bioRxiv Subject Collection: Systems Biology

Reversible Opto-Chemical activation of KRASG12V signaling with near single-cell precision

2026-6-30

KRAS mutations drive some of the most lethal carcinomas, and genomic and inducible systems have established many of the cellular and tissue-level consequences of KRAS mutations. However, these approaches operate at the level of oncogene expression, allowing for cellular adaptation that mask the individual role of KRAS oncoprotein signaling. Here, we developed a reversible Opto-Chemical system to activate KRAS oncoprotein signaling by chemically translocating a cytosolic mutant KRASG12V G-domain to the plasma membrane upon light or small molecule input. In MDCK cells, the G-domain bound effector proteins and its plasma membrane recruitment activated downstream signaling and reduced collective migration. In mouse small Intestinal Organoids, G-domain plasma membrane recruitment promoted increased crypt size and number under Epidermal Growth Factor deprived condition. We further showed that the increased number of crypts depended on continuous KRASG12V signaling. Finally, under the same deprived conditions, localized activation in just one budding crypt promoted crypt formation compared to controls. This system decouples oncoprotein activity from oncogene expression, allowing to investigate the KRAS contribution to early epithelial transformation.

Personalized Immunotherapy via Multiscale Tumor-Immune Modeling and Optimal Control

Asgedom, A., Kefela, Y. — 2026-6-30

Cancer remains a global health challenge requiring sophisticated understanding of tumor-immune dynamics for effective treatment design. Mathematical oncology has emerged as a rapidly evolving interdisciplinary field that uses mathematical models to enhance our understanding of cancer dynamics, including tumor growth, metastasis, and treatment response. This paper presents a comprehensive multiscale framework integrating patient-specific data, machine learning, and optimal control for personalized immunotherapy design. We develop a hybrid model that combines deterministic dynamics with stochastic elements and time delays, capturing the inherent variability and temporal lags in biological processes. The model incorporates biologically realistic Holling Type-II functional responses and is validated against longitudinal clinical data from 100+ cancer patients and patient-derived organoid experiments. Using deep neural networks with Bayesian regularization, we learn patient-specific parameter distributions from clinical biomarkers and predict treatment responses with high accuracy. Our optimal control framework, incorporating clinical constraints and toxicity limits, generates personalized treatment protocols that stabilize otherwise unstable dynamics. The framework establishes a new paradigm for precision immuno-oncology, bridging mathematical theory, computational methods, and clinical practice. Author summary Cancer remains one of the leading causes of death worldwide, and the immune system plays a crucial role in controlling tumor growth. However, the complex interactions between tumor cells and immune cells make it difficult to predict how individual patients will respond to immunotherapy. In this work, we develop a mathematical framework that integrates patient-specific data, machine learning, and optimal control to design personalized immunotherapy strategies. Our model captures the realistic dynamics of tumor-immune interactions by incorporating biologically relevant features such as time delays (representing immune response lags) and stochastic effects (representing biological variability). Using deep learning, we estimate patient-specific parameters from clinical biomarkers, enabling personalized predictions of treatment outcomes. We validate our framework against data from over 100 cancer patients and patient-derived organoid experiments, demonstrating excellent agreement. Our optimal control approach generates personalized treatment protocols that stabilize otherwise unstable tumor dynamics, achieving 78% tumor reduction compared to 52% for standard-of-care protocols. These findings suggest that therapies targeting immunological thresholds may be as important as those directly killing tumor cells, providing a new perspective for immunotherapy design. This framework bridges mathematical theory, computational methods, and clinical practice, offering a pathway toward truly personalized cancer treatment.

Explaining the pathogenesis of African swine fever using knowledge-driven regulatory network modeling

2026-6-29

A lethal DNA virus with significant economic impact on livestock farmers worldwide, the pathogenesis of African swine fever virus (ASFV) infection is complex and continues to challenge the development of effective vaccine candidates. The requirement for high-containment conditions further complicates its study, resulting in limitations in sample size and marker assessment that challenge conventional statistical analysis. In this work we demonstrate how prior knowledge of immune biology and pathogen-host proteome interactions can be leveraged and reconciled with sparse experimental data to deliver plausible mechanistically informed hypotheses describing ASFV illness progression. We apply large-scale automated mining of literature and pathway schema together with generative artificial intelligence (AI) to create closed-loop regulatory network models consisting of 133 pathogen and host proteins linked by 676 regulatory interactions. Immune regulatory tuning of these networks is reverse engineered to explain two distinct experimentally observed illness progression trajectories in only 5 markers measured every second day over a maximum of 8 days. Comparison of network model pools specific to each progression phenotype suggest that these significantly different outcomes may arise from altered regulatory tuning of genes coding for interleukin (IL)1β, tumor necrosis factor (TNF)α and Forkhead box protein (FOX)O4, potentially as a result of epigenetic adaptations. Simulated challenges with individual ASFV protein confirm broadly delayed interferon (IFN)-γI response in both phenotypes, with multigene family (MGF)505-3R offering the earliest induction and only in the more severe phenotype. Paradoxically, predictions suggest that this delay is preceded by an early IL-10 induction by this same viral protein. While added model granularity and validation is needed, we propose that this proof-of-concept knowledge driven approach offers an attractive solution to mechanistic hypothesis generation in data poor environments.

Spectral fingerprints distinguish buckling from angiogenesis in tortuous blood vessels

Kawanaka, H., Miura, T. — 2026-6-29

Physiological blood vessels are generally straight, but tortuous curvature is observed under pathological conditions across spatial scales, from large arteries to retinal microvasculature and tumor-associated vessels. Here, we compare two theoretical mechanisms of curved vessel formation—mechanical buckling and angiogenic biased random walk—within a common spectral framework. We simplify the Chaplain–Anderson angiogenesis model and an Euler–Bernoulli buckling model with surrounding-tissue support, reproduce curvature numerically, and analyze the power spectra of the resulting patterns. Buckling yields a single characteristic peak in the power spectrum, whereas angiogenesis yields k −2 scaling in the low-frequency range. Mathematical analysis explains the selective growth of a dominant buckling wavelength and scaling characteristics in the Chaplain-Anderson model. We further test these predictions using morphological descriptors (power spectrum, autocorrelation, and mean squared displacement) applied to a public retinal vessel dataset and propose a two-phase model combining angiogenic structure generation with subsequent mechanical remodeling. These results suggest that spectral fingerprints may help distinguish mechanically driven tortuosity from angiogenesis-driven tortuosity in vascular images.

Multi-timescale PTM architecture in human RNA polymerase II

2026-6-29

Summary Human RNA Polymerase II (Pol II) is characterized by a dense layer of over 775 post-translational modifications (PTMs) that form a dynamic, rewritable regulatory architecture integrating large numbers of cellular signals to coordinate transcription initiation, elongation, termination, and co-transcriptional RNA processing. While genomic information has been extensively catalogued, the potential information capacity associated with Pol II PTM patterns has remained largely unquantified. Here, we analyze the PTM sites across the human Pol II complex (Rpb1–Rpb12) and estimate the state-space information capacity using Shannon entropy theory. We first provide a theoretical upper bound of ~707.98 bits per Pol II molecule (~88.50 bytes) corresponding to ~5.68 × 10 7 bits per nucleus (~7.10 MB), assuming ~80,200 Pol II molecules per cell. We distinguish this maximal capacity from a conservative, kinetically addressable estimate of ~114.88 bits per molecule (~1.15 MB per nucleus), reflecting physiological kinetic constraints and site coupling that restrict the simultaneously addressable PTM state space in vivo. Finally, we show that major PTM classes (phosphorylation, proline isomerization, O-GlcNAcylation and ubiquitination) operate over distinct lifetimes, from seconds to minutes and hours-scale processes, supporting a multi-timescale biochemical architecture of this enzyme. Together, these results provide a quantitative information framework that distinguishes maximal PTM state-space capacity from kinetically addressable physiological regulatory capacity, supporting a view of Pol II PTM patterning as a high-dimensional, dynamically reconfigurable, multi-timescale regulatory information layer. Highlights A systems-level framework quantifies regulatory information in Pol II PTMs Known PTM modification sites provide 707.98 bits per Pol II as a regulatory upper bound Physiological kinetic constraints reduce accessible regulatory capacity to 114.88 bits Distinct PTM modification classes define fast, intermediate, and slow regulatory layers Graphical

Mammalian aging involves genome-wide splicing degeneration leading to functional decline

Zhang, S., Tyshkovskiy, A., Ying, K., Wang, S., Gladyshev, V. — 2026-6-29

Alternative splicing exhibits significant changes during development and aging, affecting the composition and variance in the transcriptome. However, it is unclear whether and how age-associated splicing dysregulation leads to functional consequences. Here, an integrative analysis of transcriptome data across mouse and human tissues revealed that aging is characterized by systematic deterioration of the fidelity of RNA splicing, here termed splicing degeneration, a measure of functional alteration of reading frame and domain configuration of protein products. Genes with higher aging-associated splicing degeneration were more conserved and enriched for processes such as RNA metabolism and antigen presentation. By assessing alternative splicing events associated with functional deterioration, we quantified the degree of splicing degeneration. Its level increased with age but was alleviated following calorie restriction or rapamycin treatment, indicating that it can serve as a new molecular hallmark of aging. Mechanistically, through a comprehensive meta-data analysis, we discovered that splicing degeneration is associated with age-associated changes in specific splicing factors, which in turn showed a strong association with age-related transcriptome changes. Overall, our study demonstrates the intricate relationship between aging and genome-wide splicing degeneration, revealing a promising target for aging interventions acting to reverse splicing degeneration.

A Mecom—Cdk6 toggle switch governs hematopoietic stem-to-multipotent fate transitions via distinct multistable landscapes

Martinez, J., Dey, A., McDermott, M., Rudolph, K., MacLean, A. — 2026-6-29

Hematopoietic stem cells are required to regenerate the blood system throughout life. We previously discovered that transitions between quiescent stem and multipotent progenitor states are controlled by mutual inhibition between Mecom and Cdk6 , with upstream regulation from the insulin-like growth factor (IGF) signaling pathway. To investigate the dynamics of exit from quiescence and the stem-to-multipotent cell state transition, we modeled the Mecom – Cdk6 regulatory network via coupled nonlinear differential equations. Bifurcation analysis revealed that the model permits tetrastability, with two stable intermediate states, suggesting that stem cell exit from quiescence proceeds via multiple fine-scale transitions. Perturbation of Mecom self-activation reorganized the multistable landscape, producing two IGF-dependent landscapes with distinct geometries. At high IGF, transitions proceeded only through an intermediate state, whereas at low IGF, a distinct landscape emerged permitting direct transitions between cell states. Stochastic simulations and minimum action path analysis showed that the multipotent attractor is deep at high IGF, whereas low IGF promotes a transition to quiescence by stabilizing the stem cell state. Simulated pharmacological intervention via CDK4/6 inhibitors destabilized the multipotent state and favored transitions towards a more stem-like quiescent state. Together, these results demonstrate how IGF signaling, Mecom self-activation, and Cdk6 inhibition jointly shape early stem cell fate decisions by dictating the accessible cell states on multistable landscapes and the transition paths that connect them.

Curating MitoCore: A Standardized Small-Scale Human Metabolic Model as Platform for Proteomics Integration and Disease Modeling

Lange, E., Santamaria, A., Heyer, R. — 2026-6-29

Motivation Central human metabolism powers cellular processes, yet its dysregulation in disease remains poorly understood. While comprehensive genome-scale metabolic models like Human-GEM are available, their size limits interpretability and computational efficiency. Conversely, the smaller MitoCore model is more manageable but lacks the standardized annotations and curated gene-protein-reaction (GPR) associations necessary for omics integration like protein-constrained modeling. Improving MitoCore’s annotation quality is therefore essential for its use in integrative workflows. Results We systematically updated MitoCore to enhance compatibility with the protein-constrained modeling framework sMO-MENT. By restructuring legacy annotations and integrating data from Human-GEM and MitoMammal, we increased EC-codes from 354 to 593 and UniProt-annotated genes from 0 to 592. MitoCore captures central metabolic processes, confirmed by mapping its reactions to 51 of 106 metabolic KEGG modules. Integration of thrombocyte proteomics and experimental ATP data for original and curated models showed an increase in mapped proteins (228 to 294) and reactions with kcat values (295 to 310), adding 33 protein-constrained reactions. Consequently, prediction errors for exchange fluxes and ATP production decreased by 19% and 89%, respectively, with 100% of ATP predictions falling within the 95% confidence interval (compared to 16% for the original model). Finally, we implemented a continuous integration/continuous deployment pipeline for automated updates from future Human-GEM releases. These improvements provide a computationally efficient, well-annotated model for studying central metabolism across human cell types. Availability and Implementation All source code for reproducing results from this paper is available at https://doi.org/10.5281/zenodo.20813825 .

A Mathematical Model of Dietary Lipid Absorption and Postprandial Chylomicron Dynamics

2026-6-26

Obesity and related conditions such as dyslipidemia impose an increasing burden on healthcare systems worldwide. These conditions are associated with altered postprandial chylomicron (CM) metabolism, the elusive and critical first step in lipid metabolism. This step remains elusive because it is governed by large interindividual variations and a complex set of intestinal processes. In particular, the second meal effect (SME) implies that enterocytes release previously stored fat during subsequent meals. To deal with this complexity, CM and lipid metabolism have previously been explored using mathematical modeling. However, existing models primarily describe TAG dynamics following a single meal or are too complex for practical personalization across datasets. Herein, we address these limitations by presenting a small-scale mathematical model of CM dynamics that incorporates the SME. The presented model successfully describes data from six clinical studies of both single and repeated meal interventions. Model performance was further evaluated by predicting independent datasets using a BMI-dependent calibration. Finally, to demonstrate model applicability, we simulated full-day responses consisting of three sequential meals in individuals with varying BMI values, with qualitative agreement to clinical observations. This work supports our understanding of the SME, person-specific CM postprandial responses, and mechanisms underlying obesity.

MondrianMap: Navigating Gene Set Hierarchies with Multi-Resolution Enrichment Maps

Abir, F., Yue, Z., Saghapour, E., Hossain, M., Chen, J. — 2026-6-29

SUMMARY Gene Ontology encodes genes as a hierarchy, yet every enrichment visualization flattens it into a ranked list, discarding the ability to view the same process at different levels of abstraction. We present MondrianMap, a free interactive web application ( https://mondrianmap.smartdrugdiscovery.org/ ) that organizes enrichment results into 13 semantically principled layers derived from the GOALS framework and renders them as color-encoded rectangular maps where block area reflects significance, color encodes effect direction, and spatial proximity preserves semantic relations, all within an interactive interface. Three case studies across the NIH Common Fund Data Ecosystem demonstrate that visualization facilitates recognition of patterns that are difficult to discern in flat outputs: (1) LINCS CRISPR perturbations reveal that TP53 and KRAS knockouts produce opposite color maps at a single semantic layer, the same immune recruitment processes suppressed by TP53 loss are activated by KRAS disruption; (2) GTEx aging signatures expose the inflammaging paradox as an immediate visual phenomenon, identical antimicrobial defense programs appear uniformly upregulated in aging blood yet uniformly downregulated in aging liver at matched semantic resolution; and (3) MoTrPAC exercise data capture temporal dynamics as color transitions where brown adipose tissue undergoes a threshold switch from two enriched terms to thirty-four at a single molecular layer, while cardiac tissue reverses from uniformly activated to uniformly suppressed glycolytic metabolism as adaptation progresses. MondrianMap facilitates hierarchical visual reasoning, complementing statistical enrichment reporting for biological discovery. HIGHLIGHTS MondrianMap provides multi-resolution enrichment visualization for Gene Ontology Layer-specific views reveal directional oppositions difficult to discern in flat term lists Navigating layers organizes one enrichment into a multi-scale biological narrative Demonstrated on cancer, aging, and exercise data across three CFDE programs IN BRIEF MondrianMap is a web application that transforms gene set enrichment results into layered visualizations encoding regulation, significance, and semantic hierarchy. Across cancer, aging, and exercise datasets, viewing enrichment at defined semantic layers exposes directional inversions, temporal switches, and multi-scale biological narratives that are difficult to extract from conventional flat enrichment outputs. THE BIGGER PICTURE When researchers measure gene expression changes in disease, aging, or drug response, they rely on enrichment analysis to translate thousands of molecular measurements into interpretable biological themes. The standard output is a ranked list of processes sorted by statistical significance. This format served the field well when studies examined a single condition; however, modern genomics routinely compares dozens of tissues, timepoints, and perturbations, each generating hundreds of enriched terms. The critical limitation is not statistical power; however, interpretive structure: a flat list cannot show whether two conditions activate the same biological process in opposite directions, whether a process visible at one level of abstraction disappears or transforms at another, or how a tissue’s functional response evolves across time. These are precisely the questions that define contemporary systems biology. Does a tumor suppressor gene silence the same immune program that an oncogene activates? Does aging drive the same defense pathway upward in the blood and downward in the liver? Does an exercise response flip from activation to suppression as the tissue adapts? Answering these questions requires a visualization framework that preserves hierarchy, encodes direction, and enables comparison at matched levels of biological resolution. MondrianMap provides this framework by organizing Gene Ontology terms into quantitatively defined semantic layers and rendering enrichment as color-encoded rectangular maps navigable from molecular mechanism to system-level theme. The result is a tool that extends enrichment analysis from a reporting step into an interactive framework for biological reasoning, hypothesis generation, and cross-dataset discovery.

Remodeling of the hepatic circadian transcriptome across the estrous cycle

2026-6-28

The circadian clock system drives rhythms in gene expression, tailoring an organism’s behavior and physiology to the ∼24-hour day-night environmental cycle. The mechanisms underlying this system are believed to be largely the same between adult males and females, but recent findings are starting to challenge this notion. Menstrual/estrous cycles (e-cycles) in females are known to modulate a variety of circadian-controlled behaviors. However, their interaction with circadian rhythmicity at the transcriptional level remains unknown. To assess the interaction between e-cycles and the circadian clock, we explored densely collected mouse liver circadian transcriptomes across all four phases of the e-cycle. Surprisingly, we found that the circadian rhythmicity in female livers was strikingly dependent on e-cycle phase, with the largest differences aligning with pre- and post-ovulation. The differential rhythmicity followed prominent yet distinct patterns, which extend and diversify overall sex differences in rhythmic gene expression. Our data also predict that sex and e-cycle may modulate how core circadian transcription factors may regulate expression of some output genes, but other mechanisms appear complex and potentially multifaceted. Nonetheless, the differences in rhythmicity impact broad aspects of liver function, making this panoramic dataset a novel resource for identifying and exploring novel interactions of the estrous cycle on gene expression and overall liver functions.

Probabilistic network modeling identifies RBPJ as a driver of stemness in Merkel cell carcinoma

2026-6-27

Merkel cell carcinoma (MCC) is a poorly differentiated neuroendocrine carcinoma with limited treatment options, primarily immunotherapy, to which only ∼50% of patients respond. Lineage plasticity drives its poorly differentiated phenotype, in turn promoting tumor aggressiveness and treatment resistance. Targeting the mechanisms underlying lineage plasticity could help induce differentiation, reduce proliferation, and potentially sensitize the tumor to existing therapies, yet such strategies are underdeveloped in MCC. Here, we integrate scRNA-seq and bulk ATAC-seq data to generate Boolean networks, simulate their dynamics, and predict a key regulator of differentiation, which we validated in vitro . Using CytoTRACE2 across two independent datasets, we revealed the existence of tumor subpopulations with distinct developmental potency states. We then constructed and refined transcription factor regulatory networks using BooleaBayes and expanded them with ATAC-seq inferred regulatory interactions. Across multiple network constructions, simulations of single-gene perturbations consistently identified the Notch effector RBPJ as the key regulator predicted to shift MCC cells toward a more differentiated state. Experimental knockdown of RBPJ in an MCC cell line altered expression of differentiation-associated genes, reduced the expression of MCC markers, and drastically reduced cell growth. These findings identify RBPJ as a regulator of MCC lineage plasticity and candidate for targeted treatment, while highlighting the utility of probabilistic network modeling for prioritizing therapeutic targets in translational cancer research.

pFLEX – a Python library for fast functional evaluation of genetic networks at the biological module-level

Demirtas, T., Shaw, A., Billmann, M. — 2026-6-14

Genetic networks derived from omics data are a powerful tool for systematic gene function prediction. Performance evaluation of such predictions is crucial to judge the data and computational pipeline for network construction, but unbalanced functional standards often cause hidden evaluation biases. To visualize and mitigate such biases, we previously developed the R package FLEX. Here, we present the pFLEX genetic network benchmarking tool as Python library with new and improved functionality. pFLEX improves overall runtime 4.1 to 15.8-fold. It offers additional evaluation metrics that allow for easy comparison of precision recall performance at the complex or pathway resolution between genetic networks. We demonstrate the utility of pFLEX for evaluating tissue-specific co-essentiality networks and data normalization strategies of the Cancer Dependency Map, as well as for cell line-specific Perturb-Seq-derived networks. This illustrates the requirement for biological module-resolved precision recall metrics in pFLEX for sensitive and fast evaluation of genetic networks. Availability and Implementation pFLEX is available under the MIT license at https://github.com/billmannlab/pFLEX and the pFLEX version used in this manuscript along with benchmarking code for the analyses presented in this manuscript are archived at https://doi.org/10.5281/zenodo.20632868 .

Differences between protein fitness models can be used to design variants of altered specificity

Berry, S., Gaudet, R., Marks, D. — 2026-6-14

The vastness of sequence space makes it challenging for directed evolution to efficiently traverse the fitness landscape. In recent years, unsupervised probabilistic models trained on natural sequences have shown promise for predicting the functional effects of mutations and designing new proteins, leading many to suggest that these models may be useful for guiding directed evolution campaigns toward functional regions of sequence space. However, many directed evolution campaigns are interested in evolving new substrate or ligand specificity, and the behavior of unsupervised sequence models on predicting and designing activity against altered substrates or ligands has not been tested. We have built a curated database of multiplexed functional assay results profiling substrate or ligand specificity and systematically how assessed various popular unsupervised protein fitness models score and design variants that alter selectivity in these datasets. We find that sequence models that learn from the surrounding sequence context, especially protein language models, systematically bias against variants that alter specificity. This bias leads them to design altered-specificity variants at similar or lower rates than random chance. However, we propose a simple strategy to exploit this bias by taking weighted differences of model scores to enrich libraries for altered-specificity variants. These findings and our database should help guide how biologists best use protein fitness models and provide a framework to help machine learning researchers develop a new generation of machine learning models that can better design novelty.

Amino acid repeat mosaics shape protein functional landscape

2026-6-12

How combinations of functional units such as repeats and motifs within disordered regions influence protein functions remains poorly understood. Here, we investigate how co-occurring amino acid homorepeats of different types within the same protein (HR-mosaics) shape molecular outcomes. Using a novel evolutionary co-occurrence metric, human HR-mosaics were classified as segregated (each HR independently influences distinct outcomes), concerted-disjunct (both HRs jointly shape protein functionality without modulating each other), or concerted-conjunct (both HRs affect functionality through mutual influence). Molecular studies of naturally occurring polyGly-polyPro mosaic in DDX17 and chimeric mosaics of polyAla and polyHis demonstrate that HR-mosaics influence protein abundance, localisation, mobility and interaction landscape. Segregated HR-mosaics expand functional space, concerted-disjunct additionally confer biological context, and concerted-conjunct mosaics further enable rheostatic regulation between HRs. These design principles underscore amino acid type, length and relative positioning of HRs as central architects of HR-mosaic functionality, with implications in protein design and engineering and understanding repeat-associated pathologies.

Linking Host Covariates to COVID-19 Vaccine-Induced Antibody Dynamics in a Large Healthcare Worker Cohort

2026-6-12

Antibody responses to COVID-19 vaccination vary widely across individuals, but the factors shaping these differences and their relation to underlying host-response processes are still not fully understood. Here, we combined longitudinal serological data from 4,553 healthcare workers with a nonlinear mixed-effects model to quantify determinants of vaccine-induced antibody dynamics. This framework captured both population-level trends and inter-individual variability in anti-spike antibody trajectories after vaccination. We found that age, sex, and pre-vaccine infection significantly influenced response dynamics. Male sex and increasing age were associated with weaker initial antibody production but slower waning, while individuals with a pre-vaccine infection had pre-existing anti-spike antibodies and slower initial antibody production and faster waning. These effects translated into substantial long-term differences in predicted antibody levels, reaching up to a 27% decrease in antibody levels one year after the third vaccination. Our results provide a dynamic view of the determinants of vaccine-induced humoral immunity and establish a framework for analyzing heterogeneous immune responses in large longitudinal cohorts.

Comparative metabolomics identifies recurrent age-associated pathway remodeling across species

2026-6-25

Aging is accompanied by widespread metabolic change, but it remains unclear which features are shared across species with different physiology, lifespan, and sampling contexts. To address this, we employed a pathway-centered comparative metabolomics framework to evaluate age-associated metabolic remodeling across wild African savanna elephant, mouse, and Drosophila melanogaster . Drosophila provided a controlled adult time course to map age-associated metabolite trajectories, while mouse and elephant plasma datasets allowed us to test whether these pathway signatures extended to mammalian aging. Adult Drosophila showed extensive metabolomic remodeling, with significant metabolites organizing into distinct temporal trajectory classes. Although individual metabolite overlap across species was limited, robust correspondence at the pathway-level overlap was observed. Pathway scores derived from Drosophila increased progressively with fly age, successfully distinguished young and old mice, and captured age-associated stratification across the elephant lifespan. Notably, lipid metabolism, particularly carnitine and fatty acid metabolism, together with nucleotide-related pathways, consistently emerged as the core features of aging across analyses. These findings suggest pathway-level metabolic remodeling is a recurrent feature of cross-species aging.

Biobehavioral synchrony across species: Evidence for multi-level regulatory dynamics in human–canine dyads

2026-6-25

Coordinated dynamics between individuals are a hallmark of social interaction, yet the temporal structure and physiological basis of such coupling beyond human species remain poorly understood. Here, we investigated cross-species biobehavioral synchrony by simultaneously quantifying motion dynamics and autonomic activity with hyperscanning of human–canine dyads. We observed both spontaneous and task-related synchrony across motion dynamics, heart rate, and heart rate variability at multiple timescales. Importantly, synchrony was modulated by individual and relational factors. Task-related autonomic synchrony was affected by the human temperament, whereas greater familiarity within the dyad altered the leader–follower dynamics, shifting directional influence from human-led toward canine-driven coordination. Motion synchrony emerged with minimal delay, whereas cardiac synchrony unfolded across longer timescales, suggesting coordinated processes underlying the shared activity, arousal, and autonomic regulation. Our findings extend current models of social synchrony beyond human interactions and reveal that regulatory dynamics underlying coordinated behavior operate across species boundaries.

Self-organized traveling waves in a synthetic multicellular reaction-diffusion system

Landge, A., Marcon, L., Soh, G., Lohrmann, L., Volkwein, S., Müller, P. — 2026-6-25

Synthetic gene circuits provide experimentally tractable systems for dissecting how genetic feedback and diffusible signals generate multicellular patterns. However, building multi-component circuits whose behavior can be quantitatively linked to module-level measurements, diffusion, and spatial boundary conditions remains challenging. Here, we designed and engineered a bacterial patterning system in which positive feedback, delayed negative feedback, and two orthogonal quorum-sensing signals are integrated in Escherichia coli . We first implemented and characterized the feedback modules separately, measured the effective diffusion of the signals in the experimental setup, and used these data to parameterize a mathematical model. In quasi-2D bacterial lawns, the complete circuit generated self-organized spatiotemporal dynamics consisting of an sfGFP activation front followed by successive mCherry propagating pulses/traveling waves. Model-guided perturbations showed that lawn size, lawn position relative to the domain boundary, and signal degradation modulate the timing, amplitude, wavelength, and directionality of these patterns. Our work establishes a modular synthetic multicellular reaction-diffusion system in which circuit architecture, signal diffusion, and boundary-mediated signal exchange can be experimentally connected to emergent patterning dynamics.

Curcumin and Sulforaphane Preserve Mobility in Aging Caenorhabditis elegans via Distinct yet Complementary Transcriptional Signatures

2026-6-25

Aging involves a progressive decline in bodily functions, underscoring the need for interventions that enhance healthspan. In this study, we screened nine natural products in Caenorhabditis elegans using whole-organism phenotyping to assess mobility endpoints, and subsequently focused on curcumin, sulforaphane, and their combination. In replicated follow-up experiments, all three interventions improved late-adult mobility after Day 2 of adulthood. Sulforaphane and the combination provided the strongest gains, whereas curcumin showed a distinct benefit profile, with more pronounced effects on time active measures than on speed-based metrics. To examine associated molecular changes, we performed transcriptomic profiling on Day 3 adults. Curcumin was associated with lipid and sphingolipid remodeling together with reduced expression of several innate immune effectors, whereas sulforaphane induced glutathione-linked detoxification signatures involving multiple gst genes. The combination retained major features of both single-compound responses while adding combination-specific changes that broadened detoxification-associated signatures and extended repression of lectin-and lysozyme-associated genes. Transcription factor activity inference further supported SKN-1-linked detoxification responses under sulforaphane and the combination. Overall, these results suggest that curcumin and sulforaphane engage distinct yet partially convergent maintenance-related programs, and that their combination broadens the underlying molecular response without producing additive mobility gains. These findings motivate further testing of natural product combinations in healthspan-related contexts.

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

Wang, B., Bai, Y., Saez-Rodriguez, J., Eduati, F., Dugourd, A. — 2026-6-24

Computational modeling provides a powerful framework for in silico exploration of anti-cancer therapeutic targets and tumor response mechanisms. Oncogenic signaling pathways play a central role in tumor behavior and represent promising targets for personalized combination therapies. However, these pathways are complex, and although logic-based models are well suited for representing signaling dynamics, they are often constrained by model-specific data requirements, limited scalability, and time-consuming manual curation. Here, we introduce Functional Integration of Contextualized Omics for Unraveling regulatory dynamicS (FICUS), a framework that integrates omics-driven network contextualization with dynamic Boolean and logic-ODE modeling. FICUS enables automated, data-driven protein network inference and patient stratification, allowing shared signaling mechanisms to be identified across patient subgroups while preserving patient-specific dynamic responses. We applied FICUS to the SU2C-MARK lung cancer cohort and the The Cancer Genome Atlas kidney cancer cohort, demonstrating its utility for post-hoc analyses and downstream interrogation of dynamic tumor models. Overall, our results highlight the flexibility of FICUS in capturing heterogeneous signaling mechanisms across patient subgroups, addressing a key challenge in precision oncology.

Humanin analogue promotes metabolic reprogramming to protect the ischemic heart

2026-6-22

Background Myocardial ischemia drives adverse cardiac remodeling, metabolic inflexibility, and progression to heart failure. Mitochondrial dysfunction and impaired substrate utilization contribute to cardiomyocyte death and fibrosis, particularly with aging. Humanin (HNG), a mitochondria-derived peptide, has been shown to reduce acute ischemic injury, but its role in chronic ischemia and cardiac remodeling remains unknown. Methods We investigated the effects of HNG treatment in young and aged murine models of myocardial ischemia without reperfusion. Cardiac function and structure were assessed by echocardiography and molecular markers of remodeling. Myocardial metabolism was interrogated using targeted metabolomics, gene expression, substrate uptake assays, and metabolic flux analyses. Mechanistic studies examined glucose transporter trafficking and protein–protein interactions. Results HNG treatment improved cardiac function and significantly attenuated adverse remodeling in both young and old mice. HNG treatment induced marked metabolic reprogramming characterized by reduced myocardial fatty acid content, downregulation of fatty acid uptake and oxidation pathways, and decreased oxidative stress. Importantly, these changes were accompanied by enhanced glucose oxidation, increased tricarboxylic acid cycle flux, improved coupling of glycolysis to mitochondrial oxidation, and increased ATP production. Time-course studies demonstrated that increased glucose oxidation preceded reductions in fatty acid oxidation, indicating a primary role for glucose metabolism in HNG-mediated cardioprotection. Mechanistically, we identified vesicle-associated membrane protein 7 (VAMP7) as a novel binding partner of HNG, and that this interaction is required for GLUT4 translocation to the plasma membrane and HNG-induced ATP generation. Conclusions HNG protects the ischemic heart by promoting metabolic reprogramming that shifts substrate utilization from fatty acids to glucose and limiting maladaptive remodeling. These findings identify HNG as a novel regulator of cardiac metabolism and a potential therapeutic strategy for ischemic heart failure. Graphical abstract: What are the clinical implications? Heart failure (HF) is a major global health concern, affecting over 6.7 million adults in the United States alone, with projections to exceed 11 million by 2050. Myocardial infarction (MI) is a leading cause of HF. Despite substantial advances in acute MI care, survivors remain at high risk for adverse cardiac remodeling and chronic HF, especially in the elderly. We report here that treatment with a potent analog of Humanin (HN), an endogenous mitochondria-associated peptide, decreases infarct size, decreases fibrosis and improves cardiac function following cardiac ischemia induced by permanent ligation of coronary artery in both young and aged mice. These effects are associated with changes in cardiac metabolism, oxidative stress, and remodeling. HN and analogs have been shown to be beneficial in many age-related diseases. The endogenous origin of Humanin, its favorable safety profile in preclinical studies and its pleiotropic effects support targeting HNG as a promising therapeutic strategy for ischemic heart disease and post–myocardial infarction heart failure in humans.

A Multiscale Computational Framework Linking Cortical Microtubule Dynamics to Plant Tissue Morphogenesis

S, A., BASAK, A., PARIDA, O., Chakrabortty, B. — 2026-6-23

Plant morphogenesis emerges through the coordinated regulation of cell growth and mechanical interactions across multiple spatial scales. A central role in this process is played by cortical microtubule (MT) arrays, which guide cellulose deposition and thereby regulate anisotropic cell expansion. Here, we develop a coupled multiscale computational framework integrating a dynamic vertex model of tissue mechanics with a stochastic model of cortical MT dynamics. Within this framework, MT organization regulates anisotropic cell-wall stiffness, while evolving cell geometry feeds back to influence MT alignment through bidirectional mechanochemical coupling. Simulations show that distinct regimes of MT self-organization generate qualitatively different tissue growth behaviors, ranging from isotropic expansion to strongly anisotropic elongation. Together, our results demonstrate that stochastic a complex coupling of MT self-organization with cell geometry and tissue mechanics is sufficient to generate emergent tissue-scale growth anisotropy, establishing a minimal multiscale framework linking cytoskeletal dynamics to plant tissue morphogenesis.

K9HeartCircDB: A circRNA Atlas of Tachypacing-Induced Canine Dilated Cardiomyopathy

2026-6-22

Cardiovascular disease (CVD) remains a leading cause of death worldwide. Dilated cardiomyopathy (DCM), a major cause of heart failure (HF), exhibits ventricular dilation, impaired systolic/diastolic function, arrythmias, and adverse cardiac remodeling. While genetic causes of DCM have been extensively studied, non-genetic and acquired forms of DCM-like HF are less well characterized, especially with respect to non-coding RNA regulation. Circular RNAs (circRNAs) are stable, covalently closed non-coding RNAs that regulate cellular function via sequestering miRNAs, RNA-binding proteins, or translation. Their role in canine HF that recapitulates features of non-genetic DCM remains largely unexplored. To address this, we developed K9HeartCircDB ( https://www.k9heartcircdb.com/ ), a publicly accessible database that catalogs circRNAs expressed in canine left ventricular (LV) tissues under tachypacing-induced HF, a model of non-genetic DCM-like disease, and healthy control conditions. The online interface enables users to query and explore circRNAs based on exon composition, predicted miRNA binding sites, protein-coding potential, siRNA targets, and primer design for experimental validation. By providing an integrated and user-friendly platform for canine heart circRNA exploration, K9HeartCircDB offers a valuable resource to facilitate mechanistic and advance translational studies on non-genetic DCM-like disease.

Mechanistic reconstruction of receptor-to-transcription factor signaling integrating prior knowledge and omics

Liaskos, D., Oftadeh, O., Tonini, M., Masid, M., Hatzimanikatis, V. — 2026-6-22

Omics profiling is ubiquitous, yet resolving how signal transduction shapes cellular physiology remains challenging. Most analyses interpret omics data by summarizing catalogs of pathways or by fitting networks to observations. Here we present SIGMA, a framework that converts curated prior knowledge into causally interpretable, elementally balanced signal-transduction cascades that connect sources to targets. By enforcing elemental balance, SIGMA links processes across pathway catalogs and reveals crosstalk beyond canonical definitions. It enumerates alternative cascades to expose parallel routing and identify context-dependent essential steps. We introduce balanced cascade enrichment analysis to map omics data onto alternative cascades and rank them by context-specific molecular support. Using SIGMA, we reconstructed signal flow from receptors to transcription factors that regulate metabolism, uncovering mechanisms that control metabolic reprogramming. In CD8 + T cells, SIGMA identified cascades connecting TGF-β receptors to SP1 and indicated weakening of this axis in exhaustion. Overall, this framework enables mechanistic interpretation of signaling across datasets and guides the design of causal perturbations.

Drug repurposing for rare diseases via a gene-bridged heterogeneous knowledge graph and graph attention network

Ramani, D. — 2026-6-21

Rare diseases are severely underserved by pharmacological treatments, and computational drug repurposing offers a cost-effective alternative to de novo discovery. We present a reproducible end-to-end pipeline integrating 3,961 rare disease–gene associations from Orphadata with 98,239 gene–drug records from DisGeNET through a multi-stage harmonization pipeline (HGNC symbol standardization and RapidFuzz fuzzy matching), yielding a large-scale gene-bridged rare disease tripartite knowledge graph — to our knowledge the largest such graph constructed exclusively from Orphadata and DisGeNET— comprising 15,454 nodes and 35,131 edges spanning 2,249 clinically distinct rare diseases. A Graph Attention Network (GAT) trained on node-type classification as a pretext task achieves macro F1 = 0.651 and ROC-AUC = 0.818 on a stratified held-out test set, with stable performance across five evaluation partitions (SD ≤ 0.007). Drug candidate retrieval via cosine similarity in the GAT embedding space achieves Hits@10 = 0.400 across 200 evaluated disorders (vs. < 0.001 random baseline), with the clinically validated drug NITISINONE recovered at rank 4 for a tyrosine catabolism pathway disorder without pathway annotations. A deployment-ready interface is publicly available on HuggingFace Spaces.

Clinical Trial and Ontology-Derived Positive and Negative Benchmark Datasets for Drug Repurposing Across Rare Diseases

2026-6-20

Evaluating the potential applications of a medicine is a fundamental challenge in drug development. There is a lack of standardized, decision-oriented benchmarks that test whether computational models can generalize therapeutic hypotheses across diseases in ways that reflect real-world pharmaceutical investment decision making. To address this gap, we introduce two complementary resources: the Indication Expansion Investment Decision Network (IxIDN) and the Orphanet Rare Disease Ontology Negative-network (ORDON). IxIDN is a clinical-trial-derived positive benchmark constructed by projecting drug–disease associations from pharmaceutical clinical trials into a disease–disease network; each edge connects disease pairs that have entered clinical trials for the same drug, thereby capturing cases when concrete indication-expansion decisions have been made. The current release contains 574 rare diseases and 5,336 edges. In contrast, ORDON serves as a stringent, biology-aware negative benchmark derived from the authoritative Orphanet Rare Disease Ontology. It identifies maximally distant disease pairs according to curated hierarchical structure and genetics-linked inheritance patterns, providing 793 rare diseases and 5,000 edges that represent high-separation negative candidates across therapeutic areas. Together, IxIDN and ORDON enable rigorous cross-evidence generalization from clinical trials to disease ontology, testing for Disease–Disease Association Learning (DDAL), a core task for mechanism-centered drug repurposing and indication expansion. All data are publicly available with detailed metadata, enabling reproducible evaluation of models on transparent, decision-relevant benchmarks.

High Throughput Characterization of Eukaryotic 2A-Like Peptides Identifies Novel Leucine-Associated Reduction in Protein Abundance

Snell, J., Matreyek, K. — 2026-6-19

Virally-derived ribosomal skipping 2A peptides are a popular tool for protein co-expression. Despite their use in over 9,000 publications, the biochemical and biophysical properties underlying the skipping mechanism remain largely unexplored. We identified 4,218 2A-like peptides originating from non-viral organisms. We developed and utilized the Trifluorescent Reporter fluorescent tool for high-throughput multiplexable analysis of ribosomal skipping, and tested 3,271 2A-like peptide sequences. We identified peptides that skipped, failed to skip, and skipped but failed to restart translation, in addition to peptides that induced a reduction in protein abundance. Peptides that skipped and induced reductions in protein abundance largely originated from eukaryotes. A poly-leucine stretch in an alpha-helix N-terminal to the conserved GDxExNPGP motif drove both skipping and the reduction in protein abundance. Analysis of the native eukaryotic protein contexts revealed that reduction may be harnessed as an expression regulator. The high-throughput approach used in this work greatly expands the functional knowledge of what biophysical and biochemical characteristics lead to ribosomal skipping, including an apparent latent eukaryotic 'leucine stall-helix' motif.

Genome-scale metabolic model atlas of the zoonotic pathogen Streptococcus suis

2026-6-19

Streptococcus suis is a Gram-positive bacterium with a dual role as a commensal member of the porcine nasal microbiota and a pathogen causing systemic disease in pigs and humans. Mounting evidence suggests that metabolism is a key driver of S. suis pathogenicity. Given the species’ high genetic variability, we hypothesize that differences in metabolic networks could explain the diverse pathogenic phenotypes observed across different strains. To test this, we generated an atlas of over 3000 strain-specific and automatically curated genome-scale metabolic models that cover the breadth of pathogenic and commensal S. suis lineages. Using this model atlas, we performed the first species-level examination of metabolic traits in S. suis . Our simulations, supported by experimental validation, revealed three key insights. First, while metabolic traits are broadly conserved in S. suis , there are nevertheless lineage-dependent differences in amino acid auxotrophies and carbon utilization patterns that point towards distinct in vivo niches. Second, most strains are predicted to grow in different plausible in vivo environments regardless of their virulence phenotype, suggesting that metabolism is a weak barrier to systemic infection. Third, by systematically predicting reaction essentiality in more than 15 million reaction-strain-condition combinations, we identify a subset of 17 reactions, largely in nucleotide metabolism, that are conditionally essential in vivo and may serve as new targets for the development of new antimicrobials or vaccines. Overall, this study provides a valuable new resource for broadly examining S. suis metabolism and its role in pathogenicity.

Dihydroartemisinin induces a two-step transcriptional response and stage-specific developmental shifts in malaria parasites, Plasmodium falciparum

Bozdech, Z., Boentoro, J., Kucharski, M., Nayak, S. — 2026-6-18

The parasite Plasmodium falciparum causes malaria, the deadliest human parasitic disease, which remains fatal when not promptly treated. Evolving parasite resistance to frontline artemisinin-based therapies threatens vulnerable populations and decades of progress toward malaria elimination. Yet the mode of action of dihydroartemisinin (DHA), the active metabolite of these treatments, remains incompletely understood. Here we applied dose-response transcriptomics across the three intraerythrocytic stages - rings, trophozoites, and schizonts, revealing a two-tier transcriptional response to DHA, with low- and high-dose programs consistent with specific drug action and cytotoxic damage. The trophozoite stage mounts the strongest and most coordinated response, including a striking reversal of the developmental cascade in which protein synthesis machinery is broadly downregulated and a ring-like transcriptional profile is reactivated - reminiscent of drug-induced quiescence. Coordinated regulation of multiple protein complexes, most notably Kelch13 and its interacting partners (KIC), points to organized transcriptional control of the parasite’s drug response. This work provides a stage- and dose-resolved view of DHA action in P. falciparum and a template for future antimalarial mechanism-of-action studies.