Gene Regulatory Network Analysis

The paper derives stationary equations for non-Markovian biochemical systems, transforming them into Markovian problems and enabling noise estimation.

The paper shows that constant calcium stress causes stochastic nuclear localization pulses of Crz1 in yeast, proposing a model where time delays and stochasticity convert analog calcium signals into digital pulses.

This paper introduces a supervised learning method using synthetic data to reconstruct gene regulatory networks from experimental time-series, showing superior performance compared to unsupervised methods.

This paper infers reaction networks from perturbation data using an evolutionary strategy that evolves network topologies and parameters to match experimental data.

The paper investigates how a bistable molecular switch like CaMKII can encode both short and long timescale information through analog signaling around stable states.

This paper designs a synthetic cell-cell signaling circuit that enables long-distance communication through active AHL pulse propagation in bacterial consortia.

The paper analyzes noise in biochemical complex formation by studying how model parameters affect noise in homomer and heteromer complexes.

The paper develops a stochastic model to analyze optimal feedback mechanisms for cell number regulation, finding an optimal feedback strength that balances noise suppression and energy costs.

The paper introduces a measure of trait tractability (ST) based on gene expression states in yeast, showing it is constrained by natural selection and predicts the determinability of traits from expression data.

The paper introduces Ac3net, an improved version of C3NET for inferring large-scale directional gene/protein networks and shows it outperforms existing methods.

This paper develops feedback-ISA (f-ISA) to model feedback mechanisms in signaling pathways, showing it can explain phosphorylation levels in MAPK and AKT pathways across breast cancer cell lines and conditions.

The paper introduces a novel spatio-temporal method for modeling biological systems and demonstrates its effectiveness in analyzing electrical stimulation effects on mesenchymal stem cells.

FPtool provides a preliminary genotype-phenotype fingerprint of mathematical models by comparing two biological scenarios, validated on a lung legionella infection model.

The paper introduces a method combining empirical Bayes and information theory to reconstruct gene regulatory networks from single-cell data, improving accuracy with prior information.

This paper introduces a machine learning method to analyze single-cell gene expression data and reconstruct dynamic gene regulatory networks during cell state transitions, with application to hematopoiesis.

The paper shows that protein degradation, not just transcription, drives coordinated fluctuations in protein abundance in single cells.

The paper explains dynamic compensation in biological circuits using model-free control theory and provides simulations to illustrate homeostasis.

The paper introduces a new antagonistic regulatory system with a unique setpoint and integral/double integral action, demonstrating its asymptotic stability through analytical methods.

The paper reviews open source software and data standards in multicellular systems biology and highlights areas for growth to enable community-driven frameworks.

The paper improves a Bayesian method for gene activity state estimation by addressing limitations in handling outliers and modeling uncertainty in co-regulation networks.

The paper designs a synthetic bacterial community with population density control using feedback loops and molecular sequestration, showing absorbance methods overestimate population density when ccdB is used.

This paper introduces the geometric hidden Markov model (GHMM) to capture low-dimensional dynamics of biological processes from time series data, showing it can predict oscillatory behavior in a synthetic biomolecular circuit.

The paper develops an efficient algorithm to infer gene regulatory networks in Drosophila melanogaster embryogenesis, identifying key regulatory interactions and demonstrating its computational efficiency.

The paper develops a network model to analyze the regulation of histidine kinase CheA by chemoreceptors, revealing a dual regulation mechanism involving ATP binding and P1 binding or phosphotransfer.

The paper uses a dynamical model to analyze gene regulatory networks and suggests GATA2 drives erythroid lineage specification by repressing PU.1, while GATA1 is more involved in downstream differentiation.

This paper introduces a Bayesian method for model calibration and identifiability analysis, showing it provides more robust and precise results than existing approaches.

The paper identifies a recurrent structure in gene regulatory networks across five organisms, showing it enables dynamical encoding and information integration through a reservoir-computing paradigm.

The paper introduces rxncon 2.0, a language for defining signal transduction networks that balances mechanistic precision with scalability for executable molecular systems biology.

The paper develops a C-based reusable software library implementing the Gillespie Direct Method for stochastic simulation of chemical reactions.

The paper shows that genetic regulation at the population level depends on cell-to-cell variability in regulatory factor concentrations and the 5' UTR of the regulator gene.