ArticleJournal of the Royal Society, Interface2013
Designing experiments to understand the variability in biochemical reaction networks.
Article in Journal of the Royal Society, Interface, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 78 citations in OpenAlex.
- Uncertainty Quantification forChemical reviews · 2026Review
- Sequential design of single-cell experiments to identify discrete stochastic models for gene expression.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2024Article
- Quantifying single-cell diacylglycerol signaling kinetics after uncaging.Biophysical journal · 2023Article
- MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division.Biophysical journal · 2023Article
- Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.Frontiers in cell and developmental biology · 2023Article
- Deep reinforcement learning for optimal experimental design in biology.PLoS computational biology · 2022Article
- Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level.PLoS computational biology · 2022Article
- Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks.Entropy (Basel, Switzerland) · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- BAYESIAN INFERENCE OF STOCHASTIC REACTION NETWORKS USING MULTIFIDELITY SEQUENTIAL TEMPERED MARKOV CHAIN MONTE CARLO.International journal for uncertainty quantification · 2020Article
- Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments.Complexity · 2020Article
- Cell-machine interfaces for characterizing gene regulatory network dynamics.Current opinion in systems biology · 2019Article
- The finite state projection based Fisher information matrix approach to estimate information and optimize single-cell experiments.PLoS computational biology · 2019Article
- Distribution shapes govern the discovery of predictive models for gene regulation.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Identification of gene regulation models from single-cell data.Physical biology · 2018Article
- Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber.Biophysical journal · 2017Article
- Reconstructing dynamic molecular states from single-cell time series.Journal of the Royal Society, Interface · 2016Article
- Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth.Nature communications · 2016Article
- Finite state projection based bounds to compare chemical master equation models using single-cell data.The Journal of chemical physics · 2016Article
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori, to decide whether a single-cell experiment might be beneficial, is currently only possible in systems where either the chemical master equation is computationally tractable or a Gaussian approximation is appropriate. Here, we provide formulae for computing the information provided by measured means and variances from the first four moments and the parameter derivatives of the first two moments of the underlying process. For stochastic kinetic models for which these moments can be either computed exactly or approximated efficiently, the derived formulae can be used to approximate the information provided by single-cell distribution experiments. Based on this result, we propose an optimal experimental design framework which we employ to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression. Subsequently, we compare the information content of a set of experiments which have been performed in an engineered light-switch gene expression system in yeast and show that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.