Gene Regulatory Network Analysis

jFuzzyMachine is a fuzzy logic-based regulatory network inference tool that identifies more regulatory edges than ARACNe and provides interaction directionality in high-throughput biological data.

The paper analyzes the energetic costs of cellular signaling, showing that ATP consumption and chemical potential determine the information capacity of kinase-phosphatase loops, with yeast signaling pathways approaching optimality.

The paper introduces Sign Boolean Networks and evaluates their extensibility by analyzing how they can grow while maintaining original functions.

The paper identifies minimal node sets that determine network dynamics, finding that dynamic decision-makers are not static hubs.

The paper updates modelbase, an open-source Python package for constructing and analysing dynamic models, enhancing its capabilities for model reuse, isotope labelling, and SBML compatibility.

PyEcoLib simulates E. coli stochastic size dynamics by modeling division as a jump process linked to cell size and continuous growth.

The paper introduces a multistaged-hyperparallel optimization method to accelerate fuzzy logic regulatory model inference, reducing computation time from 485.6 hours to 9.6 hours for a molecular regulation problem.

The paper introduces a computational method to design genetic circuits that control the formation of multicellular shapes from a single cell in human cells.

The paper uses multinucleate cells to generate true time series of gene expression, revealing structured regulatory dynamics in cell differentiation through reachability graphs.

The paper develops a dynamical model to identify optimal gene regulation strategies for cellular responses to environmental shifts based on selective pressures.

This study develops an in silico Drosophila Patient Model to identify optimal combinatorial therapies for colorectal cancer using Boolean networks and patient-specific data.

This paper develops a mathematical model to study how cells can use diffusible signals to grow into clusters with different shapes, identifying rules for generating rod-like structures and multiple protrusions.

The paper benchmarks inducible promoters in budding yeast to evaluate their dynamic control and leakiness, identifying key limitations and introducing new tools for precise gene regulation.

The paper shows yeast cells use phosphorylation of Whi5 to temporarily remember environmental signals and decide to enter the cell cycle.

The paper identifies five patterns of cell signaling pathways linked to cell behavior by analyzing phosphorylation levels and their fluctuations using flow cytometry.

This paper introduces a stochastic dynamical systems model to predict gene expression from methylation data using a gene regulatory network, and provides software for implementation.

The paper introduces Fly T-WEoN, a tool for generating context-specific Gene Regulatory Networks in D. melanogaster by filtering known regulations using epigenomic data.

The paper investigates how different forms of robustness in chemical switches - noise, topology, and rates - are largely independent, with some topologies showing higher robustness across multiple dimensions.

This paper introduces DRMN, a method to infer cell type-specific transcriptional networks from multi-omic data, showing it accurately predicts cis-regulators and models network dynamics.

The paper analyzes bacterial cell size data to show that a commitment size model better explains both mean and fluctuation profiles than degradation-based models.

This paper models co-translational dimerization to enable tunable nonlinearity in synthetic gene circuits.

The paper compares different computational methods for simulating cellular biochemistry and identifies discrepancies in results, emphasizing the need for critical evaluation of models.

The paper introduces a parallel implementation of a modified Finite State Projection algorithm to solve the chemical master equation more efficiently.

The paper constructs a transcriptomic network model from time-series single-cell data to identify feedback loops driving cell fate transitions in mouse embryos.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

The paper designs synthetic genetic circuits in E. coli to demonstrate Turing pattern formation through an activator-inhibitor mechanism.

The paper develops a dynamical population model and type-1 IFFL controller to simulate and improve wound healing dynamics.

The paper uses mathematical modeling to compare how different mechanisms of molecular switch activation affect signaling pathway response properties.

The paper constructs synthetic gene circuits using bacterial repressors to study how auxin and salicylic acid crosstalk translates dynamic hormone inputs into precise and tunable cellular responses.

The paper shows that accurate prediction of genetic circuit behavior requires multidimensional characterization of parts, using computational models and experimental verification.