Accurate reconstruction of dynamic gene expression and growth rate profiles from noisy measurements
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