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.
HetNetEX is a novel method that replaces the computationally expensive and resolution-limited permutation-based XSwap approach with an exact analytical inference technique to efficiently calculate significance for connectivity in heterogeneous biomedical knowledge graphs.
This paper demonstrates that the failure of standard Normal perturbation kernels in high-dimensional ABC-SMC is caused by the combination of summary-statistic-induced covariance overinflation and dimension-driven step-size overconcentration, and proposes the Cauchy kernel as a robust default alternative that maintains positive acceptance rates and significantly improves posterior approximation accuracy regardless of dimension.
This paper presents a systematically curated and standardized update of the MitoCore human metabolic model, which significantly enhances its annotation quality and compatibility with protein-constrained modeling, thereby improving the accuracy of disease-related metabolic predictions and establishing a framework for automated future updates.
By integrating multi-omics profiling with enhancer-promoter interaction mapping in the LUHMES model, this study elucidates the coordinated regulatory networks driving human dopaminergic neuron differentiation, identifying key transcription factors like MYT1, ISL2, and NHLH2 as crucial maturation drivers and LCOR as a negative regulator.
This study employs chemoproteomics to identify novel ppGpp-binding targets in plants and cyanobacteria, revealing distinct regulatory mechanisms such as the inhibition of pyrimidine metabolism in chloroplasts and the activation of glycogen synthesis and carboxysome aggregation in cyanobacteria, thereby expanding our understanding of how photosynthetic organisms adapt to environmental stress.
The paper introduces Dis2Vec, a representation learning framework that generates biologically grounded disease embeddings from genetic and phenotypic data to enable systematic knowledge repurposing, improve therapeutic transferability predictions for investment decisions, and facilitate unsupervised clustering of rare and non-rare diseases.
This longitudinal plasma proteomics study identifies a reproducible five-protein sequential cascade, led by the IL-33/ST2 axis and culminating in myometrial contractile proteins, that serves as the molecular trigger for spontaneous human labour onset.
By phenotyping a library of marine heterotrophic bacteria, this study reveals that while growth on diverse carbon and nitrogen sources is weakly linked to phylogeny, it is organized around substrate properties like C:N stoichiometry and degree of reduction, alongside strain-specific traits that challenge simple yield predictions and offer new principles for modeling global biogeochemical cycles.
This paper introduces TabularQual, a spreadsheet-based format and bidirectional converter that bridges human-readable logical model representations with the standardized SBML-qual XML format to enhance the annotation, curation, exchange, and interoperability of regulatory and signaling models.
This study introduces a novel Cellular Omics-Structural Integration (COSI) platform that simultaneously maps single-cell gene expression and super-resolution ultrastructure to identify specific gene sets governing liver sinusoidal endothelial fenestrations, thereby providing new molecular markers for assessing and treating metabolic diseases like NASH and diabetes.