📄 Category

systems biology

96 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.

A Reproducible Dual-Model Constraint-Based Framework for Exploring Hepatic Energy Metabolism Under Stachys affinis-Derived Short-Chain Fatty Acid Scenarios

This study employs a reproducible dual-model constraint-based framework to demonstrate that stachyose-derived short-chain fatty acids from *Stachys affinis* significantly increase hepatic ATP production in a dose-dependent manner, while simultaneously identifying and cross-validating a critical propionate catabolism gap in the Recon3D model through targeted pathway restoration.

Nguyen, A. T., Nguyen, B. A.2026-03-30📄 systems biology

Targeting cancer-associated fibroblasts for treatment of ER+ breast cancer: A mathematical modeling perspective and optimization of treatment strategies

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.

Akman, T., Pietras, K., Köhn-Luque, A., Acar, A.2026-03-30📄 systems biology

A Systems Framework for Quantifying Programmability and Persistence Across Mammalian Cell Types

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.

Chauhan, V., Chen, M., Sridharan, A. T., Pan, L.2026-03-30📄 systems biology

A Cohort-Based Global Sensitivity Benchmark of MRI-Derived Whole-Heart Electromechanical Models in Healthy Hearts

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.

Rahmani, S., Pouliopoulos, J., W. C. Lee, A., Barrows, R. K., Solis-Lemus, J. A., Strocchi, M., Rodero, C., Qayyum, A., Lashkarinia, S., Roney, C., Augustin, C. M., Plank, G., Fatkin, D., Jabbour, A. (…)2026-03-30📄 systems biology

Glycan Reachability Analysis: A Bottleneck-Aware Frameworkfor Inferring Tissue-Specic Glycan Biosynthetic Potential fromTranscriptomics

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.

Matsui, Y.2026-03-27📄 systems biology

VIOLIN: A modular framework for scalable reconciliation of heterogeneous interaction graphs

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.

Luo, H., Hansen, C. E., Arazkhani, N., Telmer, C. A., Tang, D., Zhou, G., Spirtes, P., Miskov-Zivanov, N.2026-03-25📄 systems biology

The Gibbs free energy landscape based on liver metabolome revealed thermodynamic robustness against fasting and obesity

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.

Abekawa, T., Ohno, S., Hirayama, A., Soga, T., Kuroda, S.2026-03-25📄 systems biology

NeighborFinder: an R package inferring local microbial network around a species of interest

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.

Sola, M., Paravel, A., Auger, S., Chatel, J.-M., Plaza Onate, F., Le Chatelier, E., Leclerc, M., Veiga, P., Frioux, C., Mariadassou, M., Berland, M.2026-03-25📄 systems biology

A graph-based learning approach to predict the effects of gene perturbations on molecular phenotypes

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.

Jin, Y., Sverchkov, Y., Sushkova, A., Ohtake, M., Emfinger, C., Craven, M.2026-03-25📄 systems biology