Gene Regulatory Network Analysis

The paper extends Power Law Modeling with monoids to predict kinetic and genomic parameters in molecular networks, specifically estimating binding site numbers in metabolic pathways and allosteric systems.

The paper develops a framework to implement nearly-homeostatic biological networks by analyzing parameter impacts and using feedback-free systems to approximate steady-state responses.

This study investigates how resource constraints affect stochastic gene expression by modeling mRNA and protein dynamics under ribosomal limitations and identifies sub-Poisson fluctuations and noise minimization.

This study examines how incorporating measurement variance into an ODE-based model parameterization improves accuracy by using inverse stabilized variance weighting.

The paper analyzes scRNA-seq data to infer clone-specific gene regulatory networks in tumors and shows distinct network architectures and treatment-induced rewiring across cancer types.

This paper introduces a spatial stochastic model of nuclear mRNA with telegraph transcriptional dynamics and shows it can be approximated by a Poisson-Beta distribution for efficient parameter inference.

The paper combines bond graph modeling with feedback control theory to analyze biochemical oscillators, showing phase margin as a scalar indicator of oscillation presence and structure.

The paper shows that individual yeast cells signal consistently over time, indicating that their signaling pathways transmit information precisely.

The paper estimates burst frequency and size parameters of gene expression in G1 and G2/M phases using mechanistic models on single-cell RNA-seq data from mouse cells.

This study mathematically characterizes the phase diagram of protein distribution shapes in autoregulatory gene circuits, identifying three steady-state shapes and their parameter regions.

This study develops a computational workflow to assess drug effects on NFκB signaling dynamics, finding that temporal coding capacity is robust to pharmacological perturbations.

CLERA identifies gene regulatory programs and dynamic models from single-cell RNA sequencing data using representation learning and sparse model discovery.

The paper introduces GMGTS, a method that accelerates mixed-effects inference for biochemical networks by replacing numerical integration with gradient matching, improving computational efficiency.

NeighbourNet constructs cell-specific co-expression networks to uncover regulatory patterns in single-cell data, improving efficiency and enabling downstream analysis.

The paper investigates whether cell length and volume can be used interchangeably in cell cycle analysis and finds they can be, due to small cell width fluctuations.

This paper introduces LiPLike, a method to predict gene regulatory networks with higher certainty by avoiding false positives caused by ambiguous regulatory interactions.

BooLEVARD quantifies paths leading to node activation or repression in Boolean models, enhancing signal strength analysis in signaling networks.

The paper shows that power-law behavior in mRNA counts after induction can estimate the number of regulatory steps in gene expression.

The paper investigates how gene regulatory networks influence transcriptional variability in Escherichia coli under different perturbations and identifies genetic properties that explain gene-to-gene differences in transcriptional variation.

The paper experimentally defines and models phase portraits of genetically encoded oscillators in yeast, showing how noise influences frequency and amplitude, enabling noise-guided design of synthetic protein dynamics.

This paper introduces a multi-layer feedback control strategy for synthetic biology systems, demonstrating precise regulation in gene expression and population growth across scales.

This paper presents a modeling framework linking molecular gene regulation to embryonic patterning, showing that a two-step enhancer integration strategy improves the robustness and evolvability of gene expression patterns.

This study shows that biologically meaningful Boolean functions stabilize random Boolean network dynamics more than random functions, across varying network sizes and connectivities.

The paper uses a topology-based theory to predict and validate independent control of G2-M and G1-S checkpoints in fission yeast cell cycle by analyzing CDK-cyclin complex dynamics.

The paper develops a method to engineer synthetic cis-regulatory elements that control gene expression in response to signals, showing they can be designed using quantitative rules.

This study investigates toggle-n networks to understand the dynamics of differentiation into multiple cell states, showing co-expression of transcription factors and the role of cytokine signaling and regulatory asymmetry.

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

The paper introduces an entropy-based method for model selection using time-stamped snapshot data, demonstrating its ability to identify the correct mechanistic model from simulated data.

This paper develops a terminator strength prediction model and a GAN-based terminator sequence generator for E. coli, achieving high prediction accuracy and reliable sequence design.

The paper introduces a new modeling paradigm, UnifiedGreatMod, that integrates fine-grained and coarse-grained biological information to study system behavior, demonstrating its application in simulating E. coli metabolism and analyzing cell responses to infection.