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 proposes Assembly Theory, a framework that quantifies causation as a material property through the assembly index and copy number to establish a testable metrology for distinguishing life and its derivatives from non-living matter by identifying structures with persistent copies in regimes of deep causal possibility.
This paper presents an extended Dynamical Mean Field Theory framework that integrates slow epigenetic feedback into gene regulatory networks, revealing how such feedback dynamically reshapes the effective potential landscape to govern stable and oscillatory cellular states and cell fate decisions.
This paper establishes that gradient-based task affinity estimation in multi-task learning is fundamentally unreliable unless tasks share at least 30-40% of training instances, a critical threshold that standard benchmarks like MoleculeNet and TDC fail to meet, thereby explaining years of inconsistent results in the field.
This paper presents the first systematic study demonstrating that while FiLM-based target conditioning significantly outperforms other fusion architectures and enables predictions in data-scarce scenarios, it can also degrade performance due to distribution mismatches, while simultaneously exposing critical flaws in standard molecular benchmarking practices and validating the generalization of context-conditional representations to future chemical space through rigorous temporal evaluation.
This paper introduces the concept of strong regulatory graphs to address scalability issues in biological logical modeling by defining update rules where vertices become active or inactive only upon unanimous influence from predecessors, otherwise adopting an ambiguous state, and explores the resulting existence of phenotype attractors.
This paper demonstrates that Support Vector Machines can be implemented using chemical reaction networks by leveraging their steady-state dynamics to model the algorithm's computational aspects, thereby establishing a novel biochemical framework for machine learning.
The paper proposes ALIGNED, a neuro-symbolic framework based on Abductive Learning that integrates data-driven learning with existing biological knowledge to predict genetic perturbation responses while systematically refining and re-discovering mechanistic insights despite data-knowledge inconsistencies.
This paper provides a complete analytical characterisation of the mixed and toxic phases in the HeMiTo framework for prion-like protein dynamics, deriving exact solutions that explain the transition from exponential growth to logistic saturation and offering a unified mechanistic description of neurodegenerative disease progression.
This paper proposes a scalable bilevel integer programming framework utilizing infeasibility-based Benders decomposition and a novel subspace separation technique to efficiently enumerate all minimal controls for synchronously driving Boolean networks toward desired phenotypes in disease modeling.
By integrating extensive experimental data into a mathematical model, researchers identified a previously hypothesized negative feedback loop that reveals how a spontaneously patterning reaction-diffusion network, combining activator-inhibitor and substrate depletion mechanisms, robustly governs trichoblast cell fate specification in Arabidopsis roots.