Inference of Quantitative Models of Bacterial Promoters from Time-Series Reporter Gene Data

Abstract

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity

“…(6) Low-affinity DNA binding sites, unstable protein complexes, and DNA supercoiling can play crucial roles in regulating transcription. Investigating transcriptional dynamics necessitates both live imaging methods with high resolution (Skupsky et al, 2010;Suter et al, 2011;Evans et al, 2012;Friedman, Mumm, & Gelles, 2013;Gebhardt et al, 2013;Hocine et al, 2013;Kouno et al, 2013;Lickwar, Mueller, & Lieb, 2013;Yunger et al, 2013;Sidaway-Lee et al, 2014;Annibale & Gratton, 2015;Camunas-Soler et al, 2015;Gocheva et al, 2015;Roberts et al, 2015;Rybakova et al, 2015a;Corrigan et al, 2016;Tantale et al, 2016) and quantitative computer simulations with appropriate theories and models (Skupsky et al, 2010;Suter et al, 2011;Wang et al, 2012;Maina et al, 2014;Choubey, Kondev, & Sanchez, 2015;Stefan et al, 2015;Rybakova et al, 2015a,b;Corrigan et al, 2016;Tantale et al, 2016). Specifically, integrating diverse sets of data makes it possible to present a coherent dynamic picture of gene transcription in bacteria ).…”

Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs

“…The optimization procedure made use of the fminsearchbnd function of Matlab available at mathworks.com/matlabcentral/fileexchange. We evaluated the results by performing an a-posteriori identifiability analysis using a procedure analogous to bootstrapping as described before [72], resulting in confidence intervals for the parameter estimates (S1 Fig).…”

On Observability and Reconstruction of Promoter Activity Statistics from Reporter Protein Mean and Variance Profiles

“…An estimate of the average gene activation over the population of cells may thus be obtained by regularized inversion of the reporter synthesis dynamics [21]. Provided accurate knowledge of the latter, reconstruction of the promoter activity allows one to investigate gene expression regulatory mechanisms, a crucial step toward inference of gene regulatory networks [18].…”