensor and to provide a basis for the characterization of other intein-mediated biosensors. This type of biosensor can be used as the basis for novel treatments of thyroid diseases and can be adapted to measure the concentrations of other hormones, giving rise to a series of mathematically characterized modular biosensors.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"20 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747759","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":"In silico biomarker analysis of the adverse effects of perfluorooctane sulfonate (PFOS) exposure on the metabolic physiology of embryo-larval zebrafish","authors":"Rayna M Nolen, Lene H. Petersen, Karl Kaiser, Antonietta Quigg, D. Hala","doi":"10.3389/fsysb.2024.1367562","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1367562","url":null,"abstract":"Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant in global aquatic ecosystems with increasing concern for its toxicity to aquatic wildlife through inadvertent exposures. To assess the likely adverse effects of PFOS exposure on aquatic wildlife inhabiting polluted ecosystems, there is a need to identify biomarkers of its exposure and toxicity. We used an integrated systems toxicological framework to identify physiologically relevant biomarkers of PFOS toxicity in fish. An in silico stoichiometric metabolism model of zebrafish (Danio rerio) was used to integrate available (published by other authors) metabolomics and transcriptomics datasets from in vivo toxicological studies with 5 days post fertilized embryo-larval life stage of zebrafish. The experimentally derived omics datasets were used as constraints to parameterize an in silico mathematical model of zebrafish metabolism. In silico simulations using flux balance analysis (FBA) and its extensions showed prominent effects of PFOS exposure on the carnitine shuttle and fatty acid oxidation. Further analysis of metabolites comprising the impacted metabolic reactions indicated carnitine to be the most highly represented cofactor metabolite. Flux simulations also showed a near dose-responsive increase in the pools for fatty acids and acyl-CoAs under PFOS exposure. Taken together, our integrative in silico results showed dyslipidemia effects under PFOS exposure and uniquely identified carnitine as a candidate metabolite biomarker. The verification of this prediction was sought in a subsequent in vivo environmental monitoring study by the authors which showed carnitine to be a modal biomarker of PFOS exposure in wild-caught fish and marine mammals sampled from the northern Gulf of Mexico. Therefore, we highlight the efficacy of FBA to study the properties of large-scale metabolic networks and to identify biomarkers of pollutant exposure in aquatic wildlife.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"71 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376211","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":"Mathematical modeling of temperature-induced circadian rhythms","authors":"Lingjun Lu, Yannuo Li, Rene Schloss, Ioannis P. Androulakis","doi":"10.3389/fsysb.2024.1256398","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1256398","url":null,"abstract":"The central circadian pacemaker in the suprachiasmatic nuclei (SCN) aligns the phase and period of autonomous molecular oscillators in peripheral cells to daily light/dark cycles via physiological, neuronal, hormonal, and metabolic signals. Among different entrainment factors, temperature entrainment has been proposed as an essential alternative for inducing and sustaining circadian rhythms in vitro. While the synchronization mechanisms for hormones such as glucocorticoids have been widely studied, little is known about the crucial role of body temperature as a systemic cue. In this work, we develop a semi-mechanistic mathematical model describing the entrainment of peripheral clocks to temperature rhythms. The model incorporates a temperature sensing-transduction cascade involving a heat shock transcription factor-1 (HSF1) and heat shock response (HSR) pathway to simulate the entrainment of clock genes. The model is used to investigate the mammalian temperature entrainment and synchronization of cells subject to temperature oscillations of different amplitudes and magnitudes and examine the effects of transitioning between temperature schedules. Our computational analyses of the system’s dynamic responses reveal that 1) individual cells gradually synchronize to the rhythmic temperature signal by resetting their intrinsic phases to achieve coherent dynamics while oscillations are abolished in the absence of temperature rhythmicity; 2) alterations in the amplitude and period of temperature rhythms impact the peripheral synchronization behavior; 3) personalized synchronization strategies allow for differential, adaptive responses to temperature rhythms. Our results demonstrate that temperature can be a potent entrainer of circadian rhythms. Therefore, in vitro systems subjected to temperature modulation can serve as a potential tool for studying the adjustment or disruption of circadian rhythms.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" 571","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140382927","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":"A robust ensemble feature selection approach to prioritize genes associated with survival outcome in high-dimensional gene expression data","authors":"Phi Le, Xingyue Gong, Leah Ung, Hai Yang, Bridget P Keenan, Li Zhang, Tao He","doi":"10.3389/fsysb.2024.1355595","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1355595","url":null,"abstract":"Exploring features associated with the clinical outcome of interest is a rapidly advancing area of research. However, with contemporary sequencing technologies capable of identifying over thousands of genes per sample, there is a challenge in constructing efficient prediction models that balance accuracy and resource utilization. To address this challenge, researchers have developed feature selection methods to enhance performance, reduce overfitting, and ensure resource efficiency. However, applying feature selection models to survival analysis, particularly in clinical datasets characterized by substantial censoring and limited sample sizes, introduces unique challenges. We propose a robust ensemble feature selection approach integrated with group Lasso to identify compelling features and evaluate its performance in predicting survival outcomes. Our approach consistently outperforms established models across various criteria through extensive simulations, demonstrating low false discovery rates, high sensitivity, and high stability. Furthermore, we applied the approach to a colorectal cancer dataset from The Cancer Genome Atlas, showcasing its effectiveness by generating a composite score based on the selected genes to correctly distinguish different subtypes of the patients. In summary, our proposed approach excels in selecting impactful features from high-dimensional data, yielding better outcomes compared to contemporary state-of-the-art models.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":" 88","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221450","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":"Calcium oscillations in HEK293 cells lacking SOCE suggest the existence of a balanced regulation of IP3 production and degradation","authors":"Clara Octors, Ryan E. Yoast, Scott M. Emrich, Mohamed Trebak, James Sneyd","doi":"10.3389/fsysb.2024.1343006","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1343006","url":null,"abstract":"The concentration of free cytosolic Ca2+ is a critical second messenger in almost every cell type, with the signal often being carried by the period of oscillations, or spikes, in the cytosolic Ca2+ concentration. We have previously studied how Ca2+ influx across the plasma membrane affects the period and shape of Ca2+ oscillations in HEK293 cells. However, our theoretical work was unable to explain how the shape of Ca2+ oscillations could change qualitatively, from thin spikes to broad oscillations, during the course of a single time series. Such qualitative changes in oscillation shape are a common feature of HEK293 cells in which STIM1 and 2 have been knocked out. Here, we present an extended version of our earlier model that suggests that such time-dependent qualitative changes in oscillation shape might be the result of balanced positive and negative feedback from Ca2+ to the production and degradation of inositol trisphosphate.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"8 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240991","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 insights in cell physiology","authors":"Geneviève Dupont, Didier Gonze","doi":"10.3389/fsysb.2024.1335885","DOIUrl":"https://doi.org/10.3389/fsysb.2024.1335885","url":null,"abstract":"Physiological processes are governed by intricate networks of transcriptional and post-translational regulations. Inter-cellular interactions and signaling pathways further modulate the response of the cells to environmental conditions. Understanding the dynamics of these systems in healthy conditions and their alterations in pathologic situations requires a “systems” approach. Computational models allow to formalize and to simulate the dynamics of complex networks. Here, we briefly illustrate, through a few selected examples, how modeling helps to answer non-trivial questions regarding rhythmic phenomena, signaling and decision-making in cellular systems. These examples relate to cell differentiation, metabolic regulation, chronopharmacology and calcium dynamics.","PeriodicalId":73109,"journal":{"name":"Frontiers in systems biology","volume":"35 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247824","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}