Gene Regulatory Network Analysis

The paper applies dynamic mode decomposition to analyze yeast cell division cycle gene dynamics, identifying key genes and temporal patterns in gene expression.

The paper shows that mutational destabilisation enables rapid evolution of novel sensory functions in AraC proteins through a BIF mechanism, outperforming traditional allosteric modes.

The paper models and computes decision making errors in a molecular signaling system with two outputs using single cell measurements of NFκB and ATF-2 concentrations.

This study presents a hybrid model of stem cell differentiation driven by epigenetic modifications, showing how random inheritance of epigenetic states can induce cell differentiation, dedifferentiation, and transdifferentiation.

This study uses fine-grained mathematical models to explore how ATP levels affect decision-making capacity in a two-gene regulatory network, showing higher energy supports more cell states.

The paper extends a gene regulatory network model for Arabidopsis thaliana root-hair cell fate to better recover mutant phenotypes and quantify cell type distribution patterns.

This paper develops a distributed feedback control system in microbial consortia to precisely regulate gene expression across different cell populations.

This paper introduces dSort-Seq, a deep-learning-assisted Sort-Seq method for high-throughput profiling of gene expression characteristics with high precision, and demonstrates its application in studying noise and dose-response relationships in E. coli.

The paper introduces GADY, an evolutionary algorithm for bottom-up design of synthetic bacteria, with a script in Gro cellular programming language for bioremediation applications.

The paper develops a method to quantify gene expression memory and identify regulators of cell state transitions, showing that modulating PI3K and TGF-β pathways can reduce drug resistance in melanoma.

The paper shows that collective cAMP signaling drives rapid, asynchronous cell state transitions in Dictyostelium, challenging the idea of synchronous development.

This paper develops a scalable synthetic communication platform for mammalian cells using orthogonal receptors and coiled-coil peptides to enable logic gate computations in cell consortia.

The paper investigates factors affecting genetic circuit performance and develops a model to predict performance under untested conditions.

This paper uses optimal control theory to model morphogen-driven tissue patterning, showing how cells can achieve precise and reproducible fate decisions despite noise and dynamic feedback.

FitMultiCell provides a scalable pipeline for modeling, simulating, and parameterizing multi-scale multi-cellular processes using Morpheus and pyABC.

This paper investigates the Poisson representation linking discrete and continuous models of stochastic gene regulatory networks, showing it holds for unregulated genes and approximates for regulated ones in large protein number limits.

The paper shows that dimerization networks can perform diverse computations with varying inputs and are versatile across cell types, especially when large enough.

This paper develops a theory to quantify and correct bias in transcriptional parameter inference from single-cell data by analyzing the effects of extrinsic noise.

The paper constructs and analyzes gene regulatory networks for breast cancer subtypes, adjacent normal tissues, and normal tissues to identify subtype-specific and pre-cancerous regulatory features and candidate genes.

The paper evaluates gene regulatory network prediction methods in early human embryos, finding that MICA, combining mutual information with chromatin accessibility, provides more reproducible and biologically meaningful results.

ReDirection assigns reaction outcomes and dissociation constants to user-defined biochemical networks using a numerical method based on stoichiometry matrices.

The paper introduces PaLD, a method for analyzing time course transcriptomic data, and shows it identifies gene regulatory network structures not found by other methods.

The paper introduces CiTruss, a statistical framework that learns gene networks and eQTLs from SNP and allele-specific expression data, revealing insights into gene regulation mechanisms.

The paper develops a method to automatically discover structurally identifiable and interpretable dynamical models from biological data using SINDy-PI and additional procedures.

Catalyst introduces a Julia library for modeling and simulating chemical reaction networks with high performance and flexibility.

This study models macrophage phenotype transitions using a stochastic gene-regulatory network, identifying bistable and tristable states and their transition dynamics.

The paper develops a platform to identify inducible biosensors from microbial sources, demonstrating its ability to find novel sensors responsive to inflammatory conditions in the gut.

The paper uses interventional data to infer causal networks for blood traits, revealing regulatory relationships among 788 genes.

The paper assesses whether gene regulatory networks can predict gene sensitivity in cancer cell lines and finds that mRNA abundance is a better predictor than inferred regulatory activity.

This paper introduces a Bayesian model to infer transcription factor activity from gene expression data using logical rules and applies it to bulk and single-cell transcriptomics to study fibroblast plasticity.