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.
This study demonstrates that *Akkermansia muciniphila* alleviates fructose-induced metabolic syndrome by reprogramming the gut microbiota and metabolome to elevate oleoylethanolamide levels, which subsequently activates the gut-hypothalamus axis to improve metabolic health.
This paper introduces a simulation-conditioned generative framework that combines coarse-grained mechanistic models with foundation image models to produce biologically realistic synthetic patterns, enabling the inference of initial experimental conditions from real biological data where experimental samples are scarce.
By integrating harmonized proteogenomic data from 11 CPTAC cohorts, this study reveals how somatic mutations, particularly TP53 loss, and lineage-specific factors drive distinct, context-dependent rewiring of the ubiquitin proteasome system, thereby defining new mechanistic frameworks and therapeutic vulnerabilities for precision degrader therapy.
This study utilizes real-time PTR-MS monitoring combined with respiratory and behavioral tracking to quantify how inter- and intra-individual differences in oral processing and swallowing significantly influence the kinetics of aroma release from food.
This study develops and calibrates a mechanistic ordinary differential equation model of the canonical aryl hydrocarbon receptor pathway using time-resolved gene expression data from diverse ligands, revealing that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events.
This study addresses the challenge of kinetic parameter uncertainty in mitochondrial fatty acid oxidation (mFAO) modeling by constructing an ensemble of 51 validated computational models, which successfully link residual enzyme activity to clinical symptoms and reveal distinct pathophysiological mechanisms between long-chain and short/medium-chain mFAO deficiencies.
This study demonstrates that the Gibbs energy accessible during metabolic activation acts as a thermodynamic constraint that limits long-term cell growth by trapping cells in low-growth states, a mechanism confirmed experimentally by showing that the size of conserved metabolite pools dictates steady-state ATP production rates.
By systematically analyzing over 59,000 network topologies, this study reveals that sequential drug efficacy is governed by specific bistability-enabling motifs—specifically a positive feedback loop coupled with antagonistic crosstalk—which allow the first drug to reconfigure the system into a suppressed state inaccessible to concurrent treatment, provided a critical therapeutic window is respected.
DrugPTM-Bench is a large-scale, curated benchmark dataset that standardizes drug-induced, cell type-specific protein post-translational modifications across multiple dimensions to enable robust predictive modeling of drug mechanisms of action and signaling dynamics in imbalanced biological settings.
This paper proposes a quantitative dynamical model demonstrating that targeting high-frequency "NT-genes"—which serve as bidirectional hubs connecting normal and tumor gene regulatory networks—offers an optimal strategy for achieving partial cancer phenotype reversal while minimizing escape routes and preserving normal tissue function.