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 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.
This study utilizes multi-omic profiling to map the distinct, time-resolved molecular responses of abdominal subcutaneous adipose tissue to acute endurance and resistance exercise in sedentary adults, revealing specific mechanisms involving angiogenesis, metabolism, and exerkine release that underpin exercise-induced metabolic health benefits.
The paper introduces MAPLE, a framework utilizing structured validation schemas to facilitate collaboration between large language models and human modelers, thereby ensuring the accurate, provenance-rich, and reproducible extraction of calibration data for quantitative systems pharmacology models while mitigating common LLM errors like hallucination.
This study utilizes a network pharmacology framework combined with experimental validation to identify and confirm the therapeutic potential of repurposing the drugs TUDCA and arundine for Alzheimer's disease by rescuing disease-relevant phenotypes through the regulation of G protein signaling.
The paper introduces the Toroidal Search Algorithm (TSA), a novel topology-inspired metaheuristic that eliminates boundary stagnation through toroidal geometry and winding numbers, demonstrating superior performance in high-dimensional optimization and successfully solving inverse parameterization problems in mathematical oncology.
TwinCell is a large causal cell model that leverages in vitro perturbation data and a multiomics interactome to identify and interpret upstream therapeutic targets driving disease-to-healthy state transitions, outperforming existing methods in both zero-shot and clinical validation scenarios.
This study from the MoTrPAC consortium reveals that acute endurance and resistance exercises trigger distinct, temporally ordered multi-omic responses in human skeletal muscle, characterized by early epigenetic and signaling changes that precede transcriptomic and proteomic shifts to regulate processes such as autophagy, angiogenesis, and protein turnover.
This study identifies nicotinic acid riboside (NaR) as a liver clock-controlled circulating metabolite that engages the prefoldin complex to modulate unfolded protein response signaling and adipogenesis in a time-dependent manner, thereby linking systemic circadian rhythms to tissue-specific physiological outcomes.
The Molecular Transducers of Physical Activity Consortium (MoTrPAC) study successfully established a framework for investigating the distinct acute and chronic physiological and molecular adaptations to endurance and resistance exercise in healthy sedentary adults, demonstrating high sample collection success and significant improvements in fitness parameters following supervised training.
This paper introduces a neural posterior estimation framework for likelihood-free parameter inference in spatiotemporal stochastic biological models, demonstrating its ability to accurately recover biologically interpretable parameters for cell migration from both summary statistics and raw spatial data without relying on surrogate approximations or explicit noise models.