Gene Regulatory Network Analysis

OPTIMIS: Optimizing Personalized Therapies through Integrated Multiscale Intelligent Simulation

This paper develops an AI framework integrating stochastic and deterministic models to simulate multiscale biological systems, enabling adaptive treatment strategies through reinforcement learning.

A Quantitative Design Guideline for Biomolecular Positive Feedback Systems

The paper provides a quantitative design guideline for positive feedback in biomolecular systems, showing how feedback strength relates to saturation parameters and demonstrating its application in gene regulation models.

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph Transformer

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

Phase-space distance between stationary states mudulates phenotypic plasticity in breast cancer

The paper identifies a geometric control principle in non-conservative gene regulatory networks that determines phenotypic transitions in breast cancer by analyzing phase-space distances and bifurcation structures.

Flux organizations and control modes in antagonistically combined negative feedback loops

This paper analyzes how two antagonistic negative feedback loops regulate compensatory responses to environmental changes, identifying three control modes and their implications for homeostasis and rheostasis.

Synthetic ratio computation for programming population composition and multicellular morphology

This paper introduces genetic devices for precise control of cell-type ratios and multicellular morphology in synthetic biology systems.

Context-Aware Synthetic Promoter Design Using Neural Networks Enables Rewiring of Eukaryotic Transcriptional Networks

The paper designs synthetic promoters in yeast using neural networks to rewiring transcriptional networks with high repression rates.

BioOS: A Gene-Driven Digital Twin Runtime for Emergent Plant Development

BioOS is a mechanistic runtime for plant development that simulates gene regulation and morphogenesis, achieving high accuracy on multiple developmental benchmarks.

SPSID: A single-parameter shrinkage inverse-diffusion for denoising gene regulatory networks

SPSID denoises gene regulatory networks by distinguishing direct from indirect interactions, outperforming existing methods in simulations and benchmarks.

BoolDog: integrated Boolean and semi-quantitative network modelling in Python

BoolDog is a Python package for Boolean and semi-quantitative network modeling, enabling simulation, analysis, and integration with continuous ODE systems.

Generative design of synthetic gene circuits for functional and evolutionary properties

This paper uses a generative model to design synthetic gene circuits that achieve specific functional and evolutionary properties.

Cell type-specific gene regulatory network inference from single cell transcriptomics with ctOTVelo

The paper introduces ctOTVelo, a method for inferring cell type-specific gene regulatory networks from single-cell transcriptomics data, achieving state-of-the-art performance.

Generative AI-based design of hybrid transcriptional activator proteins with new DNA-binding specificity

The paper uses a variational autoencoder to generate hybrid transcriptional activators with new DNA-binding specificity, showing functional variants that recognize multiple promoters.

Trans-acting Determinants of Gene Expression: Effects of Transcription Factor Affinity, Abundance, and Localization

The paper investigates how transcription factor affinity, abundance, and localization affect gene expression and finds that promoter binding-site strength has the most significant impact.

Bringing the Genetically Minimal Cell to Life on a Computer in 4D

A hybrid 4D whole-cell simulation of the minimal bacterium JCVI-syn3A integrates spatial reaction-diffusion, metabolism, polymer chromosome dynamics, and division across a complete 105-minute cell cycle.

The Stochastic System Identification Toolkit (SSIT) to model, fit, predict, and design experiments

The paper introduces SSIT, a toolkit for stochastic modeling and analysis, demonstrating its application on yeast and breast cancer datasets.

Impact of retroactivity on information flows in engineered synthetic biological circuits

This paper quantifies how retroactivity affects information flow in synthetic biological circuits and identifies strategies to mitigate its effects.

Filtered finite state projection method for the analysis and estimation of stochastic biochemical reaction networks.

This paper introduces the Filtered Finite State Projection (FFSP) method to solve high-dimensional stochastic filtering problems in biochemical reaction networks, demonstrating its effectiveness with numerical examples and real biological data.

Analytical Solutions for the Time-Dependent Dynamics of Stochastic Gene Expression with mRNA-sRNA Interactions

This paper derives an analytical solution for stochastic gene expression dynamics with mRNA-sRNA interactions and develops a parameter-inference method for efficient kinetic parameter estimation.

Combining model-based and data-driven models: an application to synthetic biology resource competition

This paper combines mechanistic and machine learning models to address resource competition in synthetic biology, demonstrating improved predictions and physical consistency.

Stochasticity in mammalian cell growth rates drives cell-to-cell variability independently of cell size and divisions

This study shows that stochastic growth fluctuations, not cell size or division, drive cell-to-cell growth variability in lymphocytic leukemia cells.

Why Boolean network control tools disagree: a taxonomy of control problems

The paper surveys Boolean network control tools, identifies inconsistencies in their results, and introduces a metric to prioritize mutations based on predicted phenotypic impact.

Genetically encoded assembly recorder temporally resolves cellular histories in cellulo and in vivo

This paper introduces GEMINI, a tool that records cellular activity histories with high temporal resolution, and demonstrates its application in tracking transcriptional changes and inflammation dynamics in vivo.

The Dynamics of Inducible Genetic Circuits

This paper uses statistical mechanical models to study how endogenous signaling parameters, such as effector concentrations, influence the stability of genetic circuits in living cells, contrasting with traditional approaches that rely on Hill functions and manually tuned parameters.

Fast, long-range intercellular signal propagation through growth assisted positive feedback

The paper constructs and tests synthetic circuits in E. coli to show that positive feedback-regulated secondary signals can propagate faster and farther than diffusion-limited signals.

Inferring gene-regulatory networks using epigenomic priors

This paper improves gene-regulatory network inference by using epigenomic priors, showing better accuracy with DNA methylation data compared to chromatin accessibility data.

Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling

The paper develops a framework to assign context-dependent weights to gene regulations from literature, improving biological predictions and mathematical modeling of disease-specific networks.

Synthetic gene circuits that selectively target RAS-driven cancers

This paper develops synthetic gene circuits that selectively target RAS-driven cancers by integrating multiple RAS sensors to enhance selectivity and therapeutic potential.

Spectral theory of stochastic gene expression: a Hilbert space framework

This paper provides a unified Hilbert space framework for the spectral theory of stochastic gene expression and shows that deterministic models fail to capture relaxation rates under strong autoregulation.

Leveraging Twin Information for Inference of Gene Regulatory Networks

This paper introduces TwINFER, a framework using twin cell information to improve gene regulatory network inference, resolving directionality and reducing false positives in network motifs.