Bacteria are able to respond to a variety of environmental stresses, which poses food safety problems when these bacteria are food-borne pathogens. Addition of salt, one of the most ancient and common way of preserving food, subjects the bacteria to an osmotic stress to which some may survive. However, the molecular mechanisms of adaptation in food-born pathogens are largely unknown. As a first step towards better understanding these adaptation processes on the molecular level, Delphine Ropers and Aline Métris from the Institute for Food Research in Norwich (UK), invited researcher in IBIS this year, have developed a qualitative model of the osmotic stress response in the model bacterium Escherichia coli for which more information is available in the literature. The model has allowed to reproduce the behavior of E. coli cells adapting to an osmotic stress by including the regulatory mechanisms involved in the process. This work has been published in the International Journal of Food Microbiology and in Data in Brief . It paves the way to modelling stress responses of other foodborne pathogens like Salmonella to stresses relevant for the food industry, for which much less is known.

The tool used for the qualitative modeling and simulation of the regulatory mechanism underlying osmotic stress is Genetic Network Analyzer (GNA). This tool describes the dynamics of gene regulatory networks by means of PLDE models, as described in Section . GNA has been integrated with the other bioinformatics tools distributed by Genostar (http://www.genostar.com/). Version 8.7.2 of GNA was released by IBIS and Genostar this year and has been deposited at the Agence pour la Protection des Programmes (APP). Some bugs have been corrected in the new version and the program has been adapted to the latest versions of Java and the software platform of Genostar. Version 8.7.2 supports the SBML standard and is also capable of exporting its models to the newly-developed standard for qualitative models, SBML Qual. This standard has been elaborated by the community of developers of logical and related modeling tools (CoLoMoTo), in which the GNA developers participate.

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 ). 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.

Valentin Zulkower, former PhD student in IBIS, developed novel methods for the analysis of reporter gene data obtained in microplate experiments, 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. This work was presented at the major bioinformatics conference ISMB/ECCB and published in the special issue of Bioinformatics associated with the conference last year. The linear inversion methods have been implemented in the Python package WellFARE and integrated in the web application WellInverter (Section ). Funded by the Institut Français de Bioinformatique (IFB), Yannick Martin is currently extending 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.

While the use of microplate readers results in population-level measurements of gene expression, for many applications it is mandatory to monitor gene expression over time on the level of individual cells. Several developments in the past decade have enormously extended the capabilities to achieve this, in particular the combination of fluorescence time-lapse microscopy for precisely quantifying gene expression in single cells and microfluidics technology for cultivating bacteria in confined spatial compartments and under well-controlled experimental conditions. One of the most wide-spread microfluidics devices is the so-called mother machine shown in Figure . A major problem is that software for image analysis (segmentation, tracking, lineage reconstruction, ...) adapted to the requirements of mother machine applications are still missing. IBIS therefore collaborates with the BEAGLE project-team for the adaptation of their tool FluoBacTracker to the analysis of time-lapse movies of fluorescent reporter expression and bacterial growth in microfluidics devices. This collaboration is supported by the Technology Transfer and Innovation department of Inria, in the framework of the Inria Hub program, and has allowed the hiring of Cyril Dutrieux as a software engineer in IBIS.

Adaptation of bacterial growth to changes in environmental conditions, such as the availability of specific carbon sources, is triggered at the molecular level by the reorganization of metabolism and gene expression: the concentration of metabolites is adjusted, as well as the concentration and activities of enzymes, the rate of metabolic reactions, the transcription and translation rates, and the stability of proteins and RNAs. This reprogramming of the bacterial cell is carried out by i) specific interactions involving regulatory proteins or RNAs that specifically respond to the change of environmental conditions and ii) global regulation involving changes in the concentration of RNA polymerase, ribosomes, and metabolite pools that globally affect the rates of transcription, translation, and degradation of all RNAs and proteins. While these phenomena have been well studied in steady-state growth conditions, much less is known about adaptation during growth transitions. In particular, only very few data are available on changes in the concentration and activity of the transcription and translation machineries and almost no data exost for the dynamic response of the degradation machinery.

In the framework of the PhD thesis of Manon Morin, supported by a Contrat Jeune Scientifique INRA-Inria (2012-2015), the collaboration of Delphine Ropers with Muriel Cocaign-Bousquet and Brice Enjalbert at INRA/INSA de Toulouse has allowed to disentangle the role of post-transcriptional regulation from other regulatory interactions in the dynamic adaptation of central carbon metabolism in E. coli. In a multi-scale analysis of a wild-type strain and its isogenic mutant attenuated for the protein CsrA, a variety of experimental data have been acquired in relevant conditions, including growth parameters, gene expression levels, metabolite pools, enzyme activities and metabolic fluxes. Data integration, metabolic flux analysis and regulation analysis revealed the pivotal role of post-transcriptional regulation for shaping carbon metabolism. In particular, the work has shed light on csrA essentiality and has provided an explanation for the glucose-phosphate stress observed in the mutant strain. A paper summarizing the work has been published in Molecular Microbiology this year . A follow-up study conducted with various mutant strains of the carbon storage regulator system has elucidated the role of post-transcriptional regulation in the dynamics of glycogen storage and consumption, as well as the key role of the latter compound for bacterial fitness. A paper summarizing the work is being prepared for publication.

The collaboration with INRA/INSA de Toulouse is continued in the context of the PhD thesis of Thibault Etienne, funded by an INRA-Inria PhD grant, with the objective of developing models able to explain how cells coordinate their physiology and the functioning of the transcription, translation, and degradation machineries following changes in the availability of carbon sources in the environment.

At the single-cell level, the processes that govern single-cell dynamics in general and gene expression in particular are better described by stochastic models. Modern techniques for the real-time monitoring of gene expression in single cells enable one to apply stochastic modelling to study the origins and consequences of random noise in response to various environmental stresses, and the emergence of phenotypic variability. The potential impact of single-cell stochastic analysis and modelling ranges from a better comprehension of the biochemical regulatory mechanisms underlying cellular phenotypes to the development of new strategies for the (computer assisted or genetically engineered) control of cell populations and even of single cells.

Work in IBIS on gene expression and interaction dynamics at the level of individual cells is addressed in terms of identification of intrinsic noise models from population snapshot data, on the one hand, and the inference of models focusing on cellular variability within isogenic populations from fluorescence microscopy gene expression profiles, on the other hand. Along with modelling and inference comes analysis of the inferred models in various respects, notably in terms of identifiability, single-cell state estimation and control. Other problems related with single-cell modelling and extracellular variability are considered in eukaryotic cells through external collaborations.

In the context of the response of yeast cells to osmotic shocks, in collaboration with the LIFEWARE project team and colleagues from Université Paris Descartes and University of Pavia (Italy), Eugenio Cinquemani has investigated the use of mixed effects-modelling and identification techniques to characterize individual cell dynamics in isogenic cell populations. Mixed-effects models are hierarchical models where parametric response profiles of individuals is subject to inter-individual parameter variability following a common population distribution. Starting from identification approaches in pharmacokinetics, we have developed and applied inference methods to microfluidics data, with a focus on the response of budding yeast to osmotic shocks. Results were described in a publication in PLoS Computational Biology . A study of statistical validation methods for mixed-effects and alternative stochastic modelling paradigms has been presented at the IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE) in Magdeburg . In collaboration with the project-team BIOCORE at Inria Sophia-Antipolis - Méditerranée, the approach is now being investigated for the joint modelling of growth and gene expression in E. coli, based on single-cell microfluidics data from growth arrest-and-restart experiments. Further challenges stemming from this activity toward modelling and identification of extrinsic noise in individual cells are part of the recently started ANR project MEMIP (Section ).

Work on identification and state estimation for single-cell gene network dynamics has been focused on the reconstruction of promoter activity profiles from fluorescent reporter data. In a stochastic, intrinsic noise modelling context, Eugenio Cinquemani addressed the problem of inferring promoter activity statistics over a cell population, such as mean and variance, from analogous statistics of the reporter output, as obtained from so-called population snapshot data. This nontrivial extension of the deterministic promoter activity deconvolution problem from population-average data is the first, crucial step toward reconstruction of promoter activity regulation and inference of stochastic network models. Earlier results, concerning parameter identifiability of stochastic promoter activity models and reconstruction of promoter activity distributions in the special case of single-switch systems, were further developed in a contribution to the HSB conference this year . The relationship between the spectrum of the promoter process (cross-correlation function) and the mean-variance profiles of fluorescent reporter readouts was derived and demonstrated on examples, laying down the bases for a full-blown observability analysis and the development of spectrum estimation methods.

The collaboration of Eugenio Cinquemani with Marianna Rapsomaniki (IBM Zurich Research Lab, Switzerland), Zoi Lygerou (University of Patras, Greece) and John Lygeros (ETH Zurich, Switzerland) is moving on to applications of joint work published in Bioinformatics last year. Deployment of the methods developed into an efficient cluster-based software for the inference of protein kinetics in single cells from Fluorescence Recovery After Photobleaching (FRAP) experiments is under study. Exploitation of the same methods for the simulation and analysis of more general biochemical processes in single cells is part of the ongoing research efforts.

The ability to experimentally control the growth rate is crucial for studying bacterial physiology. It is also of central importance for applications in biotechnology, where often the goal is to limit or even arrest growth. Growth-arrested cells with a functional metabolism open the possibility to channel resources into the production of a desired metabolite, instead of wasting nutrients on biomass production. The objective of the RESET project, supported in the framework of the Programme d'Investissements d'Avenir (Section ), is to develop novel strategies to limit or completely stop microbial growth and to explore biotechnological applications of these approaches.

A foundation result for growth control in bacteria was published in the journal Molecular Systems Biology last year. In that publication, we described an engineered E. coli strain where the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. The publication also presented a biotechnological application of the synthetic growth switch in which both the wild-type E. coli strain and our modified strain were endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. This work is being continued in several directions in the context of the RESET project by Célia Boyat. In order to further explore the possibility of transferring this technology to biotechnology companies, we participated in the Challenge Out of Labs (http://www.linksium.fr/lancez-vous/resultat-challenge-out-of-labs/) organized by Linksium, the local incubator for technology transfer and start-up building. The presentation by Hans Geiselmann was selected for further development by Linksium.

In a review recently accepted for publication in Trends in Microbiology , we have put the scientific results mentioned above in a broader context. As illustrated by the synthetic growth switch, reengineering the gene expression machinery allows modifying naturally evolved regulatory networks and thereby profoundly reorganizing the manner in which bacteria allocate resources to different cellular functions. This opens new opportunities for our fundamental understanding of microbial physiology and for a variety of applications. We describe how recent breakthroughs in genome engineering and the miniaturization and automation of culturing methods have offered new perspectives for the reengineering of the transcription and translation machinery in bacteria as well as the development of novel in vitro and in vivo gene expression systems. In our paper, we review different examples from the unifying perspective of resource reallocation, and discuss the impact of these approaches for microbial systems biology and biotechnological applications.

Whereas the synthetic growth switch has been designed for biotechnological purposes, the question can be asked how resource allocation is organized in wild-type strains that have naturally evolved. Recent work has shown that coarse-grained models of resource allocation can account for a number of empirical regularities relating the the macromolecular composition of the cell to the growth rate. Some of these models hypothesize control strategies enabling microorganisms to optimize growth. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of the PhD thesis of Nils Giordano is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. In collaboration with the BIOCORE project-team, we formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin’s Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in E. coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment. A paper describing the above results was published in PLoS Computational Biology this year .