npj Systems Biology and Applications

Dose-regimen design requires choosing how much drug to give, when to give it, and how treatment should vary across patients. Mechanistic pharmacokinetic-pharmacodynamic (PK/PD) and quantitative systems pharmacology (QSP) models can predict treatment responses, but optimizing dosing inputs depends on model-specific sensitivity derivations or derivative-free search. Here, we introduce DiffDose, a differentiable programming framework for mechanistic open-loop dose-regimen optimization that uses automatic differentiation (AD) to handle clinically interpretable dose amounts and administration times as differentiable controls. We evaluate our method in three settings: fixed-schedule dose-amplitude optimization in OptiDose PK/PD benchmarks; dose-timing optimization in a chemotherapy-induced neutropenia model with state-dependent delay; and individualized mosunetuzumab dosing in a QSP virtual population. Across these examples, AD produced gradients consistent with references, reduced model-specific derivative work, and shortened benchmark time to solution. DiffDose thereby turns mechanistic PK/PD and QSP models from tools that evaluate prespecified regimens into gradient-based engines for individualized regimen design.

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

Disease reprograms cells through changes in gene regulation, yet identifying these changes remains a major challenge. We introduce NetDes-Duo, a computational method that jointly infers transcription factor regulatory network models for two related conditions using scRNA-seq data. The networks are optimized to have minimal topological differences, while the associated ODE models recapitulate single-cell gene expression trajectories for both conditions. On synthetic benchmarks, NetDes-Duo outperformed methods that infer each network independently. NetDes-Duo was applied to neutrophil reprogramming in naive and tumor-bearing mice, and the network-simulated dynamics reproduced the observed cell state transitions. The naive landscape had two well-separated basins, whereas the tumor-bearing landscape was more continuous, with three shallower basins. Perturbation and driving simulations also identified Cebpb as a key driver of the tumor-bearing transition, consistent with emergency granulopoiesis literature. We expect NetDes-Duo to be a broadly applicable framework for uncovering the regulatory logic of disease-associated cell state transitions.

High-dimensional population balance equations (PBEs) provide a natural framework for modeling heterogeneous cell populations, but their direct numerical solution becomes computationally prohibitive when the internal state space contains many molecular variables. We propose a hybrid mechanistic-machine learning framework for reducing and simulating PBEs defined over high-dimensional intracellular coordinates. The cell population is described by a number density n(x, t), where x [isin] [R]N represents gene and protein states associated with macrophage activation. A dynamics-preserving autoencoder maps this state space to a low-dimensional latent coordinate z [isin] [R]d, with d << N, while retaining key qualitative features of the underlying gene regulatory network, including attractor structure and multistability. Mechanistic information from the original regulatory dynamics is used to construct interpretable drift and diffusion terms for the reduced latent-space PBE. The reduced PBE is solved using a stochastic Lagrangian particle representation, in which particles evolve according to stochastic differential equations (SDEs) corresponding to the latent drift and diffusion fields. The resulting latent-space solution is subsequently decoded and propagated back into the original state space to recover physically interpretable cellular dynamics. We demonstrate the framework on macrophage polarization under cytokine-dependent regulation, including gene knockout perturbations. Overall, the proposed framework provides a computationally tractable and mechanistically interpretable route for integrating single-cell genomic data with population balance models of cell-state dynamics.

Oscillatory and saturating dynamics in regulatory gene networks govern processes such as cell cycle timing, circadian rhythms, and inflammatory signaling, and sparse regression methods such as SINDy offer an interpretable method for recovering their governing equations from time-series data. However, these networks are typically governed by Hill-type nonlinearities that become nearly collinear with polynomial candidates over most bounds of an oscillating curve, and placing them in a shared library produces ill-conditioned regressions and unreliable coefficient estimates. Standard approaches such as ridge regularization, library normalization, and orthogonalization can marginally improve conditioning but cannot remove the structural multicollinearity of these systems, in turn limiting our ability to produce accurate parameter estimates. To address this issue, we propose the Regulation-Informed Sparse Identification of Nonlinear Dynamics (RI-SINDy) method, which removes known regulatory terms from the regression and recovers their coefficients directly from an asymptotic drift-balance condition. Because the time averaged derivative of a bounded trajectory approaches zero, the coefficients of regulatory nonlinearities can be computed independently by constraining them to offset the mean contribution of the remaining terms. We demonstrate that this method effectively reduces library collinearity and improves conditioning numbers by 3-45x, substantially improving coefficient recovery and regression accuracy. We test the RI-SINDy method on experimental Hes1 data and on synthetic Goodwin and NF-{kappa}B models. On Hes1, it achieves the lowest trajectory error among the compared methods (Traditional SINDy, SR3, and Nullcline Reconstruction). On the synthetic systems, it recovers the correct sparse structure with lower trajectory error than SR3 and Nullcline Reconstruction, and in a Goodwin noise sweep it recovers all active terms at noise levels up to 15%, where Traditional SINDy and E-SINDy fail to. In conclusion, the RI-SINDy method provides a robust framework to overcome the issue of multicollinearity for system identification in biological systems.

Pericytes (PCs) have recently emerged as critical regulators of cerebral blood flow (CBF) and represent a promising therapeutic target for various cerebrovascular pathologies. Given the complex array of biochemical and mechanical stimuli these cells integrate, a multiscale modeling framework is essential to quantify the impact of selective interventions on pericyte contractile machinery and blood flow restoration. Here, we introduce a computational framework to evaluate capillary pericyte responses across diverse experimental interventions and conditions (ex vivo and in vivo). To capture pharmacological modulation of the contractile apparatus, we developed a homogeneous intracellular model that incorporates key properties of robust control systems. In this framework, vascular tone generation depends strictly on intracellular calcium concentration (Ca2+), which emerges from a complex electrochemical equilibrium established by transmembrane ion (Na+, K+, Cl-) gradients, luminal mechanical forces, and external ligand concentrations. The resulting fraction of phosphorylated cross-bridges generates contractility, which is integrated into the strain energy function governing the constitutive behavior of the vascular wall. The model was successfully validated across four distinct experimental and pharmacological interventions (including pinacidil, high external K+, U46619, and nimodipine), demonstrating close agreement with observed ex vivo and in vivo vascular responses. By establishing a quantitative bridge between pericyte electrophysiology and microvascular mechanics, this framework provides a valuable foundation for evaluating targeted therapeutic strategies to alleviate tissue ischemia in stroke and vascular dementia.

Cardiopulmonary exercise testing reveals cardiovascular and respiratory limitations not apparent at rest, but similar measurements can arise from different interacting regulatory mechanisms, preventing physiological causal inference from data alone. Mechanistic computational models can separate these mechanisms in silico. However, whole-body cardiorespiratory models contain hundreds of parameters, making global sensitivity analysis and calibration challenging. We present OpenCRS, an open-source Python cardiorespiratory model coupling closed-loop 0D lumped-parameter circulation with gas exchange and integrated autonomic and respiratory control. The framework incorporates baroreflex, chemoreflex, pulmonary stretch receptors, central command, and neuromuscular drive, together with novel representations of atrial dynamics and exercise baroreflex set-point resetting. We introduce a scalable calibration pipeline that (i) applies a derivative-based global sensitivity measure (DGSM) directly to the simulator, reducing 272 parameters to 72 influential ones; (ii) trains Gaussian process emulator surrogates within iterative History Matching to exclude implausible parameter regions; and (iii) performs Bayesian calibration (MCMC), inferring a joint posterior with Hamiltonian Monte Carlo (No-U-Turn sampler) under a Gaussian copula prior. A single maximum a posteriori parameter set simultaneously reproduced 50 literature-derived rest and exercise targets (45/50 within 1 SD, all within 1.83 SD), with the rest-to-exercise transition emerging from the models embedded feedback rather than independent fitting. Simulator-based DGSM agreed with constrained Sobol indices (mean Spearman rank correlation 0.79). Sensitivity analysis identified influential physiological mechanisms. Only 2-16 parameters contributed >2% of the Sobol total-effect sensitivity per target. Resting cardiovascular targets were driven by unstressed volumes and cardiac mechanics, while exercise shifted influence towards autonomic efferent regulation. Respiratory outputs remained most sensitive to chemoreflex and gas exchange parameters. These shifts capture coordinated cardiorespiratory adaptation to metabolic demand. More broadly, the framework provides a population-level prior with quantified uncertainty for cardiovascular digital twins and a reusable route for calibrating high-dimensional, regulated physiological models.

High-throughput single-cell assays are widely used to quantify distributions of cell size, morphology, and molecular content across thousands of cells. However, such population distributions do not reveal how the measured cellular states change within individual cells over time. We introduce division-resolved inference of flow and trajectories (DRIFT), a computational framework that infers the dynamics of a measured cellular state from population distributions collected over time, without synchronizing or tracking individual cells. DRIFT solves a population-balance equation to separate state progression from the redistribution caused by cell division in proliferating populations. In simulations of growth and division perturbations, DRIFT recovered the ground-truth mean volume trajectories across simulated single-cell lineages. In live L1210 leukemia cells, DRIFT inferred perturbation-specific volume trajectories that were consistent with longitudinal single-cell measurements. Beyond cell volume, DRIFT also inferred DNA-content dynamics from fixed-cell flow cytometry in L1210 cells, consistent with independent DNA-synthesis assays. In live HeLa cells, DRIFT inferred cell area dynamics that were validated by continuous imaging. Overall, DRIFT converts endpoint measurements of cell populations into division-resolved cellular dynamics, providing a scalable strategy for high-throughput drug-response screening and mechanistic investigation.

Analysis of circadian patterns in time-series data requires computational methods that can accommodate several factors, including variable sampling resolution, replicate number, and missing values. Most existing tools simplify rhythmicity to a strict dichotomy based solely on a single p-value threshold. This leads to a level of uncertainty that affects many biological targets. We developed CODAC (Circadian Oscillation Detection Analysis and Comparison), a framework that integrates nonlinear constrained optimization with a multicriteria rhythmicity classification scheme to evaluate rhythmic patterns without relying on a single statistical cutoff. This approach allows CODAC to identify and exclude medium-confidence rhythms rather than force them into a rhythmic/arrhythmic dichotomy. CODAC comprises four modules: (i) CODAC_single estimates rhythmicity within a single group; (ii) CODAC_flex extends this to identify distinct waveform types within one group; (iii) CODAC_compare performs pairwise comparisons across two or more groups to detect rhythmic or arrhythmic changes; and (iv) CODAC_multi handles more complex designs involving multiple-group comparisons. Using in silico simulations and public transcriptomic datasets, we show that CODAC performs comparably to established methods while providing additional flexibility for rhythm classification and comparison. Taken together, CODAC provides a flexible and open-source package for circadian timeseries analysis with automated visualization tools.

Under current systemic treatment of metastatic cancer, a drug is frequently prescribed at maximum tolerable dose (MTD) until either unacceptable toxicity or progression. Unfortunately, in many patients this treatment strategy leads to the development of treatment resistance. Evolutionary therapy approaches aim to forestall or delay treatment resistance in cancer by exploiting eco-evolutionary interactions. A well-known implementation is the adaptive therapy protocol of Zhang et al., in which tumour burden thresholds are used to guide strategic treatment holidays. Deep reinforcement learning (DRL) has recently been used to optimise these approaches. However, research combining DRL with evolutionary therapy approaches has so far focused on time to progression (TTP) as a performance metric, and has not quantified safety in terms of robustness to delayed treatment restart or included patient preferences regarding quality of life (QoL) in treatment design. In our study, we use a DRL agent informed by a mathematical two-population tumour growth model to design treatment schedules for patients with non-small cell lung cancer (NSCLC). The agent is trained on a virtual patient cohort using parameters previously fitted to data from patients with NSCLC treated with erlotinib. Beyond TTP, we focus on improving robustness to delayed treatment restart and on how individual preferences and values impact QoL experienced during treatment. We compare TTP, robustness and QoL under the DRL policy, the adaptive therapy protocol of Zhang et al., and MTD. We introduce a robustness metric ``margin-to-failure'' (MTF), and compare quality-adjusted-survival (QAS) across different patient preference profiles. Finally, we explore reward shaping to assess how QoL preferences can be incorporated into DRL-based treatment design. To evaluate our results, we consider different decision intervals, defined as the time between dosing adjustments. The DRL policy achieved greater median TTP, MTF, and QAS across all treatment decision intervals compared to the other two protocols. As decision intervals increased, TTP under DRL declined gradually towards that achieved under MTD. In contrast, the Zhang et al. protocol performed inconsistently and could result in premature progression. Additionally, a population-level policy trained on a cohort of virtual patients produced an interpretable treatment rule that extended TTP for most previously unseen patients and indicated that treatment should resume at a lower tumour burden when monitoring is less frequent. These findings show that DRL can balance the benefit of preserving drug-sensitive cells to suppress resistance against the risk of unsafe tumour regrowth. Reward shaping further showed how treatment strategies could be adjusted to reflect different patient preferences. Together, these results provide a biologically informed approach for designing robust and patient-centred evolutionary therapies in fast-growing cancers such as NSCLC.

Modern medicine implicitly assumes that physiological responses to intervention are predictably determined by administered treatments. However, physiological systems containing intrinsic delays between the detection of a stimulus and the biological response may violate this assumption. We investigate the human glucose-insulin system as described by the Ultradian model and mathematically demonstrate that clinically relevant forcing protocols-such as pulsatile insulin delivery and step-wise glucose infusion, both commonly used in intensive care units (ICUs)-can induce sustained temporal chaos that may hamper accurate prediction of the physiological response. If not accounted for, these chaotic dynamics could create difficulties in achieving optimal dosing and timing when administering glucose and insulin in clinical or home care settings. This phenomenon, termed delay-induced uncertainty (DIU), arises from the interaction between physiological delay, intrinsic shear near a limit cycle, and external forcing. Using the Ultradian glucose-insulin model, we compute top Lyapunov exponents to quantify predictability. Across a range of pulsatile and step-wise forcing regimes, including stochastic amplitudes drawn from Markov processes, we observe positive Lyapunov exponents, indicating sustained chaos. Our results suggest that delayed endocrine regulation may fundamentally limit the predictive value of the models used to develop glycemic management strategies, with implications for clinical protocols in the ICU.

Organisms rely on feedback control mechanisms to maintain key biological variables within functional ranges despite persistent perturbations. Understanding how effectively these mechanisms maintain homeostasis is fundamental, yet quantitative evaluation of feedback performance remains challenging in nonlinear biological systems. Our previously developed framework, Control Ratio (CoRa), addresses this challenge by isolating the contribution of a feedback interaction through controlled comparison with an otherwise identical system in which that interaction has been removed. However, because CoRa evaluates adaptation solely through steady-state responses, it cannot distinguish controllers that ultimately recover to the same state but follow markedly different transient trajectories, despite the potentially profound physiological consequences of those dynamics. Here, we introduce CoRaDyn, a framework for evaluating adaptation as a dynamic process rather than solely as a steady-state outcome. Building on the comparative strategy introduced in CoRa, CoRaDyn quantifies the cumulative effect of feedback throughout the post-perturbation response, generating a time-dependent characterization of feedback performance. This approach reveals how the contribution of feedback depends not only on system parameters but also on the time horizon over which adaptation is evaluated, allowing distinct physiological objectives -- such as rapid recovery or the generation of transient pulses -- to be systematically compared. We demonstrate CoRaDyn using a gene regulatory circuit implementing proportional-integral-derivative (PID) control. Whereas CoRa predicts identical perfect adaptation for all controllers containing integral feedback, CoRaDyn discriminates their transient performance, quantifies the dynamic contributions of proportional and derivative control, and reveals trade-offs that are invisible to steady-state analyses. By extending feedback evaluation from endpoints to the full adaptation process, CoRaDyn broadens the scope of biological questions that can be addressed using the CoRa framework and provides a general approach for comparing feedback architectures when transient dynamics are central to biological function.

Breast cancer organotropism is driven by interactions between tumor cells and organ-specific microenvironments that support metastatic growth. To better understand the metabolic basis of organ-specific metastasis, we integrated single-cell transcriptomics with constraint-based systems biology to generate context-specific metabolic models of breast cancer metastasis to the liver, bone, and brain. Our analysis identified both common and organ-specific metabolic changes, suggesting that metastatic cells share a core metabolic program while also adapting to the metabolic environment of each target organ. Primary tumors with metastatic potential showed early alterations in nucleotide metabolism, transport reactions, and energy-related pathways, indicating metabolic changes before metastatic spread. Metabolic transformation analysis identified key metabolic regulators involved in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, redox balance, and metabolite transport. Integration with CRISPR gene essentiality data further highlighted metabolically important genes as potential therapeutic targets. In addition, analysis of organ-specific secreted metabolites revealed distinct metabolic signatures associated with metastatic colonization of the liver, bone, and brain. Overall, our single-cell-informed metabolic modeling approach shows that breast cancer organotropism is associated with both shared and organ-specific metabolic adaptations. The study provides a framework for identifying potential metabolic vulnerabilities that could be targeted to treat metastatic breast cancer.

Cells respond to their environment through protein networks often dysregulated in cancer, making dynamical modelling crucial. Limitations in experimental data and computational resources motivate coarse-graining methods to build low-dimensional descriptions. Yet classical approaches to coarse-grained modelling rely on strong assumptions, leaving it unclear when partial experimental observations support reduced descriptions of system dynamics. Here we show that symbolic regression (SR) provides a data-driven way to test whether, and how compactly, the dynamics of a signalling system coarse-grain over the measured variables, and, when they do, infers mechanistically interpretable models. In synthetic enzyme systems, SR recovers Michaelis-Menten kinetics for the two-step mechanism and under three-step extensions. As data quality is degraded, SR simplifies toward effective kinetic laws while preserving correct theoretical limits. Applied to published time-resolved ERK phosphorylation data, SR identifies compact phospho-ERK rate laws in selected cancer-relevant gene overexpression contexts, yielding interpretable kinetic effects. A sparse neural ODE baseline requires few inputs where SR succeeds, but on average more where it fails, indicating that, where a reduced model is learnable at all, SR failure is associated with more complex dynamics that a simple mathematical model cannot describe. Together, these findings establish symbolic regression as a way to test when a compact coarse-grained description is warranted, generating hypotheses where one holds and motivating potential new measurements where it does not.

Methylglyoxal (MGO) is a metabolic byproduct of sugar metabolism involved in the formation of advanced glycation end products (AGEs) and its accumulation disrupts protein function, redox balance and cellular viability. Yet the metabolic rewiring required to divert MGO overflow and prevent these cytotoxic effects remains largely unknown. To address these gaps, we used flux sampling and flux balance analysis in genome scale metabolic model iJO1366 of E. coli. High glucose increased MGO flux from 0.16 mmol/gDCW/h to 1.87 mmol/gDCW/h in simulation. Large scale in silico screening of metabolic reactions identified that increasing threonine uptake, thereby increasing ethanol flux, reduced glucose induced increase in MGO flux from 1.87 mmol/gDCW/h to 0.405 mmol/gDCW/h. In silico inhibition of ethanol production (NAD+ regeneration) inhibited the MGO lowering potential of threonine. Threonine supplementation actively drives the acetaldehyde to ethanol flux to provide localized NAD+ relief, which subsequently enhances the flux of payoff phase in glycolysis (glyceraldehyde 3-phosphate), thereby efficiently draining the stagnated DHAP pool and shutting down the overflow towards MGO production. Unexpectedly, under reduced oxygen conditions, high glucose caused only a minimal increase in MGO flux, which was not reduced by threonine supplementation. Our study also decoded the mechanistic details behind this paradox. Though this model driven hypothesis requires further validation in vivo, these stoichiometric predictions have applications in metabolic engineering, for commercial MGO production. Furthermore, in vivo studies focused on bacterial stress, gut microbiome imbalances and AGE related diseases might benefit from these simulations

Cancer cells rapidly rewire their metabolism, from efficient energy production toward anabolic processes, to sustain uncontrolled growth. Decoding such metabolic shifts is essential for uncovering novel therapeutic targets. To map systems-level metabolic changes across cancer types, we built context-specific genome-scale metabolic models for eight tissues (lung, thyroid, stomach, prostate, liver, kidney, colon, and breast) using gene expression data from The Cancer Genome Atlas (TCGA). Applying constraint-based modelling, we then identified differentially regulated pathways through flux enrichment analysis, revealing tissue-specific rewiring: branched chain amino acid metabolism was suppressed in breast cancer; sphingolipid metabolism was downregulated in colon, kidney, and thyroid but upregulated in breast. We further propose a model-driven pipeline to identify and characterise metabolic vulnerabilities. We first identify synthetic lethal reactions in normal tissues and their corresponding single lethal counterparts in cancers, thereby enabling the identification of metabolic "collateral lethal" reaction pairs for each cancer. Model-predicted collateral lethal gene pairs, including CMPK1-AK in colon, ALDOA-PGD in prostate, and SLC25A26-UQCRB in liver models, were supported through computational validation using DepMap data on gene essentiality. Subsequently, we show how to interpret metabolic rewiring in cancer tissues while accounting for any collateral lethal pairs. In summary, our results establish a systemic framework for decoding metabolic rewiring and synthetic lethal vulnerabilities in cancer.

Abstract Background: Immune-mediated skin diseases frequently co-occur, but the molecular architecture connecting clinically distinct disorders, and the extent to which molecular proximity predicts shared therapeutic opportunity, remain incompletely defined. We constructed a multilayer skin diseasome integrating transcriptomic overlap, pathway enrichment, protein-interactome topology, curated disease associations, real-world clinical co-occurrence, and drug-perturbation signatures across psoriasis (PS), atopic dermatitis (AD), hidradenitis suppurativa (HS), vitiligo (VL), and chronic spontaneous urticaria (CSU), using sodium lauryl sulfate-induced irritant contact dermatitis (SLS-ICD) as a mechanistically distinct barrier-injury comparator. Methods: Differentially expressed genes (DEGs) were derived from publicly available NCBI Gene Expression Omnibus datasets and compared using exclusive pairwise and higher-order intersections. Gene Ontology biological-process enrichment characterized shared and condition-specific programs. Interactome-based network separation (S_AB) quantified the relative topological proximity of disease modules. DisGeNET enrichment generated candidate disease +associations, a selected subset of which was examined descriptively for patient co-occurrence in TriNetX. Candidate compounds were ranked by reversal of disease-associated expression signatures against LINCS L1000 perturbation profiles. Results: The largest exclusive DEG intersection occurred between PS and HS (2,060 genes), followed by AD-PS-HS (490), VL-PS (464), AD-PS (402), and VL-PS-HS (379). Fourteen genes were shared across PS, AD, HS, VL, and CSU but not SLS-ICD. Neutrophil-associated processes recurred across the immune-mediated conditions, whereas SLS-ICD was dominated by keratinization and epidermal-differentiation programs. PS and HS had the lowest observed network-separation value (S_AB = -0.594); SLS-ICD had positive S_AB values in all five comparisons, although this separation did not persist once module sizes were harmonized. DisGeNET linked the index conditions to diverse immune, cardiovascular, neurologic, and neoplastic categories; 42 of these candidate diseases were observed to co-occur with an index condition in the queried TriNetX cohorts. Drug-signature overlap was greatest between PS and CSU, despite their marginal interactome separation, indicating that interactome proximity and predicted pharmacologic reversal capture different dimensions of disease relatedness. mTOR/PI3K-pathway inhibitors were prominent among the psoriasis reversal candidates, while methotrexate and mycophenolate mofetil emerged from both the CSU signature and the curated gene set associated with HIV infection. Conclusion: Multilayer integration identified a connected molecular core among five immune-mediated skin diseases and a distinct SLS-ICD injury module. The discordance between interactome proximity and drug-signature overlap suggests that no single layer adequately defines disease relatedness. These findings provide a hypothesis-generating framework for prioritizing disease links and repurposing candidates, but controlled epidemiologic analyses and experimental validation are required before clinical or therapeutic inference.

Obesity is a major health concern worldwide and is caused by an excessive fat accumulation in the body due to adipogenesis. Recent studies suggest that excessive stress or disruption of circadian rhythms can enhance adipogenic commitment and lead to obesity. Moreover, this adipogenic commitment occurs at a specific time of day, highlighting a correlation with the circadian rhythm. However, the dynamical nature of this kind of stress-induced adipogenic differentiation remains an unresolved puzzle. Herein, we proposed a mathematical model centred on the key transcription factor PPAR{gamma}, associated with adipogenic differentiation, and demonstrated that adipogenic commitment is a bistable, irreversible differentiation process in which slow positive feedback regulation of PPAR{gamma} primarily controls the differentiation commitment threshold. The model reproduced the experimental observations regarding the extent of adipogenesis induced by constant and pulsatile stress input signals and explained the correlation between adipogenic differentiation time and circadian phase. It further predicts how to control the adipogenic commitment even under stressful conditions. Overall, our study elucidates how stress signalling dynamically governs adipogenesis.

Metabolic profiles are used to quantify disturbance, classify disease and infer biochemical function, yet these endpoints retain different information. We combine secondary analysis of 386 metabolites in six Arabidopsis thaliana mutation accumulation lines, known-disease classification in 129 public human metabolic-disease profiles, and an analytically solvable kinetic model. A conditional occupancy model separates annotation-bin breadth from hit count and measured bin sizes: the plant low-versus-high fitness comparison changes from a two-sided exact permutation p = 0.10 to p = 0.30 after calibration. Unsigned metabolite profiles achieve mean macro recall of 0.911 and 0.895 in separate EDTA and heparin subsets, compared with 0.624 and 0.382 for a burden baseline. However, controlled loss of 50% of held-out measurements reverses representation ranking in heparin, giving signed profiles a recall advantage of 0.108 with a conditional bootstrap interval of 0.063-0.155. A spectral decomposition identifies the within-pathway contrasts attenuated by smoothing. The kinetic model then shows that lower concentration and faster relaxation can coexist with reduced desired product flux, while a null-space criterion states when observations identify a metabolic task. The results connect assay coverage, diagnostic information and functional identifiability. Human performance is internally validated, and the kinetic predictions remain uncalibrated to patients.

Single-cell genome-scale metabolic models (scGEMs) enable characterization of metabolic heterogeneity underlying cellular states and phenotypes. However, methodological choices during scGEM construction can substantially alter model structure and predictions, challenging the reliability and comparability of resulting analyses. Here, we established a systematic benchmark to assess three key construction factors: data preprocessing method, model extraction method (MEM) and gene expression threshold. We evaluated 26 strategies representing different combinations of these factors across nine scRNA-seq datasets in three dimensions: accuracy, sensitivity to expression perturbation and computational feasibility. We found that MEM had the greatest influence on most accuracy metrics, data preprocessing strongly influenced the discrimination of cellular identities, and expression threshold balanced model completeness and cellular specificity. These findings indicate that strategy performance varies across evaluation criteria. Our benchmark provides practical guidance for strategy selection and an empirical basis for standardized evaluation and future scGEM method development.