<?xml version="1.0" encoding="UTF-8" ?>
<rdf:RDF xmlns:admin="http://webns.net/mvcb/" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:prism="http://purl.org/rss/1.0/modules/prism/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/">
<channel rdf:about="https://biorxiv.org">
<admin:errorReportsTo rdf:resource="mailto:biorxiv@cshlpress.edu"/>
<title>bioRxiv Subject Collection: Systems Biology</title>
<link>https://biorxiv.org</link>
<description>
This feed contains articles for bioRxiv Subject Collection "Systems Biology"
</description>

<items>
<rdf:Seq>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.27.754711v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.30.755827v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.30.755659v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.30.754912v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.30.755718v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.30.755644v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755460v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755490v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755524v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755448v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755324v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755377v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.29.755382v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.24.754086v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.755161v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.754175v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.754405v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.755110v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.754797v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.28.754951v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.27.754802v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.26.754619v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.25.754499v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.25.754297v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.25.751968v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.24.754258v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.23.753516v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.18.752660v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.23.753896v1?rss=1"/>
<rdf:li rdf:resource="https://www.biorxiv.org/content/10.64898/2026.09.21.753254v1?rss=1"/>
</rdf:Seq>
</items>
<prism:eIssn/>
<prism:publicationName>bioRxiv</prism:publicationName>
<prism:issn/>

<image rdf:resource=""/>
</channel>
<image rdf:about="">
<title>bioRxiv</title>
<url>https://www.biorxiv.org/sites/default/files/bioRxiv_article.jpg</url>
<link>https://www.biorxiv.org</link>
</image>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.27.754711v1?rss=1">
<title>
<![CDATA[
A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.27.754711v1?rss=1
</link>
<description><![CDATA[
Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.
]]></description>
<dc:creator><![CDATA[ Zhu, H., Wan, C., Yang, M., Fang, Y., An, Z., Swaminathan, P., Dang, P., Li, Z., Wang, J., Wang, Y., Chen, Y., Ma, A., Ma, Q., Kelley, M. R., Cao, S., Fishel, M. L., Zhang, C. ]]></dc:creator>
<dc:date>2026-10-02</dc:date>
<dc:identifier>doi:10.64898/2026.09.27.754711</dc:identifier>
<dc:title><![CDATA[A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755827v1?rss=1">
<title>
<![CDATA[
Multi-omics profiling identifies a metabolic and energetic signature associated with neuronal dENL/AF9 suppression in aged flies 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755827v1?rss=1
</link>
<description><![CDATA[
Maintenance of neuronal function during aging requires metabolic adaptation, yet how metabolite-sensitive chromatin regulators contribute to this process remains unclear. YEATS-domain proteins, including ENL and AF9, recognize histone acylation marks and are positioned at the interface between cellular metabolism and transcriptional regulation. We previously showed that pan-neuronal suppression of dENL/AF9, the single Drosophila orthologue of mammalian ENL and AF9, extends lifespan, preserves healthspan, and enhances oxidative-stress resistance. Here, we used multi-omics and biochemical analyses to define the molecular state associated with neuronal dENL/AF9 suppression. Transcriptomic profiling revealed a markedly stronger response in aged than young male fly heads, with prominent enrichment of fatty-acid degradation and several intermediary metabolic pathways in the aged dataset. Guided by this age-related transcriptional response, we focused subsequent profiling on aged heads, where independent molecular layers converged on a distinct metabolic and energetic signature. Lipidomics identified reduced abundance of multiple triglyceride-related and fatty acid derivatives, while metabolomics revealed changes in central-carbon/TCA-, nicotinamide-, and purine-associated features. Proteomics implicated mitochondrial and ATP-associated processes, accompanied by increased ATP abundance and a higher NAD/NADH ratio. Matched lipidome-metabolome integration further identified citrate as a cross-omics feature inversely associated with altered triglyceride and ceramide species, linking central-carbon and lipid-associated components of the molecular signature. Together, these findings identify a stronger transcriptional response to neuronal dENL/AF9 suppression in aged flies and a distinct metabolic and energetic molecular signature in aged fly heads, providing candidate molecular correlates of the previously established longevity phenotype and a framework for studies of neuronal aging biology.
]]></description>
<dc:creator><![CDATA[ Yeewa, R., Noisagul, P., Dissook, S., Panto, C., Poound, P., JANTRAPIROM, S., Lo Piccolo, L. ]]></dc:creator>
<dc:date>2026-10-02</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755827</dc:identifier>
<dc:title><![CDATA[Multi-omics profiling identifies a metabolic and energetic signature associated with neuronal dENL/AF9 suppression in aged flies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755659v1?rss=1">
<title>
<![CDATA[
A global signature of microbiome resilience to climate stress 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755659v1?rss=1
</link>
<description><![CDATA[
As climate change accelerates, the microbial communities sustaining organisms and ecosystems worldwide face an uncertain future, with some proving resilient to stress while others collapse, yet no generalizable framework exists to distinguish the two beforehand. Using amplicon sequencing data from ~29,000 microbiomes, we constructed over 440 matched network pairs spanning three of the most pressing stressors: drought, warming, and salinity. We show that keystone microbes with high connectivity, functional redundancy, and metabolic flexibility, are disproportionately likely to survive stress, and that baseline network and functional properties predict community-level structural and functional stability even as taxonomic composition shifts stochastically. These relationships held across the diverse hosts, ecosystems, and regions we tested. Because these properties can be derived from standard, low-cost sequencing collected before disturbance occurs, our findings turn baseline microbiome data into an early-warning system for identifying which communities are most vulnerable to climate stress.
]]></description>
<dc:creator><![CDATA[ Rawstern, A., Reyes, A., Li, V., Fowler, J., Hay, E., Hernandez, D., Katula, A., Gebriel, A., Afkhami, M. ]]></dc:creator>
<dc:date>2026-10-02</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755659</dc:identifier>
<dc:title><![CDATA[A global signature of microbiome resilience to climate stress]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.754912v1?rss=1">
<title>
<![CDATA[
Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.754912v1?rss=1
</link>
<description><![CDATA[
Cerebrospinal fluid (CSF) contains brain derived proteins that can be leveraged as biomarkers for Alzheimer's disease (AD) and other neurodegenerative diseases. However, converting protein intensities to a disease signal remains a challenge because physiology driven changes to the localization of proteins across CSF subcompartments are obscured when CSF is analyzed alone. Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal. Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EVps). In a ROS/MAP cohort of 48 participants, integrating CSF and EVps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls. Notably, the joint signal identified as many brain related biomarker candidates as studies using substantially larger cohorts. By demonstrating that integration of CSF and EVps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.
]]></description>
<dc:creator><![CDATA[ Brown, B. R. P., Li, X., Grasty, M. R., Miranker, A. D., Gursoy, G. ]]></dc:creator>
<dc:date>2026-10-02</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.754912</dc:identifier>
<dc:title><![CDATA[Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755718v1?rss=1">
<title>
<![CDATA[
Population reweighting and reporter distortion obscure ribosome regulation during translation inhibition 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755718v1?rss=1
</link>
<description><![CDATA[
Bacteria dynamically adjust their ribosome allocation as growth conditions change, but how this regulation unfolds within individual cells remains poorly understood. Established relationships between ribosome allocation and growth are largely derived from population measurements under steady-state conditions, which may not capture the trajectories followed by individual cells during perturbation. Differential growth can reweight heterogeneous cellular states over time, causing population-level measurements to diverge from the regulatory trajectories followed by individual cells. Here, we use lineage-resolved single-cell microscopy to follow growth and ribosomal reporter dynamics in Escherichia coli during translation inhibition without growth-dependent reweighting of the sampled lineages. Unexpectedly, fluorescent protein fusions to the ribosomal proteins S2 and L9 decrease in fluorescence following chloramphenicol treatment, whereas transcriptional reporters driven by the ribosomal promoters initially increase. The decrease in ribosomal fusion fluorescence persists across multiple fluorescent proteins and is reproduced during tetracycline treatment, indicating that it is not specific to a particular fluorophore, ribosomal protein or translation inhibitor. Although some fusion reporters retain a relationship with cellular growth within physiological states, this relationship is itself reporter-dependent. These results reveal two independent requirements for quantitative measurements of dynamic cellular responses: longitudinal sampling is needed to distinguish within-cell regulation from growth-dependent population reweighting, and reporter behaviour must be validated when the perturbation alters the processes generating the measured signal.
]]></description>
<dc:creator><![CDATA[ Li, R., Abbott, K., Bakshi, S. ]]></dc:creator>
<dc:date>2026-10-02</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755718</dc:identifier>
<dc:title><![CDATA[Population reweighting and reporter distortion obscure ribosome regulation during translation inhibition]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755644v1?rss=1">
<title>
<![CDATA[
Dynamical stability and accuracy of moment closure approximations for bursty gene expression with nonlinear degradation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755644v1?rss=1
</link>
<description><![CDATA[
Moment-closure methods provide a widely used approach for approximating low-order statistics of stochastic biochemical reaction networks, but the resulting closed equations may exhibit physically meaningless dynamics. Here, we systematically investigate the stability of second-order moment-closure approximations for a bursty gene expression model with nonlinear protein degradation. We consider the Gaussian, log-normal, and negative-binomial closures. By analyzing the resulting dynamical systems, we show that the Gaussian closure fails the proposed stability criterion for all parameter values, whereas both the log-normal and negative-binomial closures possess a unique physically accessible fixed point and are stable if and only if kB(1 + 2B) [&ge;] 1, where B is the mean burst size and k is the dimensionless protein synthesis rate. Moreover, this condition is automatically satisfied when the steady-state mean protein copy number is at least one, suggesting that it is mild in biologically relevant regimes. Numerical comparisons over a broad parameter range further show that the negative-binomial closure is the most accurate, while the Gaussian closure is the least accurate. We further develop higher-order negative-binomial moment-closure schemes and demonstrate that increasing the closure order substantially improves predictive accuracy. These results highlight the importance of considering dynamical stability, in addition to numerical accuracy, when assessing moment-closure approximations.
]]></description>
<dc:creator><![CDATA[ Zhou, M., Jia, C. ]]></dc:creator>
<dc:date>2026-10-01</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755644</dc:identifier>
<dc:title><![CDATA[Dynamical stability and accuracy of moment closure approximations for bursty gene expression with nonlinear degradation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755460v1?rss=1">
<title>
<![CDATA[
From seven combination hypotheses to one testable interaction: a gated agentic AI QSP workflow applied to healthy ageing interventions 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755460v1?rss=1
</link>
<description><![CDATA[
Large language model (LLM) agents can propose combination therapies and construct supporting mechanistic models far quicker than either can be verified. To address this gap, we built a gated agentic AI quantitative systems pharmacology (Ai QSP) workflow where no hypothesis reaches a report until it clears strict hurdles for precedent, evidence, structural integrity, and release. We applied this pipeline end to end to healthy-ageing interventions. It began with a 34 state model calibrated on clinical trial data for semaglutide and metformin. Locked and evaluated against an independent post hoc DNA methylation trial, the model captured the direction of change across all 9 organ and system clocks, but the magnitude was far too small: it predicted about 0.14 years per year where 1.6 were observed, halting tier promotion. The workflow then generated seven nutritional combination hypotheses (H1H7) with 48 new parameters encoded in SBML. Identifiability analysis showed that 37 of these were completely unconstrained by available data. Trimming the model down to its lead pairing - a food derived bioactive (Agent A) plus a single-strain probiotic (Agent B left six identifiable parameters and locked the uncalibrated interaction term at zero. From this reduced model, we derived an 84 day trial with a 28 day washout, requiring 356 participants for 80% power at an interaction ratio of 0.75. A 9,000 person virtual trial confirmed the design (80.5% power), but revealed an operational trap: an assay floor at 24 mg/kg slashed power to 33% and skewed estimates toward the null, forcing a protocol-level censoring condition before study lock. Testing the pipeline across downstream operational stages and comparative oncology programmes showed that unconstrained generation consistently over-reports novelty, topological entry points can masquerade as biological mechanism, and governance gates remain vulnerable to operator overrides. Ultimately, the workflow delivered no silver bullet: its output is a single testable interaction, an assay hardened trial design, and an audit trail documenting why the other six hypotheses were shelved. Agent identities, doses, and agent specific sources are withheld in this version pending patent filing.
]]></description>
<dc:creator><![CDATA[ Goryanin, I., Goryanin, I., Damms, B. ]]></dc:creator>
<dc:date>2026-10-01</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755460</dc:identifier>
<dc:title><![CDATA[From seven combination hypotheses to one testable interaction: a gated agentic AI QSP workflow applied to healthy ageing interventions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755490v1?rss=1">
<title>
<![CDATA[
The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755490v1?rss=1
</link>
<description><![CDATA[
Cell-type atlases now catalogue hundreds of distinct human cell identities across development, each annotated with mitochondrial abundance, dominant fuel pathway, and lineage of origin, yet no computable model connects the topology of the lineage tree to the metabolic rate the resulting organs display in the adult body. Using a curated 396-node human cell-lineage tree spanning the zygote to terminal somatic identities, we test whether organ-level standard metabolic rate (SMR) is better predicted by developmental time (lineage depth) or by terminal fate identity (anatomical compartment). We find that lineage depth explains essentially none of the variance in a cell's metabolic tier (r = 0.11, R2 approximately 1.2%), whereas compartment identity explains roughly three-quarters of it (approximately 75%), and that the mean mitochondrial volume fraction of an organ's constituent terminal cell types tracks the organ's classical literature-derived SMR with r = 0.90 across five canonical reference-man organ groups. These results motivate a five-layer computable framework: lineage as a formal tree grammar, information-theoretic structure of the tree, metabolic switches as runtime operators, compilation to organ-level energetic networks, and execution as organism-level bioenergetics. We also introduce the metabolic commitment-horizon model: the hypothesis that a cell's metabolic tier is fixed at a discrete lineage-commitment event and thereafter held constant, rather than accumulated continuously with differentiation time. In a worked held-out test (5-fold cross-validation, n = 396), a nearest-commitment-ancestor predictor beat a global-mean null (MAE 0.752 vs 0.809, p = 0.015) but was not significantly better than a lineage-depth regression (delta MAE = -0.042, 95% CI -0.098 to +0.012, p = 0.175), so we report the commitment-horizon model as supported descriptively but not yet discriminated predictively on these data. We connect this within-species result to the classical, cross-species rate-of-living hypothesis, treat insulin resistance as an acquired perturbation of the clock, survey machine-learning and deep-learning tools that could extend the framework on primary single-cell data, and close with eight testable predictions and falsification conditions.
]]></description>
<dc:creator><![CDATA[ Martin Sevilla, A., Pan, L. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755490</dc:identifier>
<dc:title><![CDATA[The Metabolic Organ Clock: A Computable Framework Linking Cell-Lineage Architecture to Emergent Organ Bioenergetics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755524v1?rss=1">
<title>
<![CDATA[
Stress-recovery dynamics reveal a separatrix in transcriptomic landscapes across organs and aging 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755524v1?rss=1
</link>
<description><![CDATA[
Aging is often characterized statically as molecular profiles, but how these profiles may affect the capacity of systems to respond to the external environment remains elusive. We perturbed young and aged mice via transient sleep deprivation and profiled transcriptomes from six organs immediately after stress and following recovery. The same perturbation induced complex temporal dynamics for each gene in an organ- and age- dependent manner. A shape parameter distinguished genes that recovered toward control expression from those that continued to drift away during recovery. Transcriptomic vector fields were inferred from these transitions using neural ordinary differential equations, revealing distinct stability regimes across organ systems. Interestingly, that of testis from the young mice contained a separatrix bounding the region within which perturbed state can return toward control. These findings establish transient perturbation and recovery as a framework for probing age-dependent transcriptomic stability and identifying boundaries that determine whether a perturbed system can return.
]]></description>
<dc:creator><![CDATA[ Xu, B., Ji, S., Lin, Z., Deng, C., Li, L. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755524</dc:identifier>
<dc:title><![CDATA[Stress-recovery dynamics reveal a separatrix in transcriptomic landscapes across organs and aging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755448v1?rss=1">
<title>
<![CDATA[
A mechanistic, mortality-referenced biological age from routine blood tests: construction and temporal validation of IQN-BIOAGE-01 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755448v1?rss=1
</link>
<description><![CDATA[
Background. Blood-based biological age clocks predict mortality well, but they are statistical composites. They do not say which physiological systems make a person biologically older, and they cannot say how an intervention would move the clock or whether moving it would change risk. Methods. We built IQN-BIOAGE-01, a quantitative systems pharmacology (QSP) model of mortality-referenced biological age. A systemic senescent load follows saturating-removal stochastic dynamics and drives eight latent organ/hallmark axes, which are observed through 13 routine blood, blood pressure and body size measurements. Death is the first passage of the load over a threshold lowered by poor organ reserve, and biological age (BAmech) is the age at which the reference population has the same 10year risk. The model was calibrated on NHANES III (14,049 adults; 2,736 deaths in 20 years) and tested on a locked NHANES IV 1999 2010 holdout (24,650 adults; 1,867 deaths). Two later versions were accepted or rejected by rules written before their validation results. We confronted the inflammation axis with three anti-inflammatory outcome trials, audited common implementations of the comparator clock, and locked a registration-ready external validation. Results. On the hold-out, Harrell's C was 0.842 for BAmech, 0.842 for PhenoAge and 0.823 for chronological age. The hazard ratio per SD of the biological age gap was 1.55 (1.48 1.62) for BAemech and 1.45 (1.40 1.50) for PhenoAge. Risk ordering transferred across eras (calibration slope 1.01 1.03), but absolute 10year risk was overpredicted (10.4% against 7.4% observed); life-table recalibration moved observed-to-expected from 0.72 to 0.81. The inflammation axis carried most of the gap. A joint re-estimate did not converge and a converged, biological-variation-anchored version narrowly failed its pre-written identifiability criterion, so v1.0 remains the locked primary. The outcome evidence available so far, chiefly topline results from one anti-inflammatory trial, suggested that only a small fraction of a pharmacological CRP fall may carry the risk implied by the model's cross-sectional structure (posterior median pass-through 0.22). Common software implementations of PhenoAge differed from the published formula by 0.7 2.0 years. Conclusions. A mechanistic model can match a leading statistical blood clock in discrimination while adding an organ-level decomposition, explicit statements of what the data can and cannot identify, and a structure in which interventions can be simulated. Because inflammation dominates blood-based biological age while lowering CRP with drugs may carry little of the implied risk, a change in a blood clock should not be read as a change in mortality risk without outcome evidence. Validation is Tier 1 (internal and temporal, within one survey programme); the external test is locked and awaiting data access.
]]></description>
<dc:creator><![CDATA[ Goryanin, I., Damms, B., Goryanin, I. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755448</dc:identifier>
<dc:title><![CDATA[A mechanistic, mortality-referenced biological age from routine blood tests: construction and temporal validation of IQN-BIOAGE-01]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755324v1?rss=1">
<title>
<![CDATA[
Cyclic uremia without kidney injury drives cardiovascular disease with rapid onset of immunosenescence and heart failure 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755324v1?rss=1
</link>
<description><![CDATA[
End-stage kidney disease (ESKD) is a major driver of cardiovascular disease (CVD) and requires dialysis treatment leading to a unique metabolic phenotype with cyclic transitions between accumulation and clearance of uremic toxins. Although ESKD patients display the highest cardiovascular mortality among CKD patients, the effect of cyclic uremia on cardiovascular disease remains subject to discussion. Existing mouse models rely on induction of kidney injury and do not resolve the interconnected pathophysiological effects of metabolic, hypertensive and endocrine renal failure. They are therefore unable to distinguish uremia-driven effects from well-established drivers of cardiovascular disease such as hypertension. Here, we established a simple, reproducible, and sex-inclusive mouse model of uremia in absence of kidney injury or hypertension leveraging a bistable vesico-peritoneal shunt (VPS). Strikingly, we identify cyclic uremia as an independent driver of ESKD-induced CVD, that induces heart failure with preserved ejection fraction (HFpEF), vascular inflammation and immunosenescence. As such, the VPS represents the first preclinical model that enables investigation of cyclic uremia as a driver of cardiovascular disease.
]]></description>
<dc:creator><![CDATA[ Schaefer, G. J., Droste, P., Schikarski, C., Li, X., Kohl, M., Lutterbach, N., de Loor, H., Koch, L., Menzel, S., Zhang, L., Long, Q., Vogt, K., Andries, A.-S., Babler, A., Schumacher, D., Schneider, K., Hoeft, A., Boor, P., Meijers, B., Schneider, R. K., Schneider, C. V., Honne, A., Kramann, R., Hoeft, K. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755324</dc:identifier>
<dc:title><![CDATA[Cyclic uremia without kidney injury drives cardiovascular disease with rapid onset of immunosenescence and heart failure]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755377v1?rss=1">
<title>
<![CDATA[
Reducing growth-medium complexity reveals nutrient-responsive programs in a near-minimal bacterium 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755377v1?rss=1
</link>
<description><![CDATA[
Mesoplasma florum is a fast-growing, near-minimal bacterium and an emerging model for systems and synthetic biology. However, its dependence on complex serum-containing media limits experimental control and complicates the interpretation of cellular phenotypes. Here, we developed CMRL-AT, a serum-free, quasi-defined medium that supports rapid growth comparable to the commonly used ATCC 1161 medium. Despite supporting similar biomass, CMRL-AT profoundly reshaped the M. florum transcriptome, with approximately one-third of the annotated protein-encoding genes being differentially expressed relative to ATCC 1161. These changes revealed distinct physiological programs associated with rapid growth in complex medium and higher nutrient acquisition in CMRL-AT, illustrating how medium composition alters the functional priorities of a near-minimal cell. Transcriptome profiling across six energy sources further uncovered distinct sugar-responsive expression programs. Combining these responses with transcription-unit organization, protein-domain predictions, and metabolic context resolved fructose- and sucrose-responsive modules, and allowed the assignment of previously ambiguous phosphotransferase system components to specific sugar-utilization pathways. CMRL-AT provides an experimental framework to help resolving gene functions, refining metabolic models, and designing reduced genomes adapted to defined environments.
]]></description>
<dc:creator><![CDATA[ Duval, A., Gagnon, J., Jeanneau, S., Matteau, D., Jacques, P.-E., Rodrigue, S. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755377</dc:identifier>
<dc:title><![CDATA[Reducing growth-medium complexity reveals nutrient-responsive programs in a near-minimal bacterium]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.29.755382v1?rss=1">
<title>
<![CDATA[
A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.29.755382v1?rss=1
</link>
<description><![CDATA[
Estimating the postmortem submersion interval (PMSI) remains challenging due to complex biological and environmental interactions during aquatic decomposition. This study proposes a reduced mechanistic ordinary differential equation (ODE) model linking tissue degradation, microbial activity, and dissolved oxygen dynamics. Calibrated against the total aquatic decomposition score (TADS) trajectory for Northern Adriatic Sea cases, the model preserves a strictly monotone TADS--time relationship, ensuring unique numerical inversion from observed TADS to PMSI. Synthetic observation experiments evaluated how data selection influences parameter recovery, practical identifiability, and robustness under measurement uncertainty. Microbial measurements improved parameter recovery for microbial dynamics, whereas dissolved oxygen measurements constrained oxygen-consumption and renewal processes. Combining both types of observation optimized the recovery of coupled parameters. Trajectory reconstruction, profile likelihood analysis, boundary-hit frequencies, and Monte Carlo simulations confirmed these findings. Importantly, while all observation strategies reproduced TADS with comparable accuracy, they differed substantially in recovering underlying biological parameters and latent-state dynamics. Thus, accurately reproducing decomposition scores alone is insufficient for reliable parameter inference. This framework provides a transparent and biologically interpretable foundation for the mechanistic estimation of PMSI. Future applications to case-level and multi-site datasets may enable site-specific recalibration and improved predictive evaluation across diverse aquatic environments.
]]></description>
<dc:creator><![CDATA[ Akin, U., Aykir, O. F., PELEN, N. N., Kaplan, M. ]]></dc:creator>
<dc:date>2026-09-30</dc:date>
<dc:identifier>doi:10.64898/2026.09.29.755382</dc:identifier>
<dc:title><![CDATA[A Reduced Mechanistic Model for Aquatic Decomposition of a Human Body]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.24.754086v1?rss=1">
<title>
<![CDATA[
Mapping a genome-scale in vivo knockout screen to a mechanistic network model identifies VAV2, RASA1, and LEPR as regulators of cardiomyocyte hypertrophy 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.24.754086v1?rss=1
</link>
<description><![CDATA[
Cardiomyocyte hypertrophy is a leading clinical predictor of heart failure, yet newly identified candidate genes often remain disconnected from the signaling mechanisms that govern cardiomyocyte growth. We developed a computational-experimental pipeline that integrates genome-scale mouse knockout phenotypes with a logic-based differential equation model of hypertrophic signaling. Among 9,605 genes evaluated by the International Mouse Phenotyping Consortium, 939 knockout lines induced abnormal heart morphology. Directional curation of hypertrophy-related sub-phenotypes followed by interaction-based network expansion mapped 37 genes to the signaling model. Virtual knockdown screening identified five candidates with concordant in vivo and in silico effects: LRIG1 and CBL as predicted negative regulators and VAV2, RASA1, and LEPR as predicted positive regulators. Mechanistic subnetwork analysis linked these candidates to distinct receptor-proximal, Ras, PI3K-AKT, and MAPK signaling axes. In neonatal rat cardiomyocytes, siRNA-mediated depletion of VAV2, RASA1, or LEPR reduced phenylephrine-induced cell growth, supporting their cell-autonomous contribution to hypertrophy. Quantitative phenotyping further validated the predicted decreased cardiac hypertrophy for VAV2 and LEPR knockouts but identified potential age-dependent mechanisms for RASA1 knockout. Overall, this study establishes the application of network models to translate from in vivo phenotypic screens into pathway mechanisms.
]]></description>
<dc:creator><![CDATA[ Watkins, L. D., Saucerman, J. J. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.24.754086</dc:identifier>
<dc:title><![CDATA[Mapping a genome-scale in vivo knockout screen to a mechanistic network model identifies VAV2, RASA1, and LEPR as regulators of cardiomyocyte hypertrophy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.755161v1?rss=1">
<title>
<![CDATA[
Proteomic comparison of Marburg and Kasokero virus infection in natural host Egyptian rousette bat reveals distinct antiviral pathways 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.755161v1?rss=1
</link>
<description><![CDATA[
Bats are natural reservoir hosts for numerous zoonotic viruses, yet the molecular mechanisms enabling viral persistence without overt disease remain incompletely understood. The Egyptian rousette bat (Rousettus aegyptiacus, ERB) is the sole known natural reservoir of Marburg virus (MARV) and a key host in the enzootic cycle of the tick-borne Kasokero virus (KASV), providing a unique system to compare host responses to different RNA viruses within the same species. Here, we applied serial cross-sectional serum proteomics integrated with tissue-specific viral kinetics to characterize systemic host responses to experimental MARV and KASV infection in captive-reared bats. Nearly 16 % of detected proteins (67/419) were found exclusively in infected animals, yet canonical acute inflammatory signatures driving pathology in other susceptible hosts were absent (e.g., in humans and non-human primates). Differential abundance and detection analyses identified both shared and virus-dependent responses, with MARV infection eliciting limited and transient perturbations, while KASV induced broader and more sustained engagement of complement, lectin pathway, and hepatometabolic proteins. Network-based analysis uncovered coordinated proteasome modules, including circulating immunoproteasome complexes, consistent with enhanced antigen-processing capacity is a feature of these virus-host dynamics. Together, these findings reveal how ERBs mount structured, virus-dependent systemic responses, offering new insight into mechanisms underlying viral infection in natural reservoir hosts.
]]></description>
<dc:creator><![CDATA[ Genovese, B. N., Randhawa, N., Neely, B. A., Grigorean, G., Schuh, A. J., Amman, B. R., Elbert, J. A., Anthony, S. J., Mazet, J. A. K., Towner, J., Bird, B. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.755161</dc:identifier>
<dc:title><![CDATA[Proteomic comparison of Marburg and Kasokero virus infection in natural host Egyptian rousette bat reveals distinct antiviral pathways]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.754175v1?rss=1">
<title>
<![CDATA[
SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.754175v1?rss=1
</link>
<description><![CDATA[
Tissue microenvironments comprise cellular and acellular components whose three-dimensional (3D) architecture guides disease fate. Direct imaging of intact specimens by light-sheet and multiphoton microscopy, and computational reconstruction from serial sections, have established that 3D spatial context reveals cell and tissue organization inaccessible at single planes. Computational reconstruction in particular can leverage archived human tissue, benefiting from the cost-effectiveness, robustness, scalable storage, workflow compatibility, and century-long pathobiology knowledge of histology, and can integrate multiple spatial modalities. However, sectioning can introduce tears and folds, and computational alignment can further distort tissue integrity. Here we introduce SpaReg, a sparsity-based 3D reconstruction method spanning histology, spatial proteomics and spatial transcriptomics. Across multiple organs, SpaReg robustly reconstructs large tissue volumes with preserved subcellular morphology despite sectioning artifacts. On a standardized histology benchmark, SpaReg achieves the best balance between 3D reconstruction accuracy and tissue integrity, and on spatial transcriptomics benchmarks it ranks among the leading methods while scaling to hundreds of sections and millions of cells in a dataset that several existing methods fail to process. Preservation of subcellular morphology by SpaReg also enables training of a Hematoxylin and Eosin (H&E)-based epithelial, T and B cell classifier, generating single-cell-resolved 3D maps directly from H&E. Applied to pancreatic tissue containing pancreatic ductal adenocarcinoma arising from an intraductal papillary mucinous neoplasm, these maps reveal that 2D sections overestimate immune exclusion, and resolve lymphoid aggregates in 3D. SpaReg, therefore, provides a scalable foundation for morphologically faithful, multimodal 3D atlases and spatially informed disease modeling
]]></description>
<dc:creator><![CDATA[ Pawar, R., Jacob, T., Raphael, R., Byrnes, E., Watkins, S., Soong, T. R., Singhi, A., Uttam, S. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.754175</dc:identifier>
<dc:title><![CDATA[SpaReg: sparsity-based 3D reconstruction of tissue microenvironments at native resolution across morphological and spatial molecular modalities]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.754405v1?rss=1">
<title>
<![CDATA[
TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.754405v1?rss=1
</link>
<description><![CDATA[
Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.
]]></description>
<dc:creator><![CDATA[ Kumar, R., Li, S., Jiang, P., Hannenhalli, S. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.754405</dc:identifier>
<dc:title><![CDATA[TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.755110v1?rss=1">
<title>
<![CDATA[
Integrative proteomic analysis and molecular dynamics simulations of ANKLE2 reveal mechanisms of microcephaly 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.755110v1?rss=1
</link>
<description><![CDATA[
ANKLE2 is a scaffolding protein with crucial roles in neuroprogenitor cell division and embryonic brain development. Pathogenic variants in ANKLE2 cause primary microcephaly, a congenital disorder characterized by impaired neurodevelopment. Nonetheless, the underlying dysfunction of ANKLE2 is not understood. Here, we define the ANKLE2 protein interaction landscape, which includes cell division proteins and novel microcephaly candidates in the APC (anaphase-promoting complex). Analysis of six pathogenic variants reveals changes in this interactome that likely result in microcephaly. Using molecular simulations, we identify the structural consequences of five pathogenic substitutions, including disruption of important helical structures. Combined, these techniques suggest loss of interaction with the PP2A (protein phosphatase 2A) complex is a common mechanism for ANKLE2 pathogenesis. Phosphoproteomics reveals widespread alterations in ANKLE2- and PP2A-dependent phosphorylation of cell division proteins. Together, our integrative proteomics and simulation approach improves the molecular understanding of ANKLE2 function and its pathogenic changes in primary microcephaly.
]]></description>
<dc:creator><![CDATA[ Fishburn, A. T., Peddamallu, V., Lopez, N. J., Bilkic, I. M., Hixson, I. J., Mambou, E., Pan, H., Bonaventure, B., Florio, C. J., Skawinski, C. L. S., Lopez Ramos, M., Bandivadekar, P. R., Becker, S. S., Gidugu, B. L., Fishburn, J. L. A., Gudinas, A. P., Barlow, J. H., Mai, D. J., Ahn, S.-H., Johnson, J. R., Link, N. L., Shah, P. S. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.755110</dc:identifier>
<dc:title><![CDATA[Integrative proteomic analysis and molecular dynamics simulations of ANKLE2 reveal mechanisms of microcephaly]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.754797v1?rss=1">
<title>
<![CDATA[
Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.754797v1?rss=1
</link>
<description><![CDATA[
Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.
]]></description>
<dc:creator><![CDATA[ Rarani, Z. H., Keshari, S., Sachan, A., Saini, A., Pease, N. A., Fan, J., Fu, A., Liu, Y. C., Gurkar, A. U., Delgoffe, G. M., Singh, H., Das, J. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.754797</dc:identifier>
<dc:title><![CDATA[Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.754951v1?rss=1">
<title>
<![CDATA[
Comprehensive in silico analysis reveals candidate regulatory mechanisms underlying selective cerebellar vulnerability in pontocerebellar hypoplasia 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.754951v1?rss=1
</link>
<description><![CDATA[
Pontocerebellar hypoplasia (PCH) is a group of ultrarare, neurodegenerative disorders characterized by cerebellar and pontine hypoplasia. Genetic analysis over the last two decades has revealed an increasing number of pathogenic variants in a wide range of broadly expressed genes functioning in RNA processing, tRNA metabolism, and translation. However, the mechanisms linking these ubiquitous processes to brain region-specific vulnerability are unknown. Here, we established a multi-level variant-to-function in silico framework to predict the molecular consequences of PCH-associated variants in TSEN complex genes. These variants were predicted to have heterogeneous effects on diverse protein properties, including stability, subcellular localization, and degradation, supporting variant-specific rather than uniform disease mechanisms. Complementary transcriptomic analyses showed that PCH-associated genes were not globally enriched in the prenatal cerebellum. Instead, their expression was coordinated in a stage- and cell type-specific manner during cerebellar development. We therefore hypothesize that multiple PCH-associated genes are regulated by a common set of transcription factors, providing an explanation of the selective vulnerability of the cerebellum and to the phenotypic convergence of genetically diverse PCH subtypes. In summary, this study prioritizes candidate variants for biochemical, cellular, and in vivo validation, and identifies regulatory programs, cell lineages, and developmental windows for targeted, mechanistically informed disease modelling.
]]></description>
<dc:creator><![CDATA[ Branco, L., Mayer, S. ]]></dc:creator>
<dc:date>2026-09-29</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.754951</dc:identifier>
<dc:title><![CDATA[Comprehensive in silico analysis reveals candidate regulatory mechanisms underlying selective cerebellar vulnerability in pontocerebellar hypoplasia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.27.754802v1?rss=1">
<title>
<![CDATA[
Quantifying Chemical Fluxes Underlying Gut Microbiota-Host Interactions 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.27.754802v1?rss=1
</link>
<description><![CDATA[
To resolve how the gut microbiota shapes human health, it is essential to quantify the exchange of chemicals between microbes and the host. Much as the dose of a drug determines its pharmacological effect, the magnitude of a specific chemical flux determines its impact. Here, we establish from first principles how fluxes can be estimated by integrating key parameters from human digestive physiology and microbial metabolism. We apply this framework to three cases: the exchange of fermentation products, the production of toxins by bacterial pathogens, and nitrogen homeostasis. Our analysis identifies three directly measurable host-level parameters which, in concert with microbial activity, shape flux magnitudes: intestinal transit time, absolute microbial abundance in feces, and fecal mass loss. The simultaneous quantification of these parameters provides a feasible yet essential step toward a mechanistic and quantitative description of gut microbiota-host interactions across health and disease.
]]></description>
<dc:creator><![CDATA[ Arnoldini, M., Cremer, J. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.27.754802</dc:identifier>
<dc:title><![CDATA[Quantifying Chemical Fluxes Underlying Gut Microbiota-Host Interactions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.26.754619v1?rss=1">
<title>
<![CDATA[
GEMOT: Towards Mechanistic World Models for Biology 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.26.754619v1?rss=1
</link>
<description><![CDATA[
Scientific discovery seeks mechanisms that explain observations and predict beyond the measurements that produced them. Whereas large language models (LLMs) encode knowledge implicitly, mechanistic world models organise it as a parsimonious set of explicit, modular, reusable mechanisms whose predictions can be scored against data. In biology, where measurements are sparse and noisy, mechanistic world modelling must discover latent states and governing equations jointly, yet the prior knowledge that could constrain this search is largely unstructured. We introduce gemot, an agentic framework for mechanistic world modelling, and evaluate it on 18 published biological problems spanning molecular biology, epidemiology, and immune-cell differentiation, with up to 1,755 training measurements, 65 observables, 170 experimental conditions, and 3 data modalities per problem. gemot is auditable by construction: a semantic layer records each hypothesis, and a Model Context Protocol layer separates hypothesis formulation from numerical evaluation, so hypothesis scoring cannot be fabricated. On every problem, autonomously constructed models match or exceed the reference models in fit and parsimony, and generalise where held-outs exist. We demonstrate that gemot can formulate novel, biologically plausible mechanistic hypotheses. This work will enable decoding of interventional biological data into competing mechanistic hypotheses and design experiments that distinguish between them, and may serve as a blueprint for mechanistic world modelling in other domains.
]]></description>
<dc:creator><![CDATA[ de Pomereu, T., Snelling, B., Froehlich, F. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.26.754619</dc:identifier>
<dc:title><![CDATA[GEMOT: Towards Mechanistic World Models for Biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.25.754499v1?rss=1">
<title>
<![CDATA[
Quantitative machine learning of protein interactions reveals the multiscale organization and molecular syntax of signaling networks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.25.754499v1?rss=1
</link>
<description><![CDATA[
Cells employ dense networks of transient protein-protein interactions mediated by modular peptide-binding domains and unstructured peptidic motifs for high-fidelity information processing. How these networks physically execute computations through protein interactions governed by complex intra- and intermolecular mechanisms remains indiscernible from current, sparse and non-quantitative, maps of the human interactome. Here, we introduce a quantitative statistical mechanical modeling (QSM) approach for machine learning domain-peptide affinities with experimental-level accuracy. Leveraging a new, principled algorithm for data harmonization and a biophysically informed neural network architecture, QSM learns to predict dissociation constants directly from amino acid sequences with calibrated confidence. We use QSM to construct the first quantitative drafts of human signaling networks and study these networks across three physical scales--recognition mechanisms of modular binding domains, combinatorial logic of multi-dentate proteins, and pathways inferred from de novo inference of protein interaction networks. We find that (i) modular domains, based on their binding preferences, selectivities, and strengths, fall into a limited number of biophysical equivalence groups, (ii) those domains, along with peptidic motifs, are "syntactically" combined within proteins to yield multivalent recognition mechanisms, and (iii) the organization of cellular function can be traced back to algorithmically detectable modules induced by domain-mediated interactions. In aggregate, these analyses instantiate a tractable roadmap towards a comprehensive, mechanistic, and simulatable articulation of the systems biology of signaling.
]]></description>
<dc:creator><![CDATA[ Rogers, J. R., Cardani, L. P., Shah, N. H., AlQuraishi, M. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.25.754499</dc:identifier>
<dc:title><![CDATA[Quantitative machine learning of protein interactions reveals the multiscale organization and molecular syntax of signaling networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.25.754297v1?rss=1">
<title>
<![CDATA[
A thermoregulatory design principle for transitions into hypometabolism 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.25.754297v1?rss=1
</link>
<description><![CDATA[
Mammals entering torpor or hibernation undergo an abrupt transition from normothermia to hypothermia, yet how thermoregulation enables this switch remains poorly understood. Here, we identify dynamical signatures that precede these transitions and a mathematical principle that can generate them. In fasting-induced torpor in mice, body-temperature fluctuations increased before torpor onset, providing an early-warning signal that tracked proximity to the transition better than temperature decline alone. A heat-balance model showed that reducing how strongly the effective heat-loss coefficient depends on body temperature reorganizes thermoregulatory stability, allowing a low-temperature equilibrium to emerge while the normothermic state remains stable. This organization is consistent with a symmetry-broken pitchfork involving a saddle-node. Similar increases in temperature fluctuations preceded hibernation onset in hamsters. These findings link pre-transition temperature dynamics to changes in the underlying thermoregulatory landscape and provide a framework for detecting and understanding transitions from normothermia to hypothermia.
]]></description>
<dc:creator><![CDATA[ Sugimoto, H., Sunagawa, G., Nakagawa, S., Sakurai, T., Yamaguchi, Y., Kuroda, S. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.25.754297</dc:identifier>
<dc:title><![CDATA[A thermoregulatory design principle for transitions into hypometabolism]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.25.751968v1?rss=1">
<title>
<![CDATA[
Accessing Enzyme Kinetic Data and Prediction Methods at Scale 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.25.751968v1?rss=1
</link>
<description><![CDATA[
Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.
]]></description>
<dc:creator><![CDATA[ Alwer, S., Escoffier, H., Taha, K., Boorla, V., Yu, H., Santra, S., Wang, Z., Egwu, C., Osinuga, A., Dey, S., Raghunath, V. S., Zare, F., McGoldrick, J., Weder, J.-N., Kerkhoven, E., Luo, X., Maranas, C. D., Zheng, L., Wittig, U., Chowdhury, R., Saha, R., Toepfer, N., Sauter, T., Fleming, R. M. T. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.25.751968</dc:identifier>
<dc:title><![CDATA[Accessing Enzyme Kinetic Data and Prediction Methods at Scale]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.24.754258v1?rss=1">
<title>
<![CDATA[
Limit-pushing overexpression reveals constraints on protein abundance 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.24.754258v1?rss=1
</link>
<description><![CDATA[
Proteins are often classified as toxic or non-toxic without measuring the abundance reached, leaving constraints on tolerable protein abundance unresolved. We established a limit-pushing approach in Saccharomyces cerevisiae combining strong inducible expression with gTOW-mediated high-copy selection to counteract copy-number compensation while measuring protein abundance and growth. Nearly all of approximately 80 chromosome I proteins severely inhibited growth or reduced viability at sufficiently high abundance. We established IE50, the expression level associated with a 50% reduction in growth rate, to quantify their widely varying overexpression tolerance. IE50 was positively associated with predicted structural order and cytoplasmic localization propensity and negatively associated with sulphur content. Single-cell imaging linked higher tolerance to proteins remaining cytoplasmic without becoming aggregation-positive and revealed abundance-dependent changes in localization and organelle morphology. At extreme abundance, Fun12, Nup60, and Pex22 generated distinct large-scale intracellular states through specific sequence regions. These findings establish overexpression toxicity as a quantitative property linked to protein characteristics and reveal both constraints on tolerable abundance and sequence-dependent capacities for intracellular organization.
]]></description>
<dc:creator><![CDATA[ Namba, S., Nishida, Y., Boone, C., Andrews, B., Moriya, H. ]]></dc:creator>
<dc:date>2026-09-28</dc:date>
<dc:identifier>doi:10.64898/2026.09.24.754258</dc:identifier>
<dc:title><![CDATA[Limit-pushing overexpression reveals constraints on protein abundance]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.23.753516v1?rss=1">
<title>
<![CDATA[
Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.23.753516v1?rss=1
</link>
<description><![CDATA[
H2O2 released to the extracellular space (ECS) regulates diverse physiological processes, yet its concentrations and spatial distribution in tissues remain poorly defined. This uncertainty hampers mechanistic understanding of redox signaling. Here, we used reaction-diffusion modeling to estimate extracellular H2O2 concentrations and transport ranges in various scenarios. Idealized analytical models were combined with numerical models incorporating localized NADPH oxidase (NOX) clusters, ECS microstructure, membrane permeability, and the thioredoxin- and GSH-dependent clearance systems. Using maximal neutrophil and NOX superoxide/H2O2 release rates, we obtained upper bounds for extracellular H2O2. Adjacent to isolated average-sized, fully active NOX2 clusters H2O2 peaked at ~540 nM at adhesion cell-cell separations, and decreased radially over ~50-100 nm. At the receptor cell surface, peak concentration decreased inversely with intercellular separation, to <5 nM at 1 m separation. Radial decrease here, for this wide separation, was over ~2.5 m. Even the former maximal extracellular concentrations induce just a minimal, highly localized oxidation of the intracellular Prdx, Trx and GSH pools. In turn, maximally activated neutrophils carry ~2000 such NOX2 clusters, inducing 10s of M peak ECS H2O2 concentrations. These cause extensive Prdx and Trx oxidation near the exposed membranes. However, the GSH-dependent system still sustains a strong transmembrane gradient if the permeation barrier remains intact, and ECS H2O2 concentrations decay to sub-M within a few m of the source cell. Extracellular H2O2 concentrations scaled linearly with source flux in all the examined conditions. These results establish stringent constraints on autocrine, juxtacrine and next-cell paracrine H2O2 signaling.
]]></description>
<dc:creator><![CDATA[ Itacarambi, A., Gouveia, M., Travasso, R. D. M., Salvador, A. ]]></dc:creator>
<dc:date>2026-09-25</dc:date>
<dc:identifier>doi:10.64898/2026.09.23.753516</dc:identifier>
<dc:title><![CDATA[Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.18.752660v1?rss=1">
<title>
<![CDATA[
PyKappa: Rule-based modeling in Python 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.18.752660v1?rss=1
</link>
<description><![CDATA[
Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.
]]></description>
<dc:creator><![CDATA[ Alpay, B. A., Lin, A., Damiani, A. C., Fontana, W. ]]></dc:creator>
<dc:date>2026-09-24</dc:date>
<dc:identifier>doi:10.64898/2026.09.18.752660</dc:identifier>
<dc:title><![CDATA[PyKappa: Rule-based modeling in Python]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.23.753896v1?rss=1">
<title>
<![CDATA[
Chaperone isoform and interactome mapping reveals functional diversification of DNAJA2-DNAJA4 complexes via stress-regulated isoforms 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.23.753896v1?rss=1
</link>
<description><![CDATA[
The human HSP70 chaperone network maintains cellular proteostasis through a diverse repertoire of HSP70s and co-chaperones. Here we examine alternative isoforms and co-chaperone hetero-complexes as additional sources of network complexity. To that end, we systematically mapped the isoform, tissue-expression, and interaction landscapes of the human HSP70 network, revealing a modular interactome containing known and novel DNAJ-DNAJ interactions. We found both tissue-expression isoform divergence as well as widespread alternative-canonical isoform co-expression, suggesting additional modes of functional diversification. Focusing on the uncharacterized DNAJA2-DNAJA4 hetero-complex, we identified the stress-inducible isoform DNAJA4-CTD-II. DNAJA4-CTD-II formed hetero-complexes with DNAJA2 and DNAJA4, with both interactions enhanced following sodium arsenite stress. Functionally, DNAJA4-CTD-II co-localized with TDP-43 aggregates and significantly suppressed their accumulation in a DNAJA2-dependent manner. Together, our data reveal extensive, uncharted isoform and interaction complexity within the HSP70 network, and uncover isoform-dependent hetero-complex remodeling as a new layer of chaperone network regulation.
]]></description>
<dc:creator><![CDATA[ Kadah, T., Akaree, N., Brodov-Nevo, A., Meller, A., Levy-Adam, F., Shalgi, R. ]]></dc:creator>
<dc:date>2026-09-24</dc:date>
<dc:identifier>doi:10.64898/2026.09.23.753896</dc:identifier>
<dc:title><![CDATA[Chaperone isoform and interactome mapping reveals functional diversification of DNAJA2-DNAJA4 complexes via stress-regulated isoforms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.21.753254v1?rss=1">
<title>
<![CDATA[
Age-Related Remodeling of Cross-Tissue Transcriptional Coordination in the Human Motor System 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.21.753254v1?rss=1
</link>
<description><![CDATA[
How aging is coordinated across the anatomically distinct tissues that collectively constitute the human motor system remains unclear. Here, we integrated data from the Genotype-Tissue Expression (GTEx) project and Gene Expression Omnibus (GEO), comprising 7,145 samples from 15 human tissues spanning three functional levels of motor control, signal transmission, and peripheral execution, and reconstructed age-related transcriptional trajectories using generalized additive models. Most age-related genes were shared across multiple tissues, yet their direction of regulation, effect magnitude, and temporal trajectories showed marked tissue specificity. Despite the clear central-to-peripheral functional organization of the motor system, transcriptional aging did not follow a stable temporal sequence or spatial gradient along this functional chain. Instead, cross-tissue transcriptional synchrony progressively increased from early to mid-adulthood, reached a relatively high level around midlife, and subsequently declined in later life, a pattern supported at both the gene and pathway levels. This late-life loss of coordination was selective, prominently involving the peripheral nerve-skeletal muscle axis and multiple tissue connections involving the striatum. Our analyses indicate that transcriptional aging of the human motor system is characterized by tissue-specific responses built upon a broadly shared molecular basis of aging, together with age-dependent reorganization of cross-tissue coordination and selective late-life decoupling.
]]></description>
<dc:creator><![CDATA[ Nie, H., Sakuma, K. ]]></dc:creator>
<dc:date>2026-09-24</dc:date>
<dc:identifier>doi:10.64898/2026.09.21.753254</dc:identifier>
<dc:title><![CDATA[Age-Related Remodeling of Cross-Tissue Transcriptional Coordination in the Human Motor System]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-09-24</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
