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 derives stoichiometry-based topological bounds on the dynamical growth rate of scalable chemical reaction networks, showing that the maximum amplification factor—a quantity defined via a von Neumann max-min problem over feasible fluxes—controls these growth limits without requiring specific kinetic laws or parameters.
This paper establishes bounds on the Kullback–Leibler divergence to equilibrium for mass-action chemical reaction networks by linking decay rates to stoichiometric singular values and convexity parameters within a generalized gradient flow framework, offering a novel tool to quantify slow relaxation and plateau behaviors in biological systems.
This paper introduces the concept of structural contractivity for biological interaction networks, demonstrating that they are nonexpansive and strictly contractive under specific conditions using non-standard polyhedral norms, thereby explaining their robustness and ability to entrain to periodic inputs.
This paper employs a stochastic model and Fokker-Planck analysis to demonstrate that positive gene autoregulation loops achieve greater resilience against noise and perturbations through the formation of larger oligomers, which increase Hill coefficients and sharpen regulatory non-linearities.
This paper introduces a scalable, model-based framework using dynamical trap spaces to define and efficiently identify "dynamical phenotypes" in Boolean networks, thereby linking biomarkers and external inputs to cell outcomes without requiring full attractor enumeration.
This paper presents a scalable hybrid modeling framework that integrates modular mechanistic kinetic models with machine learning-driven analytics and Bayesian optimization to predict and optimize mRNA yield and quality during the in vitro transcription process.
This study investigates how effector molecules and allosteric regulation influence the multistable dynamics and phenotypic outcomes of inducible three-gene toggle switch networks, revealing that the specific biological mechanism of effector control over dual-function proteins fundamentally alters available dynamic regimes and offers a tunable framework for encoding complex gene regulatory behaviors.
This paper reviews the application of control systems engineering to synthetic biology, focusing on feedback and feedforward architectures that enable robust regulation of biomolecular factors for cell fate reprogramming despite environmental uncertainties, while highlighting the constraints imposed by the physical realizability of control laws within living cells.
This paper introduces "collaborative graphical lasso," a novel method enhanced by the XStARS model selection criterion and implemented in the R package `coglasso`, which improves multi-omics network reconstruction by harmonizing information across molecular layers through a collaborative penalty and dual regularization scheme.
This paper investigates the phase transition of the largest connected component in duplication-divergence growing graphs with symmetric coupled divergence, identifying a critical divergence rate and demonstrating how the inclusion or exclusion of non-interacting vertices in duplication events influences the transition's characteristics and its relationship to bond percolation.