Section: New Results

Inference of bacterial regulatory networks from reporter gene data

The use of fluorescent and luminescent reporter genes allows real-time monitoring of gene expression, both at the level of individual cells and cell populations (Section  3.2 ). In order to fully exploit this technology, we need methods to rapidly construct reporter genes, both on plasmids and on the chromosome, mathematical models to infer biologically relevant quantities from the primary data, and computer tools to achieve this in an efficient and user-friendly manner. For instance, in a typical microplate experiment, 96 cultures are followed in parallel, over several hours, resulting in 10,000-100,000 measurements of absorbance and fluorescence and luminescence intensities. Over the past few years, we put into place an experimental platform and data analysis software, notably the WellReader program (Section  6.4 ), to allow biologists to make the most out of the information contained in reporter gene expression data. An invited review on the analysis of fluorescent reporter gene data was published in the proceedings of the Third International Workshop on Hybrid Systems Biology (HSB 14) [25] .

Valentin Zulkower, in the framework of his PhD thesis, has developed novel methods for the analysis of reporter gene data, based on the use of regularized linear inversion. This allows a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles, to be solved in a mathematically sound and practical manner. We have evaluated the validity of the approach using in-silico simulation studies, and observed that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data, like in WellReader . We have applied the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods were shown capable of reliably reconstructing time-course profiles of growth rate, promoter activity, and protein concentration from weak and noisy signals at low population volumes. Moreover, they captured critical features of those profiles, notably rapid changes in gene expression during growth transitions. The linear inversion methods have been implemented in the Python package WellFARE , and integrated by Michel Page in the web application WellInverter (Section  6.3 ). This work was presented at the major bioinformatics conference ISMB/ECCB 2015 and published in the special issue of Bioinformatics associated with the conference [24] . The Institut Français de Bioinformatique (IFB) accepted a proposal to extend WellInverter into a scalable and user-friendly web service providing a guaranteed quality of service, in terms of availability and response time. This web service will be deployed on the IFB platform and accompanied by extensive user documentation, online help, and a tutorial.

Over the years, the above tools have been used in several studies in IBIS directed at the experimental mapping of gene regulatory networks in E. coli. An example is the motility network of E. coli, studied by Diana Stefan in the context of her PhD thesis. The main thrust of this work lies in clarifying and solving methodological issues in the automated inference of quantitative models of gene regulatory networks from time-series gene expression data, also called reverse engineering in the bioinformatics literature. The application of existing reverse engineering 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. In her PhD thesis, Diana Stefan systematically investigated, using a combination of models and experiments, the importance of this bias and possible corrections. She measured with the help of fluorescent reporter genes the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, protein concentrations and global physiological effects were estimated by means of kinetic models of gene expression. The results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, it was shown 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. The paper describing the work was published in PLoS Computational Biology [23] .

In addition to reporter gene data, a variety of other experimental data can be used for the mapping of gene regulatory networks. For example, using Chromatin Immunoprecipitation-sequencing (ChIP-seq) experiments, Stéphan Lacour and colleagues have identified a large number of target promoters of the sigma factor ${\sigma }^S$ during the transition from exponential to stationary phase. Sigma factors are accesory subunits of RNA polymerase, allowing the recognition of specific promoter sequences by the transcriptional machinery, and ${\sigma }^S$ is known to specifically accumulate in a variety of stress conditions. The study, published in Scientific Reports [21] , has confirmed the importance of ${\sigma }^S$ for redirecting RNA polymerase to promoters that drive the expression of genes necessary for the survival of E. coli after nutrient exhaustion. Furthermore, the results highlight the role of ${\sigma }^S$ in the regulation of several noncoding RNAs.