142 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 paper introduces an optimal algorithmic approach that leverages algorithmic complexity to infer asynchronous Boolean network models from noisy experimental data, effectively balancing model fit, size, and computational efficiency.
This paper introduces Deep Mechanistic Models (DMMs), a framework that integrates semi-supervised representation learning with ordinary differential equation models to reveal that mammary MAPK signalling heterogeneity is primarily driven by pathway-extrinsic factors rather than core pathway variations, while simultaneously using model discrepancies to identify novel biological mechanisms.
This paper introduces a covariant hierarchy of descriptors (cBST1–cBST3) for Biochemical Systems Theory that ensures coordinate consistency under nonlinear reparameterizations and demonstrates through quantitative validation on yeast glycolysis models that these higher-order terms significantly reduce approximation errors and accurately predict truncation behavior compared to classical methods.
This paper systematically characterizes the volume dependence of stochastic detailed balance in chemical reaction networks by introducing a four-type classification, distinguishing between parameter-dependent and structurally enforced balance, and identifying network classes that satisfy detailed balance for all volumes and rate constants while enabling the construction of systems with double-well global potentials.
By integrating deep proteomic profiling with QTL mapping in a genetically diverse mouse model of MASLD, this study constructs a comprehensive gene-protein-phenotype network that identifies the E3 ubiquitin ligase Ubr1 as a central regulator linking proteostasis and lipid metabolism to disease susceptibility.
This study introduces a scalable, untargeted metabolomics platform using Xenopus oocytes incubated in human serum to successfully deorphanize and characterize the substrate specificities of five human solute carrier proteins, recovering known substrates and identifying novel transported metabolites.
This study employs a quantitative biophysical model to demonstrate that store-operated calcium entry (SOCE) enhances skeletal muscle force during resistance exercise by sustaining calcium release, but can exacerbate fatigue during high-intensity training due to phosphate accumulation, a trade-off that is modulated by mitochondrial phosphate uptake.
This study introduces a novel supervised clustering approach for high-coverage single-cell RNA sequencing data to quantitatively characterize *C. elegans* mRNA landscapes, revealing unprecedented cell-type-specific gene expression programs and identifying novel tissue-restricted genes that were previously undetectable at lower coverage thresholds.
This paper introduces ChatGEM, an agentic platform that leverages natural language and retrieval-augmented generation to democratize genome-scale metabolic modeling, enabling researchers without computational expertise to perform complex simulations and successfully identify optimal microbial strains for bioproduction.
This study identifies specific gene regulatory network architectures, including completely disconnected structures and those with self-activation or universal inhibition, that are theoretically capable of supporting multi-fate cellular decisions through the simultaneous stability of all single high states.