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<title>bioRxiv Subject Collection: Systems Biology</title>
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This feed contains articles for bioRxiv Subject Collection "Systems Biology"
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<title>bioRxiv</title>
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<link>https://www.biorxiv.org</link>
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<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.718509v1?rss=1">
<title>
<![CDATA[
Dynamic cancer dormancy and awakening emerge from tumor microenvironment feedback in a minimal theoretical model 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.718509v1?rss=1
</link>
<description><![CDATA[
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. Through this conditioning factor, the microenvironment feeds back on cancer-host interactions and reshapes their competitive balance. Unlike a model with fixed interactions, this feedback allows dormancy and awakening to emerge as dynamic outcomes of microenvironmental change. We show that this minimal coupling is sufficient to generate distinct long-term regimes. Across these regimes, feedback generates thresholds and history dependence, so that the same cancer population can follow different fates depending on whether the microenvironment is already primed. Our framework reduces these dynamics to experimentally and biologically interpretable parameters linked to conditioning factor production, clearance, tolerance, and microenvironment-dependent changes in cancer-host competition. More broadly, it provides a quantitative basis for testing how collective microenvironmental feedback shapes cancer dormancy and awakening, and for designing experiments to uncover the mechanisms that awaken dormant cancer cells.
]]></description>
<dc:creator><![CDATA[ Yanez Feliu, G. A., Rossato, G., Valleriani, A., Cipitria, A. ]]></dc:creator>
<dc:date>2026-04-17</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.718509</dc:identifier>
<dc:title><![CDATA[Dynamic cancer dormancy and awakening emerge from tumor microenvironment feedback in a minimal theoretical model]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.718493v1?rss=1">
<title>
<![CDATA[
Serum protein profiling reveals hallmark-level aging trajectories and strain-specific resilience in CB6F1J and C57BL/6J male mice. 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.718493v1?rss=1
</link>
<description><![CDATA[
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, pathway-specific changes in inflammatory, vascular, intercellular signaling, metabolic, and regenerative processes, with distinct patterns observed between strains. CB6F1J mice exhibited directional shifts across multiple pathways, while C57BL/6J mice showed broader but more heterogeneous changes. Cross-strain comparisons demonstrated shared pathway-level trends alongside variable protein-level concordance. This approach enables non-terminal assessment of aging within individuals and resolves heterogeneity in aging trajectories using minimally invasive sampling. These findings support the use of circulating protein signatures to quantify biological aging and provide a framework for translating non-invasive proteome-based assessment of aging to human studies.
]]></description>
<dc:creator><![CDATA[ Liao, G. Y., Pettan-Brewer, C., Ladiges, W. C. ]]></dc:creator>
<dc:date>2026-04-17</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.718493</dc:identifier>
<dc:title><![CDATA[Serum protein profiling reveals hallmark-level aging trajectories and strain-specific resilience in CB6F1J and C57BL/6J male mice.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.717254v1?rss=1">
<title>
<![CDATA[
Orchard management alters citrus root and rhizosphere microbiomes with functional consequences for plant performance 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.717254v1?rss=1
</link>
<description><![CDATA[
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 remained comparatively stable, suggesting stronger host selective forces within root tissues. Mulched rhizospheres were enriched with saprotrophic fungi and metabolically diverse bacteria, while non-mulched soils contained taxa typically associated with nutrient cycling, like Rhizobium, Sphingomonas, and Nitrososphaera. Interactions between mulch and glyphosate further reshaped bacterial communities and corresponded with reduced tree physiological performance, including photosynthesis rates. To verify whether these microbial shifts were contributing to these plant phenotype changes, we conducted a greenhouse experiment using field-derived soil microbiota. Active microbiota from mulch-treated soils reduced citrus seedling establishment and root growth relative to microbiota from non-mulched soils, whereas heat-killed controls eliminated these negative effects, demonstrating a causal relationship between management-induced microbiota changes and decreases in plant performance. In contrast, humic acid influenced plant growth primarily through direct abiotic effects rather than microbial community-level traits. Together, our results show that orchard management practices can restructure citrus microbiomes and generate community-level traits that influence plant performance, highlighting the importance of incorporating microbial ecology and microbiome information when designing and testing crop management strategies.
]]></description>
<dc:creator><![CDATA[ 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. ]]></dc:creator>
<dc:date>2026-04-17</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.717254</dc:identifier>
<dc:title><![CDATA[Orchard management alters citrus root and rhizosphere microbiomes with functional consequences for plant performance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.718270v1?rss=1">
<title>
<![CDATA[
SEC-seq reveals translation-focused metabolic strategies for high IgG productivity in clonal CHO cells 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.718270v1?rss=1
</link>
<description><![CDATA[
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 that transcription alone does not explain intra-clonal secretion heterogeneity. By integrating SEC-seq with single-cell metabolic and secretory task scoring, we find that CHO cells accommodating recombinant protein expression burden have more active translation-associated pathways and suppressed energy-intensive endogenous secreted protein processing. Three high-secreting clones converge on this translation-focused state but differ in their subpopulation composition and energy/redox programs coupled to IgG output: one highly productive clone shows a low-growth, glycolytic, NAD/one-carbon-associated and UPR-activated program; a second shows increased oxidative phosphorylation and fatty-acid {beta}-oxidation, and a third shows higher lipid-uptake with modest central carbon metabolism. Genes such as Aldoa, Ndufab1, Acsl5, Mthfd2 showed clone-specific correlations with IgG, linking glycolysis, mitochondrial respiration, fatty-acid metabolism, and redox to secretion. Together, these results demonstrate that SEC-seq can resolve IgG-coupled metabolic-secretory wiring within and between CHO clones, providing a framework to identify subpopulation and circuit features to engineer or select for improved recombinant protein production.
]]></description>
<dc:creator><![CDATA[ Tat, J., Lay, F. D., Stevens, J., Lewis, N. E. ]]></dc:creator>
<dc:date>2026-04-17</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.718270</dc:identifier>
<dc:title><![CDATA[SEC-seq reveals translation-focused metabolic strategies for high IgG productivity in clonal CHO cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.718573v1?rss=1">
<title>
<![CDATA[
A Modular Machine Learning Framework for Small-Molecule Drug Repurposing Based on Organ Permeability, Target Binding, and Biomarker Modulation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.718573v1?rss=1
</link>
<description><![CDATA[
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 binding, and biomarker modulation. Unlike conventional repurposing efforts that rely on single-point predictions or disconnected steps, SCOUT integrates these factors into a unified predictive funnel, significantly reducing trial-and-error and prioritizing candidates with the highest probability of therapeutic relevance. As a proof of concept, we applied SCOUT to Alzheimer disease, where blood-brain barrier (BBB) penetration is an important hurdle. The framework first predicts the unbound brain-to-plasma partition coefficient, Kpuu, using SMILES-based embeddings and achieved robust performance (Accuracy: 0.90, Recall: 0.94), then integrates machine learning to predict binding affinity with BACE1, a key Alzheimer target that was used in this work as a proof of concept. By combining these modules, SCOUT reduced the candidate search space by 99.9% and identified hits with diverse mechanisms of action. Critically, SCOUT extends beyond hit identification by employing mechanistic modeling to simulate how these candidates might modulate specific disease biomarkers (e.g. Amyloid-beta (A{beta}) peptides). This hybrid approach ensures repurposed candidates are not only chemically viable but also physiologically active, providing a rational and resource-efficient pipeline for drug development.
]]></description>
<dc:creator><![CDATA[ Arora, H. S., Dhakal, S., Psarellis, Y., Chandrasekaran, S., Mavroudis, P. D., Pillai, N. ]]></dc:creator>
<dc:date>2026-04-17</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.718573</dc:identifier>
<dc:title><![CDATA[A Modular Machine Learning Framework for Small-Molecule Drug Repurposing Based on Organ Permeability, Target Binding, and Biomarker Modulation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.12.717976v1?rss=1">
<title>
<![CDATA[
Mapping kidney trait heritability to individual cells reals disease-specific remodeling of genetic risk architecture 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.12.717976v1?rss=1
</link>
<description><![CDATA[
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 RNA-seq atlas of 304,652 kidney cells spanning five clinical conditions (healthy reference, acute kidney injury [AKI], COVID-19-associated AKI [COV-AKI], diabetic kidney disease [DKD], and hypertensive chronic kidney disease [H-CKD]). We validate enrichment patterns using Slide-seqV2 spatial transcriptomics from 920,088 beads across 44 pucks, demonstrating strong cross-platform concordance (Spearman rho = 0.72-0.89). Disease-condition-specific analysis reveals dramatic remodeling of genetic risk distribution across cell types, with fibroblasts gaining T2D enrichment in DKD (Delta = +1.07) and immune cells dominating IgAN risk across all conditions (Cohens d = 1.40). Gene-level correlation analysis identifies condition-specific molecular programs, including mitochondrial gene dominance for eGFRcys and PDE4D emergence for T2D/UACR. By integrating scDRS rank shifts with druggability databases, we nominate three high-priority therapeutic targets-PDE4D (roflumilast), ITGB6 (STX-100), and SPP1 (anti-OPN antibody)-each showing disease-specific upregulation in distinct cell populations. The Kidney Genetic Disease Cell Atlas provides a resource for understanding the cellular basis of kidney disease heritability and identifying condition-specific therapeutic opportunities.
]]></description>
<dc:creator><![CDATA[ Hu, H. ]]></dc:creator>
<dc:date>2026-04-16</dc:date>
<dc:identifier>doi:10.64898/2026.04.12.717976</dc:identifier>
<dc:title><![CDATA[Mapping kidney trait heritability to individual cells reals disease-specific remodeling of genetic risk architecture]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.13.718310v1?rss=1">
<title>
<![CDATA[
Opto-MDMi: a dual-lock optogenetic system for robust activation of endogenous p53 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.13.718310v1?rss=1
</link>
<description><![CDATA[
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: Opto-MDMi (LOVTRAP), which regulates the nuclear translocation of p53-activating peptides, and Opto-MDMi (LOV2-PMI), which controls the binding activity of these peptides by photocaging them within the AsLOV2 domain. Through extensive in vitro screening and live-cell assays, we discovered that truncating the J helix of LOV2 effectively restricts the movement of fused inhibitory peptides, thereby masking their interaction with MDM2/MDMX under dark conditions. By combining these two regulatory layers into a dual-lock system, we achieved robust light-dependent activation of endogenous p53 while significantly suppressing basal activity in the dark. Our findings not only provide a potent tool for p53 research but also establish a general design principle for optogenetically regulating functional peptides with the LOV2 domain, offering a versatile framework for the future development of optogenetic actuators.
]]></description>
<dc:creator><![CDATA[ Tsuruoka, T., Sumikama, T., Nakashima, S., Goto, Y., Aoki, K. ]]></dc:creator>
<dc:date>2026-04-16</dc:date>
<dc:identifier>doi:10.64898/2026.04.13.718310</dc:identifier>
<dc:title><![CDATA[Opto-MDMi: a dual-lock optogenetic system for robust activation of endogenous p53]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.10.717853v1?rss=1">
<title>
<![CDATA[
EML4-ALK Fusion Rewires Transcriptomic, miRNA, and CAF-Associated Programs in Non-Small Cell Lung Cancer 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.10.717853v1?rss=1
</link>
<description><![CDATA[
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 miRNA-mRNA regulatory axis, encompassing dysregulated miRNAs, target repression and sponging, and fusion-specific regulatory pairs; and (iii) the tumor microenvironment, with emphasis on immune and stromal infiltration, particularly cancer-associated fibroblast (CAF)-linked extracellular matrix (ECM) and adhesion programs. Our analyses revealed a distinct reprogramming pattern in fusion-positive NSCLC, marked by activation of metabolic and proteostasis pathways, including N-glycan metabolism coupled to ER export, together with attenuation of immune-stromal communication, adhesion, ECM, calcium signaling, and PI3K/VEGF-axis transcription relative to fusion-negative NSCLC. We also identified fusion-associated microRNA perturbations, including an exclusively upregulated miR-3065-centered regulatory hub predicted to repress ECM- and adhesion-related genes (PDGFRB, CTSK, COL4A2, SPARC, FBN1, and LUM) in fusion-positive tumors, in contrast to broader miRNA network rewiring in fusion-negative tumors targeting ciliary and mitotic hubs. Tumor microenvironment analysis further distinguished the subtypes, with fusion-positive tumors showing reduced CAF infiltration relative to fusion-negative tumors and concordant gene-CAF associations. By linking mechanistic insight with candidate biomarkers and targetable pathway nodes, this work provides a basis for precision strategies in both fusion-positive and fusion-negative cohorts and broadens the therapeutic perspective beyond kinase inhibition alone.
]]></description>
<dc:creator><![CDATA[ Mishra, D., Agrawal, S., Malik, D., Pathak, E., Mishra, R. ]]></dc:creator>
<dc:date>2026-04-16</dc:date>
<dc:identifier>doi:10.64898/2026.04.10.717853</dc:identifier>
<dc:title><![CDATA[EML4-ALK Fusion Rewires Transcriptomic, miRNA, and CAF-Associated Programs in Non-Small Cell Lung Cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.15.718822v1?rss=1">
<title>
<![CDATA[
NovoGlyco: mapping protein glycosylation in prokaryotes 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.15.718822v1?rss=1
</link>
<description><![CDATA[
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 novel glycans, their composition, and linking chemistry. An interactive dashboard allows exploration of glycan features and modified proteins. We demonstrate the NovoGlyco platform across published glycoproteomics datasets, spanning human pathogens, Asgard archaea, and environmental enrichment cultures, and identify previously unreported flagella O-glycans in the opportunistic pathogen Campylobacter fetus. In summary, NovoGlyco provides a scalable framework for unbiased exploration of microbial glycoproteomes in both single-organism and metaproteomic contexts.
]]></description>
<dc:creator><![CDATA[ Pabst, M., Soic, D. ]]></dc:creator>
<dc:date>2026-04-16</dc:date>
<dc:identifier>doi:10.64898/2026.04.15.718822</dc:identifier>
<dc:title><![CDATA[NovoGlyco: mapping protein glycosylation in prokaryotes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.13.718172v1?rss=1">
<title>
<![CDATA[
Deep quantitative phosphoproteomics identifies non-canonical pH-sensitive yeast phosphorylation networks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.13.718172v1?rss=1
</link>
<description><![CDATA[
Changes in cellular pH act as potent upstream signals that rewire phosphorylation networks controlling cellular function and disease. In yeast, this response is thought to arise mainly from pH-dependent perturbations to membrane integrity and cell wall stress, which are relayed through TORC2- and PKC-dependent signaling pathways. Yet several proteins exhibit acid-dependent phosphorylation independently of TORC2 and PKC, pointing to additional, uncharacterized acid stress signaling mechanisms. To investigate this possibility, we performed SILAC-based deep phosphoproteomics to distinguish phosphoproteome changes that are dependent on plasma membrane and cell wall stress from those that are independent. Across more than 19,000 unique phosphosites, we identified over 1,000 sites whose acid stress-dependent regulation occurs outside the canonical acid stress pathways. These noncanonical targets were significantly enriched in proteins associated with the plasma membrane, GTPase-mediated signaling, and endocytosis. Motif analysis revealed enrichment of acidophilic substrates implicating the membrane-tethered casein kinase Yck1 as a major mediator of this response. In contrast, canonical acid stress signaling preferentially involved basophilic kinase substrates, while acid-repressed responses were enriched for proline-containing phosphosites involved in cell cycle progression. Collectively, these results uncover a distinct acid-responsive phosphorylation network that operates independently of plasma membrane and cell wall integrity signaling.
]]></description>
<dc:creator><![CDATA[ Su, X., Gajri, A., Torres, M. P. ]]></dc:creator>
<dc:date>2026-04-15</dc:date>
<dc:identifier>doi:10.64898/2026.04.13.718172</dc:identifier>
<dc:title><![CDATA[Deep quantitative phosphoproteomics identifies non-canonical pH-sensitive yeast phosphorylation networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.14.718551v1?rss=1">
<title>
<![CDATA[
Tracing cell communication programs across conditions at single cell resolution with CCC-RISE 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.14.718551v1?rss=1
</link>
<description><![CDATA[
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 variation by deconvolving communication into interpretable modules defined by unique sender cells, receiver cells, ligands, and condition associations. We applied this framework to a COVID-19 cohort with varying disease severity and a lung transplant cohort with acute allograft dysfunction. In both contexts, CCC-RISE successfully identified disease-relevant communication programs and traced them to specific cellular subpopulations, often crossing conventional cell-type boundaries. This approach offers a robust pipeline enabling the identification of disease-relevant signaling subpopulations that are invisible to aggregate methods.

HighlightsO_LICCC-RISE enables integrative analysis of cell-cell communication across multiple conditions at single-cell resolution
C_LIO_LICCC-RISE deconvolves signaling patterns into modules defined by their sender cells, receiver cells, LR pairs, and experimental conditions/samples
C_LIO_LIAnalysis at single-cell resolution uncovers signaling activity within and across conventional cell types
C_LI
]]></description>
<dc:creator><![CDATA[ Ramirez, A., Thomas, N., Calabrese, D. R., Greenland, J. R., Meyer, A. S. ]]></dc:creator>
<dc:date>2026-04-15</dc:date>
<dc:identifier>doi:10.64898/2026.04.14.718551</dc:identifier>
<dc:title><![CDATA[Tracing cell communication programs across conditions at single cell resolution with CCC-RISE]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.13.718123v1?rss=1">
<title>
<![CDATA[
Gene Expression Variability with Feedback Regulation Implemented via Protein-Dependent Cell Growth 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.13.718123v1?rss=1
</link>
<description><![CDATA[
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 an explicit solution for the protein concentration distribution in state space and represent it as a Bessel function in Laplace space. For growing populations, we find that the distribution satisfies a Heun differential equation with singular boundary conditions. By addressing the central connection problem for the Heun equation, we quantify the population-level protein distribution and determine the Mathusian parameter, which characterizes population growth. This work provides a comprehensive analytical framework for understanding how stochastic protein synthesis impacts gene expression variability and population dynamics.
]]></description>
<dc:creator><![CDATA[ Zabaikina, I., Bokes, P., Singh, A. ]]></dc:creator>
<dc:date>2026-04-15</dc:date>
<dc:identifier>doi:10.64898/2026.04.13.718123</dc:identifier>
<dc:title><![CDATA[Gene Expression Variability with Feedback Regulation Implemented via Protein-Dependent Cell Growth]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.13.718184v1?rss=1">
<title>
<![CDATA[
Targeting HIV at its core: A mathematical model for optimizing Tat Inhibitor-based therapies toward enhanced functional cure strategies 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.13.718184v1?rss=1
</link>
<description><![CDATA[
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 and non-infectious virions, while explicitly accounting for the therapeutic effects of reverse transcriptase inhibitors (RTIs), protease inhibitors (PIs), and Tat transcription inhibitors.

Analytical results establish positivity and boundedness of solutions and derive the effective reproduction number Re using the next-generation matrix approach. Stability analysis shows that the virus-free equilibrium is locally asymptotically stable when Re < 1, while viral persistence occurs when Re > 1. Numerical simulations were performed to investigate therapy interactions, viral rebound following treatment interruption, and the impact of drug efficacy on viral set-points and latent reservoir dynamics.

To further explore therapy interactions, three-dimensional viral set-point surfaces and heat maps were generated to examine how combinations of infection inhibition, viral production inhibition, and transcriptional inhibition influence viral dynamics. The simulations reveal that Tat inhibition suppresses viral transcription, thereby reducing the transition of infected cells into productive infection and limiting viral propagation when combined with conventional ART mechanisms. The therapy parameter planes further demonstrate that strong transcriptional inhibition promotes the transition of infected cells into deep latency, supporting the emerging block-and-lock strategy for functional HIV cure. In addition, a three-dimensional eradication boundary surface and therapy cube were constructed to identify regions of parameter space where Re < 1, corresponding to successful viral control. These visualizations show that viral eradication is unlikely when therapies act independently but becomes achievable when multiple therapeutic mechanisms act simultaneously.

Overall, the results highlight the critical role of transcriptional inhibition through Tat-targeting therapies in complementing existing ART regimens. By simultaneously suppressing viral replication and promoting deep latency, Tat-based combination strategies may significantly reduce viral rebound and contribute to long-term functional control of HIV infection.
]]></description>
<dc:creator><![CDATA[ Waema, R., Adongo, C., Lago, S., Ogutu, K. ]]></dc:creator>
<dc:date>2026-04-15</dc:date>
<dc:identifier>doi:10.64898/2026.04.13.718184</dc:identifier>
<dc:title><![CDATA[Targeting HIV at its core: A mathematical model for optimizing Tat Inhibitor-based therapies toward enhanced functional cure strategies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.12.718001v1?rss=1">
<title>
<![CDATA[
iCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.12.718001v1?rss=1
</link>
<description><![CDATA[
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 growth conditions and gene essentiality benchmarks. The model achieved high confidence essential gene prediction (precision = 0.77) and recapitulated pathways targeted by clinically available antifungals. Integration with transcriptomic and metabolomic data enabled iCNG99 to capture condition-specific metabolic adaptations and to identify candidate vulnerabilities in drug tolerance, revealing metabolic adaptations associated with survival within host conditions and drug susceptibility. Together, iCNG99 provides a systems-level computational platform for investigating C. neoformans metabolism and for prioritizing antifungal vulnerabilities.
]]></description>
<dc:creator><![CDATA[ Feng, C., Hu, P., Zhu, Y., Ke, W., Gao, X., Ding, C., Zhai, B., Wang, L., Dai, Z. ]]></dc:creator>
<dc:date>2026-04-15</dc:date>
<dc:identifier>doi:10.64898/2026.04.12.718001</dc:identifier>
<dc:title><![CDATA[iCNG99: a validated genome-scale metabolic model of Cryptococcus neoformans strain H99]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.11.717857v1?rss=1">
<title>
<![CDATA[
Equation-Based Integration of Flux Balance Analysis with Diffusion for Spatio-Temporal Simulation of Microbial Communities 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.11.717857v1?rss=1
</link>
<description><![CDATA[
Spatio-temporal interactions shape microbial community dynamics. Metabolism, through competition and cross-feeding, is a foundational mechanism of these interactions. Flux balance analysis enables efficient simulation of steady-state metabolism. Integrating these simulations through time, using dynamic flux balance analysis, provides temporal predictions of growth and metabolism. Incorporating spatial context, through partial differential equations, enables spatio-temporal simulation of microbial communities. In this chapter, we step through this sequential process, moving from steady-state, to temporal, to spatio-temporal simulation of microbial community metabolism. We provide an illustrative example using the modeling software COMETS (Computation of Microbial Ecosystems in Time and Space) to simulate interacting bacterial colonies of Bifidobacterium longum subsp. infantis and Anaerobutyricum hallii (previously Eubacterium hallii). Within this simulation, both competition and cross-feeding influenced the production of butyrate leading to an intermediate optimal interaction distance for metabolite production. We outline each step and provide open-source code such that this simulation can serve as a template for future spatio-temporal simulations of microbial community metabolism.
]]></description>
<dc:creator><![CDATA[ Senya, F., Siegel, R., Dukovski, I., Bernstein, D. B. ]]></dc:creator>
<dc:date>2026-04-14</dc:date>
<dc:identifier>doi:10.64898/2026.04.11.717857</dc:identifier>
<dc:title><![CDATA[Equation-Based Integration of Flux Balance Analysis with Diffusion for Spatio-Temporal Simulation of Microbial Communities]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.10.717685v1?rss=1">
<title>
<![CDATA[
Hyperbolic stratification of protein intrinsic disorder and structure-mediated interactions in the human protein interactome 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.10.717685v1?rss=1
</link>
<description><![CDATA[
Classical models of protein-protein interactions (PPIs) focus on stable, structure-driven interfaces between folded domains, yet recent work highlights the central role of intrinsic disorder and phase separation in shaping dynamic, multivalent associations. How these interaction modes are reflected in the large-scale organization of PPI networks remains unclear.

Here, we map the human interactome onto a hyperbolic representation, integrating sequence- and structure-derived features to test whether network organization reflects distinct molecular interaction strategies. Radial position defines a continuum: central proteins are enriched in folded domains, structural complexity, and post-translational modifications, whereas peripheral proteins show increased intrinsic disorder and liquid-liquid phase separation (LLPS) propensity. Angular organization further reveals communities structured by characteristic domain architectures or disorder-linked motifs.

Combined analysis of intrinsic disorder, LLPS propensity, and binding-mode diversity uncovers interaction patterns associated with distinct molecular functions and motif repertoires. Condensate-associated proteins span multiple communities while retaining shared short linear motif signatures. Together, these results show that the hyperbolic map links sequence composition, structural organization, and network topology, providing a framework to interpret protein interaction behavior and to guide functional analysis within the human interactome.
]]></description>
<dc:creator><![CDATA[ Hause, F., Sorokin, O., Huettelmaier, S., Sinz, A. ]]></dc:creator>
<dc:date>2026-04-14</dc:date>
<dc:identifier>doi:10.64898/2026.04.10.717685</dc:identifier>
<dc:title><![CDATA[Hyperbolic stratification of protein intrinsic disorder and structure-mediated interactions in the human protein interactome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.10.717623v1?rss=1">
<title>
<![CDATA[
Modeling and dissecting bidirectional feedback in gene-metabolite systems using the CausalFlux method 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.10.717623v1?rss=1
</link>
<description><![CDATA[
Predicting cellular behaviors, a central task in systems biology and metabolic engineering, can be enhanced through integrative modeling of processes such as gene regulation and metabolism. Information flow from gene regulation (modeled via a gene regulatory network) to metabolism (modeled via a genome-scale metabolic model) is well-studied, but the reciprocal regulation of genes by metabolites is less explored. We introduce CausalFlux, a method that models bidirectional feedback between genes and metabolites, in order to predict steady-state reaction fluxes under wild-type (WT) or perturbed (e.g., gene knockout/KO) conditions. CausalFlux does so by iteratively performing causal surgery on a Bayesian gene regulatory network and constraint-based analysis of a coupled metabolic model. CausalFlux enabled us to assess the impact of two-way feedback in several testbed models and real-world biological systems by comparing its predictions to those of TRIMER, a state-of-the-art model of gene-to-metabolite one-way feedback. Incorporating bidirectional feedback, as in CausalFlux, improved the Spearman correlation between actual and predicted fluxes in 92% of the 39 distinct simulation conditions relative to TRIMER. For predicting growth/no-growth phenotype following single-gene KOs in E. coli, CausalFlux achieved a balanced accuracy of 0.79 in identifying essential genes, and TRIMER achieved 0.71 for the same task, again highlighting the importance of modeling two-way feedback. In ablation studies that further dissect the role of specific metabolite[-&gt;]gene feedback edges in E. coli, the F1 scores of gene essentiality predictions decreased by 7.5% and 13% upon ablation of feedback edges from any metabolite to the crp gene and the 10 metabolic feedback genes with the highest influence on the KO genes, respectively. Finally, we highlight the application of CausalFlux to predict the essentiality of several hundred genes under different media conditions. Overall, our findings show that CausalFlux can crucially utilize information on feedback metabolites to predict trends in reaction fluxes and qualitative (growth/no-growth) outcomes; thereby encouraging future systems modeling efforts to carefully incorporate not only gene-to-metabolite but also metabolite-to-gene interactions.

AvailabilityCode pertaining to the CausalFlux method, and its benchmarking and application is publicly available at: https://github.com/BIRDSgroup/CausalFlux.

Author summaryThe myriad processes within a living cell, such as gene regulation or metabolism, are tightly interconnected. Modeling these interconnected processes can offer a deeper mechanistic understanding of cellular behaviors, as well as guide efforts that engineer the metabolic output of a cell. In this work, we develop a novel integrated model of gene regulation and metabolism that incorporates bidirectional feedback between these two processes, via the concept of metabolite-induced causal surgery on a gene regulatory network and gene-induced constraints on the fluxes of metabolic reactions. Our model, which we call CausalFlux, represents an advance over most existing models that capture just the one-way gene-to-metabolism feedback (i.e., genes coding for enzymes that control metabolic reactions). Our CausalFlux methodology opens up an unique opportunity to quantify the impact of two-way feedback in gene-metabolite systems, via comparison of CausalFluxs predictions to those of TRIMER, a published model incorporating one-way feedback alone. For predicting reaction fluxes in testbed models and essential genes in E. coli, quantitative comparison of the performance of CausalFlux vs. TRIMER showed that accounting for two-way feedback leads to more accurate and biologically meaningful predictions. CausalFlux also enabled us to quantify the effect of two-way feedback by comparing prediction performance before and after ablation of certain feedback edges from metabolites to genes. Overall, our findings highlight the importance of modeling gene regulation and metabolism as two-way interconnected systems within a living cell, and encourage future works to incorporate gene{leftrightarrow}metabolite feedback into their analyses.
]]></description>
<dc:creator><![CDATA[ Subramanian, N., Kumar, S. P., Rengaswamy, R., Bhatt, N. P., Narayanan, M. ]]></dc:creator>
<dc:date>2026-04-13</dc:date>
<dc:identifier>doi:10.64898/2026.04.10.717623</dc:identifier>
<dc:title><![CDATA[Modeling and dissecting bidirectional feedback in gene-metabolite systems using the CausalFlux method]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.09.717564v1?rss=1">
<title>
<![CDATA[
Recently emerged Fusarium chemotypes reprogram wheat defence and detoxification networks during Fusarium head blight development 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.09.717564v1?rss=1
</link>
<description><![CDATA[
Fusarium head blight (FHB) is a major threat to global wheat production and food safety due to contamination with mycotoxins, such as deoxynivalenol (DON). The emergence of new mycotoxin chemotypes, including 7--hydroxy,15-deacetylcalonectrin (3ANX), presents an evolving challenge for disease management and resistance breeding. Here, we performed a field-based, systems-level proteome analysis of wheat infected with Fusarium graminearum strains belonging to the common 15ADON and recently emerged 15ADON/3ANX chemotypes. Across host and pathogen, we quantified more than 9,200 proteins, providing extensive coverage of infection-associated molecular responses. Infection with 15ADON/3ANX strains suppressed canonical wheat detoxification pathways while promoting structural and oxidative defence responses. Concurrently, the fungal proteome of 15ADON/3ANX-producing strains indicated altered mitochondrial ribosome function and alternative virulence strategies. Further investigation of the host-pathogen interface defined hub protein networks negatively regulating classical detoxification markers, suggesting coordinated regulation of host defence responses regardless of chemotype. Molecular responses were linked to field phenotypes by quantification of DON-3-glucoside/DON ratios and disease severity, defining positive correlations in 15ADON infections, which were abolished upon 15ADON/3ANX infection, indicating chemotype-specific evasion or suppression of host defenses. These findings demonstrate reprogramming of host-pathogen interaction networks and reveal molecular targets that may inform chemotype-aware breeding strategies to enhance crop resilience.
]]></description>
<dc:creator><![CDATA[ Ramezanpour, S., Alijanimamaghani, N., McAlister, J. A., Dale, A., Cordwell, S. J., Hooker, D., Geddes-McAlister, J. ]]></dc:creator>
<dc:date>2026-04-13</dc:date>
<dc:identifier>doi:10.64898/2026.04.09.717564</dc:identifier>
<dc:title><![CDATA[Recently emerged Fusarium chemotypes reprogram wheat defence and detoxification networks during Fusarium head blight development]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.09.717485v1?rss=1">
<title>
<![CDATA[
Pushing the limits of SCP: bacSCP, a proof-of-concept study to investigate heterogeneity of bacteria by single cell proteomics. 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.09.717485v1?rss=1
</link>
<description><![CDATA[
Single-cell proteomics (SCP) has emerged as a powerful approach to quantify protein expression variability at cellular resolution, yet most state-of-the-art workflows are tailored to eukaryotic cells with only one study exploring how single bacteria can be analyzed by mass spectrometry. Here, we established bacSCP, a protocol extending SCP to bacterial cells, facing analytical challenges such as the thick bacterial cell wall hampering lysis, the extremely small cell size and resultant low protein content, and the consequently relatively high level of contaminating proteins from external sources. Using this bacSCP pipeline, we quantified more than 50 bacterial proteins from single Bacillus subtilis and Escherichia coli cells. Upon heat stress, we reproducibly observed up to 8-fold upregulation of chaperones including GroEL, GroES, and ClpC for a B. subtilis {Delta}mcsB strain. Importantly, single-cell measurements revealed potential heterogeneity within the heat-stressed subpopulation, enabling interrogation of stress-response variability at the proteome level. These results demonstrate the feasibility of bacSCP and provide a foundation for studying bacterial stress adaptation and phenotypic diversity with single-cell proteomic resolution.
]]></description>
<dc:creator><![CDATA[ Leodolter, J., Thierer, T., Mechtler, K., Matzinger, M. ]]></dc:creator>
<dc:date>2026-04-13</dc:date>
<dc:identifier>doi:10.64898/2026.04.09.717485</dc:identifier>
<dc:title><![CDATA[Pushing the limits of SCP: bacSCP, a proof-of-concept study to investigate heterogeneity of bacteria by single cell proteomics.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.07.717118v1?rss=1">
<title>
<![CDATA[
Antimony 3: Extending human-readable model definitions for SBML Level 3 Core and Packages 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.07.717118v1?rss=1
</link>
<description><![CDATA[
Antimony is a human-readable language for defining and sharing models developed by the systems biology community. It enables scientists to describe biochemical networks with a simple syntax, while supporting seamless conversion to and from the Systems Biology Markup Language (SBML) community standard. Since Antimonys original release, both SBML and modeling practices have evolved significantly, creating a need to update Antimony to maintain its standards compliance and practical relevance. In this paper, we introduce Antimony 3, a comprehensive update that formalizes its cumulative improvements and extends its support for SBML Level 3 Core and Flux Balance Constraints (FBC), Distributions, Layout, and Render packages. Antimony 3 enables model specifications that combine kinetic reactions with flux balance analysis, represent uncertainty using probability distributions, add biological context through annotations, and define publication-ready visualizations, all within a unified plain-text format. Antimony 3 is delivered as a lightweight C/C++ library with a stable C API. It is available through official bindings for Python, Julia, and JavaScript/WebAssembly, as well as a cross-platform desktop GUI, which enables straightforward use across scripting environments, desktop applications, and browser-based tools. Antimony 3 is released as open-source software under the BSD 3-Clause License and is available at https://github.com/sys-bio/antimony.

Author SummaryBiological models are typically stored in standardized formats that ensure compatibility across different software tools, but these formats rely on verbose, machine-readable syntax that is difficult for humans to write or inspect directly. Antimony addresses this challenge by providing an intuitive, text-based language for defining biological models that can be automatically converted to and from the Systems Biology Markup Language (SBML). Since Antimonys original release in 2009, the SBML standard and common modeling workflows have expanded significantly. We developed Antimony 3 to support these advances, enabling researchers to write a single human-readable text file that defines reaction networks, constraint-based objectives, uncertainty in parameters and initial conditions, semantic annotations linking to biological databases, and model diagrams. Antimony 3 is provided as open-source software with broad support across computational environments, making it accessible to researchers in a wide range of workflows.
]]></description>
<dc:creator><![CDATA[ Heydarabadipour, A., Smith, L. P., Hellerstein, J. L., Sauro, H. M. ]]></dc:creator>
<dc:date>2026-04-10</dc:date>
<dc:identifier>doi:10.64898/2026.04.07.717118</dc:identifier>
<dc:title><![CDATA[Antimony 3: Extending human-readable model definitions for SBML Level 3 Core and Packages]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.07.717029v1?rss=1">
<title>
<![CDATA[
Cell-Type-Resolved Pseudobulk Classification Across Independent Cohorts Identifies Microglial PTPRG as a Transcriptional Hub in Alzheimer's Disease 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.07.717029v1?rss=1
</link>
<description><![CDATA[
Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and widespread cerebral pathology. Understanding cell-type-specific molecular mechanisms underlying AD is critical for identifying precise therapeutic targets.

We applied a supervised machine learning approach to single-nucleus RNA sequencing data from the ROSMAP cohort, aggregating gene expression profiles into pseudobulk representations across six major brain cell types.

Systematic evaluation of all possible cell-type combinations identified microglia and astrocytes as the most discriminative cell types for AD classification. A logistic regression model trained on 228 highly variable genes achieved robust classification performance on held-out ROSMAP samples (balanced accuracy 0.87, AUC 0.89) and generalized to an independent cohort from the Seattle Alzheimers Disease Brain Cell Atlas (balanced accuracy 0.86, AUC 0.92), demonstrating cross-cohort reproducibility that remains uncommon in computational AD research. Among the 72 genes selected by the model, microglial PTPRG exhibited the highest absolute coefficient. Gene Set Enrichment Analysis (GSEA) revealed that microglia-expressed genes were enriched for chronic immune activation and inflammatory signaling, while astrocyte-associated genes implicated protein homeostasis stress and HSF1-mediated chaperone pathways. Weighted Gene Co-expression Network Analysis (WGCNA) further showed that PTPRG operates within fundamentally different gene network contexts in AD and NCI microglia, with AD networks characterized by inflammatory dysregulation and NCI networks reflecting homeostatic immune surveillance. Cell-cell communication analysis identified established AD risk genes including APOE, GRN, PSEN1, and CLU among the top neuronal ligands predicted to regulate microglial PTPRG, positioning it as a convergence point for disease-relevant neuronal signals. Correlation analysis further revealed that excitatory and inhibitory neurons couple to microglial PTPRG through distinct biological processes, implicating divergent mechanisms of AD-associated microglial dysregulation.

Collectively, these findings establish microglial PTPRG as a central hub integrating neuronal signaling and inflammatory dysregulation in AD pathology.
]]></description>
<dc:creator><![CDATA[ Anwer, D., Marchi, A., Montaldo, N. P., Kerkhoven, E. J., Gilis, J., Polster, A. V. ]]></dc:creator>
<dc:date>2026-04-10</dc:date>
<dc:identifier>doi:10.64898/2026.04.07.717029</dc:identifier>
<dc:title><![CDATA[Cell-Type-Resolved Pseudobulk Classification Across Independent Cohorts Identifies Microglial PTPRG as a Transcriptional Hub in Alzheimer's Disease]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.08.717309v1?rss=1">
<title>
<![CDATA[
Rhythmic gene expression and behavioral plasticity in harvester and carpenter ants 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.08.717309v1?rss=1
</link>
<description><![CDATA[
We examined the overlap in the genes associated with daily rhythms and with behavioral plasticity in ants. We first investigated the daily rhythms of gene expression in the harvester ant, Pogonomyrmex barbatus, and how the rhythmic genes overlap with others previously shown to be associated with plasticity of foraging behavior. Then, to consider whether the overlap is conserved across ant species, we compared rhythms of gene expression in the diurnal, desert harvester ants with those previously reported for a distantly related nocturnal, subtropical carpenter ant, Camponotus floridanus. First, daily transcriptomes in P. barbatus showed that most genes were expressed in light-dark (LD) and constantly dark (DD) conditions at about the same levels; only 11 genes showed at least a two-fold change in expression. Network analysis identified eleven modules of P. barbatus genes under LD conditions. Of these 11 clusters, modules C1 and C2 seem to be central nodes of the gene expression network, because they are the most highly connected in LD, and show the strongest preservation in DD vs. LD, and contain core clock gene Period. Only one module, C2, showed significant overlap with P. barbatus genes that have 24h-rhythmic expression in both LD and DD. There was significant overlap between modules C1, C2, C10, C11, and P. barbatus genes found previously to be associated with plasticity in the regulation of foraging activity to manage water loss. A comparison of the daily transcriptome of P. barbatus with that of C. floridanus showed significant overlap of 24h-rhythmic genes in LD. Modules C1 and C2 of P. barbatus also overlap with C. floridanus genes previously shown to differ in expression rhythms in nurses and foragers. In combination, these results indicate that genes linking plasticity of the circadian clock and of behavior may be broadly conserved in ants.
]]></description>
<dc:creator><![CDATA[ Das, B., Gordon, D. M. ]]></dc:creator>
<dc:date>2026-04-10</dc:date>
<dc:identifier>doi:10.64898/2026.04.08.717309</dc:identifier>
<dc:title><![CDATA[Rhythmic gene expression and behavioral plasticity in harvester and carpenter ants]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-10</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.07.714784v1?rss=1">
<title>
<![CDATA[
Interplay of the ribosome A and CAR sites 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.07.714784v1?rss=1
</link>
<description><![CDATA[
Protein translation is a highly regulated process influenced by multiple factors at the initiation, elongation, and termination stages. One notable regulatory element of the ribosome is the CAR interaction surface, a three-residue motif in the structure of the ribosome composed of C1274 and A1427 of S. cerevisiae 18S rRNA (corresponding to C1054 and A1196 in E. coli 16S rRNA) and R146 of ribosomal protein Rps3. CAR is highly conserved and positioned adjacent to the amino-acyl (A site) decoding center. It establishes hydrogen bonds with the +1 codon next in line to enter the ribosome A site, acting as an extension of the tRNA anticodon and forming base-stacking interactions with nucleotide 34 of the tRNA. However, despite CARs enzymatically strategic positioning within the ribosome, its functional relationship with the A site remains poorly characterized. Using molecular dynamics (MD) simulations, we examined the interplay between the A site and CAR site, revealing sequence-dependent modulation of H-bonding and {pi}-stacking interactions within and between the two sites. These findings highlight the interplay between the A site and CAR site, suggesting a structural and functional connection between these two regions of the ribosome that may contribute to mRNA sequence-specific tuning of translation elongation.

GRAPHICAL ABSTRACT

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/714784v1_ufig1.gif" ALT="Figure 1">
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]]></description>
<dc:creator><![CDATA[ Raval, M., Zhou, Y., Lynch, M., Krizanc, D., Thayer, K., Weir, M. P. ]]></dc:creator>
<dc:date>2026-04-09</dc:date>
<dc:identifier>doi:10.64898/2026.04.07.714784</dc:identifier>
<dc:title><![CDATA[Interplay of the ribosome A and CAR sites]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.06.716803v1?rss=1">
<title>
<![CDATA[
Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.06.716803v1?rss=1
</link>
<description><![CDATA[
Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing.

In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters.

Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.
]]></description>
<dc:creator><![CDATA[ Wang, D., Froehlich, F., Stapor, P., Schaelte, Y., Huth, M., Eils, R., Kallenberger, S., Hasenauer, J. ]]></dc:creator>
<dc:date>2026-04-09</dc:date>
<dc:identifier>doi:10.64898/2026.04.06.716803</dc:identifier>
<dc:title><![CDATA[Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.07.716866v1?rss=1">
<title>
<![CDATA[
Time-dependent memory of hypoxia exposure influences tumor invasion dynamics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.07.716866v1?rss=1
</link>
<description><![CDATA[
Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.
]]></description>
<dc:creator><![CDATA[ Sadhu, G., Jain, P., Meena, R. K., George, J. T., Jolly, M. K. ]]></dc:creator>
<dc:date>2026-04-09</dc:date>
<dc:identifier>doi:10.64898/2026.04.07.716866</dc:identifier>
<dc:title><![CDATA[Time-dependent memory of hypoxia exposure influences tumor invasion dynamics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.06.716859v1?rss=1">
<title>
<![CDATA[
Improved inference of multiscale sequence statistics in generative protein models 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.06.716859v1?rss=1
</link>
<description><![CDATA[
High dimensionality and multiscale statistical structure are pervasive features of biological data, posing fundamental challenges for modeling. Because model inference generally proceeds with far fewer data than parameters, statistical patterns across scales are often unevenly represented. Protein sequences provide a paradigmatic example: statistics across homologs are inherently multiscale, displaying collective correlations among conserved residue sectors that encode function, alongside localized correlations corresponding to physical contacts outside these sectors. Standard regularization strategies used to mitigate undersampling during model inference have been shown to capture these patterns unevenly, a bias that compromises generative models of protein sequences by limiting their ability to produce both functional and diverse proteins. This limitation is exemplified by Boltzmann machine-based generative models, which so far have required post hoc corrections to recover functionality, at the cost of reduced sequence diversity. Here, we introduce the stochastic Boltzmann Machine (sBM), a new regularization strategy that more accurately captures different correlation scales. Through analyses of theoretical models with known ground-truth parameters and experiments on the chorismate mutase family, we show that sBM effectively mitigates distortions in the estimation of model parameters, enabling the generation of functional sequences with greater diversity and without the need for post hoc corrections. These results advance the inference of generative models that more faithfully reflect the evolutionary constraints shaping protein sequences.
]]></description>
<dc:creator><![CDATA[ Chauveau, M., Kleeorin, Y., Hinds, E., Junier, I., Ranganathan, R., Rivoire, O. ]]></dc:creator>
<dc:date>2026-04-09</dc:date>
<dc:identifier>doi:10.64898/2026.04.06.716859</dc:identifier>
<dc:title><![CDATA[Improved inference of multiscale sequence statistics in generative protein models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.06.716692v1?rss=1">
<title>
<![CDATA[
UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.06.716692v1?rss=1
</link>
<description><![CDATA[
Agent-based models (ABMs) are widely used to study complex multiscale biological systems, particularly in cancer research. However, their high-dimensional parameter spaces, stochasticity, and computational costs pose significant challenges for uncertainty quantification, calibration, and systematic comparison of competing mechanistic hypotheses. PhysiCell has evolved into a growing ecosystem of open-source tools supporting physics-based multicellular modeling, including model construction, visualization, and data integration. However, despite these advances, systematic support for uncertainty-aware model analysis, scalable parameter exploration, and formal calibration workflows remains limited. Here, we introduce UQ-PhysiCell, an open-source Python package that enables uncertainty quantification, calibration, and model selection for PhysiCell models using a modular and scalable workflow. UQ-PhysiCell acts as a manager of PhysiCell simulation inputs and outputs, including parameters, initial conditions, rules, and MultiCellDS-compliant objects, and provides automated orchestration of large ensembles of simulations. The framework supports multiple levels of parallelism to accelerate the analysis, including the parallel execution of independent simulations, stochastic replicates, and downstream analysis tasks. UQ-PhysiCell integrates seamlessly with established Python libraries for sensitivity analysis, optimization, Bayesian inference, and surrogate modeling, allowing users to construct customized pipelines that match their modeling goals and computational resource requirements. By decoupling model execution from statistical analysis and emphasizing extensibility and reproducibility, UQ-PhysiCell lowers the barrier to applying rigorous uncertainty-aware methodologies to agent-based modeling and supports the systematic evaluation of PhysiCell models in biological and biomedical research.

Author summaryWe developed UQ-PhysiCell to address a key challenge in agent-based modeling: the systematic quantification of uncertainty in complex stochastic simulations. PhysiCell is widely used to model multicellular biological systems, particularly in cancer research; however, practical tools for uncertainty analysis, calibration, and model comparison are often developed in an ad hoc manner. This makes the results difficult to reproduce and limits the ability to rigorously evaluate competing biological hypotheses. UQ-PhysiCell provides a flexible Python framework that manages the inputs and outputs of PhysiCell simulations and enables large-scale computational analysis. We designed the software to be modular, allowing users to build their own analysis pipelines and combine different methodologies for sensitivity analysis, calibration, and model selection. Rather than enforcing a single workflow, UQ-PhysiCell supports customization to match specific scientific questions and computational requirements. To make uncertainty-aware analyses feasible for computationally intensive agent-based models, UQ-PhysiCell implements multiple parallelism strategies, enabling the concurrent execution of simulations, stochastic replicates, and downstream analyses. By promoting reproducibility, scalability, and methodological flexibility, UQ-PhysiCell helps researchers move beyond single best-fit simulations toward more reliable and interpretable computational modeling.
]]></description>
<dc:creator><![CDATA[ L. Rocha, H., Bucher, E., Zhang, S., Deshpande, A., Bergman, D. R., Heiland, R., Macklin, P. R. ]]></dc:creator>
<dc:date>2026-04-08</dc:date>
<dc:identifier>doi:10.64898/2026.04.06.716692</dc:identifier>
<dc:title><![CDATA[UQ-PhysiCell: An extensible Python framework for uncertainty quantification and model analysis in PhysiCell]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.06.714260v1?rss=1">
<title>
<![CDATA[
Quantification of domain-specific intrinsic capacity using mortality data 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.06.714260v1?rss=1
</link>
<description><![CDATA[
Functional health is centered on five domains of Intrinsic Capacity (IC): locomotion, cognition, vitality, psychological and sensory capacity. Therefore, measuring IC at the domain-specific level is the cornerstone for developing preventive interventions to help individuals preserve their independence. In this study, we used 63 clinical features from the UK Biobank to develop  IC age, an 18-year mortality risk estimator that approximates an individuals biological age associated with the decline of each IC domain. By establishing proteomic surrogates of IC age, we find immune system activation across domains and provide a proteomic framework that may facilitate scalable monitoring of functional health decline.
]]></description>
<dc:creator><![CDATA[ Fuentealba, M., Zhai, T., Aldajani, S., Gladyshev, V. N., Snyder, M., Furman, D. ]]></dc:creator>
<dc:date>2026-04-08</dc:date>
<dc:identifier>doi:10.64898/2026.04.06.714260</dc:identifier>
<dc:title><![CDATA[Quantification of domain-specific intrinsic capacity using mortality data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.05.716273v1?rss=1">
<title>
<![CDATA[
An AI-Assisted Workflow for Reconstruction, Extension, and Calibration of Quantitative Systems Pharmacology Models. 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.05.716273v1?rss=1
</link>
<description><![CDATA[
AbstractsO_ST_ABSBackgroundC_ST_ABSQuantitative systems pharmacology (QSP) models provide mechanistic insight into drug response but are limited by labor-intensive, expert-driven workflows. We developed an AI-assisted QSP (AI-QSP) framework that integrates large language models (LLMs) with SBML-based modeling to enable automated reconstruction, extension, and calibration of mechanistic models.

MethodsThe framework was applied to a published CAR-T QSP model. The model was reconstructed in SBML and extended via LLM-guided prompts to incorporate key resistance mechanisms: T-cell exhaustion, PD-1/PD-L1 checkpoint regulation, and tumor antigen escape. Model development followed an iterative expert-in-the-loop workflow. The resulting model (21 reactions, 9 species) was calibrated to synthetic benchmark data using 19-parameter optimization. Model credibility was assessed using ASME V&V 40 and ICH M15 principles, including global sensitivity and profile-likelihood analyses.

ResultsThe calibrated model reproduced benchmark dynamics with high accuracy (mean log-RMSE = 0.132). Sensitivity analysis identified CAR-T killing and bystander cytotoxicity as dominant drivers of tumor response. Profile-likelihood analysis showed 71% of parameters were practically identifiable, with remaining parameters prioritised for future data-driven refinement.

ConclusionsAI-assisted QSP modeling enables reproducible, scalable model reconstruction and evolution while maintaining mechanistic transparency and regulatory alignment. This framework provides a foundation for accelerating model-informed drug development in cell and gene therapies.
]]></description>
<dc:creator><![CDATA[ Goryanin, I., Checkley, S., Demin, O., Goryanin, I. ]]></dc:creator>
<dc:date>2026-04-07</dc:date>
<dc:identifier>doi:10.64898/2026.04.05.716273</dc:identifier>
<dc:title><![CDATA[An AI-Assisted Workflow for Reconstruction, Extension, and Calibration of Quantitative Systems Pharmacology Models.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.03.716310v1?rss=1">
<title>
<![CDATA[
Kinome profiling allows examination and prediction of kinase inhibitor cardiotoxicity 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.03.716310v1?rss=1
</link>
<description><![CDATA[
BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement.

ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity.

MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases.

ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recurrently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases.

ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.
]]></description>
<dc:creator><![CDATA[ Tabet, J. S., Joisa, C. U., Jensen, B. C., Gomez, S. M. ]]></dc:creator>
<dc:date>2026-04-07</dc:date>
<dc:identifier>doi:10.64898/2026.04.03.716310</dc:identifier>
<dc:title><![CDATA[Kinome profiling allows examination and prediction of kinase inhibitor cardiotoxicity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-04-07</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
