{"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\/2026.04.16.718710v1?rss=1'>Evaluating splicing factor and kinase network crosstalk through global phosphoproteomics<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Crowl, S., Singh, S., Zhang, T., Naegle, K. M.<\/span><div class=\"wp-block-rss__item-excerpt\">Both splicing and kinase signaling are biochemical processes that fundamentally determine and shape cell physiology. Although there has been some indication that there is an interaction between the two &#8212; splicing can alter the availability of exons encoding kinase targets and kinases can phosphorylate splicing factors &#8212; it has yet to be established the extent to which altering splicing factor expression impacts kinase signaling networks. In this work, we implemented a data-driven analysis using ENCODE RNA-sequencing data and prior work [&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\/2026.04.17.719123v1?rss=1'>Water-mediated productivity dynamics in shifting coral reef communities<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Vetter, J., Engelhardt-Stolz, K. E., Dietzmann, A., Woehrmann-Zipf, F., Ziegler, M.<\/span><div class=\"wp-block-rss__item-excerpt\">Mass mortality of reef-building stony corals has driven widespread community shifts towards reefs dominated by soft corals and macroalgae. Although physical competition for space between these organisms plays an important role, non-contact water-mediated interactions have been proposed to modulate organismal performance and community functioning, yet their independent effects remain poorly resolved. Here, we experimentally tested the hypothesis that water-mediated interactions generate non-additive effects on community productivity, altering ecosystem functioning during phase shifts. Using two controlled incubation experiments with representative stony [&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\/2026.04.17.719305v1?rss=1'>Multimodal analysis of molecular remodeling in aging spleen identified global and cell type specific changes<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Vlajic, K., Luciano, A., Merrihew, G. E. E., Attar, S., Sanchez, C. R., Riffle, M., Beliveau, B., Sweetwyne, M. T., Tsantilas, K. A., Churchill, G. A., MacCoss, M. J., Schweppe, D. K.<\/span><div class=\"wp-block-rss__item-excerpt\">Aging reshapes the cellular and molecular landscape of mammalian tissues. These changes can be progressive, preceding linearly with age, or occur as abrupt transitions of the course of lifespan. To investigate the age-dependent cellular and molecular shifts we profiled matched proteomes and transcriptomes from male and female murine spleens across eight time points, from stable adults through late life. The spleen was chosen to integrate understanding of age-dependent changes associated with immune surveillance, inflammaging, and immune-related proteostasis. Male and female [&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\/2026.04.19.719409v1?rss=1'>Cell-type-resolved Metabolic Flux Inference Reveals Stromal Metabolic Reprogramming Across Human Cardiomyopathies<\/a><\/div><time datetime=\"2026-04-21T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 21, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Sakuma, T., Ohno, S., Shimizu, H.<\/span><div class=\"wp-block-rss__item-excerpt\">Metabolic remodeling is a hallmark of cardiomyopathy, yet which cell types bear the metabolic burden and how cell-type-specific contributions are disrupted remain unclear. Here, we developed a cell-type-resolved genome-scale metabolic flux inference pipeline optimized for post-mitotic cardiac tissue by maximizing ATP synthesis rather than biomass production and applied it to a single-nucleus transcriptomic atlas of human cardiomyopathies (78 donors, 869,449 nuclei). Metabolic impairment in dilated cardiomyopathy (DCM) was most profound in stromal cells, whereas myeloid cells exhibited opposing metabolic activation. [&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\/2026.04.17.716662v1?rss=1'>Conserved metabolic vulnerabilities across pathogenic coronaviruses nominate host-directed therapeutic targets<\/a><\/div><time datetime=\"2026-04-20T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 20, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Dohai, B., El Assal, D. C., Kang, M., Jaiswal, A., Poulet, C., Daakour, S., Nelson, D. R., Falter-Braun, P., Twizere, J.-C., Salehi-Ashtiani, K.<\/span><div class=\"wp-block-rss__item-excerpt\">Pathogenic coronaviruses profoundly rewire host cell metabolism to support viral replication, yet whether these metabolic alterations expose shared and actionable vulnerabilities remains unclear. By integrating transcriptomic profiles from cells infected with SARS-CoV, SARS-CoV-2, and MERS-CoV with genome-scale metabolic models, we identify conserved and virus-specific metabolic perturbations affecting mitochondrial transport, nucleotide biosynthesis, fatty acid metabolism, and redox balance. Despite distinct transcriptional responses, all three viruses converge on a limited set of metabolic reactions whose flux ranges deviate strongly from healthy states. [&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\/2026.04.20.719630v1?rss=1'>SugarBase: mapping glycomolecule precursors in microbes<\/a><\/div><time datetime=\"2026-04-20T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 20, 2026<\/time> <span class=\"wp-block-rss__item-author\">by van Ede, J. M., Sorensen, M. C. H., van Loosdrecht, M., Pabst, M.<\/span><div class=\"wp-block-rss__item-excerpt\">Glycan biosynthesis relies on nucleotide-activated sugars, essential metabolites across all domains of life, yet their usage in microbes is poorly understood. Here we present SugarBase, a mass spectrometry and bioinformatic pipeline for untargeted exploration of microbial nucleotide sugar networks. SugarBase resolves the chemical complexity of microbial metabolism by combining narrow-window DIA fragmentation with a chemistry-informed parent ion identification algorithm. Applying SugarBase across a broad phylogenetic range of microbes revealed extensive, species-specific nucleotide sugar profiles, including many candidates with no existing [&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\/2026.04.15.718746v1?rss=1'>Extreme genome reduction selectively retains modular regulatory architecture in Prochlorococcus MED4: conserved transcriptional modules reveal core physiological regulatory programs<\/a><\/div><time datetime=\"2026-04-19T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 19, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Johnson, Z., Sadler, N., Garcia, M., Li, X., Rozum, J., Anderson, L. N., Zhang, T., Feng, S., QIAN, W.-J., Cheung, M., Bohutskyi, P.<\/span><div class=\"wp-block-rss__item-excerpt\">Prochlorococcus MED4 is a minimal photoautotroph whose extreme genome streamlining extends to its transcriptional regulatory architecture, yet it dominates high-light oligotrophic surface waters and drives marine carbon cycling. Despite ecological significance, MED4 remains genetically intractable, lacking molecular tools to characterize regulatory mechanisms and construct a transcriptome-wide regulatory map. To address this, we assembled an RNA-seq compendium of 253 samples, including 207 new samples capturing transcriptional responses across three classes of experiments: diverse environmental perturbations, a 24-hour circadian cycle, and phage [&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\/2026.04.14.718573v1?rss=1'>A Modular Machine Learning Framework for Small-Molecule Drug Repurposing Based on Organ Permeability, Target Binding, and Biomarker Modulation<\/a><\/div><time datetime=\"2026-04-17T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 17, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Arora, H. S., Dhakal, S., Psarellis, Y., Chandrasekaran, S., Mavroudis, P. D., Pillai, N.<\/span><div class=\"wp-block-rss__item-excerpt\">With nearly 90% of drug candidates failing in clinical trials due to poor efficacy or toxicity, drug repurposing has emerged as a vital strategy to accelerate the delivery of life-saving treatments. However, most current drug repurposing approaches fail to account for the physiological barriers and downstream biological impacts that dictate therapeutic success. To bridge this gap, we present SCOUT (Screening Candidates via Organ Uptake and Target-binding), a modular machine learning-driven framework for drug repurposing by simultaneously modeling organ permeability, drug-target [&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\/2026.04.14.718270v1?rss=1'>SEC-seq reveals translation-focused metabolic strategies for high IgG productivity in clonal CHO cells<\/a><\/div><time datetime=\"2026-04-17T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 17, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Tat, J., Lay, F. D., Stevens, J., Lewis, N. E.<\/span><div class=\"wp-block-rss__item-excerpt\">Chinese hamster ovary (CHO) cells are the dominant host for therapeutic protein production, yet intra- and inter-clonal heterogeneity in manufacturing phenotypes, and the underlying metabolic and secretory circuitry, remain poorly defined at single-cell resolution. Here, we apply secretion encoded single-cell sequencing (SEC-seq) to simultaneously measure transcriptomes and secreted IgG in single-cells from a parental production cell line and five CHO clones, each varying in cell-specific productivity. IgG mRNA and recombinant protein secretion are only moderately correlated across single cells, indicating [&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\/2026.04.14.717254v1?rss=1'>Orchard management alters citrus root and rhizosphere microbiomes with functional consequences for plant performance<\/a><\/div><time datetime=\"2026-04-17T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 17, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ginnan, N., Jones, R., Wu-Woods, J., Pervaiz, T., El-kereamy, A., Ashworth, V. E., Hamid, M. I., Dawson, E. K., Strauss, S. L., Stajich, J., Rolshausen, P., Roper, M. C.<\/span><div class=\"wp-block-rss__item-excerpt\">Agricultural management practices act as ecological disturbances that can restructure soil and plant-associated microbial communities, but the functional consequences of these microbial shifts on crop performance remain poorly understood. Here, we examined how common orchard inputs, including wood mulch, glyphosate, and humic acid, affect citrus root and rhizosphere microbiomes and tree performance over a three-year field experiment. Mulch emerged as the dominant driver of microbiome structure, significantly altering bacterial and fungal community composition and increasing rhizosphere alpha diversity. Root microbiomes [&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\/2026.04.14.718493v1?rss=1'>Serum protein profiling reveals hallmark-level aging trajectories and strain-specific resilience in CB6F1J and C57BL\/6J male mice.<\/a><\/div><time datetime=\"2026-04-17T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 17, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Liao, G. Y., Pettan-Brewer, C., Ladiges, W. C.<\/span><div class=\"wp-block-rss__item-excerpt\">Aging is characterized by coordinated molecular and physiological changes across multiple biological systems, yet the ability to quantify these processes non-invasively within individuals remains limited. Here, we establish a framework for quantifying hallmark-level features of aging in mice using serum protein array profiles obtained from a single blood draw. Serum protein expression was profiled in groups of CB6F1J and C57BL\/6J male mice at 8 and 32 months of age and mapped to established hallmarks of aging. Hallmark-level analyses revealed coordinated, [&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\/2026.04.14.718509v1?rss=1'>Dynamic cancer dormancy and awakening emerge from tumor microenvironment feedback in a minimal theoretical model<\/a><\/div><time datetime=\"2026-04-17T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 17, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Yanez Feliu, G. A., Rossato, G., Valleriani, A., Cipitria, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Cancer cell dormancy is characterized by late relapse and therapy resistance, yet the mechanisms that awaken dormant cells remain poorly understood. The tumor microenvironment has emerged as a key driver of these state transitions. Here we present a theoretical framework based on evolutionary game theory in which interactions between cancer and host cells are coupled explicitly to a changing tumor microenvironment. Cancer cells produce a conditioning factor that is cleared by the microenvironment and tolerated only up to a threshold. [&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\/2026.04.10.717853v1?rss=1'>EML4-ALK Fusion Rewires Transcriptomic, miRNA, and CAF-Associated Programs in Non-Small Cell Lung Cancer<\/a><\/div><time datetime=\"2026-04-16T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 16, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Mishra, D., Agrawal, S., Malik, D., Pathak, E., Mishra, R.<\/span><div class=\"wp-block-rss__item-excerpt\">O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC=&quot;FIGDIR\/small\/717853v1_ufig1.gif&quot; ALT=&quot;Figure 1&quot;&gt; View larger version (42K): org.highwire.dtl.DTLVardef@183ec5org.highwire.dtl.DTLVardef@1c12ef4org.highwire.dtl.DTLVardef@1f1ab9dorg.highwire.dtl.DTLVardef@139dcb2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG This study establishes an integrative framework that combines paired mRNA\/miRNA profiling with immune microenvironmental features to clarify how EML4-ALK fusions shape transcriptomic and post-transcriptional networks in Non-small cell lung cancer (NSCLC). Using paired mRNA-seq and miRNA-seq data generated from the same patients, we compared fusion-positive and fusion-negative NSCLC across three interconnected layers: (i) transcriptome architecture, including differential expression, pathway, and network analyses; (ii) the [&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\/2026.04.13.718310v1?rss=1'>Opto-MDMi: a dual-lock optogenetic system for robust activation of endogenous p53<\/a><\/div><time datetime=\"2026-04-16T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 16, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Tsuruoka, T., Sumikama, T., Nakashima, S., Goto, Y., Aoki, K.<\/span><div class=\"wp-block-rss__item-excerpt\">Optogenetics has emerged as a powerful technology for manipulating biological functions with high spatiotemporal resolution, yet the precise control of endogenous molecules remains a significant challenge. In this study, we developed Opto-MDMi, a dual-lock optogenetic platform designed to control the activity of endogenous p53, a master regulator of cell cycle and apoptosis. The p53 pathway is strictly governed by its negative regulators, MDM2 and MDMX, which inhibit p53 through direct binding and ubiquitination. Our system integrates two distinct light-responsive modules: [&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\/2026.04.12.717976v1?rss=1'>Mapping kidney trait heritability to individual cells reals disease-specific remodeling of genetic risk architecture<\/a><\/div><time datetime=\"2026-04-16T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 16, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Hu, H.<\/span><div class=\"wp-block-rss__item-excerpt\">Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function and disease, yet the cell-type-specific mechanisms through which these variants act remain largely unknown. Here, we construct the Kidney Genetic Disease Cell Atlas by applying single-cell disease relevance scoring (scDRS) to map GWAS signals for six kidney-related traits-estimated glomerular filtration rate (eGFR), cystatin C-based eGFR (eGFRcys), blood urea nitrogen (BUN), urinary albumin-to-creatinine ratio (UACR), type 2 diabetes (T2D), and IgA nephropathy (IgAN) onto a comprehensive single-nucleus [&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\/2026.04.15.718822v1?rss=1'>NovoGlyco: mapping protein glycosylation in prokaryotes<\/a><\/div><time datetime=\"2026-04-16T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 16, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Pabst, M., Soic, D.<\/span><div class=\"wp-block-rss__item-excerpt\">Protein glycosylation in prokaryotes shows extraordinary diversity including species-specific monosaccharides, non-canonical attachment sites, and variable glycan architectures that challenge existing glycoproteomics approaches. Current strategies are largely tailored to eukaryotic systems and depend on predefined glycan databases or prior biochemical knowledge, limiting their application to microbes. Here we present NovoGlyco, a modular glycoproteomics platform for untargeted characterisation of prokaryotic protein glycosylation from shotgun proteomics data. NovoGlyco integrates de novo oxonium ion discovery, sequence tag matching, and mass offset binning to identify [&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\/2026.04.12.718001v1?rss=1'>iCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99<\/a><\/div><time datetime=\"2026-04-15T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 15, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Feng, C., Hu, P., Zhu, Y., Ke, W., Gao, X., Ding, C., Zhai, B., Wang, L., Dai, Z.<\/span><div class=\"wp-block-rss__item-excerpt\">Cryptococcus neoformans is a ubiquitous environmental fungus that can also cause life-threatening infections in immunocompromised individuals. As a competent pathogen, Cryptococcus needs to reprogram its metabolism to adapt to the drastic differences between environmental niches and host niches. A well-curated genome-scale metabolic model (GEM) is a powerful tool to facilitate the investigation of the metabolic resilience of an organism. Here we reconstructed and validated iCNG99, a GEM for C. neoformans reference strain H99, and evaluated its predictive performance across 43 [&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\/2026.04.13.718184v1?rss=1'>Targeting HIV at its core: A mathematical model for optimizing Tat Inhibitor-based therapies toward enhanced functional cure strategies<\/a><\/div><time datetime=\"2026-04-15T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 15, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Waema, R., Adongo, C., Lago, S., Ogutu, K.<\/span><div class=\"wp-block-rss__item-excerpt\">Human immunodeficiency virus (HIV) persistence remains a major barrier to cure due to the existence of long-lived latent reservoirs that evade immune clearance and persist despite combination antiretroviral therapy (ART). Although ART effectively suppresses viral replication, treatment interruption often leads to rapid viral rebound originating from these latent reservoirs. In this study, we develop a deterministic mathematical model describing the in vivo dynamics of HIV infection incorporating uninfected CD4+ T cells, infected cells, latent reservoirs, deep latent reservoirs, and infectious [&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\/2026.04.13.718123v1?rss=1'>Gene Expression Variability with Feedback Regulation Implemented via Protein-Dependent Cell Growth<\/a><\/div><time datetime=\"2026-04-15T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 15, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Zabaikina, I., Bokes, P., Singh, A.<\/span><div class=\"wp-block-rss__item-excerpt\">Variability in gene expression among single cells and growing cell populations can arise from the stochastic nature of protein synthesis, which often occurs in random bursts. This study investigates the variability in the expression of a growth-sustaining protein, whose concentration is regulated by a negative feedback loop due to cell growth-induced dilution. We model the distribution of protein concentration using a Chapman-Kolmogorov equation for single cells and a population balance equation for growing cell populations. For single cells, we derive [&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\/2026.04.14.718551v1?rss=1'>Tracing cell communication programs across conditions at single cell resolution with CCC-RISE<\/a><\/div><time datetime=\"2026-04-15T00:00:00-05:00\" class=\"wp-block-rss__item-publish-date\">April 15, 2026<\/time> <span class=\"wp-block-rss__item-author\">by Ramirez, A., Thomas, N., Calabrese, D. R., Greenland, J. R., Meyer, A. S.<\/span><div class=\"wp-block-rss__item-excerpt\">Cell-cell communication (CCC) mediates coordinated cellular activities that vary dynamically across time, location, and biological context. While various tools exist to infer CCC, they typically aggregate data according to pre-defined cell types, obscuring critical single-cell heterogeneity. Furthermore, because signaling pathways and cell populations operate in a coordinated manner, an integrative analytical approach is essential. To address these challenges, we developed CCC-RISE, an extension of the tensor-based method Reduction and Insight in Single-cell Exploration (RISE). CCC-RISE identifies integrative patterns of single-cell [&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}]}}