Accurate reconstruction of dynamic gene expression and growth rate profiles from noisy measurements
Vidal Pena, G. A.; Vidal Cespedes, C. I.; rudge, t. j.
Show abstract
Cells face changing environments to which they sense and respond in complex ways, changing their rates of gene expression and growth. Measuring these dynamics is therefore essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of gene expression and growth rate profiles from typically noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for estimation of dynamic gene expression rates and biomass growth rates from noisy measurement data, and show that it is several times more accurate than current approaches. We applied our method to multiple promoter-reporter fusion genes. Gene expression rates of such promoter-reporter fusions are typically used as a proxy for transcription rates. However, using our method we show that fusion gene expression rate dynamics are determined at least by the promoter of interest and the downstream reporter. O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/435606v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@eded2corg.highwire.dtl.DTLVardef@6e1778org.highwire.dtl.DTLVardef@1c6e853org.highwire.dtl.DTLVardef@1bee0a1_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Quantifying massively parallel microbial growth with spatially mediated interactions 94%
- An off-lattice discrete model to characterise filamentous yeast colony morphology 94%
- A spatio-temporal model to reveal oscillatorphenotypes in molecular clocks: Parameterestimation elucidates circadian gene transcriptiondynamics in single-cells 94%
Similar papers in this journal
- Inferring kinetic parameters of oscillatory gene regulation from single cell time series data 95%
- Computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth 95%
- Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using Approximate Bayesian Computation 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.