{"id":3114,"date":"2023-01-17T20:50:35","date_gmt":"2023-01-18T02:50:35","guid":{"rendered":"https:\/\/kermitmurray.com\/msblog\/?page_id=3114"},"modified":"2023-01-17T20:50:35","modified_gmt":"2023-01-18T02:50:35","slug":"biorxiv-systems-biology","status":"publish","type":"page","link":"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-systems-biology\/","title":{"rendered":"BioRxiv Systems Biology"},"content":{"rendered":"\n<div class=\"wp-block-caxton-grid relative\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-columns caxton-grid-block\" style=\"padding-top:0;padding-left:0;padding-bottom:0;padding-right:0;grid-template-columns:repeat(12, 1fr)\" data-tablet-css=\"padding-left:em;padding-right:em;\" data-mobile-css=\"padding-left:em;padding-right:em;\">\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 8\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"https:\/\/www.biorxiv.org\/alertsrss\" target=\"_blank\" rel=\"noreferrer noopener\">Journal Home<\/a><\/strong><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-caxton-section relative\" style=\"grid-area:span 1\/span 4\"><div class=\"absolute absolute--fill\"><div class=\"absolute absolute--fill cover bg-center\" style=\"background-color:;background-image:linear-gradient( );\"><\/div><div class=\"absolute absolute--fill\" style=\"background-color:;background-image:linear-gradient( );opacity:1;\"><\/div><\/div><div class=\"relative caxton-section-block\" style=\"padding-top:5px;padding-left:5px;padding-bottom:5px;padding-right:5px\" data-mobile-css=\"padding-left:1em;padding-right:1em;\" data-tablet-css=\"padding-left:1em;padding-right:1em;\">\n<p><strong><a href=\"http:\/\/connect.biorxiv.org\/biorxiv_xml.php?subject=systems_biology\" target=\"_blank\" rel=\"noreferrer noopener\">RSS<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n<ul class=\"has-dates has-authors has-excerpts wp-block-rss\"><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.28.728220v1?rss=1'>Development positions malignant cellular states but does notexplain their diversification<\/a><\/div><time datetime=\"2026-06-01T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">June 1, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Poyatos, J. F.<\/span><div class=\"wp-block-rss__item-excerpt\">Epithelial cancers are often described as aberrant reactivations of embryonic or tissue-forming programs, but whether malignant cellular-state diversification is actually constrained by developmental trajectories remains unclear. Here, we present a quantitative framework to test this idea in pancreatic ductal adenocarcinoma (PDAC). Using representation learning on large-scale single-cell data, we build a reference space that captures the main axes of normal foregut and pancreatic epithelial variation. Malignant cells can be mapped into this space, showing that developmental biology helps interpret their [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.29.727857v1?rss=1'>Extracellular vesicles from Manila clam (Ruditapes philippinarum): tailored isolation from hemolymph and insights into water-derived vesicles<\/a><\/div><time datetime=\"2026-06-01T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">June 1, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Moccia, V., Dalla Rovere, g., Minh, T. T., Zendrini, A., Kleinjan, M., Roelofs, M., Berto, P., Zeev-Ben-Mordehai, T., Zaal, E. A., Bergese, P., Radeghieri, A., Milan, M., Wauben, M. H. M., Zappulli, V.<\/span><div class=\"wp-block-rss__item-excerpt\">Extracellular vesicles (EVs) are evolutionarily conserved mediators of intercellular communication released by cells into biological fluids and the extracellular environment. Despite their growing relevance in biomedical and veterinary research, knowledge on EVs in marine bivalves remains limited. The aim of this study was to optimize tailored protocols for EV isolation from the hemolymph of the Manila clam (Ruditapes philippinarum) based on density gradient ultracentrifugation (dgUC) or size exclusion chromatography (SEC). EV-enriched fractions were identified through nanoparticle tracking analysis, protein quantification, [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.28.728221v1?rss=1'>iSsus3744: A Genome-Scale Model-Guided Strategy for Rational Media Design for Cultivated Pork<\/a><\/div><time datetime=\"2026-05-31T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 31, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Gomez Romero, S. I., Vigliotti, M., Ramirez Lopez, V., Nguyen, K., Marchitto, V., Boyle, N. R.<\/span><div class=\"wp-block-rss__item-excerpt\">Cultivated meat production is currently limited by high production costs and an incomplete understanding of cellular metabolism in agriculturally relevant species. Genome-scale metabolic models (GEMs) have successfully guided media optimization in biopharmaceutical systems but have not been widely applied to cultivated meat. In this study, we present iSsus3744, the first genome-scale metabolic reconstruction for Sus scrofa and demonstrate its application for rational media design in cultivated pork production. iSsus3744 was reconstructed using HumanGEM and Recon3D as template models and further [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.27.728139v1?rss=1'>Growth-resolved genome-scale metabolic modeling of Priestia megaterium SR7 validated by chemostat and 13-C flux analysis<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Chang, K. Y. W., Song, Y., Hing, N. Y. K., Vethathirri, R. S., Wang, Y., Thompson, J. R.<\/span><div class=\"wp-block-rss__item-excerpt\">Priestia megaterium SR7 is a promising candidate chassis for bioprocess engineering, but its development is limited by the availability of condition-grounded, mechanistic models that can translate experimental measurements into predictive design hypotheses. Here, we present PMSR7, a genome-scale metabolic model for SR7, and evaluate it under a growth-resolved chemostat framework spanning a dilution-rate series. Stable steady states were established across the growth regime, with the highest dilution rate (D = 1.1538 per hr) excluded from growth interpretation due to biomass [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.27.728132v1?rss=1'>Intestinal Dysbiosis in Necrotic Enteritis: Dissecting the Roles of Eimeria and Clostridium perfringens<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Liu, J., Guo, J., Whitmore, M. A., Tobin, I., Kim, D. M., Zhang, G.<\/span><div class=\"wp-block-rss__item-excerpt\">Necrotic enteritis (NE), caused by Clostridium perfringens, is major enteric disease in poultry with substantial economic impact. NE is frequently triggered by co-infection with Eimeria spp., yet the relative contributions of Eimeria and C. perfringens to NE-induced dysbiosis and disease progression remain poorly defined. To address this, Cobb broiler chickens were challenged with Eimeria maxima, C. perfringens, or both, and ileal and cecal microbiota were analyzed using 16S rRNA gene sequencing and shotgun metagenomics. Temporal dynamics of intestinal microbiota shifts [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.27.728028v1?rss=1'>EndoTwin-W: glycodelin-A and CA-125 as non-invasive biomarkers of endometrial receptivity derived from a multiscale computational digital twin<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Goyal, R.<\/span><div class=\"wp-block-rss__item-excerpt\">Endometrial receptivity assessment currently requires invasive tissue biopsy, yet recent randomized trials have questioned the clinical utility of biopsy-based approaches. Here we present EndoTwin-W, a four-layer mechanistic computational model that simulates human endometrial remodeling from hormone inputs through receptor binding, pathway scoring, and continuous-time Markov chain cell-state transitions across 17 cell states. Transition rates were optimized against scRNA-seq and microarray data, then validated by 5-fold cross-validation on an independent bulk RNA-seq cohort (n=236 biopsies), achieving significant correlations for 16 of [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.27.728168v1?rss=1'>Analyzing the dynamics in defense\/counter-defense games among hosts and pathogens<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Dwivedi, S., Ona, L., Schuster, S.<\/span><div class=\"wp-block-rss__item-excerpt\">In the dynamic interplay between hosts and pathogens, hosts may produce a defense compound that acts as a toxin to deter pathogen attack. Conversely, pathogens may evolve to produce a counter-defense enzyme, neutralizing the host&#039;s toxin. This evolutionary arms race incurs costs for both parties, prompting adaptations and strategic shifts. We conceptualize this interaction as an asymmetric game, with hosts and pathogens as players, and their potential responses &#8211; defense, counter-defense, or inaction &#8211; as their strategic options. In this [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.28.728621v1?rss=1'>Ultrasensitive response in bacterial replication initiation<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Sassi, A. S., Pigolotti, S.<\/span><div class=\"wp-block-rss__item-excerpt\">Bacteria are able to coordinate cell growth and genome replication in different growth conditions.The DNA-binding protein DnaA is responsible for determining initiation of replication,thereby playing a central role in this coordination. Theoretical and experimental studies have shown that stability of the cell cycle requires an ultrasensitive response, i.e., a sharp dependence of the initiation firing rate on the cell volume. However, the source of such ultrasensitivity remains elusive. In this work, we elucidate how the structure and binding affinities of [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.29.728632v1?rss=1'>Modeling of Glucosinolate Biosynthesis During Biotic Stress as a Function of mRNA<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Earle, J., Neefjes, A. C. M., Ploeger, X. S. D., van Laar, M., Van Wees, S. C. M., Schuurink, R. C., van Dijk, A. D. J., Bleeker, P., Hoefsloot, H.<\/span><div class=\"wp-block-rss__item-excerpt\">Glucosinolates are an important group of specialized metabolites in the Brassicaceae family, playing a role as defensive compounds against biotic attackers. In response to biotic stress, plants upregulate glucosinolate biosynthesis in part by increasing the abundance of enzymes in the glucosinolate biosynthetic pathway. As an increase in enzyme abundance is often preceded by an increase in the corresponding mRNA levels, the dynamic changes in mRNA levels should capture the information required to infer how metabolite levels change over time. In [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.29.728731v1?rss=1'>Explainable machine learning reveals an RBP regulatory logic of exon skipping<\/a><\/div><time datetime=\"2026-05-30T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 30, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Raghav, Y., Paul, A., Anderson, R., Karthyk, S., Iturralde, A., Vyas, J., Dy, J., Jones, B. C., Castaldi, P. J., Platig, J.<\/span><div class=\"wp-block-rss__item-excerpt\">RNA binding proteins (RBPs) regulate the life cycle of an mRNA, often through RBP-RNA interactions. This life cycle includes splicing, whereby the intronic sequence of a pre-mRNA is removed and the exons are joined together. However, the patterns of RBP binding that lead to different splicing outcomes are still incompletely understood. Here, we build machine learning models from RBP-RNA binding and knockdown RNA-seq data for over 168 RBPs in two cell lines (HepG2 and K562) to better understand the binding [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.26.727952v1?rss=1'>Multi-state Continuous-Time Markov Chain Modeling for Chronic Kidney Disease Progression<\/a><\/div><time datetime=\"2026-05-29T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 29, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Li, Q., Chu, W., Shahriyari, L.<\/span><div class=\"wp-block-rss__item-excerpt\">This paper presents a unified six-state Continuous-Time Markov Chain (CTMC) framework for Chronic Kidney Disease (CKD) progression, with CKD stages 1-5 modeled as transient states and death as an absorbing state. Under a non-homogeneous CTMC formulation, we derive integral representations for transition probabilities, state distributions, sojourn times, and survival-related quantities. We then study the homogeneous case as a tractable baseline and provide explicit formulas for key quantities. Although the methodology is rooted in standard multi-state theory, these expressions are often [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.28.728366v1?rss=1'>Reconstructing heterogeneous metabolic trajectories of E. coli diauxie via a dynamical Maximum Entropy Principle<\/a><\/div><time datetime=\"2026-05-29T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 29, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ferrero, A., Kratzl, F. P., Kelley, L., Korolev, K., Masoero, D., Segre, D., Dukovski, I., de Martino, D.<\/span><div class=\"wp-block-rss__item-excerpt\">The glucose-acetate diauxic shift in E. coli is classically described as an abrupt, population-wide switch from glucose to acetate consumption. Recent experiments challenge this view, revealing a robust intermediate regime of co-consumption whose single-cell basis remains unresolved: does it reflect coexisting specialized subpopulations, or genuine mixed metabolic states within individual cells? We first develop a two-state consumer-resource model in which cells optimally grow on either glucose or acetate, and show that observed co-consumption trajectories cannot be decomposed into convex combinations [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.27.728330v1?rss=1'>Endocytosis suppresses stochastic collapse in fibroblast-macrophage circuits under shared resource competition<\/a><\/div><time datetime=\"2026-05-29T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 29, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Inoue, K.-i., Ishii, Y., Hariyama, M.<\/span><div class=\"wp-block-rss__item-excerpt\">Interdependent multicellular circuits must maintain stable coexistence despite competition for shared environmental resources. Fibroblast-macrophage circuits represent a conserved signaling architecture in which fibroblasts produce colony-stimulating factor 1 (CSF) to support macrophages, whereas macrophages produce platelet-derived growth factor (PDGF) to support fibroblasts. Previous analytical models proposed receptor-mediated endocytosis as a stabilizing negative-feedback mechanism, but these formulations assumed spatial homogeneity and independently assigned carrying capacities. Here, we constructed a spatial agent-based fibroblast-macrophage circuit model using PhysiCell to investigate how PDGF and CSF [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.25.727308v1?rss=1'>Learning dynamical systems with biochemically informed neural ordinary differential equations<\/a><\/div><time datetime=\"2026-05-28T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 28, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Fonseca, L. L., Laubenbacher, R., Boettcher, L.<\/span><div class=\"wp-block-rss__item-excerpt\">Ordinary differential equation models of biochemical reactions are often formulated as stoichiometric systems in which the dynamics arise from a collection of interacting processes. A central challenge is that the functional form of each process is rarely known a priori and may be difficult to infer from data. We propose biochemically informed neural ordinary differential equations (BINODEs), a neural-ODE framework that retains the stoichiometric structure of mechanistic models while representing individual processes by neural networks. In BINODEs, the outputs of [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.26.725436v1?rss=1'>Spatiotemporal remodeling of cytoskeletal and junction networks during somatic cell reprogramming<\/a><\/div><time datetime=\"2026-05-28T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 28, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Samson, R., Kitaygorodsky, J., Tersigni, M., Tursun, T., Hu, Q., Hardy, W. R., Trcka, D., Rost, H., Wrana, J. L., Samavarchi-Tehrani, P., Gingras, A.-C.<\/span><div class=\"wp-block-rss__item-excerpt\">Summary\/AbstractReprogramming somatic cells into induced pluripotent stem cells involves a dramatic reorganization of the cytoskeleton and junctions during the critical mesenchymal-to-epithelial transition stage. While protein abundance changes have been profiled, the spatiotemporal dynamics of protein-protein associations involving these structural components remain poorly resolved. Here, we present a time-resolved proximity proteomics resource that maps cytoskeletal and junctional remodeling across 27 baits during the early stages of reprogramming. We identified over 1100 high-confidence interactions, including many not previously reported, capturing the dynamic [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.22.727332v1?rss=1'>hB-PAC: A non-invasive aging clock for quantifying individual differences in aging<\/a><\/div><time datetime=\"2026-05-27T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 27, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ando, Y., Yada, Y., Kashima, M., Bessho, Y., Hirata, H., Naoki, H., MATSUI, T.<\/span><div class=\"wp-block-rss__item-excerpt\">Aging progresses heterogeneously among individuals, and aging clocks that estimate biological age have been developed to quantify this heterogeneity. However, existing methods depend on invasive tissue sampling or long-term longitudinal data, and a mathematical framework that explicitly quantifies individual differences in aging has not yet been established. Here, we employed a progeroid zebrafish model (klotho mutant; kl-\/-) and developed a hierarchical Bayesian framework, termed the hierarchical Bayesian-Multimodal Aging Clock (hB-MAC), which integrates non-invasive snapshot data of behavior and morphology with [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.21.727040v1?rss=1'>A Cross-tissue Temporal Multi-omics Atlas Reveals the Molecular Architecture of Pressure Injury<\/a><\/div><time datetime=\"2026-05-26T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 26, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Jiang, F., Li, L., Zhao, Y., Zhang, T., Li, X., Wei, J., Liu, X., Jia, Y., An, M., Jiao, X.<\/span><div class=\"wp-block-rss__item-excerpt\">Pressure injury induces progressive necrosis of skin and deep muscle, yet how mechanical loading reshapes molecular programs across tissues and time remains poorly defined. To address this gap, we established a rat pressure-injury model and performed longitudinal integrated profiling of skin and deep muscle across four stages, combining transcriptomics, proteomics and untargeted metabolomics with histological assessment. This multi-layered atlas revealed a staged injury program dominated by early transcriptional activation of innate immunity and complement-coagulation crosstalk, accompanied by neutrophil-associated responses and [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.20.726705v1?rss=1'>Fatty acid metabolic interactome atlas linked to cellular longevity<\/a><\/div><time datetime=\"2026-05-23T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 23, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Naaz, A., Gao, M., Zhang, Y., Dorajoo, R., Kennedy, B. K., Alfatah, M.<\/span><div class=\"wp-block-rss__item-excerpt\">Fatty acid biosynthesis is a central metabolic process required for membrane formation, organelle maintenance, and cellular proliferation, yet its broader relationship with stress responses and cellular aging remains incompletely understood. Here, we combined human and yeast interactome analyses with transcriptomic profiling and chronological lifespan assays to investigate the systems-level organization of fatty acid metabolic pathways and their relationship to cellular longevity. Integrated interactome mapping of mammalian fatty acid metabolic regulators, including ACACA, FASN, SCD, ACSL, ELOVL, and SLC27 family proteins, [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.20.726524v1?rss=1'>A conserved transcriptomic model defines metabolic resilience and vulnerability in obesity<\/a><\/div><time datetime=\"2026-05-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Su, Y.-Y., Bundalian, L. T., Chen, Y.-C., Gjermeni, E., Gille, B., Richter, S., Jasaszwili, M., Palma-Vera, S., Hoffmann, A., Ghosh, A., Wolfrum, C., Bluher, M., Peleg, S., Garten, A., Le Duc, D., Lin, C.-C.<\/span><div class=\"wp-block-rss__item-excerpt\">BackgroundObesity arises from a complex interplay of genetic and environmental factors, with alterations of transcriptional networks that integrate metabolic, immune, and regulatory pathways. Conventional measures such as body mass index (BMI) quantify body size but fail to capture the molecular heterogeneity underlying divergent metabolic outcomes. We therefore sought to construct a gene expression-based transcriptomic representation of obesity, using BMI as a practical training anchor, and to use this framework to delineate transcriptional programs associated with metabolically healthy and pathogenic obesity, [&hellip;]<\/div><\/li><li class='wp-block-rss__item'><div class='wp-block-rss__item-title'><a href='https:\/\/www.biorxiv.org\/content\/10.64898\/2026.05.20.726376v1?rss=1'>A Simulation of Semi-Infectious Particles and Genome Complementation Reproduces Interferon Response by Respiratory Epithelial Cells in vitro during Influenza A Virus Infection<\/a><\/div><time datetime=\"2026-05-22T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">May 22, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Dal-Castel, P. C., Resnick, J. D., Sluka, J. P., Gallagher, M. E., Helfers, M., Bird, I. M., Ratcliff, J. D., Grady, S. L., Glazier, J. A.<\/span><div class=\"wp-block-rss__item-excerpt\">In the respiratory epithelium, interferon (IFN)-induced antiviral resistance acts as a defense against infection. Influenza A viruses (IAVs) have evolved multiple strategies to counteract these defenses, including expression of the viral protein NS1, which inhibits both IFN production and the IFN-mediated transcription of Interferon Stimulated Genes (ISG) in infected cells. However, experiments show that this inhibition is imperfect, especially at a low multiplicity of infection (MOI). One hypothesis to describe this phenomenon relies on the presence of Semi-infectious Particles (SIPs) [&hellip;]<\/div><\/li><\/ul>\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\/>\n\n\n\n<h4 class=\"wp-block-heading\">Related Journals<\/h4>\n\n\n<ul class=\"su-siblings\"><li class=\"page_item page-item-3099\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biochemistry\/\">BioRxiv Biochemistry<\/a><\/li>\n<li class=\"page_item page-item-3112\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-bioinformatics\/\">BioRxiv Bioinformatics<\/a><\/li>\n<li class=\"page_item page-item-3132\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-biophysics\/\">BioRxiv Biophysics<\/a><\/li>\n<li class=\"page_item page-item-3188\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-cancer-biology\/\">BioRxiv Cancer Biology<\/a><\/li>\n<li class=\"page_item page-item-3190\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-pharmacology-and-toxicology\/\">BioRxiv Pharmacology and Toxicology<\/a><\/li>\n<li class=\"page_item page-item-3193\"><a href=\"https:\/\/kermitmurray.com\/msblog\/links\/journal-feeds\/biochemistry-journal-feeds\/biorxiv\/biorxiv-zoology\/\">BioRxiv Zoology<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Related Journals<\/p>\n","protected":false},"author":1,"featured_media":2652,"parent":3087,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3114","page","type-page","status-publish","has-post-thumbnail","hentry","entry"],"_links":{"self":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/comments?post=3114"}],"version-history":[{"count":1,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114\/revisions"}],"predecessor-version":[{"id":3115,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3114\/revisions\/3115"}],"up":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/pages\/3087"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/media\/2652"}],"wp:attachment":[{"href":"https:\/\/kermitmurray.com\/msblog\/wp-json\/wp\/v2\/media?parent=3114"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}