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 study introduces and validates a multiscale in-silico framework that links pericyte electrophysiology and intracellular calcium dynamics to vascular wall mechanics, successfully predicting capillary contractility across diverse experimental and pharmacological conditions to support therapeutic strategies for cerebrovascular pathologies.
This paper demonstrates through ovarian cancer modeling that while statistical metrics like AIC and BIC help balance goodness-of-fit with parsimony, selecting the most biologically insightful model requires a deliberate trade-off between statistical simplicity and the inclusion of essential physiological mechanisms to avoid unidentifiability and overfitting.
This study demonstrates that temporal fluctuations in gene regulatory interaction strengths—specifically additive versus multiplicative noise—can drive opposing phenotypic outcomes in cell-fate decision networks by differentially reshaping the occupancy of co-expression and single-high states, thereby pushing developmental systems toward either progenitor-like or terminally differentiated fates.
This study integrates transcriptomic data from multiple Escherichia coli K-12 adaptive laboratory evolution experiments to identify a conserved core of 109 genes that consistently remodel motility, chemotaxis, transport, and biofilm functions across diverse evolutionary trajectories.
This paper introduces Evidence-Constrained Mechanistic Synthesis (ECMS), a framework that transforms heterogeneous biological evidence into constraints on a fixed ensemble of mechanistic hypotheses to optimize drug regimen selection while preserving distinct representations of evidence, uncertainty, and decision preferences.
By developing a quantitative rule-based model of TLR4 signaling that revealed a consistent failure to predict pathway deactivation, researchers identified a previously unknown TANK-dependent IKKε checkpoint that negatively regulates the MyD88-IRAK1-TRAF6 module to restrain inflammation.
This paper introduces a nonlinear growth balance analysis (GBA) framework to model the fitness costs of suboptimal protein allocation in growing cells, demonstrating that accounting for complex resource trade-offs and enzyme kinetics yields predictions consistent with experimental patterns and offers a practical tool for metabolic engineering.
This study utilizes multi-omics analysis of a human Krabbe disease model to reveal novel metabolic dysregulations, including enhanced de novo ceramide synthesis and impaired N-glycosylation, which suggest promising therapeutic strategies involving tezacaftor-mediated substrate reduction and galactose supplementation.
This study establishes that the exactness of projecting spatial stochastic biological systems onto zero-dimensional count-based models depends on an aggregate-rate lumpability condition, and demonstrates that information-theoretic risks derived from spatial correlations serve as robust, transferable diagnostics for predicting the practical failure of such mean-field approximations across molecular, epidemic, and ecological scales.
This paper proposes and validates a compositional data analysis framework for Isotopic Metabolic Flux Analysis (I-MFA) that utilizes isometric log-ratio transformations to replace standard Euclidean distance calculations, thereby significantly reducing estimation errors and narrowing confidence intervals compared to traditional methods.