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        <title>Frontiers in Systems Biology | Data and Model Integration section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/systems-biology/sections/data-and-model-integration</link>
        <description>RSS Feed for Data and Model Integration section in the Frontiers in Systems Biology journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-14T18:32:31.566+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2026.1729027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2026.1729027</link>
        <title><![CDATA[Mathematical modeling of bone remodeling after surgical menopause]]></title>
        <pubdate>2026-02-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anna C. Nelson</author><author>Edwina F. Yeo</author><author>Yun Zhang</author><author>Carley V. Cook</author><author>Sophie Fischer-Holzhausen</author><author>Lauryn Keeler Bruce</author><author>Pritha Dutta</author><author>Samaneh Gholami</author><author>Brenda J. Smith</author><author>Ashlee N. Ford Versypt</author>
        <description><![CDATA[Osteoporosis is a skeletal pathology characterized by decreased bone mass and structural deterioration resulting from an imbalance in bone metabolic processes. Estrogen deficiency in postmenopausal women leads to an increased risk of osteoporosis, while women who have undergone complete oophorectomies display an even higher risk due to the sudden decrease in estrogen. Some evidence indicates that bone loss slows in the period beyond 15 years after surgery; however, there is substantial uncertainty in clinical data. To explore the effects of surgically induced menopausal transition, here we propose a mathematical model for the bone cell dynamical responses to sudden estrogen deficiency, which extends an existing model for osteoporosis due to aging and natural menopause. Using data on key effects observed in female mice and humans after bilateral oophorectomy, this new model considers the role of osteocytes embedded within the mineralized bone matrix in regulating bone remodeling, which results in net bone loss after surgical menopause. The model parameter values in natural and surgical menopause were estimated from aggregated human clinical data from existing longitudinal studies. The new model effectively captures the previously unmodeled increase in bone loss during the first 15 years post-surgical menopause and the rebound in bone mineral density in the long-term. With this model, effects of treatments on targeting osteocyte dynamics could be explored in the future.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1651930</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1651930</link>
        <title><![CDATA[BioMedKG: multimodal contrastive representation learning in augmented BioMedical knowledge graphs]]></title>
        <pubdate>2025-12-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tien Dang</author><author>Viet Thanh Duy Nguyen</author><author>Minh Tuan Le</author><author>Truong-Son Hy</author>
        <description><![CDATA[Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential new drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from specialized Language Models (LMs) with Graph Contrastive Learning (GCL) to enhance intra-entity relationships while employing a Knowledge Graph Embedding (KGE) model to capture inter-entity relationships for effective link prediction. To address limitations in existing BKGs, we present PrimeKG++, an enriched knowledge graph incorporating multimodal data, including biological sequences and textual descriptions for each entity type. By combining semantic and relational information in a unified representation, our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes. Experimental results in PrimeKG++ and the DrugBank drug-target interaction dataset demonstrate the effectiveness and robustness of our method in diverse biomedical datasets. Our source code, pre-trained models, and data are publicly available at https://github.com/HySonLab/BioMedKG.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1656683</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1656683</link>
        <title><![CDATA[The role of neutrophil-to-lymphocyte ratio in the prognosis of chronic kidney disease: insights from the NHANES cohort study]]></title>
        <pubdate>2025-10-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Liu</author><author>Ru Wang</author><author>Jinguo Yuan</author><author>Jin Zhao</author>
        <description><![CDATA[ObjectiveTo investigate the association of neutrophil-to-lymphocyte ratio (NLR) with the cardiovascular disease (CVD) and all-cause mortality in patients with chronic kidney disease (CKD).MethodsUsing date from NHANES survey 2009–2018, 2,635 patients with CKD were eventually included in this study. The population was stratified into two groups based on the median NLR. Kaplan-Meier method with log-rank tests for significance was used for survival analysis. Weighted Cox proportional hazards regression models were employed to estimate the hazard ratio (HR) and corresponding 95% confidence interval (CI) for all-cause and CVD mortality. The potential nonlinear relationship between NLR and CVD and all-cause mortality was assessed using restricted cubic spline (RCS) models. The time-dependent receiver operating characteristic (ROC) curve was utilized to assess the precision of NLR in predicting survival outcomes.ResultsThe Kaplan-Meier curve indicated a significant difference in overall survival between the two groups (log-rank test, p < 0.0001). Compared to lower NLR group, participants in the higher NLR group had HR of 1.56 (1.30, 1.87) for all-cause mortality and 2.07 (1.51, 2.84) for CVD mortality, respectively. We observed a significant nonlinear relationship between NLR and CVD and all-cause mortality (p < 0.0001). The time-dependent ROC curve demonstrated that the areas under the curve for 1-, 3-, 5-, and 10-year survival rates were 0.69, 0.65, 0.63, and 0.62 for all-cause mortality, and 0.71, 0.67, 0.66, and 0.64 for CVD mortality, respectively.ConclusionA higher NLR is linked to an elevated risk of CVD and all-cause mortality in patients with CKD. Additionally, NLR can serve as a potential prognostic indicator for CKD patients.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1717030</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1717030</link>
        <title><![CDATA[Correction: A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools]]></title>
        <pubdate>2025-10-23T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Francesco Canonaco</author><author>Joverlyn Gaudillo</author><author>Nicole Astrologo</author><author>Fabio Stella</author><author>Enzo Acerbi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1649758</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1649758</link>
        <title><![CDATA[GETgene-AI: a framework for prioritizing actionable cancer drug targets]]></title>
        <pubdate>2025-09-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adrian Gu</author><author>Jake Y. Chen</author>
        <description><![CDATA[Prioritizing actionable drug targets is a critical challenge in cancer research, where high-dimensional genomic data and the complexity of tumor biology often hinder effective prioritization. To address this, we developed GETgene-AI, a novel computational framework that integrates network-based prioritization, machine learning, and automated literature analysis to prioritize and rank potential therapeutic targets. Central to GETgene-AI is the G.E.T. strategy, which combines three data streams: mutational frequency (G List), differential expression (E List), and known drug targets (T List). These components are iteratively refined and ranked using the Biological Entity Expansion and Ranking Engine (BEERE), leveraging protein-protein interaction networks, functional annotations, and experimental evidence. Additionally, GETgene-AI incorporates GPT-4o, an advanced large language model, to automate literature-based ranking, reducing manual curation and increasing efficiency. In this study, we applied GETgene-AI to pancreatic cancer as a case study. The framework successfully prioritized high-priority targets such as PIK3CA and PRKCA, validated through experimental evidence and clinical relevance. Benchmarking against GEO2R and STRING demonstrated GETgene-AI’s superior performance, achieving higher precision, recall, and efficiency in prioritizing actionable targets. Moreover, the framework mitigated false positives by deprioritizing genes lacking functional or clinical significance. While demonstrated on pancreatic cancer, the modular design of GETgene-AI enables scalability across diverse cancers and diseases. By integrating multi-omics datasets with advanced computational and AI-driven approaches, GETgene-AI provides a versatile and robust platform for accelerating cancer drug discovery. This framework bridges computational innovations with translational research to improve patient outcomes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1631901</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1631901</link>
        <title><![CDATA[A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools]]></title>
        <pubdate>2025-08-25T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Francesco Canonaco</author><author>Joverlyn Gaudillo</author><author>Nicole Astrologo</author><author>Fabio Stella</author><author>Enzo Acerbi</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2025.1589079</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2025.1589079</link>
        <title><![CDATA[Learning Gaussian graphical models from correlated data]]></title>
        <pubdate>2025-07-03T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Zeyuan Song</author><author>Sophia Gunn</author><author>Stefano Monti</author><author>Gina M. Peloso</author><author>Ching-Ti Liu</author><author>Kathryn Lunetta</author><author>Paola Sebastiani</author>
        <description><![CDATA[Gaussian Graphical Models (GGMs) are a type of network modeling that uses partial correlation rather than correlation for representing complex relationships among multiple variables. The advantage of using partial correlation is to show the relation between two variables after “adjusting” for the effects of other variables and leads to more parsimonious and interpretable models. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a cluster-based bootstrap algorithm to infer GGMs from correlated data. We use extensive simulations of correlated data from family-based studies to show that the proposed bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions when there are sufficient number of clusters. We apply our method to learn the Gaussian Graphic Model that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well without power loss.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1500710</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1500710</link>
        <title><![CDATA[Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data]]></title>
        <pubdate>2025-01-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andrea Angarita-Rodríguez</author><author>Nicolás Mendoza-Mejía</author><author>Janneth González</author><author>Jason Papin</author><author>Andrés Felipe Aristizábal</author><author>Andrés Pinzón</author>
        <description><![CDATA[IntroductionThe availability of large-scale multi-omic data has revolution-ized the study of cellular machinery, enabling a systematic understanding of biological processes. However, the integration of these datasets into Genome-Scale Models of Metabolism (GEMs) re-mains underexplored. Existing methods often link transcriptome and proteome data independently to reaction boundaries, providing models with estimated maximum reaction rates based on individual datasets. This independent approach, however, introduces uncertainties and inaccuracies.MethodsTo address these challenges, we applied a principal component analysis (PCA)-based approach to integrate transcriptome and proteome data. This method facilitates the reconstruction of context-specific models grounded in multi-omics data, enhancing their biological relevance and predictive capacity.ResultsUsing this approach, we successfully reconstructed an astrocyte GEM with improved prediction capabilities compared to state-of-the-art models available in the literature.DiscussionThese advancements underscore the potential of multi-omic inte-gration to refine metabolic modeling and its critical role in studying neurodegeneration and developing effective therapies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1470000</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1470000</link>
        <title><![CDATA[Intertwined roles for GDF-15, HMGB1, and MIG/CXCL9 in Pediatric Acute Liver Failure]]></title>
        <pubdate>2024-10-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ruben Zamora</author><author>Jinling Yin</author><author>Derek Barclay</author><author>James E. Squires</author><author>Yoram Vodovotz</author>
        <description><![CDATA[IntroductionPediatric Acute Liver Failure (PALF) presents as a rapidly evolving, multifaceted, and devastating clinical syndrome whose precise etiology remains incompletely understood. Consequently, predicting outcomes—whether survival or mortality—and informing liver transplantation decisions in PALF remain challenging. We have previously implicated High-Mobility Group Box 1 (HMGB1) as a central mediator in PALF-associated dynamic inflammation networks that could be recapitulated in acetaminophen (APAP)-treated mouse hepatocytes (HC) in vitro. Here, we hypothesized that Growth/Differentiation Factor-15 (GDF-15) is involved along with HMGB1 in PALF.Methods28 and 23 inflammatory mediators including HMGB1 and GDF15 were measured in serum samples from PALF patients and cell supernatants from wild-type (C57BL/6) mouse hepatocytes (HC) and from cells from HC-specific HMGB1-null mice (HC-HMGB1−/−) exposed to APAP, respectively. Results were analyzed computationally to define statistically significant and potential causal relationships.ResultsCirculating GDF-15 was elevated significantly (P < 0.05) in PALF non-survivors as compared to survivors, and together with HMGB1 was identified as a central node in dynamic inflammatory networks in both PALF patients and mouse HC. This analysis also pointed to MIG/CXCL9 as a differential node linking HMGB1 and GDF-15 in survivors but not in non-survivors, and, when combined with in vitro studies, suggested that MIG suppresses GDF-15-induced inflammation.DiscussionThis study suggests GDF-15 as a novel PALF outcome biomarker, posits GDF-15 alongside HMGB1 as a central node within the intricate web of systemic inflammation dynamics in PALF, and infers a novel, negative regulatory role for MIG.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1419809</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1419809</link>
        <title><![CDATA[Spectral expansion methods for prediction uncertainty quantification in systems biology]]></title>
        <pubdate>2024-10-03T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Anna Deneer</author><author>Jaap Molenaar</author><author>Christian Fleck</author>
        <description><![CDATA[Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1283371</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1283371</link>
        <title><![CDATA[Accessible Type 2 diabetes medication through stable expression of Exendin-4 in Saccharomyces cerevisiae]]></title>
        <pubdate>2024-09-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gia Balius</author><author>Kiana Imani</author><author>Zoë Petroff</author><author>Elizabeth Beer</author><author>Thiago Brasileiro Feitosa</author><author>Nathan Mccall</author><author>Lauren Paule</author><author>Neo Yixuan Peng</author><author>Joanne Shen</author><author>Vidhata Singh</author><author>Cambell Strand</author><author>Jonathan Zau</author><author>D. L. Bernick</author>
        <description><![CDATA[Diabetes mellitus affects roughly one in ten people globally and is the world’s ninth leading cause of death. However, a significant portion of chronic complications that contribute to mortality can be prevented with proper treatment and medication. Glucagon-like peptide 1 receptor agonists, such as Exendin-4, are one of the leading classes of Type 2 diabetes treatments but are prohibitively expensive. In this study, experimental models for recombinant Exendin-4 protein production were designed in both Escherichia coli and Saccharomyces cerevisiae. Protein expression in the chromosomally integrated S. cerevisiae strain was observed at the expected size of Exendin-4 and confirmed by immunoassay. This provides a foundation for the use of this Generally Regarded as Safe organism as an affordable treatment for Type 2 diabetes that can be propagated, prepared, and distributed locally.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1407994</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1407994</link>
        <title><![CDATA[The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology]]></title>
        <pubdate>2024-08-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ben Noordijk</author><author>Monica L. Garcia Gomez</author><author>Kirsten H. W. J. ten Tusscher</author><author>Dick de Ridder</author><author>Aalt D. J. van Dijk</author><author>Robert W. Smith</author>
        <description><![CDATA[Both machine learning and mechanistic modelling approaches have been used independently with great success in systems biology. Machine learning excels in deriving statistical relationships and quantitative prediction from data, while mechanistic modelling is a powerful approach to capture knowledge and infer causal mechanisms underpinning biological phenomena. Importantly, the strengths of one are the weaknesses of the other, which suggests that substantial gains can be made by combining machine learning with mechanistic modelling, a field referred to as Scientific Machine Learning (SciML). In this review we discuss recent advances in combining these two approaches for systems biology, and point out future avenues for its application in the biological sciences.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1394084</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1394084</link>
        <title><![CDATA[Transporter annotations are holding up progress in metabolic modeling]]></title>
        <pubdate>2024-07-24T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>John Casey</author><author>Brian Bennion</author><author>Patrik D’haeseleer</author><author>Jeffrey Kimbrel</author><author>Gianna Marschmann</author><author>Ali Navid</author>
        <description><![CDATA[Mechanistic, constraint-based models of microbial isolates or communities are a staple in the metabolic analysis toolbox, but predictions about microbe-microbe and microbe-environment interactions are only as good as the accuracy of transporter annotations. A number of hurdles stand in the way of comprehensive functional assignments for membrane transporters. These include general or non-specific substrate assignments, ambiguity in the localization, directionality and reversibility of a transporter, and the many-to-many mapping of substrates, transporters and genes. In this perspective, we summarize progress in both experimental and computational approaches used to determine the function of transporters and consider paths forward that integrate both. Investment in accurate, high-throughput functional characterization is needed to train the next-generation of predictive tools toward genome-scale metabolic network reconstructions that better predict phenotypes and interactions. More reliable predictions in this domain will benefit fields ranging from personalized medicine to metabolic engineering to microbial ecology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1377188</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1377188</link>
        <title><![CDATA[Surface-displayed silicatein-α enzyme in bioengineered E. coli enables biocementation and silica mineralization]]></title>
        <pubdate>2024-05-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Toriana N. Vigil</author><author>Nikolas K. Schwendeman</author><author>Melanie L. M. Grogger</author><author>Victoria L. Morrison</author><author>Margaret C. Warner</author><author>Nathaniel B. Bone</author><author>Morgan T. Vance</author><author>David C. Morris</author><author>Kristi McElmurry</author><author>Bryan W. Berger</author><author>J. Jordan Steel</author>
        <description><![CDATA[Biocementation is an exciting biomanufacturing alternative to common cement, which is a significant contributor of CO2 greenhouse gas production. In nature biocementation processes are usually modulated via ureolytic microbes, such as Sporosarcina pasteurii, precipitating calcium carbonate to cement particles together, but these ureolytic reactions also produce ammonium and carbonate byproducts, which may have detrimental effects on the environment. As an alternative approach, this work examines biosilicification via surface-displayed silicatein-α in bio-engineered E. coli as an in vivo biocementation strategy. The surface-display of silicatein-α with ice nucleation protein is a novel protein fusion combination that effectively enables biosilicification, which is the polymerization of silica species in solution, from the surface of E. coli bacterial cells. Biosilicification with silicatein-α produces biocementation products with comparable compressive strength as S. pasteurii. This biosilicification approach takes advantage of the high silica content found naturally in sand and does not produce the ammonium and carbonate byproducts of ureolytic bacteria, making this a more environmentally friendly biocementation strategy.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1308292</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1308292</link>
        <title><![CDATA[Context-aware knowledge selection and reliable model recommendation with ACCORDION]]></title>
        <pubdate>2024-04-18T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Yasmine Ahmed</author><author>Cheryl A. Telmer</author><author>Gaoxiang Zhou</author><author>Natasa Miskov-Zivanov</author>
        <description><![CDATA[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 (ACCelerating and Optimizing model RecommenDatIONs), 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: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, context-specific, and useful subset (15%–20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1284668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1284668</link>
        <title><![CDATA[Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression]]></title>
        <pubdate>2024-04-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samuel King</author><author>Xinyi E. Chen</author><author>Sarah W. S. Ng</author><author>Kimia Rostin</author><author>Samuel V. Hahn</author><author>Tylo Roberts</author><author>Janella C. Schwab</author><author>Parneet Sekhon</author><author>Madina Kagieva</author><author>Taylor Reilly</author><author>Ruo Chen Qi</author><author>Paarsa Salman</author><author>Ryan J. Hong</author><author>Eric J. Ma</author><author>Steven J. Hallam</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1367562</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1367562</link>
        <title><![CDATA[In silico biomarker analysis of the adverse effects of perfluorooctane sulfonate (PFOS) exposure on the metabolic physiology of embryo-larval zebrafish]]></title>
        <pubdate>2024-03-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rayna M. Nolen</author><author>Lene H. Petersen</author><author>Karl Kaiser</author><author>Antonietta Quigg</author><author>David Hala</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1335885</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1335885</link>
        <title><![CDATA[Computational insights in cell physiology]]></title>
        <pubdate>2024-03-13T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Geneviève Dupont</author><author>Didier Gonze</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1333760</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1333760</link>
        <title><![CDATA[Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity]]></title>
        <pubdate>2024-03-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Patrick C. Kinnunen</author><author>Kenneth K. Y. Ho</author><author>Siddhartha Srivastava</author><author>Chengyang Huang</author><author>Wanggang Shen</author><author>Krishna Garikipati</author><author>Gary D. Luker</author><author>Nikola Banovic</author><author>Xun Huan</author><author>Jennifer J. Linderman</author><author>Kathryn E. Luker</author>
        <description><![CDATA[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.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fsysb.2024.1363884</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fsysb.2024.1363884</link>
        <title><![CDATA[BioModels’ Model of the Year 2023]]></title>
        <pubdate>2024-02-27T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Rahuman S. Malik Sheriff</author><author>Hiroki Asari</author><author>Henning Hermjakob</author><author>Wolfgang Huber</author><author>Thomas Quail</author><author>Silvia D. M. Santos</author><author>Amber M. Smith</author><author>Virginie Uhlmann</author>
        <description><![CDATA[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.]]></description>
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