71 papers
Quantitative biology applied to Minnesota offers a fascinating glimpse into how data-driven approaches are reshaping our understanding of local ecosystems and public health. This field bridges the gap between complex mathematical models and real-world biological challenges, helping researchers predict disease spread, analyze genetic patterns, and optimize environmental strategies specific to the region. By turning raw numbers into actionable insights, these studies reveal hidden connections that traditional observation alone might miss.
At Gist.Science, we continuously monitor arXiv to bring you the freshest research in this niche. For every new preprint published in this category, our team generates both a clear, plain-language overview for general readers and a detailed technical summary for specialists. This dual approach ensures that cutting-edge findings are accessible to everyone, from students to seasoned scientists.
Below you will find the latest papers from arXiv covering Q-Bio work in Minnesota, ready for your review.
The paper introduces ECMSim, a high-performance, interactive web application that utilizes WebAssembly and compiled C++ to solve 1.37 million coupled ordinary differential equations in real time, enabling the spatiotemporal simulation of cardiac extracellular matrix remodeling and fibrosis across a 10,000-cell spatial array.
This paper introduces MetaboKG, an analysis-centric knowledge graph framework that integrates fragmented untargeted metabolomics data from public repositories and GNPS molecular networks into a unified, traceable, and semantically rich structure to enable reproducible SPARQL exploration and reuse of biochemical knowledge.
This paper evaluates four graph neural network explanation methods on breast cancer data to reveal distinct topological signatures of disease hubs and proposes a consensus framework that integrates these complementary approaches to significantly improve the prioritization of canonical cancer genes and the recovery of biologically coherent signaling pathways.
This paper establishes a Hasse-diagram-based characterization for determining whether a clustering system can be realized as the hardwired clustering system of a rooted level- network, proving that such a realization exists if and only if a specific parameter derived from each non-trivial block of the system's Hasse diagram does not exceed .
HiRes is a retrieval-augmented reaction condition recommendation system that combines hierarchical graph representations with k-NN precedent retrieval to achieve state-of-the-art prediction accuracy while providing chemists with inspectable, justifying precedents for practical synthesis planning.
This paper introduces an "information blueprint" algorithm inspired by renormalization-group techniques to identify condition-dependent transcription factor binding sites in non-coding genomic regions by compressing global sequence information into collective coordinates, a method validated on *E. coli* data to reveal novel regulatory elements across various growth conditions.
This paper proposes a novel life detection framework that distinguishes biological from abiotic chemical signatures by analyzing the statistical elemental composition and scaling laws of small molecules in ecological systems, demonstrating its potential to identify biosignatures in planetary science mass spectrometry data.
This paper introduces a computationally efficient, SSA-like simulation algorithm for piecewise-deterministic Markov processes that accurately models transcriptional bursting in gene regulatory networks, demonstrating through a toggle switch example that bimodal distributions arise from distinct burst frequencies driven by gene interactions rather than bursting alone.
This paper proposes a control-theoretic framework that redefines aging as a loss of safe controllability and introduces quantitative rules for optimizing the dose, timing, and sequence of interventions to restore biological function, arguing that this "control-value reduction" is a superior predictor of translational success compared to existing static biomarker or hallmark-based approaches.
This paper introduces \themodel{}, a novel multi-resolution transcriptome-guided diffusion framework that addresses the ill-posed nature of transcriptome-based drug design by bridging the biology-chemistry domain gap to generate molecules conditioned on desired cellular state transitions.