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.
Using a computational model, this study demonstrates that whether tuberculosis relapse is driven by residual replicating bacteria or the reactivation of non-replicating bacteria trapped in caseum depends heavily on whether patients are screened for cure upon treatment completion, suggesting that post-treatment relapse may require antibiotics with superior caseum penetration.
This paper proposes a universal landscape framework based on potential flow to describe the dynamics of Turing patterns across various network sizes and external morphogen couplings, successfully applying this quantitative model to explain SOX9 expression during digit patterning.
This study demonstrates that orchard management practices, particularly wood mulch application, significantly restructure citrus rhizosphere microbiomes in ways that causally reduce plant performance, whereas humic acid primarily affects growth through direct abiotic mechanisms.
This study constructs the Kidney Genetic Disease Cell Atlas by integrating single-cell and spatial transcriptomics with GWAS data to reveal how genetic risk architectures for kidney traits are dynamically remodeled across specific cell types and disease conditions, ultimately identifying condition-specific therapeutic targets.
NovoGlyco is a modular, untargeted glycoproteomics platform that overcomes the limitations of eukaryotic-focused methods to enable the unbiased discovery and characterization of diverse protein glycosylation in prokaryotes, including novel glycans in pathogens and archaea.
This study employs deep quantitative phosphoproteomics to reveal a distinct, non-canonical acid-responsive phosphorylation network in yeast that operates independently of the traditional TORC2 and PKC pathways, identifying the membrane-tethered kinase Yck1 as a key mediator of acid stress signaling.
This study develops a deterministic mathematical model to analyze the dynamics of HIV infection under combination antiretroviral therapy and Tat inhibitors, establishing conditions for viral clearance and evaluating strategies to optimize functional cure by targeting latent reservoirs.
This study redefines drug-tolerant persisters (DTPs) not as uniform single-cell states but as heterogeneous, population-level "idling" collections with distinct sub-states and altered ion-channel activity that collectively exhibit near-zero net growth yet remain vulnerable to ferroptosis, thereby advocating for a shift from cell-type-centric strategies to "targeted landscaping" approaches that modulate phenotypic heterogeneity to eradicate tumor drug tolerance.
This study introduces bacSCP, a novel single-cell proteomics protocol that overcomes bacterial-specific analytical challenges to quantify protein heterogeneity in individual *Bacillus subtilis* and *Escherichia coli* cells, revealing significant variability in heat-stress responses within bacterial populations.
The paper introduces CausalFlux, a novel method that integrates Bayesian gene regulatory networks with genome-scale metabolic models to capture bidirectional gene-metabolite feedback, demonstrating significantly improved accuracy in predicting reaction fluxes and gene essentiality compared to existing one-way feedback models.