Gene Regulatory Network Analysis

This paper explores mathematical models of homo-oligomerisation, showing it can generate complex dynamics, homeostasis, ultrasensitivity, and bistability in signal transduction.

The paper presents a fully interpretable deep learning model for understanding DNA regulatory codes in gene expression, validated on Drosophila embryo data.

The paper analyzes the time-dependent behavior of an auto-regulating gene under fast promoter switching, identifying six dynamical phases based on parameter space and stochastic dynamics.

The paper quantitatively estimates genetic interactions between 36 yeast cell-cycle genes using high-throughput phenotyping of 7,350 mutants and refines a mathematical cell cycle model.

The paper demonstrates metabolic coupling during rapid adaptation of single yeast cells by measuring metabolism and division in thousands of individual cells.

The paper analyzes parameter geography in PTM systems to assess robustness, finding that bistability is rare under realistic enzyme mechanisms.

The paper analyzes optimal bang-bang feedback for bursty gene expression to minimize deviation from a target protein concentration.

The paper experimentally investigates amplitude-timescale co-variation in a pulse generating incoherent feedforward loop circuit and finds that larger amplitude pulses have slower rise times.

This paper introduces decision-theoretic and statistical methods to model signaling outcomes in cells, using single-cell data to analyze decision accuracy and dynamics in biological systems.

The paper introduces an ensemble modeling framework to discover design principles in biochemical networks by analyzing successful models and identifying shared features that guide network construction.

This paper introduces genetic devices that can be dynamically tuned after construction to adjust their response functions and logic gate transitions.

The paper develops a framework to optimize perturbation experiments by identifying minimal perturbation sets that maximize network parameter inferability.

This study uses a stochastic model to show that short transcription factor residence times reduce gene expression noise and improve information transmission in gene regulation systems, especially at lower induction levels.

The paper introduces libsbmljs, a JavaScript/WebAssembly library for handling SBML models in web environments, enabling browser-based tools and simulations without server-side processing.

The paper quantifies transcription factor abundances during human erythropoiesis and reveals gene regulatory principles and imbalances in coactivator-corepressor ratios.

The paper introduces FaST, a tool for parallelising agent-based models of cell signalling on CPUs or GPUs, demonstrating performance improvements of 5.8x and 53.9x.

This paper introduces solvable stochastic models of gene expression that account for multiple states and factors affecting transcript abundance, enabling analytical and computational solutions for gene regulation.

This study shows growth feedback affects memory gene circuits differently based on network topology, with self-activation circuits losing memory faster than toggle switches.

The paper shows that transcriptional bursting in gene networks can produce rare coordinated high expression states in cancer cells, which are linked to drug resistance.

The paper develops methods to efficiently manipulate and generate Kirchhoff polynomials for analyzing non-equilibrium biochemical reaction networks, reducing symbolic complexity through graph-based factorization and simplification.

The paper introduces the SBOL Visual 2 Ontology to standardize the mapping of genetic elements to visual glyphs, facilitating tool development and integration with biological standards.

This paper develops a hybrid stochastic model to study noise effects on budding yeast cell cycle control, achieving efficient simulations matching experimental data.

corto is a lightweight R package for gene network inference and master regulator analysis, benchmarked on human tumor and normal tissue datasets.

The paper develops a mathematical model of the HNF4α-PPARγ network to show how it enables phenotypic plasticity in NAFLD by allowing hepatocytes to adopt adipocyte-like states.

The paper introduces a cell-in-the-loop approach to synthetic biology pattern formation, demonstrating checkerboard patterning through optogenetic signaling emulated in silico.

This paper proposes feedback control strategies to robustly balance synthetic toggle switch cell populations under stochastic and uncertain conditions using in-silico experiments and agent-based simulations.

This study derives analytical solutions for gene expression models to show that heavy-tailed RNA copy number distributions arise from extrinsic noise, not intrinsic stochasticity.

This paper uses Bayesian model reduction with Lasso to identify minimal reaction subsets in signaling networks that reproduce experimental data.

The paper introduces the Beacon Calculus, a process algebra for modelling biological systems, and demonstrates its application to DNA replication, DNA damage repair, and multisite phosphorylation.

The paper evaluates four inducible promoters in Synechocystis sp. PCC 6803 to identify their dynamic range, strength, and toxicity, aiming to improve synthetic biology applications in cyanobacteria.