Classical demand theory predicts that genes used often should tend toward positive control, whereas genes used rarely should tend toward negative control. We test a weaker version of this idea in Escherichia coli: effective demand may bias, but not determine, regulatory sign. Effective demand was estimated as the fraction of transcriptome conditions in which a regulatory iModulon was active, and signed regulation was summarized at module, regulator, and regulator–gene edge levels. The clearest association appeared at the regulator-weighted layer, especially for local regulons with at most 50 targets, whereas broader and edge-level summaries were positive but less precise. External refitting preserved the positive direction, while sign-shuffle null models did not clearly separate the observed slopes from random sign allocation. Taken together, the analyses are consistent with a modest residual demand-associated tendency that is filtered by regulatory architecture, physiology, and dependence among targets.
Nonalcoholic steatohepatitis (NASH) and primary biliary cholangitis (PBC) are chronic liver diseases characterized by immune dysregulation, but their shared mechanisms remain unclear. We integrated four GEO liver transcriptomic datasets to identify common differentially expressed genes, functional pathways, immune infiltration patterns, and candidate diagnostic genes. Differential expression, GO/KEGG and GSEA enrichment, protein-protein interaction network analysis, hub-gene screening, ROC analysis, bootstrap validation, and mouse-model experiments were performed. Twenty-five common differentially expressed genes were shared by NASH and PBC and were enriched in cytokine-mediated signaling, leukocyte chemotaxis, and inflammatory pathways. IL1RAP, IL1RN, CXCL2, CCL3, and CXCL9 showed favorable candidate diagnostic performance, with IL1RN displaying stable discrimination in both diseases. Immune infiltration analysis indicated abnormal M1 macrophage enrichment, and CXCL9 expression correlated positively with M1 macrophage infiltration. In NASH and PBC mouse models, histology and molecular assays supported increased expression of selected core genes. These findings suggest shared immune-related molecular features between NASH and PBC and provide candidate biomarkers for further clinical and mechanistic validation.
Multidrug resistance remains a major obstacle in cancer therapy and is driven by hyperactive efflux transporters and dysregulated signaling pathways. In this study, text mining and network analyses were performed to identify key genes associated with breast cancer drug resistance. Critical regulatory nodes were identified using the Boykov-Kolmogorov algorithm applied to a directed protein–protein interaction network. Molecular docking and molecular dynamics simulations were subsequently conducted to screen FDA-approved drugs for potential interactions with these targets. Cytotoxicity, migration, apoptosis, efflux activity, and relative gene expression assays were performed in drug-resistant and parental breast and gastric cancer cell lines to experimentally evaluate drug effects. ESR1, PPARD, and NFKB1 were identified as essential cut nodes sustaining MDR network connectivity. Drug repurposing analyses predicted celecoxib, desloratadine, and dutasteride as ligands targeting these proteins. Experimental validation demonstrated that the triple-drug combination significantly increased mitoxantrone sensitivity (P < 0.001) in multidrug-resistant breast cancer cells. This effect was accompanied by marked inhibition of drug efflux, including significant suppression of BCRP activity (P < 0.05) and up to a 20-fold reduction in BCRP gene expression (P < 0.001), while more limited effects were observed on MDR1 expression in gastric cells. Collectively, the combination treatment restored chemotherapy responsiveness, reduced cell migration, and promoted apoptosis. Overall, this study suggests that integrating network-based analysis with drug repurposing may provide a useful framework for identifying potential multidrug strategies against drug resistance. These findings offer a computational basis for further experimental validation and potential development of anti-resistance therapeutic approaches.
Turing’s reaction–diffusion framework provides a mathematical basis for diffusion-driven pattern formation in interacting systems. An early biological application of the Turing principles is the Gierer and Meinhardt (GM) model, which was developed to describe hydra head regeneration involving two species: an activator and inhibitor. However, biological networks are far beyond simplicity because they contain numerous interconnected components. In this work, we incorporate a coherent feed-forward loop (FFL) into the GM model by assuming that the activation of the inhibitor is mediated through this motif to better capture the complexity of biological systems. Our results indicate that the FFL reshapes the underlying Turing region in two distinct ways. This not only reduces the Turing instability region but also induces spatial patterns, even when the GM model is stable. This reveals that the network topology, rather than the parameters alone, can reshape the onset and structure of the diffusion-driven instability. Our findings suggest that considering the network topology is crucial for unraveling the behavior of real systems.
Generalised Lotka-Volterra (GLV) models provide an interpretable framework for inferring microbial interaction networks from time-series data. However, these models often exhibit blow-up-like (rapidly diverging) trajectories for certain parameter values, leading to numerical instability and rendering global optimisation methods ineffective, particularly when using wide parameter bounds. We present a robust strategy for parameter estimation that combines initially tight, biologically plausible bounds with an iterative re-optimisation procedure using moving bounds that gradually expand the search space. This approach avoids early exploration of divergent trajectories, enables global optimisers to initialise feasible populations, and incrementally guides the search toward parameter regions yielding good fits. While our method specifically targets the practical obstacle of blow-up-like trajectories, it also mitigates two other common pitfalls: convergence to local optima and overfitting. We validate the approach on case studies of increasing complexity. Finally, we demonstrate that experimental design critically affects practical identifiability and emphasise the often-overlooked distinction between achieving a good fit and recovering true parameter values, a caveat essential for ecological interpretation. This work contributes both a methodological advance and a conceptual clarification broadly applicable to parameter estimation in complex microbial ecology models.
Metabolic shifts are crucial for cellular proliferation, however, the roles of various metabolic pathways and their interconnections in NSCLC remain unclear. This lack of understanding in metabolic shifts precludes better cancer management as well as effective therapeutic interventions. To bridge this gap, using network biology and RNA expression data complemented with wet lab experiments, metabolism in NSCLC has been studied in detailed furnishing new insights and therapeutic avenues. This study has revealed that the Malate-Aspartate Shuttle (MAS) is the backbone of nitrogen metabolic circuit, i.e., pyrimidine and arginine metabolic pathways. Several genes from the MAS (GOT1, GOT2, MDH2) are differentially regulated and are the target of therapeutic interventions, further providing the relevance of this network. Furthermore, when GOT1 was downregulated, it affected the expression of several cell cycle related genes (CCNA2, CCNB1, CCND1) and VEGF in KRAS mutated cell lines suggesting potential synthetic lethality interaction. Therefore, these experiments provide concrete evidence that MAS components, particularly GOT1, is a potential therapeutic candidate in NSCLC that could further evaluated in broader experimental conditions.
Cancerous growth, along with other known biological and biochemical signals, also depends on the biomechanical pressure wielded by the adjacent extracellular matrix (ECM). The dynamic relationship between the growing tumor and the ECM influences tumor growth, aggressiveness, and dormancy. Additionally, with time, the stroma also becomes subject to tumor-mediated remodeling through deformation and deposition cycles. In recent years, many studies have identified the dialog between the ECM and the tumor as one of the major causes behind increased metastasis and aggression. This study is designed to assess the impact of ECM-mediated mechanical stress on multicellular spheroids by encapsulating them within a non-proteinous matrix. The non-degradable nature of the matrix is instrumental in highlighting the impact of mechanical pressure on spheroid behavior, without accounting for tumor-mediated interference. Encapsulated spheroids exhibit restrained proliferation and increased cell death with increased stress. Compressive stress also contributes to sub-cellular re-organization and chemokine secretion. Transcriptomic analysis of the entrapped spheroids displays altered metabolism and protein trafficking while rearranging cell-cell and cell-substratum adhesion. Encapsulated spheroids remain static until they recognize neighboring micro-tracks within the matrix. Trapped spheroids perform non-adhesion-mediated collective migration within the microtracks. This behavior highlights the plasticity of the multicellular spheroids at the cellular level to survive the unfavorable conditions. This study would help identify mechanical stress-mediated pathways and catalog targets for therapeutic intervention.
Type 1 diabetes (T1D) is an autoimmune disease in which the immune system attacks pancreatic beta cells, leading to high blood glucose levels and requiring lifelong insulin therapy. There is no cure, and individuals with T1D may face a reduced lifespan of up to 12 years. Defects in regulatory T cells (Tregs) are a key contributor to disease onset and are being explored as a therapeutic avenue. However, the effectiveness of Treg therapy remains uncertain. Research is further limited by the inability to directly observe pancreatic and lymph node activity during the long presymptomatic stage of T1D. In this study, we develop a mathematical model for beta and T cell dynamics. We find both Treg quality and quantity affect disease progression, and that antigen-presenting cell (APC) dynamics play a central role. Notably, Treg therapy combined with APC depletion improves outcomes, especially with strong peptide-induced APC activation.
During early embryonic development, the human neural tube is formed and patterned through spatial regionalization of cell identity, driven by gene regulatory responses to morphogen gradients. However, many of the underlying mechanisms remain unclear. Here, we integrate single-cell RNA sequencing data from in vitro emulation of neural tube patterning to develop computational models of rostral-caudal and dorsal-ventral patterning. By embedding these models in a 3D geometry, we reveal how transient morphogen signals induce irreversible patterns consistent with developmental biology and experimental data. Notably, our framework accurately captures the formation and maintenance of the isthmic organizer at the mid-hindbrain boundary, providing a realistic and mechanistic picture of neural tube patterning. This integrated approach bridges in vitro experimentation and computational modeling to uncover fundamental principles of neural development.
Colorectal cancer (CRC) is accompanied by complex metabolic alterations, but the diagnostic value and biological relevance of circulating metabolites require further clarification. In this study, untargeted serum metabolomics was performed in 90 individuals, including 45 patients with CRC and 45 healthy controls. Differential metabolites were screened and further prioritized using LASSO regression and random forest modelling, yielding an 11-metabolite panel. The random forest model showed AUCs of 0.989 in the training set and 0.906 in the testing set. To evaluate whether this panel provided information beyond common clinical variables, we compared a clinical-covariate model, the metabolite model, and a combined model in the testing set. The clinical-covariate model showed limited discrimination (AUC = 0.4089), whereas the metabolite and combined models showed higher AUCs of 0.9911 and 0.9956, respectively, with sensitivity of 1.0000 and specificity of 0.8000 at the training-set Youden cutoff. Pathway and cell-based analyses further linked phenylalanine and L-valine with mTOR-associated proliferative and migratory phenotypes, and L-carnitine with fatty-acid-oxidation-related metabolic activity in CRC cells. These findings suggest that serum metabolic profiling may provide candidate markers for CRC classification and biological stratification, although larger prospective and externally validated studies are needed before clinical translation.
Primary biliary cholangitis is an autoimmune cholestatic liver disease, but upstream immune programs linking inherited risk to hepatic remodeling and therapeutic targets remain incompletely defined. We developed a cross-tissue causal multi-omics framework integrating PBC GWAS, 731 GWAS-derived immune traits, peripheral blood transcriptomes, and liver bulk and single-cell RNA-seq. Two-sample Mendelian randomization with reverse-causation testing and independent replication identified genetically predicted CD27⁺ unswitched memory B cells as a risk-increasing immune trait for PBC. Liver transcriptomic analyses showed immune and stromal remodeling, while functional mapping prioritized 18 immune-inflammatory genes and supported an 11-gene LASSO blood signature that distinguished PBC from controls. Single-cell analyses localized the causal signal to an expanded hepatic CD27⁺ IgM memory-like B-cell state characterized by terminal, non-proliferative features, adaptive immune activation, T-cell activation programs, and chemokine-centered intercellular communication. Transcription factor activity inference highlighted a CREB1/AP-1-centered regulatory module and nominated pathway inhibitors as hypothesis-generating candidates. Together, these findings provide convergent, but inferential, evidence linking CD27-related genetic susceptibility, a PBC-associated blood transcriptional signature.
The development of multicellular organisms relies on a symphony of spatiotemporally coordinated signals that selectively regulate gene expression. In particular, G protein-coupled receptors (GPCRs), the largest superfamily of transmembrane receptors, play a pivotal role in transducing extracellular signals into physiological outcomes. Notably, neurotransmitter GPCRs, classically associated with neuronal tissue communication, are increasingly emerging as regulators of pattern formation and morphogenesis. However, how these receptors coordinate such morphogenetic processes remains poorly understood. To address this gap, we developed and employed a coupled, machine-learning-based analytical pipeline, MAPPER 2.0, that fuses quantitative and qualitative analyses of Drosophila melanogaster wing phenotypes to robustly identify both severe and more subtle phenotypes generated by RNAi expression. We phenotypically characterized the impact of RNAi-based inhibition of the 111 GPCRs and the G-protein subunits in Drosophila, a genetic model system for investigating conserved protein and gene regulatory pathways. Severe morphological phenotypes resulted from RNAi-mediated knockdown targeting several G-proteins and neuropeptide and neurotransmitter GPCRs, with seven knockdowns exhibiting greater than 80% penetrance. Beyond these strong qualitative hits, MAPPER 2.0 revealed a broader class of more subtle phenotypes, including quantitative differences in wing size, compartmental organization, and vein patterning. Quantitative reverse transcription polymerase chain reaction and meta-analysis of RNA expression data validated that positive hits are expressed in the wing disc. Overall, MAPPER 2.0 provides a phenotypic platform for drug testing and mechanism discovery in GPCR-implicated human diseases, ranging from cancers to neurological conditions.
Children are disproportionately at a greater risk of developing severe dengue disease, yet the molecular mechanisms driving the heightened susceptibility remains poorly understood. To elucidate the immune determinants of pediatric dengue pathogenesis, we profiled the transcriptomic landscape by performing RNA sequencing (RNA-Seq) on peripheral blood mononuclear cells (PBMCs) from laboratory-confirmed cases of primary and secondary pediatric dengue, as well as pediatric healthy controls. Differential gene expression (DEG) and pathway analyses were performed to delineate the transcriptional alterations. Pediatric dengue was marked by extensive transcriptional changes with altered expression of genes associated with the complement cascade macrophage-associated genes as well as immune checkpoint molecules. Notably, secondary infection had significant alterations in immune checkpoint molecules and genes associated with macrophage activation, suggesting the onset of immune exhaustion during reinfection. Differences between the primary and secondary dengue cohorts were modest relative to the transcriptional differences observed between either of the groups and healthy controls, with immune checkpoint genes showing the most consistent divergence between the two dengue cohorts. While our initial findings provide early insights into the transcriptional patterns potentially associated with disease outcomes, the dysregulated transcriptomic signatures reported here are preliminary requiring systemic functional validation to determine their biologic role in the immunopathogenesis of pediatric dengue infection.
Treatment refractoriness in chronic rheumatic disease — formalised for rheumatoid arthritis (RA) by the EULAR 2021 difficult-to-treat (D2T) criteria and affecting ~12% of patients — is not addressable by any single-pathway model. We ask whether a multi-axis dynamical-systems framework can generate, as a testable hypothesis, the attractor-like persistence of refractory disease. We present the 3-Axis Integrative Framework (3-AIF), a six-variable ordinary differential equation (ODE) system coupling mucosal tolerance (Axis 1), the energy-gated Nerve–Adipose–Immune (NAM) danger-sensing unit (Axis 2), and the integrated stress response (Axis 3) through Hill-function cross-talk and an mTORC1–AMPK metabolic gate. Under a biologically motivated parameter set, the system is multistable, with three co-existing stable attractors—healthy, refractory-disease, and severe-collapse—separated by two saddle points, and shows fold-catastrophe hysteresis, mirroring the clinical spectrum from partial response to complete refractoriness. Local sensitivity analysis ranks three Axis-2 parameters (parasympathetic resolution, danger auto-amplification, and environmental forcing) as dominant, consistent with emerging vagus nerve-targeted neuroimmune modulation in RA. Single-axis ablation confirms that cross-axis coupling is required for the fold transitions. A cross-disease transcriptomic consistency analysis across six public GEO datasets, using an expression- and variance-matched permutation null, finds the 3-AIF axis genes significantly more dysregulated than matched background genes in the two RA cohorts (p = 0.0016 and p < 0.0001) but not in the smaller non-RA cohorts. Because these datasets lack documented D2T patients, we frame the results as molecular plausibility rather than validation, and note the need for longitudinal multi-omics data in formally classified D2T cohorts.
Cardiac complications are common and clinically significant in COVID-19, yet their underlying molecular drivers remain poorly understood. Here, we integrate post-mortem histopathology with transcriptomic and microRNA (miRNA) analyses to delineate the regulatory architecture of SARS-CoV-2-induced myocardial injury. Histological examination of cardiac tissue from 29 deceased COVID-19 patients revealed pronounced immune infiltration, cardiomyocyte necrosis, and fibrotic remodeling. To gain new insights into these pathological processes, we first analyzed 42 transcriptomes from SARS-CoV-2-infected cardiomyocytes, including primary human, iPSC, and hESC-derived cells. We identified 871 differentially expressed genes (DEGs) in infected cardiomyocytes associated with immune activation, extracellular matrix (ECM) remodeling, and impaired contractile function. Based on these dysregulated genes, we then inferred miRNA–mRNA regulatory networks and, through miRTarBase analysis, we uncovered 331 miRNAs as putative regulators of these DEGs, including miR-29a, miR-145, and miR-199a, known to modulate fibrosis, ECM composition, and cardiomyocyte survival. Finally, selected candidates were evaluated in blood samples from COVID-19 patients. We profiled circulating miRNAs in plasma from COVID-19 patients and detected 32 dysregulated miRNAs, 11 of which overlapped with the predicted set. Notably, downregulation of miR-29a correlated with profibrotic signatures, while immune-regulatory miR-21 was upregulated in mild disease. Together, our multi-modal analysis reveals a miRNA-mRNA regulatory program orchestrating inflammation, fibrosis, and contractile dysfunction in the SARS-CoV-2-infected heart. These findings highlight molecular candidates for risk stratification and therapeutic targeting in COVID-19-associated cardiomyopathy.
This review explores Boolean and logical modeling as vital systems biology tools for analyzing gene regulatory networks. It traces foundational theories—from Glass and Kauffman to René Thomas—showing how discrete Boolean dynamics approximate continuous biochemical processes, and how network topology dictates dynamics like multistationarity or oscillation. Consequently, Boolean models successfully capture essential qualitative biological behavior without needing precise kinetic parameters. It also offers practical network construction guidelines.
The present study investigated the bacterial and fungal communities and cross-domain network interactions in patients with chronic Otitis media in response to the natural monoterpene medicinal 1,8-Cineol as a promising treatment option. While the bacterial community showed modest changes with preserved core genera, the fungal community underwent dramatic diversification and network expansion. This suggests that treatment may have disrupted competitive exclusion mechanisms in the latter, allowing for increased diversity and interactions, despite no significant changes in traditional diversity metrics. Investigations on the microbial community compositions in Otitis media patients upon 1,8-Cineol treatment revealed significantly different initial microbial signatures in responders and non-responders. Notably, therapy-responding COM patients demonstrated a baseline situation with significantly higher abundances of different gram-negative Pseudomonas species. In contrast, significantly elevated levels of various gram-positive Corynebacterium species were observed in the non-responder group compared to the 1,8-Cineol responding patients. These keystone taxa may represent new targets for therapeutic intervention or biomarkers for treatment response.
Spontaneous biological oscillations are typically attributed to specific architectures within metabolic or gene regulatory networks. Here, we uncover a more general mechanism arising from the interplay between cellular growth, burdensome gene expression, and nutrient availability, which can generate oscillations in both growth and gene expression. Focusing on sporulation dynamics in Bacillus subtilis, we developed a minimal model that captures these coupled processes and analytically identified the range of continuous culture conditions that give rise to oscillatory behavior. These predictions were experimentally validated in chemostat cultures. Our results demonstrate that oscillations can emerge independently of specific genetic circuit architectures, and without external forcing. More broadly, they reveal that feedback between environmental conditions and cellular states is sufficient to drive oscillatory dynamics, suggesting that such behavior may be widespread in long-term cultivation systems where gene expression, growth, and resource availability are tightly coupled.
Viruses can re-structure the architecture of host cells genome and also alter their epigenomic landscape. In this study, we investigate with polymer physics modelling virus induced genome re-organization by using available datasets from different viral infections causing: COVID-19 syndrome (SARS-CoV-2 virus), human common cold (HCoV-OC43 virus) and avian influenza (IAV-H5N1 virus). By combining recent experimental Hi-C data from virally infected human cells with in-silico polymer models, we find that each pathogen induces peculiar re-structuring at the level of A/B compartments, which can be encoded by pathogen specific re-modulations of protein-chromatin binding affinities. Specifically, more pronounced effects are observed in SARS-CoV-2 and IAV-H5N1 infected cells, where a generally enhanced A/B mixing is observed, whose symmetry degree depends on the infection. On the other hand, HCoV-OC43 infection exhibits comparatively milder effects. Overall, our results suggest that viruses associated with more severe disease phenotypes could induce deeper, large-scale chromatin architectural changes.
Nitrogen is essential for life, and microorganisms prefer ammonium as a nitrogen source. Due to the low affinity of glutamine synthetase (GS) for ammonium, E. coli must maintain high intracellular ammonium (NH4+) concentrations to sustain its rapid growth. Under ammonium limitation, E. coli imports ammonium through the transporter AmtB, but the mechanism by which membrane potential drives ammonium accumulation is unresolved. We compare six kinetic models of E. coli ammonium transport and assimilation against published experimental data. Three models assume that membrane potential affects AmtB–NH4+ binding (electro-binding). Three others assume that it drives the conformational flip of the transporter (electro-flipping). Computer experimentation reveals that the electro-binding models are 28-fold more plausible than the electro-flipping models and suggests that the membrane potential affects AmtB–NH4+ binding from the cytoplasmic side. Integrating these kinetic and thermodynamic features with existing structural information suggests a new spatiotemporal mechanism for coupling ammonia and proton flows in AmtB. Simulations further show that coordinated regulation of GS and AmtB minimizes futile cycling while maintaining rapid growth, even as transport-related Gibbs energy dissipation becomes substantial under ammonium limitation. These findings provide new insights into the energetic trade-offs underlying bacterial ammonium acquisition.
