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.
PACER is a scalable, acyclicity-guaranteed framework for causal discovery that parameterizes a distribution over valid DAGs via variable permutations and edge probabilities, enabling efficient optimization on large-scale interventional data without the numerical instability of soft constraints.
This paper employs the theory of vertically parametrized systems to provide ideal-theoretic characterizations and linear algebra conditions that resolve fundamental geometric questions regarding generic finiteness, absolute concentration robustness, and nondegenerate multistationarity in reaction networks with power-law kinetics.
The paper introduces RAG-GNN, a novel framework that integrates dynamically retrieved biomedical literature with graph neural networks to significantly enhance functional clustering and precision in cancer signaling analysis, demonstrating that combining structural topology with semantic knowledge outperforms topology-only approaches.
This paper establishes the existence and uniqueness of stationary distributions for stochastic gene regulatory networks with transcriptional bursting and provides explicit Wasserstein convergence bounds using coupling methods.
This paper introduces alternative polynomial systems that are in one-to-one correspondence with the positive equilibria of mass action networks to simplify the analysis of their geometric and algebraic properties, including toricity, equilibrium counts, multistationarity regions, and bifurcations, with specific strengthened results derived for quadratic networks.
This paper proposes replacing standard Hill functions with globally smooth, strictly positive logistic functions to model gene regulatory networks, thereby eliminating structural flaws like silent complex-valued arithmetic and expression shutdown while ensuring numerical stability and enabling advanced control strategies.
This paper introduces Probability Flow Matching (PFM), a scalable framework that learns biophysically consistent stochastic processes from time-resolved single-cell data to accurately model gene regulatory dynamics, lineage transitions, and cellular responses while overcoming the interpretability and generalization limitations of existing methods.
CMGL is a two-stage graph learning framework that improves cancer subtype classification by using evidential deep learning to estimate per-sample modality reliability, which is then used to guide cross-omics fusion and prevent noise from distorting patient similarity graphs.
This paper proposes a mechanism where stochastic noise drives cells through frustrated gene states into distinct expression basins, which are then irreversibly locked in by slow epigenetic feedback to ensure robust differentiation and homeorhesis.
This paper develops a mathematical framework for travelling waves in multi-stage enzymatic pathways that identifies activation bias as a key bifurcation parameter, introduces a reciprocal-velocity rescaling technique to normalize signal propagation in heterogeneous networks, and establishes a basis for rational model reduction when severe kinetic bottlenecks cause pathway fragmentation.