Moment-based inference predicts bimodality in transient gene expression.
about
Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biologyA computational framework for analyzing stochasticity in gene expressionUncoupled analysis of stochastic reaction networks in fluctuating environmentsAutoregulation and heterogeneity in expression of human Cripto-1Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size ExpansionCERENA: ChEmical REaction Network Analyzer--A Toolbox for the Simulation and Analysis of Stochastic Chemical KineticsSparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time SeriesTailored parameter optimization methods for ordinary differential equation models with steady-state constraintsLong-term model predictive control of gene expression at the population and single-cell levels.Stochastic sensitivity analysis and kernel inference via distributional data.Finite state projection based bounds to compare chemical master equation models using single-cell data.Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study.ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamicsLessons learned from quantitative dynamical modeling in systems biology.Exact reconstruction of gene regulatory networks using compressive sensingMapping behavioral specifications to model parameters in synthetic biology.A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in YeastUncovering distinct protein-network topologies in heterogeneous cell populations.Effect of Intrinsic Noise on the Phenotype of Cell Populations Featuring Solution Multiplicity: An Artificial lac Operon Network Paradigm.Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use itIterative experiment design guides the characterization of a light-inducible gene expression circuitWhat Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in YeastIntegrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.Spatial simulations in systems biology: from molecules to cells.Generalized method of moments for estimating parameters of stochastic reaction networks.A moment-convergence method for stochastic analysis of biochemical reaction networksSystematic identification of signal-activated stochastic gene regulation.Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank MatricesA novel role for lncRNAs in cell cycle control during stress adaptation.Osmostress-induced gene expression--a model to understand how stress-activated protein kinases (SAPKs) regulate transcriptionReconstructing dynamic molecular states from single-cell time series.Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.An effective method for computing the noise in biochemical networks.Designing experiments to understand the variability in biochemical reaction networks.Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.A general moment expansion method for stochastic kinetic models.Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space.Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber.
P2860
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P2860
Moment-based inference predicts bimodality in transient gene expression.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年学术文章
@wuu
2012年学术文章
@zh-cn
2012年学术文章
@zh-hans
2012年学术文章
@zh-my
2012年学术文章
@zh-sg
2012年學術文章
@yue
2012年學術文章
@zh
2012年學術文章
@zh-hant
name
Moment-based inference predicts bimodality in transient gene expression.
@ast
Moment-based inference predicts bimodality in transient gene expression.
@en
type
label
Moment-based inference predicts bimodality in transient gene expression.
@ast
Moment-based inference predicts bimodality in transient gene expression.
@en
prefLabel
Moment-based inference predicts bimodality in transient gene expression.
@ast
Moment-based inference predicts bimodality in transient gene expression.
@en
P2093
P2860
P356
P1476
Moment-based inference predicts bimodality in transient gene expression
@en
P2093
Christoph Zechner
Heinz Koeppl
Jakob Ruess
John Lygeros
Peter Krenn
P2860
P304
P356
10.1073/PNAS.1200161109
P407
P577
2012-05-07T00:00:00Z