NPJ Systems Biology and Applications最新文献

Although endocrine therapies and Cdk4/6 inhibitors have improved outcomes for patients with estrogen receptor positive (ER+ ) breast cancer, continuous application of these drugs often results in resistance. Upregulation of G1 and S phase kinase activities during therapy can allow cancer cells to bypass drug induced cell cycle arrest. We investigated whether inhibiting WEE1, a key G2 checkpoint regulator also involved in G1/S transition, could delay the development of resistance. We treated ER+ MCF7 breast cancer cells with palbociclib alternating with a combination of fulvestrant and WEE1 inhibitor AZD1775 for 12 months. We found that the alternating treatment delayed the development of drug resistance to palbociclib and fulvestrant compared to monotherapies. We developed a mathematical model that can simulate cell proliferation under monotherapy and alternating drug treatments. Finally, we showed that the mathematical model can be used to minimize the number of fulvestrant plus AZD1775 treatment periods while maintaining its efficacy.

Single-cell resolution time-lapse microscopy of bacterial populations is a powerful tool for assessing cellular behavior and interaction dynamics. Realizing its full potential requires accurate image analysis: segmentation of individual cell objects, tracking of persistent cells from frame to frame, and connecting of mother cells to daughters during division events. Tracking is particularly challenging in densely packed populations or when cells move significantly; Leading software often struggles. To address this, we present TrackRefiner, a tool for refinement of bacillus cell tracking data. This package was specifically designed to refine the tracking outputs of CellProfiler. Benchmarks involve non-motile, rod-shaped bacteria; extension to motile species or other morphologies remains to be demonstrated. For timelapses with frequent imaging, TrackRefiner achieved-with one exception-over 98% detection accuracy and corrected 57-100% of tracking errors. TrackRefiner is published on PyPI and Anaconda . Source code, user manuals, and the benchmark dataset are available on Github and OSF .

The Epithelial-mesenchymal transition (EMT) is a cellular state transition fundamental to development, wound healing, and cancer metastasis. The gene regulatory mechanisms underlying EMT have been extensively documented, revealing gene regulatory networks (GRNs) involving groups of mutually inhibiting transcription factors and microRNAs. Despite significant progress from both experimental and computational approaches, the details of how the EMT GRN initiates EMT in response to various external inputs is still not well understood. Here, we apply both Boolean and ordinary differential equation (ODE)-based methods to simulate a well-studied 26-node, 100-edge EMT GRN, examining its response to a wide range of single- and double-node perturbations. We evaluate the characteristics of effective EMT-inducing signals, particularly examining the amplifying role of transcriptional noise in determining the likelihood and mean transit time of an EMT. Together, these models establish a complementary framework for understanding and predicting drivers of EMT in the context of a GRN. We anticipate that this framework for a systematic study of in-silico GRN perturbations will be useful in developing increasingly accurate dynamical GRN models for various biological processes.

The development of effective interventions for neurodegenerative disorders, such as posterior cortical atrophy (a visual Alzheimer's variant), remains to be a significant clinical challenge. We introduce a computational framework using convolutional neural networks (CNNs) as in silico models to simulate visual system degeneration and evaluate intervention strategies. By modeling controlled synaptic decay and comparing three distinct retraining approaches, random data (control), accuracy-based, and entropy-based, we assess impacts on classification performance and neural representation geometry. Our results demonstrate that accuracy-based retraining outperformed other strategies, maintaining model performance and preserving optimal manifold geometry during intermediate degeneration stages. This computational analysis supports prioritizing accuracy-targeted interventions for cognitive compensation. Our framework enables rapid evaluation of intervention efficacy while elucidating computational principles underlying neurodegeneration and recovery. This approach offers a platform for refining strategies to slow visual-cognitive decline in neurodegenerative diseases, bridging mechanistic insights with clinical translation.

We seek to establish a parsimonious mathematical framework for understanding the interaction and dynamics of the response of pancreatic cancer to the NGC triple chemotherapy regimen (mNab-paclitaxel, gemcitabine, and cisplatin), stromal-targeting drugs (calcipotriol and losartan), and an immune checkpoint inhibitor (anti-PD-L1). We developed a set of ordinary differential equations describing changes in tumor size under the influence of cocktails of treatments. Parameter estimation relies on three tumor volume measurements obtained over a 14-day period in a genetically engineered pancreatic cancer model ( ${Kras}^{LSL-G12D};{Trp53}^{LSL-R172H};Pdx1-Cre$ ). Our model reproduces tumor growth in all scenarios with an average concordance correlation coefficient (CCC) of 0.99 ± 0.01. We conduct leave-one-out predictions (average CCC = 0.74 ± 0.06), mouse-specific predictions (average CCC = 0.75 ± 0.02), and hybrid, group-informed, mouse-specific predictions (average CCC = 0.85 ± 0.04). The developed mathematical model demonstrates high accuracy in fitting the experimental tumor data and a robust ability to predict tumor response to treatment. This approach has important implications for optimizing combination NGC treatment strategies.

Biological oscillators control vital rhythmic processes, and their dysregulation is associated with disorders such as cancer, sleep disturbances, and motor deficits. These oscillators often exhibit competitive interactions through mutual inhibition, and their dynamics are regulated by feedback mechanisms: positive feedback enhances synchronization, while negative feedback ensures tunability. However, the role of hybrid (positive-plus-negative) feedback in modulating competitive biological oscillators remains poorly understood. Here, we analyse seven competitive oscillators and demonstrate that hybrid feedback induces two distinct modulation modes: higher-amplitude, lower-frequency oscillations or higher-frequency, lower-amplitude oscillations, depending on hybrid feedback strengths. Furthermore, we show that oscillation tunability hinges on the asymmetry between positive and negative feedback loops. These findings deepen our understanding of oscillation regulation and could guide therapeutic strategies for diseases related to rhythm disorders.

Pre-transcriptional regulation through alternative promoter usage is a critical yet underexplored mechanism influencing gene expression in Triple-Negative Breast Cancer (TNBC), a highly aggressive and heterogeneous breast cancer subtype. While short-read RNA sequencing data are widely available, they offer limited resolution in accurately capturing transcript-level diversity. To overcome this, we focused on promoter-level quantification to infer active promoter usage and investigate transcriptional regulation dynamics in TNBC. Using RNA-seq data from 360 TNBC tumors and 88 adjacent normal tissues, we identified TNBC-specific and subtype-enriched Active Alternative Promoters (AAPs). Integration with H3K4me3 and H3K27ac ChIP-seq data confirmed a key promoter switching event in the HDAC9 gene: the promoter pr1077 was downregulated while another promoter pr1079 was specifically activated in TNBCs. This switch was epigenetically supported by differential enrichment of histone marks, implicating HDAC9 promoter switching as a tumor-specific regulatory mechanism. Further, we identified subtype-specific alternative promoters in TNBC, including basal subtype-enriched activity of SEC31A and reduced promoter usage of AKAP9, which were not reflected at the gene expression level but were evident through promoter-level analysis. Next, we identified alternative promoters of HUWE1 and FTX as independent predictors of relapse-free survival (RFS) in TNBC. Their prognostic value remained significant after adjusting for copy number alterations and transcriptomic subtypes. A 4-feature model integrating these two promoter activities with two clinical variables (Tumor size, Ki67 index) achieved an AUROC of 0.73 and improved patient risk stratification, with a Net Reclassification Improvement (NRI) of 0.40-0.48 over the clinical-only model, underscoring the potential of promoter activity as a biomarker in TNBC.

Cooperation among phenotypically distinct sub-populations within a tumor plays a key role in cancer progression. In this study, we investigated how proteolytic heterogeneity supports collective cancer invasion. In invasive MDA-MB-231 breast cancer cells which exhibit considerable variability in MMP9 expression, we show that MMP9 knockdown cells are notably smaller and softer than control cells. A computational model revealed that the invasiveness of mixed clusters containing both proteolytic and non-proteolytic cells depends on cell-cell adhesion, with non-proteolytic cell invasion requiring close proximity to proteolytic neighbors. When we assigned non-proteolytic cells the same size and stiffness as proteolytic ones, the overall invasiveness declined-highlighting that small size and deformability of non-proteolytic cells are essential for sustained collective invasion. We validated these predictions experimentally using spheroid invasion assays showing that mixed spheroids of control and MMP9 knockdown cells are the most invasive. Together, our findings demonstrate that interplay between MMP9 expression and biophysical properties enables collective invasion through enrichment of and matrix degradation by high MMP9 expressing cells at the invasive front, and squeezing of low MMP9 expressing cells through the remodeled matrix.

Environmental stressors such as radiation, pH shifts, temperature variations, and electromagnetic fields can trigger intracellular oxidative stress, upregulating voltage-gated ion channel (VGIC) gene expression. This paper presents a hybrid modeling framework integrating Hodgkin-Huxley-based electrophysiological simulations with redox-sensitive transcriptional feedback to investigate how reactive oxygen species (ROS) modulate calcium signaling and drive electrophysiological reprogramming. In healthy epithelial cells (MCF-10A), sustained oxidative perturbations induce non-voltage-gated calcium influx, mitochondrial ROS generation, and VGIC transcription, shifting membrane potential from non-excitable to excitable states. Repeated ROS or thermal pulses promote progressive VGIC expression, depolarization, mRNA accumulation, and genomic instability. A Transformer-Long Short-Term Memory (LSTM) model, trained on simulated ROS-VGIC-V_m-mutation trajectories and human datasets (GSE45827), achieved >90% accuracy in predicting tumorigenic transformation. This framework enables simulation-guided drug target identification, ion channel parameter optimization, and AI-assisted screening of VGIC-modulating compounds, bridging systems biology with predictive oncology and informing electrophysiology-based therapeutic design.