NPJ Systems Biology and Applications最新文献

Circadian clocks orchestrate behavior, physiology, and metabolism in harmony with the Earth's 24-h cycle. Low temperatures are known to disrupt circadian clocks in plants and poikilotherms; however, their effects on human circadian rhythms remain poorly understood. Here, we demonstrate that cold exposure abolishes the circadian rhythm in cultured human cells through diminishing the oscillation amplitude, which was restored upon rewarming. In addition, the oscillation amplitude of the 24-h temperature cycles was enhanced through resonance, reflecting the intrinsic frequency of the circadian clock. From a theoretical perspective, these dynamics correspond to Hopf bifurcation, which is confirmed by a mathematical model for the mammalian circadian clock. In contrast, the circadian amplitude of human hair follicle cells was not significantly sensitive to temperature changes. These observations suggest a potential evolutionary advantage of maintaining Hopf bifurcation despite robust homeostasis.

Scientific literature is being published at an exponential rate, including in the field of mammalian cell bioprocessing. At the same time, the research landscape is becoming more diverse, with the emergence of multiple specialised subfields. This rise in information availability as well as broadening of research fields has a direct impact on ease of information retrieval. While this growth offers valuable insights, it also makes information retrieval more complex. Developing effective literature search queries has become increasingly challenging. This work discusses the process of literature query search refinement and the nuances of maintaining search sensitivity and specificity in the context of multi-omics research for next-generation mammalian cell bioprocessing.

Ataxia-Telangiectasia (A-T) is a rare genetic disorder caused by ATM mutations, leading to impaired DNA repair, oxidative stress, and neurodegeneration. We developed a computational model of ATM-mediated signaling using ordinary differential equations in COPASI, capturing key processes including DNA damage sensing, cell cycle regulation, autophagy, and oxidative stress response. The model simulates physiological, ATM-deficient, and drug-treated conditions to explore repurposing strategies. We evaluated the effects of spermidine, omaveloxolone, and HDAC4 inhibition, revealing mechanisms by which these compounds modulate dysfunctional signaling. Sensitivity and stability analyses confirmed the model's robustness, while enrichment analysis validated involvement of key pathways. Our results highlight the synergistic potential of combining autophagy activation and epigenetic modulation to partially restore homeostasis in ATM-deficient cells. This work introduces a generalizable modeling framework for simulating disease-specific signaling dysfunction and identifying therapeutic interventions, illustrating the value of computational systems biology in rare disease drug repurposing.

The adaptive response of cancer cells to hypoxia, a key microenvironmental factor in solid tumors, is orchestrated by Hypoxia-inducible factor 1 (HIF-1). Recent evidence indicate that oxygen tension in tumor is dynamic, with hypoxia being frequently unstable, or cycling. Cycling hypoxia is associated with specific phenotypic outcomes for the cancers. Transcriptomic analysis shows that for most genes, expression changes in cycling hypoxia lie expectedly in between the change caused by stable hypoxia, suggesting multi-cycle averaging of dosage in the oxygen tension, and likely HIF-1 induced transcription. However, a small subset of genes show an oscillation/cycling hypoxia specific response, suggesting that the transcriptional machinery of these genes may interpret cycling HIF-1 activity differently from stably high HIF-1 activity. Here, we model a gene regulatory circuit, the incoherent feed-forward loops (IFFLs) to illustrate that there are parameter regimes in such genetic circuits where oscillatory specific transcription is plausible. In these IFFL models, HIF-1 regulates gene transcription of a target gene directly, as well indirectly via another transcription factor with an opposite effect on gene transcription. This IFFL circuit is able to generate gene expression of certain target genes that is more extreme than either normoxia or stable hypoxia, and this nonlinear IFFL behavior can result from either the dynamic nature or even the intermediate, time averaged hypoxic signal Supplementary Information 1 (Steady state analysis of IFFL circuits). This gene circuit also allows us to search for plausible signaling intermediaries involved in the IFFL mediated cycling hypoxic response. Finally, we present experimental evidence suggesting that HIF-1 can form IFFLs with two key transcription factors p53, and Notch1, resulting in cycling hypoxia-specific gene expression linked to breast cancer progression and poor prognosis. Our work aims to draw attention to genetic circuits as plausible mechanisms where temporal fluctuations in the tumor microenvironment may directly inform downstream transcription. These ideas could identify hitherto unknown HIF-1 driven mechanism of cancer progression contributing to emergent tumor heterogeneity.

Heterogeneity in breast cancer (BC) subtypes within a tumor contributes to therapy resistance and cancer recurrence. Subtype heterogeneity in tumors arises through a combination of stochastic genetic/epigenetic changes, phenotypic plasticity, and microenvironment-driven selection as the tumor evolves over time. Here, we sought to characterize how NF-κB epigenetic variability contributes to the progression of the HER2⁺ BC subtype. Initially, we used RNA to determine the expression levels of NF-κB, TWIST1, SIP1, and SLUG in two breast cancer (BC) cell lines, HCC-1954 and MDA-MB-231, classified as HER2⁺ and triple-negative breast cancer (TNBC) subtypes, respectively. Then, we built and calibrated a gene regulatory network (GRN) model that reproduces the transcriptional interactions between these genes. The model epigenetic landscape exhibits two attractor basins that reproduces the observed expression profiles of both HER2⁺ and TNBC subtypes, separated by an unstable stationary state. For validation, we used DHMEQ-treated cells, along with published patient data and in vitro results. Stochastic fluctuations in the NF-κB levels induce spontaneous irreversible transition from HER2⁺ to TNBC attractor basins at different times, contributing to subtype heterogeneity. The unstable state mediates this transition by providing a slow route between subtypes in the phase space that is susceptible to dynamic fluctuations. Mutations or drugs that change the availability of NF-κB alters the size of the subtype basins, changing the transition probabilities. Together, our findings enhance the established attractor landscape formulation and deepen understanding of BC heterogeneity, leading to more precise classification, prognosis, and targeted strategies for BC progression.

Glucagon-like peptide-1 (GLP-1) is a hormone known for its critical functions in managing blood sugar and offering cardiovascular benefits. Our study focuses on Glucagon Like Peptide 1 Receptor (GLP1R) agonists that act beyond glycemic control in cardiovascular and metabolic health. A comprehensive bioinformatic analysis was conducted, incorporating GLP1R, Gastric Inhibitory Polypeptide Receptor (GIPR), Gastric Inhibitory Polypeptide (GIP) and glucagon receptor (GCGR) to assess the effects of GLP1R agonists on gene and metabolite interactions. Interaction network analysis revealed 130 common genes among GLP1R, GLP1R/GIPR, GLP1R/GIP, and GLP1R/GIPR/GCGR associated with diabetes-related processes, including obesity and hyperglycemia. Enriched terms related to cardiovascular diseases, such as hypertension, calcium regulation in cardiac cells, and amino acid accumulation-induced mTOR activation. We also observed enrichment in gene sets linked to longevity and less recognized terms like fatty liver disease. In GLP1R/GIP, behavior-related terms and gastric acid secretion were identified; GLP1R/GIPR/GCGR linked to fibrosarcoma, thought/speech disturbances, and adipogenesis. The metabolite-gene layer revealed enrichment in galactose metabolism, platelet homeostasis, and nitric-oxide pathways. We found that GLP1R agonists network-level associations are stronger with heart diseases than sodium-glucose co-transporter 2 inhibitors, suggesting greater therapeutic benefits. Integrating networks with metabolites highlighted key interactors and clarified GLP1R agonists' mechanisms and therapeutic potential.

Complex biological systems often undergo a pre-transition phase prior to the onset of catastrophic event, during which a sharp and essential shift occurs. There is a pressing need to develop a swift and effective method for identifying such pre-transition phase or critical state, facilitating the timely implementation of tailored interventions to prevent irreversible and catastrophic transitions. Nonetheless, the identification of the pre-transition phase at the single-sample or single-cell level remains an exceedingly daunting task in modern clinical medicine, as reliance on small sample sizes often undermines the efficacy of traditional statistical methodologies. In this study, we propose a novel critical state algorithm based on individual sample data, termed single-sample network entropy (SNE), which effectively quantifies the disturbance caused by an individual sample relative to a set of reference samples, thereby revealing the pre-transition phases during biological development at the specific sample level. Our proposed method successfully identified pre-transition phases in both numerical simulations and eight real-world datasets, including an influenza infection dataset, three single-cell data (one associated with epithelial-mesenchymal transition (EMT) and two related to embryo development), and four tumor datasets: esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), and uterine corpus endometrial carcinoma (UCEC). In contrast to the existing single-sample approaches, our SNE method demonstrates enhanced effectiveness in detecting potential pre-transition phase. Moreover, it identifies two novel prognostic indicators: optimistic SNE (O-SNE) and pessimistic SNE (P-SNE) biomarkers for subsequent practical applications. Additionally, the reliability of computational findings is further strengthened by the functional roles of signaling biomarkers. Therefore, we present a novel computational approach that uncovers the pre-transition phases and signaling biomarkers of complex biological processes at the single sample or single-cell level, offering new insights and applications for early personalized biological analysis, including disease diagnosis and prognosis evaluation.

The BCL-2 protein family governs critical cell-fate decisions between survival, senescence, and apoptosis, yet the dynamical principles underlying these choices remain poorly understood. Here, we integrate mathematical modeling, bifurcation analysis, and stochastic simulations to dissect how BCL-2 network architecture encodes multistability and fate plasticity. Our coarse-grained model reveals tristable regimes requiring cooperative BH3-only and anti-apoptotic BCL-2 interactions, with stochastic fluctuations driving heterogeneous fate commitments in genetically identical cells. Comparative analysis of mechanistic models demonstrates that while bistability emerges from canonical BCL-2 interactions, robust tristability requires additional regulatory constraint, explaining the metastability of senescence in stress responses. Hybrid models further show that BH3-only binding cooperativity enables multistability, but physiological senescence likely depends on additional control mechanisms. These results establish a unified framework linking molecular interactions to cell-fate dynamics, with implications for targeting apoptosis resistance in disease.

Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We therefore designed a hypothesis-generating framework that integrates network motifs and protein disorder to explore their contribution to predictive biomarker discovery. This encouraged us to develop MarkerPredict by using literature evidence-based positive and negative training sets of 880 target-interacting protein pairs total with Random Forest and XGBoost machine learning models on three signalling networks. MarkerPredict classified 3670 target-neighbour pairs with 32 different models achieving a 0.7-0.96 LOOCV accuracy. We defined a Biomarker Probability Score (BPS) as a normalised summative rank of the models. The scores identified 2084 potential predictive biomarkers to targeted cancer therapeutics, 426 was classified as a biomarker by all 4 calculations. We detailed the biomarker potential of LCK and ERK1. This study encourages further validation of the high-ranked predictive biomarkers. The development of the MarkerPredict tool (which is available on GitHub) for predictive biomarker identification may have a significant impact on clinical decision-making in oncology.

Diabetic retinopathy (DR), a complex condition driven by inflammation, oxidative stress, and metabolic imbalances, calls for innovative treatment strategies. Engineered probiotics delivering angiotensin-converting enzyme 2 (ACE2) offer a promising strategy by leveraging gut microbiome-retina association. Advances in synthetic biology and computational techniques enable personalized, data-driven therapies. This review discusses computational approaches at multiple scales and presents an integrated framework for promoting personalized, systems-level DR management.