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 integrative structural modeling combining crosslinking mass spectrometry and AlphaFold to elucidate the assembly mechanisms of HDAC1/2 within NuRD, SIN3, and CoREST complexes, revealing that the intrinsically disordered C-terminal domain of HDAC1 folds into alpha helices upon complex formation.
By applying a follicle-aware spatial transcriptomics framework to human thyroid tissue, researchers reveal that epithelial heterogeneity is primarily organized by a stress-function tradeoff between active hormone-producing thyrocytes and spatially clustered damage-response cells, rather than by variations in hormone synthesis.
This study demonstrates that sex-specific physiological network robustness necessitates distinct biomarker panels for accurate biological age estimation, revealing that while older males currently offer the optimal signal-to-noise ratio for cost-effective geroscience trials, developing validated female-specific panels is an urgent priority to overcome the challenges posed by greater female physiological resilience.
This paper presents the first thermodynamically consistent, mechanistically derived mathematical model of the mitochondrial dicarboxylate carrier SLC25A10, based on a ping-pong framework and Bayesian inference, which elucidates how mitochondrial morphology and metabolic perturbations, particularly under SDH deficiency, regulate substrate exchange and succinate handling.
This paper introduces a publicly available collection of 1,100 synthetic benchmark problems derived from 22 published models to address the scarcity of well-curated datasets, thereby enabling the systematic evaluation and optimization of computational algorithms for dynamic modeling of cellular processes.
By integrating high-content morphological profiling, phospho-protein arrays, and systems modeling, this study reveals that neuregulin-1 uniquely drives cardiomyocyte elongation and area expansion through distinct PI3K and p38 signaling pathways, offering new mechanistic insights for selectively targeting maladaptive cardiac remodeling.
This study demonstrates that integrating postmortem metabolomics with machine learning on real Swedish forensic cases enables accurate detection and subtyping of ketoacidosis-related deaths, achieving over 90% detection accuracy and over 80% subtyping accuracy.
GRNFormer is a generalizable graph transformer framework that accurately infers gene regulatory networks from single-cell and bulk transcriptomics data across diverse species and cell types by integrating a transformer-based expression encoder with a variational graph autoencoder and transcription factor-anchored sampling, outperforming existing methods in benchmark evaluations.
This study utilizes large-scale multi-omics profiling of sedentary humans to characterize the distinct and shared temporal blood-based molecular responses to acute endurance and resistance exercise, revealing novel insights into systemic health pathways such as immune regulation, lipid metabolism, and tissue repair.
This study systematically demonstrates that promoter binding-site strength is the primary modulator of gene expression levels, followed by transcription factor localization and concentration, while affinity variations are largely buffered, revealing key performance trade-offs between these trans-acting determinants.