NPJ Systems Biology and Applications最新文献

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.

Metabolism operates under physico-chemical constraints that result in multireaction dependencies. Understanding how multireaction dependencies affect metabolic phenotypes remains challenging, hindering their biotechnological applications. Here, we propose the concept of a forcedly balanced complex that allows to efficiently determine the effects of specific multireaction dependencies on metabolic network functions in constrained-based models. Using this concept, we found that the fraction of multireaction dependencies induced by forcedly balanced complexes in genome-scale metabolic networks followed power law with exponential cut-off. We identified forcedly balanced complexes that are lethal in cancer but have little effect on growth in healthy tissue models. In addition, these forcedly balanced complexes are largely specific to models of particular cancer types. Therefore, multireaction dependencies resulting from forced balancing of complexes represent an innovative means to control cancers that, we argue, can be implemented via transporter engineering. The presented constraint-based approaches pave the way for using multireaction dependencies in metabolic engineering for diverse biotechnological applications.

c-Jun N-terminal kinase (JNK) plays a major role in the regulation of cell death. Numerous studies have highlighted how the dynamics of this kinase dictate whether cells survive in response to cellular stress or induce cell death mechanisms. However, it remains less clear how these dynamics potentially contribute to downstream gene expression patterns through regulated transcription factors like c-Jun. To investigate this question, we used a treatment strategy with the JNK agonist anisomycin to drive specific temporal dynamics of JNK activation: sustained, transient, or pulsed activation, and assessed the impact on downstream gene expression patterns. We observed that multiple gene expression patterns emerged depending on the temporal dynamics of JNK activation. Ordinary differential equation (ODE) models suggest that a subset of these clusters is mediated by mRNA stability, a finding supported by experimental datasets of mRNA decay rates. Specific gene clusters also show enrichment in specific cellular pathways, including cell death and inflammatory signaling, suggesting that JNK dynamics contribute to differential regulation of these pathways. These findings highlight another contribution of JNK dynamics to the regulation of cellular responses to stress stimuli.

Infertility affects millions of couples worldwide, and in vitro fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ~60% of patients, highlighting the need for innovative solutions to improve clinical outcomes. Challenges include the limited ability to study embryo implantation, inadequate methods to test therapeutic drugs, and lack of metrics to evaluate implantation images. To address these issues, we developed ImplantoMetrics, a Fiji plugin for quantitative assessment of trophoblast invasion in combination with a 3D-in-vitro model. ImplantoMetrics uses Convolutional Neural Network and XGBoosting to accurately measure multidimensional expansion patterns. It allows quantitative evaluation of potential therapeutic interventions in vitro and enables a complex study of trophoblast invasion. Compared to manual methods, ImplantoMetrics is ~13-times faster and reduces errors through automation. Beyond implantation research, ImplantoMetrics offers a comprehensive tool to study spheroid invasion in different biological contexts, as e.g. demonstrated here for cancer research.

The interactions between tumor and the immune system are main factors in determining cancer treatment outcomes. In Chronic Myeloid Leukemia (CML), considerable evidence shows that the dynamics between residual leukemia and the patient's immune system can result in either sustained disease control, leading to treatment-free remission (TFR), or disease recurrence. The question remains how to integrate mechanistic and data-driven models to support prediction of treatment outcomes. Starting from classical ecological modeling concepts, which allow to explicitly account for immune interactions at the cellular level, we incorporate time-course data on natural killer (NK) cell number, function, and their tumor-induced suppression into our general model of CML treatment. We identify relevant time scales governing treatment and immune response, enabling refined model calibration using tumor and NK cell time courses from different datasets. While the model successfully describes patient-specific response dynamics, critical parameters for predicting treatment outcome remain uncertain. However, by explicitly incorporating tumor load changes in response to TKI dose alterations, these parameters can be estimated and used to derive model predictions for treatment cessation. Further exploring dynamic changes in the number of functional immune cells, we suggest specific measurement strategies of immune effector cell populations to enhance prediction accuracy for CML recurrence following treatment cessation. The generalizability and flexibility of our approach represent a significant step towards quantitative, personalized medicine that integrates tumor-immune dynamics to guide clinical decisions and optimize dynamic cancer therapies.

Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a computational method that integrates multi-omic data from bulk metabolomics and single-cell transcriptomics through a Bayesian regression approach that explicitly models the timescale separation between molecular layers. We validate the method on both simulated datasets and experimental Parkinson's disease data. MINIE exhibits accurate and robust predictive performance across and within omic layers, including curated multi-omic networks and the lac operon. Benchmarking demonstrated significant improvements over state-of-the-art methods while ranking among the top performers in comprehensive single-cell network inference analysis. The integration of regulatory dynamics across molecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference.

AMP-activated protein kinase (AMPK) plays a key role in restoring cellular metabolic homeostasis after energy stress. Importantly, AMPK acts as a hub of metabolic signaling, integrating multiple inputs and acting on numerous downstream targets to activate catabolic processes and inhibit anabolic ones. Despite the importance of AMPK signaling, unlike other well-studied pathways, such as MAPK/ERK or NF-κB, only a handful of mechanistic models of AMPK signaling have been developed. Epistemic uncertainty in the biochemical mechanism of AMPK activation, combined with the complexity of the AMPK pathway, makes model development particularly challenging. Here, we leveraged uncertainty quantification (UQ) methods and recently developed AMPK biosensors to construct a new, data-informed model of AMPK signaling. Specifically, we applied Bayesian parameter estimation and model selection to ensure that model predictions and assumptions are well-constrained to measurements of AMPK activity using recently developed AMPK biosensors. As an application of the new model, we predicted AMPK activity in response to exercise-like stimuli. We found that AMPK acts as a time- and exercise-dependent integrator of its input. Our results highlight how UQ can facilitate model development and address epistemic uncertainty in a complex signaling pathway, such as AMPK. This work shows the potential for future applications of UQ in systems biology to drive new biological insights by incorporating state-of-the-art experimental data at all stages of model development.

Elucidating the mechanisms of transport kinetics in plants is crucial to develop crops that can use nutrients efficiently. The plant nitrate transporter NRT1.1 rapidly switches between high- and low-affinity transport modes to maintain an optimal uptake amidst fluctuations in nitrate levels. This functional switch is regulated by NRT1.1 phosphorylation, but the precise mechanisms remain poorly understood. Here, using an integrated molecular and systems-level modeling, we identify mechanisms underlying biphasic behaviour of NRT1.1. Phosphorylation of NRT1.1 and its binding to nitrate impacts its overall flexibility and synergistically modulates its global conformation, impacting the nitrate transport rate. Integrating these observations with a regulatory network involving kinases CIPK8/CIPK23 and calcium binding proteins CBL1/9, reveals that in high nitrate conditions, CIPK8-mediated sequestration of CBL1 disrupts the CIPK23-CBL complex required for NRT1.1 phosphorylation, switching NRT1.1 to a low-affinity mode. Together, our findings untangle the molecular complexity enabling NRT1.1 phosphorylation switch with broader implications in nitrate sensing and molecular-level adaption to fluctuating external nutrient levels.

Cancer cells frequently reprogramme their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the tasks inferred from the differential expression (TIDE) algorithm to infer pathway activity changes in the different conditions. We also explored a variant of TIDE that uses task-essential genes to infer metabolic task changes, providing a complementary perspective to the original algorithm. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki-MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.

Digital twins (DTs) can transform randomized clinical trials by improving ethical standards, including safety, informed consent, equity, and data privacy. They also enhance trial efficiency by enabling early detection of adverse events and streamlined design. This paper explores the role of DTs in personalized medicine, from pre-clinical research to post-marketing, while addressing technological, legal, and ethical challenges in implementation.