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 investigates the identifiability of stochastic differential equations derived from biochemical reaction networks, establishing conditions under which reaction rates and network structures can be uniquely determined from the diffusion law and demonstrating that distinct networks or non-identifiable rates may yield indistinguishable stochastic behaviors.
This paper develops a new theoretical framework for multicellular self-organization based on biologically-general first principles and dynamic graphs, aiming to overcome current limitations in understanding and controlling gene regulation within complex cellular networks.
This paper introduces BSTModelKit.jl, an open-source Julia package that leverages the SciML ecosystem to provide a comprehensive toolkit for constructing, simulating, and performing global sensitivity analysis on Biochemical Systems Theory (BST) models using S-system representations.
drGT is an attention-guided graph deep learning model that predicts drug response and identifies biomarkers with high accuracy across major benchmark datasets while enhancing interpretability by leveraging attention coefficients to validate known drug-target interactions and uncover novel, literature-supported drug-gene associations.
This paper reviews deterministic and stochastic network-level models to explain how Arrhenius-like temperature dependencies in individual biochemical reactions transform into complex emergent system behaviors, such as non-Arrhenius scaling and thermal limits, thereby bridging empirical temperature response curves with the molecular organization of biological systems.
This paper presents a scalable Python-based algorithm that automates the identification and classification of homeostatic mechanisms in complex biological input-output networks by extending theoretical frameworks to handle multiple inputs and directly enumerating homeostatic subnetworks from connectivity structures, thereby overcoming the combinatorial and accessibility limitations of previous graph-theoretical approaches.
The paper introduces HIDDENdb, a freely accessible web-based database that integrates large-scale perturbation screens, multi-omics data, and curated repositories to map and visualize genetic and protein co-dependency relationships, revealing functional modules and potential structural interactions across diverse biological contexts.
This paper departs from conventional dynamical systems analyses of genetic circuits by employing statistical mechanical models to examine how endogenous effector concentrations, rather than manually tuned parameters, regulate the stability of regulatory motifs, offering a more physiologically relevant perspective than traditional Hill function-based approaches.
This paper investigates how noise influences attractor transitions in Boolean networks, revealing that while global noise drives behavior based on basin sizes, local noise generates structured patterns with enhanced stability, non-trivial dominance, and broader exploration of the attractor space.
This paper introduces a diagnostic framework demonstrating that rankings of gene regulatory network inference methods exhibit significant instability across evaluation protocol axes, driven primarily by shifts in relative discrimination ability rather than base rate effects, thereby challenging the assumption of ranking invariance in current benchmarking practices.