121 papers
Systems biology moves beyond studying individual genes or proteins to understand how they work together as a complex, living network. Instead of looking at isolated parts, this field examines the intricate conversations between molecules that drive life, revealing how cellular systems respond to changes and maintain balance. It is a holistic approach that turns vast amounts of data into a coherent story of how organisms function as a whole.
At Gist.Science, we ensure these breakthroughs remain accessible to everyone by processing every new preprint in this category directly from bioRxiv. Our team generates both plain-language explanations for the curious mind and detailed technical summaries for researchers, bridging the gap between rapid scientific discovery and clear understanding.
Below are the latest preprints in systems biology, freshly curated and summarized to help you navigate the cutting edge of network science.
This study employs a mathematical modeling framework and optimal control theory to demonstrate that targeting cancer-associated fibroblasts through specific signaling inhibition strategies can significantly enhance the efficacy of endocrine therapies for ER+ breast cancer by mitigating estrogen-independent tumor growth.
This review synthesizes data from over 50 mammalian cell populations to introduce a unified Programmability & Persistence Score (PPS) and Pareto frontier analysis, enabling the quantitative selection of optimal cell types for diverse translational applications by balancing lifespan, engraftment, immunogenicity, and stress resilience.
This study establishes a global sensitivity benchmark for patient-specific four-chamber electromechanical heart models across five healthy subjects, demonstrating that hemodynamic loading conditions consistently dominate whole-heart function outputs regardless of inter-individual anatomical variations.
This paper introduces a bottleneck-aware framework called glycan reachability analysis that infers tissue-specific glycan biosynthetic potential from transcriptomics by quantifying pathway capacity based on the least-abundant component, thereby overcoming the limitations of binary threshold methods in capturing quantitative biological variation.
MINGL is a probabilistic framework that transforms discrete spatial-omics neighborhood annotations into continuous measures of tissue architecture, enabling the quantification of cellular gradients, interface heterogeneity, and hierarchical interactions across diverse biological contexts.
The paper introduces VIOLIN, a modular and configurable Python framework that systematically reconciles heterogeneous molecular interaction graphs extracted from scientific literature with curated baseline models by classifying relationships as corroboration, contradiction, flagged cases, or extensions, while demonstrating high stability, interpretability, and alignment with expert curation across various NLP systems and large language models.
By mapping the Gibbs free energy landscape of mouse liver glucose metabolism using a novel computational method, this study reveals that the organ maintains thermodynamic robustness against metabolic shifts caused by both fasting and obesity through substantial enzyme expression costs that buffer metabolite fluctuations to ensure efficient metabolic switching.
The paper introduces NeighborFinder, an efficient R package that infers interpretable, species-centered local microbial networks using cross-validated penalized regression, offering a targeted alternative to computationally intensive global network inference methods for large metagenomic datasets.
This paper presents a general graph-based machine-learning approach that accurately predicts the effects of gene perturbations on various molecular phenotypes, offering a cost-effective alternative to exhaustive experimental screening by leveraging existing data to prioritize targets, hypothesize mechanisms, and generalize across unmeasured genes and phenotypes.
This study demonstrates that FTIR spectroscopy-derived chemotypes in *Drosophila melanogaster* serve as integrative proxies for biological variation driven by sex, genotype, nutrition, and age, successfully predicting population-level differences in stress responses.