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Maximizing the information content of experiments in systems biology

Maximizing the information content of experiments in systems biology

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Reverse engineering and identification in systems biology: strategies, perspectives and challenges Integrated -omics: a powerful approach to understanding the heterogeneous lignification of fibre crops A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation.Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model.Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.Modelling proteasome and proteasome regulator activities.Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach Bayesian model comparison and parameter inference in systems biology using nested sampling Optimal experiment design for model selection in biochemical networks.Model selection in systems biology depends on experimental design.A Bayesian active learning strategy for sequential experimental design in systems biology.A MINE alternative to D-optimal designs for the linear model.Efficient Optimization of Stimuli for Model-Based Design of Experiments to Resolve Dynamical Uncertainty.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 Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology.Likelihood-free simulation-based optimal design with an application to spatial extremes.Near-optimal experimental design for model selection in systems biology Cutting the wires: modularization of cellular networks for experimental design.Model of Host-Pathogen Interaction Dynamics Links In Vivo Optical Imaging and Immune Responses.Convergence in parameters and predictions using computational experimental design.Approximate Bayesian inference for complex ecosystems.A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process.Uncertainty and variability in computational and mathematical models of cardiac physiology.Control mechanisms for stochastic biochemical systems via computation of reachable sets How to deal with parameters for whole-cell modelling.Network information analysis reveals risk perception transmission in a behaviour-influenza dynamics system.Bayesian experimental design for models with intractable likelihoods.Designing experiments to understand the variability in biochemical reaction networks.Multivariate moment closure techniques for stochastic kinetic models.PEITH(Θ) - Perfecting Experiments with Information Theory in Python with GPU support.Optimal Quantification of Contact Inhibition in Cell Populations.Evaluating genetic drift in time-series evolutionary analysis.Phosphorelay of non-orthodox two component systems functions through a bi-molecular mechanism in vivo: the case of ArcB.Identification of Gene regulation models from single-cell data.

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Maximizing the information content of experiments in systems biology

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JOURNAL.PCBI.1002888

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PLOS Computational Biology

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Maximizing the information content of experiments in systems biology

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Juliane Liepe

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Michael P H Stumpf

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Michał Komorowski

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Sarah Filippi

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P2860

Universally sloppy parameter sensitivities in systems biology models

Simulation-based model selection for dynamical systems in systems and population biology.

Optimal experimental design for parameter estimation of a cell signaling model

Oscillatory expression of the bHLH factor Hes1 regulated by a negative feedback loop

Stimulus design for model selection and validation in cell signaling

Approximate Bayesian computation in population genetics

A synthetic oscillatory network of transcriptional regulators

Causal protein-signaling networks derived from multiparameter single-cell data

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.

Data-based identifiability analysis of non-linear dynamical models.

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10.1371/JOURNAL.PCBI.1002888

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