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.
This paper develops a theoretical framework for two-node gene regulatory circuits to demonstrate how feedback topology generates "cyclic noise" that circulates through the system, thereby determining both the magnitude and temporal persistence of stochastic fluctuations.
This paper reframes bacterial sugar metabolism as a linear programming consumer choice problem, demonstrating that the classic cybernetic model's matching rule emerges naturally as a growth-maximizing strategy where sequential substrate consumption (diauxie) is the optimal corner solution for specialized enzymes, while simultaneous use occurs only as a degenerate case of equal profitability.
This paper proposes a multiscale model demonstrating that cells achieve robust gene expression against signal fluctuations through a feedback-induced learning process that drives a transition from random to structured collective modes, rather than relying on simple noise suppression.
By applying Bayesian inference to leverage prior knowledge of predictable spatiotemporal concentration patterns, this study demonstrates that cells can surpass classical sensing limits to achieve significantly higher accuracy in predicting future chemical concentrations, thereby explaining rapid and precise cell fate decisions during development.
This paper establishes that exact linear lumping of mass action reaction networks yields only trivial reductions for generic parameters, while providing an algorithmic framework to identify critical parameter sets where non-trivial lumpings become possible, thereby clarifying the distinction between structural and fine-tuned model reductions.
This paper investigates the asymptotic stability and stochastic behavior of a phosphorylation chemical reaction network with fixed substrate mass and enzyme mass scaling linearly with system size, utilizing stochastic calculus, queueing theory, and dynamical system analysis to characterize regimes with multiple equilibrium points and establish averaging principles for the underlying Markov process.
This paper introduces a robust Koopman predictor that combines an attention-free latent memory block for local temporal context and dynamic re-encoding mechanisms to correct latent drift, achieving superior long-horizon accuracy and lower inference latency compared to existing autoencoder and attention-based models.
The paper introduces MMGNN, a hierarchical graph neural network framework that decomposes molecular graphs into overlapping, interaction-specific subgraphs based on atom-type pairs and geometric proximity, demonstrating superior performance in molecular property prediction across multiple benchmarks compared to traditional single-graph approaches.
This study demonstrates that circadian output networks, particularly through serial pathways and shortest-path lengths from the core oscillator, actively buffer molecular noise to enhance the precision of rhythmic oscillations rather than merely relaying timing signals.
This paper demonstrates that the systematic information loss observed in biochemical networks under the Linear-Noise Approximation arises from its reliance on state-based trajectory descriptions, and proposes a reaction-based Gaussian framework that accurately captures information transfer by preserving the sequence of reaction events.