ArticlePLoS computational biology2011
Computation of steady-state probability distributions in stochastic models of cellular networks.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- A moment-convergence method for stochastic analysis of biochemical reaction networks.The Journal of chemical physics · 2016Article
- Build to understand: synthetic approaches to biology.Integrative biology : quantitative biosciences from nano to macro · 2016Review
- Article
- Stochastic sensitivity analysis and kernel inference via distributional data.Biophysical journal · 2014Article
- Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.PLoS computational biology · 2013Article
- Article
- Intercellular interactions, position, and polarity in establishing blastocyst cell lineages and embryonic axes.Cold Spring Harbor perspectives in biology · 2012Review
- Bayesian learning from marginal data in bionetwork models.Statistical applications in genetics and molecular biology · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.
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Registered trials
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.