Gene Regulatory Network Analysis

The paper introduces BioSWITCH, a tool that converts static gene regulatory networks into executable models to predict cellular behavior.

The paper investigates why certain phosphorelay architectures are more common in nature by analyzing their information transmission and metabolic costs using mathematical models.

The paper shows that TF-TF interactions improve co-expression signals for predicting regulatory complexes in Arabidopsis, highlighting the role of combinatorial gene regulation.

The paper investigates how an incoherent feedforward circuit involving holin and antiholin proteins regulates the precision of lysis timing in bacteriophage lambda.

The paper studies phenotypic switching in Salmonella by analyzing fliC promoter dynamics, finding that oscillatory transitions occur due to regulatory delays rather than noise-driven transitions.

This paper presents a framework for designing and evaluating miRNA-based distributed cell classifiers, showing improved prediction accuracy over single-circuit designs.

The paper presents a method to revise Boolean regulatory network models using time-series data, repairing inconsistencies and handling both synchronous and asynchronous dynamics.

The paper develops a method to generate synthetic signaling networks that mimic real biological networks in topology and dynamics, enabling testing of network inference algorithms.

The paper investigates the phosphorylation mechanism of Hog1 in yeast, finding that positive feedback enables a switch between distributive and processive phosphorylation, enhancing sensitivity and robustness.

GRNUlar infers gene regulatory networks from scRNA-Seq data using unrolled deep learning, outperforming existing methods with and without TF information.

The paper uses mathematical models to identify positive feedback regulation between Hog1 and its phosphatases in yeast osmotic stress response.

The paper introduces SimiC, a method for inferring gene regulatory networks per cell phenotype and comparing them across conditions, revealing regulatory dynamics missed by single GRN approaches.

The paper investigates how decoy sites affect gene expression noise in a transcription factor and its downstream target, showing noise reduction with decoys but counterintuitive noise increases in target proteins under nonlinear responses.

The paper introduces a PDE-constrained optimization framework to estimate cell cycle dynamics from static single-cell data, showing it can identify M-phase arrest in treated K562 cells.

This paper introduces a layered feedback control system using quorum sensing and internal signaling to maintain homeostasis in heterogeneous cell populations, showing improved robustness against cell heterogeneity.

The paper constructs a mathematical model of the JNK-p38-p53 network to identify interactions and feedbacks that determine doxorubicin response dynamics.

The paper develops a multi-scale model of CD4+ T cells integrating multiple modeling approaches to predict switch-like and oscillatory behaviors in inflammatory responses.

This paper uses a deep neural network to infer gene regulation networks from gene expression dynamics, demonstrating its ability to predict mutant behaviors and uncover regulatory mechanisms.

The paper introduces a theoretical framework for analyzing genetic circuit dynamics, focusing on structural properties to predict bistability and oscillations in biological systems.

Gene regulatory networks inferred from single-cell transcriptome and epigenome data are perturbed in silico to predict transcription-factor perturbation outcomes and to nominate factors that raise reprogramming yield.

The paper introduces a Python package to automatically generate phenomenological models of synthetic biological circuits by reducing complex chemical reaction models.

The paper presents GPU-SSA, a GPU-accelerated implementation of the Stochastic Simulation Algorithm, showing significant speedup over CPU implementations for chemical reaction simulations.

The paper shows that bursty gene expression maximizes mutual information and reduces entropy in gene networks, suggesting it enhances reliability of communication in gene regulatory systems.

This paper introduces biophysical models of cis-regulation as interpretable neural networks, demonstrating their inference from MPRAs data.

The paper demonstrates that bacteriophage lambda's lysis/lysogeny decision is governed by three design features: mutual repression, cooperative positive autoregulation of CI, and cooperative binding of the activator protein.

The paper analyzes how nonspecific transcription factor binding sites affect gene expression noise, showing that decoy sites can either amplify or reduce noise depending on their binding affinity and TF stability.

The paper shows that cellular state influences the multimodal signaling response to growth factors, enhancing context-aware decision making.

The paper investigates regulatory asymmetry in a negatively regulated single-input module (SIM) network, finding that the TF gene experiences stronger repression than its targets despite identical regulatory DNA.

This paper explores using control-based continuation to track dynamical features of biochemical systems, specifically bistable dynamics in gene regulation of pluripotency in embryonic stem cells.

This paper introduces a deep learning model called NGNC to reconstruct gene regulatory networks and infer gene dynamics from time series scRNA-seq data, achieving state-of-the-art results.