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This feed contains articles for bioRxiv Subject Collection "Systems Biology"
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<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.07.721661v1?rss=1">
<title>
<![CDATA[
Homeostatic feedback model of energy metabolism with adaptive enzyme levels exhibits problem solving behavior 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.07.721661v1?rss=1
</link>
<description><![CDATA[
Metabolic networks are typically viewed as homeostatic systems that stabilize flux, energy charge, redox balance, and metabolite availability under perturbation. However, it remains unclear whether the same feedback architectures that support metabolic robustness can also generate learning-like, experience-dependent adaptation. Here, we develop a coarse-grained dynamical model of mammalian energy metabolism to test whether prior perturbation can improve future metabolic responses. The model represents core glucose, glutamine, fatty acid, and oxidative phosphorylation pathways as coupled ordinary differential equations with Michaelis-Menten-type fluxes, product-inhibition feedback, adaptive enzyme-capacity regulation, and explicit ATP costs for enzyme adjustment. Rather than aiming to reproduce quantitative fluxes for a specific cell type, the framework is designed to expose how metabolic feedback, regulatory cost, repeated perturbation, and environmental variability interact. We use this model to ask whether adaptive enzyme regulation enables improved recovery after repeated challenges, whether such effects depend on energetic control costs, and whether environmental variability broadens or constrains the set of reachable adaptive states. This approach provides a tractable way to investigate how homeostatic metabolic regulation may give rise to experience-dependent metabolic plasticity.
]]></description>
<dc:creator><![CDATA[ de Baat, A., Levin, M. ]]></dc:creator>
<dc:date>2026-05-11</dc:date>
<dc:identifier>doi:10.64898/2026.05.07.721661</dc:identifier>
<dc:title><![CDATA[Homeostatic feedback model of energy metabolism with adaptive enzyme levels exhibits problem solving behavior]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.06.723244v1?rss=1">
<title>
<![CDATA[
Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.06.723244v1?rss=1
</link>
<description><![CDATA[
Quantitative Systems Pharmacology (QSP) models play an inherently interventional role in pharmaceutical research and development, functioning as executable causal systems for designing, evaluating, and replacing clinical trials. However, deploying QSP as an experimental planning engine remains constrained by the difficulty of translating unstructured literature descriptions of clinical or preclinical scenarios into reproducible, simulation-ready model interventions. Motivated by this issue, we propose an agent-based framework that operationalizes QSP models as intervention-ready experimental systems by automatically extracting and executing literature-derived scenarios. The framework combines semantic grounding of model entities with a large language model (LLM)-driven Scenario Extractor and a dual-agent Scenario Mapper. Rather than relying on opaque, single-shot reasoning, our pipeline converts free-text interventions into precise parameter configurations through discrete, verifiable work orders. Moreover, our dynamic Human-in-the-Loop (HITL) strategy empowers modelers to resolve biological ambiguities interactively. Across four diverse kinetic ordinary differential equation (ODE)/QSP models and seven Subject Matter Expert (SME)-curated literature scenarios, our model resolved all selected scenarios into correct executable parameter changes, including multi-dose interventions, unit conversions, no-op scenarios, and ambiguity-triggered HITL cases, demonstrating that structured collaboration between experts and agentic systems can resolve scenarios that standalone raw Systems Biology Markup Language (SBML) reasoning LLM calls handle unreliably.
]]></description>
<dc:creator><![CDATA[ Kazemeini, A., Prieto, J., Balaji Kuttae, S., Siokis, A., Singh, G., Passban, P., Andreani, T. ]]></dc:creator>
<dc:date>2026-05-11</dc:date>
<dc:identifier>doi:10.64898/2026.05.06.723244</dc:identifier>
<dc:title><![CDATA[Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.06.722433v1?rss=1">
<title>
<![CDATA[
Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.06.722433v1?rss=1
</link>
<description><![CDATA[
A key question in cell biology is how cell-scale organization emerges from a given set of molecular players and rules of interaction. Given its multiscale nature, addressing this question requires a combination of experimental perturbation, mathematical modeling, and parameter inference. We leverage recent advances in each of these fields, focusing in particular on neural-network methods for simulation-based inference, to study how cell-scale patterns of Rho GTPase activity are defined by molecular-scale activator-inhibitor interactions with filamentous actin. We show that variations in F-actin assembly dynamics can be inferred directly from experimental data by combining a mathematical model with a neural network trained to associate parameter sets with data. Our neural approach differentiates data sets more precisely than traditional summary statistics, and yields a complete and robust likelihood function for each data set. Utilizing the trained network, we demonstrate how RhoGAP tunes RhoA waves via interaction with F-actin. After showing that the known functions of RhoGAP are insufficient to explain experimentally-observed dynamics, we use neural methods to infer that RhoGAP must, at a minimum, also decrease filament nucleation rates to sustain waves. Our work yields specific, experimentally-testable predictions and illustrates how a combination of traditional forward models and modern inference tools can aid in unraveling mechanisms of self-organization.
]]></description>
<dc:creator><![CDATA[ Maxian, O., Munro, E., Dinner, A. ]]></dc:creator>
<dc:date>2026-05-11</dc:date>
<dc:identifier>doi:10.64898/2026.05.06.722433</dc:identifier>
<dc:title><![CDATA[Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.06.722954v1?rss=1">
<title>
<![CDATA[
Systemic stress states are reversed by NPBWR1 inhibition 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.06.722954v1?rss=1
</link>
<description><![CDATA[
Chronic stress is widely studied as a brain-centered driver of depression, yet its effects across the body remain unclear. Here, we define chronic stress as a coordinated molecular state across tissues in mice. Using a whole-body proteomic atlas of 13 tissues, we find that stress effects are strongest in peripheral metabolic and endocrine organs, whereas classical stress-associated brain regions show comparatively modest changes. Cross-organ analyses reveal structured, tissue-specific responses rather than uniform shifts. Inhibition of the stress-regulated receptor NPBWR1 reverses both behavioral deficits and proteomic alterations across organs in both sexes, indicating that this state is dynamically modifiable. Integration with human serum proteomics identifies shared, sex-specific signatures. Across tissues, lipid metabolism emerges as a common stress-responsive pathway, confirmed by hepatic lipid remodeling that is normalized by NPBWR1 inhibition. To facilitate exploration of these data, we provide an interactive cross-organ web-based resource. Together, these findings challenge the brain-centric view of stress pathology and define chronic stress as an organism-wide molecular state.
]]></description>
<dc:creator><![CDATA[ Stein, G., Mueller, P., Vallet, M., Cirri, E., Lange, L., Heller, E. A., Winter, J., Graeler, M. H., Ueberschaar, N., Maurer, A., Dobrowolny, H., Meyer-Lotz, G., Steiner, J., Engmann, O. ]]></dc:creator>
<dc:date>2026-05-09</dc:date>
<dc:identifier>doi:10.64898/2026.05.06.722954</dc:identifier>
<dc:title><![CDATA[Systemic stress states are reversed by NPBWR1 inhibition]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.06.723162v1?rss=1">
<title>
<![CDATA[
Computational analysis reveals the requirement for cell-cell interaction in liver repopulation by transplanted hepatocytes 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.06.723162v1?rss=1
</link>
<description><![CDATA[
Hepatocyte transplantation is a promising alternative to liver transplantation; however, it currently serves only as a temporary treatment until a donor organ becomes available. In contrast, animal studies have demonstrated ''liver repopulation'', a phenomenon in which transplanted hepatocytes progressively replace host hepatocytes. Despite extensive documentation, the mechanisms driving this process remain poorly understood. More fundamentally, it remains unclear whether liver repopulation is driven by active cell--cell interactions between host and transplanted hepatocytes that induce host cell death, or whether it can be explained solely by intrinsic differences in proliferation and survival between these populations. To address this, we performed computational simulations using an agent-based model constrained by experimental data from repopulation in uninjured rat livers. Our analysis reveals that host hepatocyte death rate is the dominant determinant of repopulation kinetics, whereas variations in proliferation rate have only a limited impact. Notably, experimentally observed repopulation dynamics were only reproduced when cell--cell interactions were incorporated, or alternatively when host hepatocyte lifespan was set to unrealistically short values (approximately 25 days). These findings support a model in which cell--cell interactions play a critical role in efficient liver repopulation. More broadly, this study establishes a conceptual and computational framework for evaluating the requirement for cell--cell interactions in tissue replacement, even in the absence of a defined molecular mechanism.
]]></description>
<dc:creator><![CDATA[ Ikuta, D., Tamaki, R., Wada, S., Onishi, K., Nishikawa, M., Sakai, Y., Katsuda, T. ]]></dc:creator>
<dc:date>2026-05-09</dc:date>
<dc:identifier>doi:10.64898/2026.05.06.723162</dc:identifier>
<dc:title><![CDATA[Computational analysis reveals the requirement for cell-cell interaction in liver repopulation by transplanted hepatocytes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.723060v1?rss=1">
<title>
<![CDATA[
Unraveling the metabolic interactions of a Dehalobacter-containinganaerobic mixed culture for bioremediation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.723060v1?rss=1
</link>
<description><![CDATA[
Organohalide-respiring bacteria (OHRB), such as Dehalobacter, play key roles in the bioremediation of anoxic environments contaminated with chlorinated aromatic compounds. These obligate anaerobes rely on syntrophic interactions to obtain essential resources--hydrogen, acetate, and corrinoid cofactors--from acetogens and fermenters. However, the metabolic interactions enabling complete reductive dehalogenation of compounds like 1,2,4-trichlorobenzene (1,2,4-TCB) to benzene remain incompletely understood. In this study, we asked: (1) What are the key microbial taxa and their functional roles within a Dehalobacter-containing anaerobic microbial community detoxifying chlorinated benzenes? (2) How do syntrophic interactions enable complete dehalogenation of 1,2,4-TCB to benzene under anaerobic conditions? (3) Can genome-resolved metagenomics and genome-scale metabolic modeling elucidate the metabolic dependencies supporting organohalide respiration in complex consortia? To address these questions, we cultivated microbial communities in batch reactors using methanol as electron donor and either 1,2,4-TCB or monochlorobenzene (MCB) as electron acceptor. In active MCB-fed cultures, benzene increased from 0 to 62.3 mol per bottle while MCB decreased from 88.3 to 22.0 mol per bottle over 120 days, with this pattern repeating across multiple substrate additions. Using genome-resolved metagenomics to identify dominant taxa and select 12 high-quality metagenome-assembled genomes (MAGs) for modeling, we reconstructed genome-scale metabolic models (GEMs) to identify candidate metabolic interactions and predict syntrophic dependencies that may support organohalide respiration in these consortia. Community flux sampling predicted that methanol, H2, acetate, and CO2 formed the dominant exchange backbone of the modeled community, while also indicating competition for shared electron donors between the two Dehalobacter populations. Model-guided minimal-community analysis further identified a narrow dechlorinating core in which all feasible minimal consortia retained a Dehalobacter member together with Methanothrix. These results provide a modeling-informed framework for hypothesis generation and future experimental validation of anaerobic consortia relevant to bioremediation.
]]></description>
<dc:creator><![CDATA[ Scott, W. T., Puentes Jacome, L. A., Nijsse, B., Wang, J., Stouten, G. R., Koehorst, J. J., Smidt, H., Edwards, E. A., Schaap, P. J., Kleerebezem, R. ]]></dc:creator>
<dc:date>2026-05-09</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.723060</dc:identifier>
<dc:title><![CDATA[Unraveling the metabolic interactions of a Dehalobacter-containinganaerobic mixed culture for bioremediation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.723030v1?rss=1">
<title>
<![CDATA[
Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.723030v1?rss=1
</link>
<description><![CDATA[
Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.
]]></description>
<dc:creator><![CDATA[ Khurana, T. K., Wu, L. Y., Gherardini, P. F., Moeinzadeh, S., Mohseni, M., Yasar, F. G., Dettloff, R., Poelma, J., Sabri, S., Scaramozza, A., Li, Y., Rouzbeh, N., Elder, N., Deshmukh, S., Majd, A., Gupta, R., Farahvashi, S., Thomas, I., Betts, C., Charbonier, F., Roberts, D., Hsiung, P.-L., Zargari-Pariset, E., Yau, R., Vila, O. F., Phillips, M., Xiao, C., Wang, J., Zhou, Y., Adhikari, P., Taing, M., Farjami, E., Javanmardi, B., Siu, M., The Cellanome Development Team,, Valente, C., Cox, C., Geiger-Schuller, K., Turley, S. J., Rozenblatt-Rosen, O., Fattahi, F., Ecker, J. R., Jones, J. R., Ga ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.723030</dc:identifier>
<dc:title><![CDATA[Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.721895v1?rss=1">
<title>
<![CDATA[
Inter- and Intra-individual Variability in Oral Food Processing and Its Impact on Aroma Release 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.721895v1?rss=1
</link>
<description><![CDATA[
Aroma perception during food consumption results from the combined effects of food composition, oral processing (such as chewing and saliva action), the release and transport of volatile compounds toward the olfactory epithelium, followed by cognitive integration in the brain. Recent advances in real-time analytical techniques, particularly Proton Transfer Reaction-Time-of-Flight Mass Spectrometry (PTR-ToF-MS), enable in vivo monitoring of aroma release with high temporal resolution and have become widely used for analyzing the composition of exhaled air. However, the interpretation of aroma release kinetics remains challenging due to substantial intra- and inter-individual variability caused by differences in physiology, anatomy, oral behavior, and respiratory patterns.

In this context, the present study was designed to quantify aroma release associated with different food oral processing (FOP) mechanisms, such as chewing and swallowing, using simple model matrices containing a single aroma compound, and to document inter- and intra-individual variability among subjects.

Real-time PTR-MS measurements were combined with self-reported oral events and simultaneous respiratory monitoring to analyze aroma release from aqueous solutions and gummy discs flavored with isoamyl acetate. The results showed that inter-individual variability was higher than intra-individual variability and allowed its quantification in aroma release. Significant differences in aroma release kinetics were observed depending on FOP protocols. The importance of considering swallowing events when analyzing aroma release data was also highlighted.
]]></description>
<dc:creator><![CDATA[ Andriot, I., Grossiord, D., Beno, N., Chabin, T., Laboure, H., Lucchi, G., Martin, C., Mourabit, O., Piornos, J. A., Saint-Georges, L., Salles, C., Trelea, I. C., Peltier, C. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.721895</dc:identifier>
<dc:title><![CDATA[Inter- and Intra-individual Variability in Oral Food Processing and Its Impact on Aroma Release]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.722902v1?rss=1">
<title>
<![CDATA[
Ensemble kinetic modelling links residual enzyme activity to clinical symptoms in mitochondrial β-oxidation defects 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.722902v1?rss=1
</link>
<description><![CDATA[
The mitochondrial fatty acid {beta}-oxidation (mFAO) is an important source of energy when carbohydrate stores are depleted. It is also involved in many diseases, including inherited fatty-acid oxidation deficiencies (mFAODs). Patients with the same genetic variant often present with clinically heterogeneous phenotypes, but the mechanisms contributing to this heterogeneity are poorly understood. To investigate the underlying pathophysiology of different mFAODs, we constructed a computational model of mFAO in human liver, based on experimentally determined enzyme kinetics. A recognised, but seldom addressed challenge in metabolic modelling is the substantial uncertainty about kinetic parameter values. Whereas experimental values of some mFAO parameters are quite reproducible, others vary by up to four orders of magnitude between different reports. To address this, we generated an ensemble of kinetic models, each with the same reaction stoichiometry and rate equations, but different kinetic parameters, sampled from distributions of literature-derived values. We also comprehensively report these values and the arguments based on which they were evaluated. The resulting models were validated against available flux data, yielding a final ensemble of 51 valid models. These models recapitulate recent findings about the accumulation of medium-chain acyl-CoAs and the concomitant depletion of free CoA (CoASH) in medium-chain acyl-CoA dehydrogenase deficiency. We applied the ensemble to a set of known mFAODs, separating them into long-chain (LC-) and short-/medium-chain (S/MC-)mFAODs. The residual activity at which clinical symptoms are known to occur corresponded well with the residual activity in the model at which pathway flux was significantly decreased in LC-mFAODs. Residual activity in S/MC-mFAODs correlated less strongly with pathway flux, but these deficiencies did show a combined flux- and CoASH-reduction effect. This comparison is of importance to researchers and clinicians, as it identifies possible ways in which insights about one mFAOD may be applied to another based on shared biochemical properties.
]]></description>
<dc:creator><![CDATA[ Odendaal, C., Krebs, O., Bakker, B. M. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.722902</dc:identifier>
<dc:title><![CDATA[Ensemble kinetic modelling links residual enzyme activity to clinical symptoms in mitochondrial β-oxidation defects]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.722708v1?rss=1">
<title>
<![CDATA[
Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.722708v1?rss=1
</link>
<description><![CDATA[
The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of textit{Cyp1a1} and textit{Ahrr} mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.
]]></description>
<dc:creator><![CDATA[ Wieland, V., Blum, T., Iriady, I., Reverte-Salisa, L., Pathirana, D., Foerster, I., Weighardt, H., Hasenauer, J. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.722708</dc:identifier>
<dc:title><![CDATA[Mathematical Modeling of the Canonical Aryl Hydrocarbon Receptor Pathway]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.722841v1?rss=1">
<title>
<![CDATA[
A unified framework links infant vulnerability with aging-related mortality dynamics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.722841v1?rss=1
</link>
<description><![CDATA[
A central question in Geroscience is whether early-life mortality, which declines from birth to sexual maturity, and late-life mortality, which grows exponentially in time, can be understood within a shared conceptual framework. We show that stochastic threshold models can explain both phases by incorporating heterogeneity in neonatal vulnerability. Using U.S. National Center for Health Statistics data, we find that infant mortality risk is strongly associated with neonatal clinical markers such as Apgar scores, gestational age, and birth weight, suggesting that initial physiological differences persist across early life. We show that the ~1/t mortality decline generically arises in stochastic threshold models via depletion of the most vulnerable, across a wide range of model specifications. Incorporating this mechanism into the Saturating-Removal model captures both the early decline and the later Gompertz acceleration, reproducing the full J-shaped mortality curve. Together, our findings link neonatal vulnerability to late-life mortality dynamics within a shared stochastic framework, supporting a life-course perspective on aging and longevity.
]]></description>
<dc:creator><![CDATA[ Shenhar, B., Strauss, T., Alon, U. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.722841</dc:identifier>
<dc:title><![CDATA[A unified framework links infant vulnerability with aging-related mortality dynamics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.722864v1?rss=1">
<title>
<![CDATA[
Acetate promotes nutritional adaptation in Escherichia coli 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.722864v1?rss=1
</link>
<description><![CDATA[
The long-held view that acetate, one of the main fermentation by-products of Escherichia coli, is toxic to microbial growth is currently challenged. Here, we demonstrate that acetate promotes E. coli adaptation to nutrient changes by accelerating growth resumption, with as little as 250 M acetate being sufficient to shorten the lag phase by several hours. Acetate was found to be consumed via acetyl-CoA synthetase very early after the nutrient change. Transcriptomics, metabolomics and 13C-isotope labeling experiments show that acetate replenishes metabolic pools in the tricarboxylic acid cycle and upper glycolysis. Single-cell analyses reveal that acetate increases the adaptation speed of individual cells switching to the new nutrient. We conclude that the reuse of excreted acetate by E. coli facilitates metabolic adaptation by transiently replenishing central metabolite pools. This work identifies an unexpected role of acetate in the nutritional adaptation of E. coli, providing new insights into the physiological relevance of overflow metabolism.
]]></description>
<dc:creator><![CDATA[ Devlin, L., Oudard, V., Barthe, M., Gosselin-Monplaisir, T., Dupin, J.-B., Condamine, F., Baudry, J., Cocaign-Bousquet, M., Millard, P., Enjalbert, B. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.722864</dc:identifier>
<dc:title><![CDATA[Acetate promotes nutritional adaptation in Escherichia coli]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.05.722856v1?rss=1">
<title>
<![CDATA[
COCOA.jl: A Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.05.722856v1?rss=1
</link>
<description><![CDATA[
Summary Recent advances in analysis of biochemical networks have contributed the identification of their modular structure based on the concept of multi reaction dependencies and kinetic coupling of reaction rates (Kueken et al., 2022; Langary et al., 2025). Existing implementations of the algorithms to study modular structure do not scale well with the size of the networks, prohibiting their application with genome-scale networks. Here, we introduce COCOA.jl, a multithreaded Julia package for identification of concordant and kinetic modules, with applications in the study of concentration robustness. Availability and implementation COCOA.jl is implemented in Julia 1.12.2 and is freely available under the MIT license at https://github.com/antoniofranky/COCOA.jl. It runs on Linux, macOS, and Windows; installation is supported via the Julia package manager. COCOA.jl can be called from Python via JuliaCall.
]]></description>
<dc:creator><![CDATA[ Schaffranke, A., Kueken, A., Nikoloski, Z. ]]></dc:creator>
<dc:date>2026-05-08</dc:date>
<dc:identifier>doi:10.64898/2026.05.05.722856</dc:identifier>
<dc:title><![CDATA[COCOA.jl: A Julia package for high-performance analysis of concordance and kinetic modules in biochemical networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.04.722718v1?rss=1">
<title>
<![CDATA[
Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.04.722718v1?rss=1
</link>
<description><![CDATA[
Large-scale mass spectrometry-based proteomic screening could reveal cellular mechanisms of drug action at systems resolution but remains limited by experimental complexity and the difficulty of extracting insight from high-dimensional datasets. Here, we describe an end-to-end platform that combines semi-automated sample preparation, rapid LC-MS/MS, and AI agent-based data analysis to enable scalable proteomic screening. In a screen of 172 compounds in HepG2 cells, we generated 1,232 proteomes with more than 8,700 quantified proteins in approximately three weeks. Agentic AI reduced data analysis and interpretation time to less than one day while translating proteomic measurements into structured mechanism-oriented summaries and experimentally testable hypotheses. Guided by this framework, we validated: (1) a cholesterol-lowering effect of methylene blue in vitro and (2) an association between loratadine exposure and increased circulating iron in matched electronic health record analyses. This work establishes a scalable platform for generating proteomic drug perturbation data and automatically converting that data into mechanistic insights and candidate translational hypotheses using AI.
]]></description>
<dc:creator><![CDATA[ Jiang, Y., Movassaghi, C. S., Munoz-Estrada, J., Sundararaman, N., Momenzadeh, A., Meyer, J. G. ]]></dc:creator>
<dc:date>2026-05-07</dc:date>
<dc:identifier>doi:10.64898/2026.05.04.722718</dc:identifier>
<dc:title><![CDATA[Robotic perturbation proteomics and AI agents enable scalable drug mechanism discovery]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.04.722656v1?rss=1">
<title>
<![CDATA[
Multi-omic analysis identifies mitochondrial dysfunction as a conserved driver of acute severity and long-term complications in RSV infection 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.04.722656v1?rss=1
</link>
<description><![CDATA[
Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in infants, older adults, and immunocompromised individuals. The molecular mechanisms linking acute RSV infection to disease severity and long-term complications remain incompletely understood.

Herein, we conducted a comprehensive multi-omic analysis of 12 independent datasets encompassing epigenomics, transcriptomics, proteomics, and metabolomics across diverse systems, including in vitro infection models, clinical cohorts, longitudinal pediatric studies, vaccination models, and multiple viral strains. Across these experimental platforms and omic analysis, RSV consistently triggered suppression of oxidative phosphorylation (OXPHOS), alongside HIF-1-driven glycolytic metabolism and mitochondrial stress response. This coordinated reprogramming was consistent across transcriptomic, proteomic, and chromatin datasets.

In adult challenge studies, symptomatic individuals exhibited prolonged OXPHOS suppression and greater activation of HIF-1 immune signaling than asymptomatic individuals. Similarly, pediatric intensive care cohorts showed comparable signatures associated with severe disease. Vaccinated mice showed attenuation of infection-induced metabolic disruption, further supporting a link between mitochondrial dysfunction and disease severity. Longitudinal analyses in pediatric samples revealed that these metabolic alterations persist for up to 1-year post-infection, with sustained metabolic dysfunction, persistent epigenetic remodeling, and single-cell evidence of epithelial remodeling, including depletion of multiciliated cells, expansion of secretory populations, and prolonged OXPHOS suppression, in children who developed wheezing. Comparative analysis across RSV strains revealed variable OXPHOS suppression and variable HIF-1 activation, indicating strain-specific differences in metabolic reprogramming.

Together, these findings establish mitochondrial dysfunction as a central and conserved feature of RSV pathogenesis, encompassing acute severity, viral strain variation, and long-term complications, and highlight mitochondrial pathways as promising therapeutic targets to mitigate both acute disease severity and post-viral sequelae. Ultimately, demonstrating that distinct viral lineages drive unique bioenergetic phenotypes establishes a foundation for predictive molecular epidemiology and gaining insight into host-pathogen dynamics in response to novel interventions.

HighlightsO_LIMulti-omic integration of 13 independent RSV datasets reveals mitochondrial dysfunction as a conserved hallmark of infection.
C_LIO_LIRSV consistently suppresses oxidative phosphorylation (OXPHOS) while activating HIF-1 signaling, glycolysis, mitochondrial stress responses, and immune pathways.
C_LIO_LIGreater mitochondrial dysfunction correlates with increased disease severity, persists in children who develop wheezing, and is partially ameliorated by vaccination.
C_LIO_LIDistinct RSV strains display variable patterns of metabolic reprogramming, linking viral genetic diversity to differential host mitochondrial responses.
C_LI
]]></description>
<dc:creator><![CDATA[ Guarnieri, J., Trovao, N. S., Schwartz, R. E. ]]></dc:creator>
<dc:date>2026-05-07</dc:date>
<dc:identifier>doi:10.64898/2026.05.04.722656</dc:identifier>
<dc:title><![CDATA[Multi-omic analysis identifies mitochondrial dysfunction as a conserved driver of acute severity and long-term complications in RSV infection]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.03.721751v1?rss=1">
<title>
<![CDATA[
Claim-Level Transparency Analysis of LLM-Generated Diagnostic Reports: A Metabolic and Endocrine Biomarker Study 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.03.721751v1?rss=1
</link>
<description><![CDATA[
Large language models are increasingly deployed in clinical decision-support contexts, yet systematic evaluation of their factual reliability in generating patient-specific diagnostic reports remains sparse, particularly for laboratory interpretation tasks. This study presents a controlled transparency experiment in which four frontier LLMs -- Claude Sonnet 4.6, Claude Opus 4.6, GPT-5.2, and Gemini 3.1 Pro -- each generated diagnostic reports for 36 patients (29 female, 7 male; aged 27-64) with biomarker profiles spanning metabolic, endocrine, and nutritional markers. A transparency engine1 extracted up to 50 claims per report (3,035 total), searched for supporting scientific evidence, and classified each claim as supported by science, plausible, or unsupported. Unsupported claims were uncommon: the transparency engine classified 2.7% of claims as unsupported (hereafter, the pipeline-measured hallucination rate; naive claim-level 95% Wilson CI: 2.2%-3.4%), with GPT-5.2 at the lowest observed rate (1.7%) and Claude Opus 4.6 at the highest (3.6%). However, mechanistic verification revealed a much larger plausibility gap: 915 claims (30.2%) were biologically reasonable but lacked a fully verified evidence chain, bringing the share of claims not fully supported by direct evidence to 32.9%. Gemini 3.1 Pro produced the highest plausible proportion (39.6%), suggesting a more conservative but less fully grounded reasoning profile. Although coarse support-level distributions were broadly similar across models (Cramers V = 0.081), claim-level analysis revealed substantial narrative divergence: 61.2% of claims were unique to a single model, and matched-claim agreement was low (Cohens kappa = 0.233), indicating that models generate substantively different clinical narratives for the same patient data despite comparable aggregate support profiles. These findings show that hallucination metrics alone understate the share of claims not fully verified under this protocol, and that claim-level mechanistic verification is needed to distinguish the proven from the merely plausible in metabolic and endocrine laboratory interpretation, with generalizability to other clinical domains requiring further study.
]]></description>
<dc:creator><![CDATA[ Yasinetsky, A., Ikonomovska, E., Geniesse, C., Yasinetsky, A. ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.03.721751</dc:identifier>
<dc:title><![CDATA[Claim-Level Transparency Analysis of LLM-Generated Diagnostic Reports: A Metabolic and Endocrine Biomarker Study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.03.722490v1?rss=1">
<title>
<![CDATA[
Bion-M 2 Biosatellite: Multisystem Mouse Responses to 30 Days in High-Latitude Orbit as a Deep-Space Analog 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.03.722490v1?rss=1
</link>
<description><![CDATA[
The combined effects of microgravity and deep-space radiation on whole-body physiology remain poorly quantified for future crewed missions. Bion-M 2, a 30-day high-latitude biosatellite carrying group-housed mice, achieved an ISS-comparable total dose with an enriched galactic cosmic ray fraction, approximating conditions beyond low-Earth orbit. A quantitative atlas of 73 physiological endpoints revealed pronounced antigravity muscle atrophy, immune and gastrointestinal remodeling, and delayed recovery of hematologic and visceral indices through 30 days post-landing. A dry-food-hydrogel diet transformed this response into a stress-dominated, densely interconnected physiological state. Pharmacological Nrf2 activation with omaveloxolone preserved hindlimb muscle mass at ground-control levels and protected visceral organs. These findings establish a systems-level baseline for mammalian adaptation to a deep-space-analog orbit and identify diet and Nrf2 activation as tractable countermeasure levers.
]]></description>
<dc:creator><![CDATA[ Andreev-Andrievskiy, A. A., Mashkin, M. A., Drugova, S. V., Shurshakov, V. A., Popov, D. V., Tarasova, O. S., Buravkova, L. B., Vinogradova, O. L., Sychev, V. N., Orlov, O. I., Bion-M 2 Team ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.03.722490</dc:identifier>
<dc:title><![CDATA[Bion-M 2 Biosatellite: Multisystem Mouse Responses to 30 Days in High-Latitude Orbit as a Deep-Space Analog]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.02.722422v1?rss=1">
<title>
<![CDATA[
Box-Counting Fractal Dimensions of Cranial Suture: Effects of Measurement Conditions and Model-Based Reproduction of Fractal-Like Patterns 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.02.722422v1?rss=1
</link>
<description><![CDATA[
1Cranial sutures are important structures associated with skull growth, and it is widely known that the cranial sutures have a fractal nature. However, the measurement conditions and analytical procedures have varied among studies, making direct comparison and interpretation difficult. In addition, the mechanisms by which such fractal-like patterns arise remain incompletely understood.

In this study, we established and validated a standardized box-counting protocol for quantifying the fractal dimension (FD) of cranial sutures. Using this protocol, we quantified FD in 45 digitized images of human lambda sutures and in eight structure-formation model variants designed to generate fractal-like patterns via distinct kernel designs (step, Gaussian, Mexican-hat, and time-dependent/dual-stage), spatially inhomogeneous inhibition (Fbase), low-frequency noise, and different initial conditions (including sine-curve initialization). We show that FD estimates are strongly affected by preprocessing (including skeletonization) and the selected scale range, explaining discrepancies across previous studies. Crucially, under the matched preprocessing and scale-range criteria, three of the eight model variants reproduce the FD of real sutures within predefined equivalence margins, supporting the notion that appropriate dynamics can produce the observed fractal-like suture behavior and providing testable hypotheses for how such patterns may emerge.
]]></description>
<dc:creator><![CDATA[ Haishi, K., Miura, T. ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.02.722422</dc:identifier>
<dc:title><![CDATA[Box-Counting Fractal Dimensions of Cranial Suture: Effects of Measurement Conditions and Model-Based Reproduction of Fractal-Like Patterns]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.01.722350v1?rss=1">
<title>
<![CDATA[
Lineage-specific lncRNAs critically determine cross-species differences in tumors 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.01.722350v1?rss=1
</link>
<description><![CDATA[
Diverse mouse models have been generated to study human tumors. Although mouse and human tumors share similar differentially expressed genes and cancer hallmarks, many drugs that work in mice fail in humans. What makes mouse models poorly recapitulate human tumors remains unclear. We postulate that transcriptional regulation by lineage-specific long noncoding RNAs (LS lncRNAs) critically determines cross-species and cross-tumor differences. To test this hypothesis, we identified LS lncRNAs, predicted their target genes, integrated 9,058 RNA-seq samples from 13 human tumors and their mouse counterparts, and analyzed transcriptional regulation by LS lncRNAs across cellular contexts. LS lncRNAs substantially and tumor-specifically reconfigure transcription and signaling, and strongly influence cancer immunity and anti-cancer drug efficacy. These results provide systematic information for exploring and interpreting human tumor mouse models and for identifying human- and tumor-specific diagnostic and therapeutic targets. They also present an analytical approach applicable to other human diseases and their mouse models.
]]></description>
<dc:creator><![CDATA[ Lin, J., Liu, X., Zhu, H. ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.01.722350</dc:identifier>
<dc:title><![CDATA[Lineage-specific lncRNAs critically determine cross-species differences in tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.01.720924v1?rss=1">
<title>
<![CDATA[
The first digital twin of Enterococcus faecium metabolism reproduces high-throughput phenotyping data 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.01.720924v1?rss=1
</link>
<description><![CDATA[
Enterococci are Gram-positive opportunistic pathogens responsible for a wide range of nosocomial infections. One enterococcocal species, Enterococcus faecium, is steadily increasing in prevalence and has been listed among major multidrug-resistant ESKAPE pathogens. To gain systems-level insights into its metabolism and support discovery of potential therapeutic targets, we constructed iDR479, a comprehensive manually curated genome-scale metabolic model (GEM) to serve as a digital twin for E. faecium TX0016 (strain DO). The reconstruction was curated through extensive homology searches and literature evidence, and further refined and gap-filled through experimental validation. Phenotypic profiling using Biolog microarrays enabled assessment of carbon source utilization, while amino acid leave-out growth assays allowed the evaluation of auxotrophies. The final refined model is 100% accurate in predicting amino acid auxotrophy and 85% accurate in predicting growth on sole carbon sources.

Discrepancies between model predictions and experimental phenotypes identified specific knowledge gaps across metabolic pathways, including unresolved carbon source utilization phenotypes, e.g., psicose, sorbitol, and palatinose utilization. Those gaps will guided future experimental characterization. Additionally, gene essentiality analysis was conducted to evaluate the predictive capacity of iDR479 model. Since no experimental gene essentiality data are currently available for E. faecium, model predictions were compared against Tn-seq experimental results from E. faecalis MMH594. Under simulated rich medium conditions, iDR479 achieved 86.7% concordance with the experimental essentiality results of E. faecalis MMH594. iDR479 thus provides a framework for studying E. faecium, offers insights into its metabolic network, and serves as a source for guiding future research and identification of therapeutic targets.
]]></description>
<dc:creator><![CDATA[ Rasmi, D. S., Krishnan, J., Hashem, Y. A., Palsson, B., Khashef, M. T., Monk, J., Aziz, R. K. ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.01.720924</dc:identifier>
<dc:title><![CDATA[The first digital twin of Enterococcus faecium metabolism reproduces high-throughput phenotyping data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.01.722176v1?rss=1">
<title>
<![CDATA[
REDUCED-PRECISION STOCHASTIC SIMULATION FOR MATHEMATICAL BIOLOGY 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.01.722176v1?rss=1
</link>
<description><![CDATA[
AO_SCPLOWBSTRACTC_SCPLOWThe stochastic simulation algorithm (SSA) is widely used to perform exact forward simulation of discrete stochastic processes in biology. However, the computational cost, driven by sequential event-by-event sampling across large ensembles, remains a computational barrier. We investigate whether reduced-precision floating-point arithmetic can accelerate SSA without degrading statistical fidelity, drawing on the success of reduced-precision methods in weather and climate modelling. We evaluate two strategies across five canonical models (birth-death, Schlogl, Telegraph, dimerisation, repressilator): (i) mixed precision, computing propensities in 16-bit while maintaining accumulators in 32-bit; and (ii) uniform precision, performing all arithmetic in 16-bit. Mixed-precision SSA produces ensemble statistics that closely match the 64-bit reference for all models, as measured by Kolmogorov-Smirnov tests and Wasserstein distances. Under uniform precision, deterministic rounding introduces systematic biases across several models, with catastrophic failures in some cases. Stochastic rounding (SR) and propensity normalisation eliminate these biases, restoring distributional fidelity across all models tested (KS p > 0.05). Our results establish mixed-precision SSA with SR as a viable acceleration strategy for mathematical biology: 16-bit formats shrink per-variable data size by 2-4x relative to fp32/fp64, yielding comparable reductions in memory footprint and up to ~ 1.5x wall-clock speedup on CPU hardware that lacks native 16-bit arithmetic. As a hardware-level acceleration, mixed-precision SSA complements algorithmic methods such as tau-leaping and maps naturally onto modern GPU and TPU architectures with native 16-bit arithmetic.
]]></description>
<dc:creator><![CDATA[ Kimpson, T., Flegg, M. B., Flegg, J. A. ]]></dc:creator>
<dc:date>2026-05-06</dc:date>
<dc:identifier>doi:10.64898/2026.05.01.722176</dc:identifier>
<dc:title><![CDATA[REDUCED-PRECISION STOCHASTIC SIMULATION FOR MATHEMATICAL BIOLOGY]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.30.722108v1?rss=1">
<title>
<![CDATA[
Accessible Gibbs energy at metabolic activation limits long-term cell growth 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.30.722108v1?rss=1
</link>
<description><![CDATA[
When exposed to a nutrient, cells activate metabolism by reorganizing metabolite pools and enzyme expression to approach the maximal growth rate permitted by physicochemical constraints. While these constraints define reachable steady states, here we propose that the Gibbs energy accessible at activation further limits which states are reached. Using minimal metabolic models, we find that limited accessible Gibbs energy can trap cells in low-growth states by constraining metabolic reorganization and imposing a proteomic burden on transport and phosphorylation reactions. To investigate this experimentally, we reconstituted the arginine deiminase pathway in vesicles, revealing that the size of a conserved pool of interconverting metabolites (arginine, citrulline, and ornithine) determines accessible Gibbs energy and constrains steady-state ATP production rate, a proxy for growth. Together, these results indicate that cellular metabolism retains memory of its initial energetic state, with accessible Gibbs energy at activation acting as a thermodynamic constraint on long-term growth.
]]></description>
<dc:creator><![CDATA[ Barreto, Y. B., Jongman, E. P. H., Patino-Ruiz, M. F., Grundel, D. A. J., Uysal, M., Coenradij, J., Poolman, B., Heinemann, M. ]]></dc:creator>
<dc:date>2026-05-05</dc:date>
<dc:identifier>doi:10.64898/2026.04.30.722108</dc:identifier>
<dc:title><![CDATA[Accessible Gibbs energy at metabolic activation limits long-term cell growth]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.05.01.722163v1?rss=1">
<title>
<![CDATA[
Network topology dictates sequential drug efficacy through bistability-mediated state switching 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.05.01.722163v1?rss=1
</link>
<description><![CDATA[
Sequential drug combinations can significantly enhance therapeutic efficacy, yet the general principles governing when and why sequential administration outperforms concurrent treatment remain poorly understood. While empirical evidence demonstrates that the order and timing of drug exposure can be critical, a mechanistic framework to predict which regulatory architectures are primed for sequential benefit is currently lacking. Here, we systematically enumerated and dynamically analysed 59,040 four-node network topologies to identify the structural design principles that dictate sequential efficacy. Our analysis reveals that only a small fraction of network architectures robustly confer a sequential advantage and identifies a minimal structural requirement for this benefit: a positive feedback loop between the primary drug target and its downstream oncogenic output, coupled with antagonistic crosstalk from a secondary drug target. We demonstrate that this architecture enables bistability, allowing the first drug to reconfigure the network into a suppressed attractor state that is inaccessible through concurrent administration. The treatment schedule determines which of two coexisting stable states the system ultimately occupies, with the gap time between doses defining a critical therapeutic window. Only when the first drug is given sufficient time to displace the system past a threshold does the sequential regimen achieve superior suppression. Our findings establish bistability-enabling network motifs as predictive determinants of sequential drug efficacy and provide a topology-based framework for the rational design of time-dependent combination therapies.
]]></description>
<dc:creator><![CDATA[ Osman, T. O., Rios, K. I., Hart, A., Shin, S.-y., Nguyen, L. K. ]]></dc:creator>
<dc:date>2026-05-05</dc:date>
<dc:identifier>doi:10.64898/2026.05.01.722163</dc:identifier>
<dc:title><![CDATA[Network topology dictates sequential drug efficacy through bistability-mediated state switching]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.30.721941v1?rss=1">
<title>
<![CDATA[
The Limits of Cross-Species WGCNA: Library Imbalance and Signal Dilution Constrain Effector Gene Recovery in Dual-Organism RNA-seq 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.30.721941v1?rss=1
</link>
<description><![CDATA[
Dual-organism RNA sequencing (RNA-seq) experiments, in which the transcriptomes of a host and a microbe are sequenced simultaneously, are increasingly used to study plant-microbe interactions. A central analytical goal is identifying effector proteins and their host targets through gene co-expression. Weighted Gene Co-expression Network Analysis (WGCNA) is the dominant tool for gene co-expression analyses, yet its ability to recover interaction-interface genes from a merged dual-organism matrix has not been systematically characterised. Here we present a simulation framework using real gene models from Hordeum vulgare (barley) and Blumeria graminis f. sp. Hordei M.Liu & Hambl (powdery mildew) to evaluate single-network WGCNA across a gradient of plant-to-fungal library size ratios (1:1-20:1), three levels of co-expression signal strength, and three WGCNA network construction types (signed, unsigned, signed hybrid). We embed 20 model effector genes (bridge genes) driven by a mixed host-pathogen eigengene and evaluate recovery using four metrics aligned with the biological objective: cross-species hub rank, top-decile hub enrichment, bridge gene detection rate, and bridge co-separation (the fraction of effector-target pairs co-assigned to the same detected module). Across 225 simulation runs (15 conditions x 5 replicates x 3 network types), bridge genes are robustly identifiable as cross-species connectivity hubs (mean rank 0.92 versus 0.50 for module genes) but co-assignment of effector-target pairs to the same module fails in 41% of runs due to scale-free topology collapse. Signal strength (2 = 0.12) and library ratio (2 = 0.22) are the primary determinants of co-separation, while network type choice accounts for less than 2%. A read-depth bias systematically inflates pathogen gene hub ranks relative to host genes at high ratios. These results establish that the method can identify effector candidates as cross-species hubs under a broad range of conditions, but reliable co-assignment requires adequate pathogen read depth and strong co-expression signal--properties that experimental design, not analytical parameterisation, must provide.
]]></description>
<dc:creator><![CDATA[ Fenn, A., Hueckelhoven, R., Kamal, N. ]]></dc:creator>
<dc:date>2026-05-05</dc:date>
<dc:identifier>doi:10.64898/2026.04.30.721941</dc:identifier>
<dc:title><![CDATA[The Limits of Cross-Species WGCNA: Library Imbalance and Signal Dilution Constrain Effector Gene Recovery in Dual-Organism RNA-seq]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.30.721884v1?rss=1">
<title>
<![CDATA[
Systems modeling identifies phenotype-determining signaling pathways controlled by phosphatase PTPRJ in diverse receptor tyrosine kinase activation settings 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.30.721884v1?rss=1
</link>
<description><![CDATA[
Protein tyrosine phosphatase receptor J (PTPRJ) restrains cell proliferation and migration by dephosphorylating receptor tyrosine kinases (RTKs) including the epidermal growth factor receptor (EGFR). PTPRJ is a purported tumor suppressor, and alterations to its expression and/or function are associated with colorectal, breast, lung, and other cancers. While there is interest in controlling PTPRJ-regulated phenotypes, efforts are limited by the complexity of PTPRJ-mediated signaling. PTPRJ dephosphorylates multiple RTKs, and the degree to which PTPRJ control of signaling and phenotypes depends on local cellular RTK activation profiles is unknown. To probe the context dependence of PTPRJ signaling regulation, we collected signaling measurements across 16 pathway nodes at two time points in a panel of HSC3 carcinoma cells engineered with different PTPRJ expression profiles. Cells were treated with three different RTK ligands, and paired phenotype measurements (viability, wound healing, xCELLigence cell index) were made. Partial least squares regression models were developed to predict relationships between PTPRJ-regulated signaling pathways and cell phenotypes. The model effectively separated contributions to variance arising from the PTPRJ expression background and growth factor context. In testing model predictions, we demonstrated that PTPRJ suppressed MET-induced cell cell proliferation via regulation of a HER3/AKT signaling axis that stabilized PTPRJ expression through an unanticipated feedback mechanism. We also found that PTPRJ regulated HSC3 cell migration via JNK signaling that was preferentially activated by MET. Our results identify new regulatory nodes through which PTPRJ influences cancer cell phenotypes and demonstrates that these processes preferentially occur in the context of distinct RTK activation states.
]]></description>
<dc:creator><![CDATA[ Hart, W. S., Knight, K. M., Rizzo, S., Lee, S. H., Fetter, R., Thevenin, D., Lazzara, M. J. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.30.721884</dc:identifier>
<dc:title><![CDATA[Systems modeling identifies phenotype-determining signaling pathways controlled by phosphatase PTPRJ in diverse receptor tyrosine kinase activation settings]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.30.721639v1?rss=1">
<title>
<![CDATA[
Supervised restricted data fusion with common, local & distinct components 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.30.721639v1?rss=1
</link>
<description><![CDATA[
In multi-block data, the dominant sources of variation are not always most relevant to a response of interest, meaning that purely exploratory decompositions may fail to recover subtle but important response-associated structure. We introduce PESCAR, a supervised extension of Penalised Exponential Simultaneous Component Analysis (PESCA) that incorporates response information directly into the estimation of common, local, and distinct (CLD) structure across multiple data blocks. This allows simultaneous multiblock decomposition and response variable influenced recovery of latent structure. Through simulation studies, we show that PESCAR can detect weak response-related components across a range of settings, including different noise levels and model-rank mis-specification. Applied to a real multi-omics dataset, PESCAR recovers biologically meaningful response-associated patterns and retains interpretable block structure. We further demonstrate that sparsity in the fitted loading matrices admits a hypergraph-based interpretability layer, summarising overlapping support patterns across components and blocks. These results show that direct incorporation of response information into multiblock decomposition can improve detection of subtle relevant signal and facilitate interpretation in complex systems.
]]></description>
<dc:creator><![CDATA[ White, F., van der Ploeg, G. R., Heintz-Buschart, A., Dong, L., Bouwmeester, H., Smilde, A., Westerhuis, J. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.30.721639</dc:identifier>
<dc:title><![CDATA[Supervised restricted data fusion with common, local & distinct components]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.30.721824v1?rss=1">
<title>
<![CDATA[
Systems-Level Transcriptomics Maps Multilevel Remodeling and Pathway-Selective Translational Alignment Across Murine Models of Cardiometabolic HFpEF 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.30.721824v1?rss=1
</link>
<description><![CDATA[
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous cardiometabolic syndrome in which the molecular programs linking metabolic stress to myocardial remodeling and diastolic dysfunction remain incompletely defined. We integrated ventricular RNA sequencing with pathway activity profiling, transcription factor inference, cell-type enrichment, phenotype association, elastic-net severity modeling, cross-lab murine validation, and human proteomic comparison to define the systems-level architecture of remodeling in the db/db + aldosterone mouse model of cardiometabolic HFpEF. HFpEF hearts exhibited a distinct transcriptomic state characterized by coordinated upregulation of collagen organization, TGF{beta} signaling, inflammatory response, and NF{kappa}B signaling, with reduced ion-channel activity and smaller shifts in oxidative phosphorylation, excitation-contraction coupling, and mechanotransduction. These pathway programs were linked to left ventricular hypertrophy and diastolic dysfunction and were accompanied by enrichment of fibroblast, myofibroblast, and macrophage signatures that tracked the same disease dimensions. Gene-level prioritization identified extracellular matrix, inflammatory, and mechanotransduction-associated candidates linked to disease severity, while transcription factor analysis revealed a broader multi-regulator architecture associated with fibrotic, inflammatory, and stress-responsive remodeling. Elastic-net modeling further showed that phenotype-derived remodeling severity was captured in an exploratory nested cross-validation framework primarily by transcription factor and fibro-inflammatory cell-program features, whereas pathway-summary scores added little incremental predictive information. In an independent HFD+L-NAME cohort, pathway remodeling showed selective reproducibility, and cross-species comparison demonstrated that concordance with human HFpEF proteomic subgroups was pathway selective rather than global. Together, these findings define a multilevel systems architecture of cardiometabolic HFpEF remodeling and support mechanistic prioritization and pathway-matched preclinical model selection.
]]></description>
<dc:creator><![CDATA[ Forouzandehmehr, A. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.30.721824</dc:identifier>
<dc:title><![CDATA[Systems-Level Transcriptomics Maps Multilevel Remodeling and Pathway-Selective Translational Alignment Across Murine Models of Cardiometabolic HFpEF]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.29.721775v1?rss=1">
<title>
<![CDATA[
A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.29.721775v1?rss=1
</link>
<description><![CDATA[
Mechanistically predicting the consequences of drug action requires distin-guishing whether molecular interactions are activating or inhibitory, yet most biomedical knowledge graphs and graph neural networks represent biology as unsigned associations. This limitation obscures regulatory logic, restricts mechanistic interpretability and reduces the accuracy of downstream therapeutic predictions. Existing approaches are further constrained by limited chemical coverage and insufficient integration of molecular and clinical data across biological scales. Here we present SIGMA-KG (SIGned Multi-omics Atlas Knowledge Graph), a large-scale signed multi-omics knowledge atlas, and FLASH (Fast Lightweight Architecture for Signed Heterogeneous GNN), a fast and lightweight signed heterogeneous graph neural network for foundation-model pretraining on biomedical knowledge graphs. SIGMA-KG integrates chemogenomic perturbations beyond approved drugs with transcriptomic, proteomic and clinical data, explicitly encoding the direction and polarity of biological and phenotypic effects. FLASH enables efficient self-supervised pretraining on this signed atlas at scale, learning transferable representations that preserve how activating and inhibitory effects compose across multi-hop biological pathways through structural balance principles. Across multiple downstream tasks (without task-specific fine-tuning), including target-specific mode-of-action prediction, drug-induced clinical response modeling and drug-drug interaction prediction, the pretrained FLASH foundation model consistently outperforms or matches nine state-of-the-art unsigned, relational and signed graph baselines while substantially improving computational efficiency. We further demonstrate the translational utility of FLASH through explainable inductive drug repurposing, identifying novel ther-apeutic candidates for four complex diseases with a 69.6% external clinical validation success rate. Together, SIGMA-KG and FLASH provide a scalable, sign-aware framework for mechanistic latent-space reasoning, advancing the predictive accuracy of drug discovery, polypharmacy design, and clinical safety assessment.
]]></description>
<dc:creator><![CDATA[ Mottaqi, M., Zhang, S., Adoremos, I., Zhang, P., Xie, L. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.29.721775</dc:identifier>
<dc:title><![CDATA[A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.29.721604v1?rss=1">
<title>
<![CDATA[
Extraction-dependent bone proteomics reveals distinct stable and dynamic protein modules during early post-exposure degradation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.29.721604v1?rss=1
</link>
<description><![CDATA[
Bone is a highly durable biological tissue widely used in forensic, archaeological, and anthropological investigations; however, efficient protein recovery and understanding of protein stability over time remain major challenges in skeletal proteomics. Here, we systematically evaluated three bone protein extraction workflows and integrated them with data-independent acquisition (DIA) mass spectrometry to assess proteome coverage, reproducibility, and temporal protein dynamics under environmentally exposed conditions. Comparative analysis demonstrated that extraction strategy is a primary determinant of detectable proteome composition. EDTA-based demineralization followed by SDS extraction provided the deepest proteome coverage and highest reproducibility, whereas guanidine hydrochloride extraction preferentially enriched collagen and extracellular matrix proteins. In contrast, acid-based extraction yielded limited protein recovery. Temporal profiling of bone samples collected at 10 and 45 days post-exposure revealed two distinct protein classes. A temporally stable module, enriched in collagens and extracellular matrix proteins including COL1A2, COL5A2, BGN, SPARCL1, and NID2, exhibited minimal abundance change, indicating resistance to environmental degradation. In contrast, temporally dynamic proteins, enriched in mitochondrial, metabolic, and intracellular pathways such as ACO2, OGDH, PDHA1, ATP5PO, and PFKM, showed marked decline over time. These findings support a two-compartment model of bone protein preservation in which matrix-embedded proteins are preferentially retained while exposed intracellular proteins undergo progressive degradation. Collectively, this study establishes an integrated framework linking extraction methodology with temporal proteome stability and identifies candidate markers for skeletal preservation assessment and temporal biomarker development in forensic and archaeological applications.
]]></description>
<dc:creator><![CDATA[ Najar, M. A., Choudhary, N., Abdulsalam, S., Sajeevan, A., Ahmad, M. N. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.29.721604</dc:identifier>
<dc:title><![CDATA[Extraction-dependent bone proteomics reveals distinct stable and dynamic protein modules during early post-exposure degradation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.04.29.721617v1?rss=1">
<title>
<![CDATA[
A Comprehensive Mathematical Model of Avidity in Cytokine Signaling 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.04.29.721617v1?rss=1
</link>
<description><![CDATA[
Multivalent ligand-receptor interactions underlie most forms of cell-cell communication, yet a general quantitative framework for "avidity" has remained elusive for over a century. Here, we derive closed-form expressions for signaling potency (EC50) in multivalent systems directly from first principles, extending exact analytical models of ternary complex equilibria to account for receptor confinement at cell surfaces. These equations unify antibody-antigen and cytokine-receptor interactions under a common mathematical framework in which potency emerges as a function of binding constants and receptor density.

In contrast to monovalent models, EC50 is no longer equal to the dissociation constant (Kd), but instead reflects receptor-dependent avidity effects that vary across cellular contexts. We validate these predictions across biophysical measurements, in vitro binding and signaling assays, in vivo murine cytokine perturbation data, and human spatial transcriptomic datasets. The framework explains longstanding empirical observations, including enhanced antibody potency through avidity and asymmetric control of cytokine signaling by receptor subunits. By embedding these equations within a regression-compatible formulation, we enable inference of signaling drivers from single-cell and spatial transcriptomic data. This work establishes a mechanistic bridge between molecular binding, receptor context, and tissue-level signaling, providing a quantitative foundation for interpreting and modeling intercellular communication in health and disease.
]]></description>
<dc:creator><![CDATA[ Douglass, E. F., Bastian, W., Mochel, J. P. ]]></dc:creator>
<dc:date>2026-05-04</dc:date>
<dc:identifier>doi:10.64898/2026.04.29.721617</dc:identifier>
<dc:title><![CDATA[A Comprehensive Mathematical Model of Avidity in Cytokine Signaling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-05-04</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
