Sensitivity, robustness, and identifiability in stochastic chemical kinetics models. - Wikidata - wikimedia - TriplyDB

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

http://www.wikidata.org/entity/Q35008293

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Maximizing the information content of experiments in systems biology Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models Estimation and discrimination of stochastic biochemical circuits from time-lapse microscopy data Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology A new stochastic model for subgenomic hepatitis C virus replication considers drug resistant mutants Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion A framework for parameter estimation and model selection from experimental data in systems biology 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 dynamics Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory.Biochemical fluctuations, optimisation and the linear noise approximation Bayesian model comparison and parameter inference in systems biology using nested sampling Mechanistic links between cellular trade-offs, gene expression, and growth Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding.Clustering reveals limits of parameter identifiability in multi-parameter models of biochemical dynamics.Iterative experiment design guides the characterization of a light-inducible gene expression circuit Ranking of Reactions Based on Sensitivity of Protein Noise Depends on the Choice of Noise Measure.Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology.Convergence in parameters and predictions using computational experimental design.Model-based design of experiments for cellular processes.A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity.Parameter identification using stochastic simulations reveals a robustness in CD95 apoptotic response.Bootstrapping least-squares estimates in biochemical reaction networks.Long-time analytic approximation of large stochastic oscillators: Simulation, analysis and inference.Bayesian design strategies for synthetic biology Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods Comparison of finite difference based methods to obtain sensitivities of stochastic chemical kinetic models.How to deal with parameters for whole-cell modelling.Feedback mechanisms control coexistence in a stem cell model of acute myeloid leukaemia.Bayesian experimental design for models with intractable likelihoods.Information processing by simple molecular motifs and susceptibility to noise Designing experiments to understand the variability in biochemical reaction networks.Fisher information metric for the Langevin equation and least informative models of continuous stochastic dynamics.A relative entropy rate method for path space sensitivity analysis of stationary complex stochastic dynamics.A general moment expansion method for stochastic kinetic models.

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P2860

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

http://www.wikidata.org/entity/Q35008293

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2011 թուականի Մայիսին հրատարակուած գիտական յօդուած

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2011 թվականի մայիսին հրատարակված գիտական հոդված

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2011年の論文

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2011年論文

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2011年論文

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2011年論文

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2011年论文

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

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Proceedings of the National Academy of Sciences of the United States of America

P1476

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models

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P2093

Maria J Costa

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Michael P H Stumpf

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Michał Komorowski

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P2860

Using a single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression

Intrinsic noise in gene regulatory networks

Stochastic gene expression in a single cell

Stochastic mechanisms in gene expression

Oscillations and variability in the p53 system

Fast evaluation of fluctuations in biochemical networks with the linear noise approximation.

Bayesian inference of biochemical kinetic parameters using the linear noise approximation.

Efficient computation of parameter sensitivities of discrete stochastic chemical reaction networks

Reverse engineering of biological complexity.

Robustness and evolution: concepts, insights and challenges from a developmental model system.

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