2023Activity reportProject-TeamMICROCOSME

3.1 Genome-scale analysis of microbial physiology

The molecular foundations of bacterial growth remain little understood today, because they involve large biochemical networks with physical and regulatory interactions across different levels of cellular organization. We investigate at the genome scale how the dynamics of gene expression and metabolism leads to microbial growth, using a combination of mathematical models and high-throughput data. The challenge is to integrate, in models of thousands of equations, multiple and heterogeneous datasets on the metabolic, transcriptomic, and proteomic level. We typically use constraint-based models to investigate the relations between microbial growth and metabolism, while the effect of growth on mRNA stability is analysed by means of non-linear mixed-effect models.

3.2 Natural and engineered resource allocation strategies in microorganisms

Microorganisms have evolved strategies to allocate their resources to different cellular functions and thus adjust their growth rate to fluctuating environments. We study these natural resource allocation strategies, by viewing cells as self-replicators that can be described using coarse-grained models and analysed by means of optimal and feedback control theory. The models take the form of systems of 5-10 nonlinear ordinary differential equations, with parameters estimated from published data or data obtained from dedicated experiments. Experimental work in the lab allows to validate model predictions on the single-cell and population level and to engineer new strategies for the reallocation of cell resources from growth to bioproduction.

3.3 Variability and robustness of microbial adaptation

The development of experimental techniques and the use of video-microscopy have led to a growing number of high-quality data showing the heterogeneity among cells in a population. We combine these single-cell data with models describing the stochastic dynamics of individual cells, such as birth-death processes, branching processes, and mixed-effect models. The models allow to investigate the origins of heterogeneity and its role in the adaptation of microorganisms to environmental changes, and to leverage population heterogeneity for biotechnological applications. In practice, this requires the extension of modelling approaches by taking into account the specificities of heterogeneity, as well as the development of appropriate methods and software for the inference of models and of biological quantities from quantitative time-course profiles of the microbial response to environmental changes.

3.4 Analysis and control of microbial communities

Heterogeneity also arises within communities consisting of different microbial species. Understanding microbial interactions is a challenging task that goes well beyond the characterization of single species, and offers great opportunities for applications, such as the control of the community for bioproduction. Indeed, suitably constructed microbial consortia carry the potential to outperform single species in the accomplishment of processes of societal interest, such as biofuel synthesis. On the theoretical side, we develop (deterministic or stochastic) models of microbial dynamics similar to those in the three other research axes, which can be used to investigate new control approaches for microbial communities. On the experimental side, the application of control strategies for biotechnological applications requires the engineering of microbial strains and the automation of experiments. To that aim we have been developing a platform for feedback control experiments allowing the real-time monitoring, data processing, evaluation, and application of control laws.

Figure 2: Research axes and methods in the MICROCOSME project-team.

4.1 Biotechnology and bioeconomy

Bioproduction imposes a strong metabolic burden on microorganisms, detrimental to their growth and the production yield. Our studies of natural resource allocation strategies lead us to explore and engineer various reallocation strategies to improve bioproduction through growth control. For instance, in the past, we have successfully implemented a growth switch in E. coli bacteria, aiming at shuttling resources, away from protein synthesis (key for bacterial growth) to the high-yield production of a metabolite of interest (glycerol) 727. We also develop and test control strategies for synthetic microbial communities, composed of populations of different E. coli strains or in consortia with other species. In the wake of our studies on the relation between growth and metabolism, we develop bioeconomy strategies for the valorisation of vegetal waste into value-added product. In one project, notably, we aim at designing E. coli strains able to efficiently degrade carbon sources derived from pre-treatment of agricultural and forestry residues (Section 9).

4.2 Health

Numerous Mycobacteria species pose serious threats to human and animal health. Mycobacteria tuberculosis strains are also known to withstand several of the antibiotics used to treat the infection. We have started to extend our microbial physiology analyses by means of constraint-based models to understand the molecular control of mycobacterial growth and characterize the relations between metabolism, pathogenicity, and growth phenotype of mycobacterial species. This may lead, in the long term, to the development of new treatments for curing tuberculosis and other mycobacterial infections.

7.1 New software

7.2 New platforms

Participants: Soraya Arias, Eugenio Cinquemani, Johannes Geiselmann, Ludovic Léau-Mercier.

8.1 Resource allocation models of bacterial growth

Participants: E. Cinquemani, H. de Jong, J. Geiselmann, A. Pavlou, D. Ropers.

Various mathematical approaches have been used in the literature to describe the networks of biochemical reactions involved in microbial growth. With different levels of detail, the resulting models provide an integrated view of these reaction networks, including the transport of nutrients from the environment and their conversion into biomass. Recent work has shown that coarse-grained models of resource allocation can account for a number of empirical regularities relating the macromolecular composition of the cell to the growth rate. A well-known example of such a growth law is the linear relation between the growth rate and the ribosomal protein concentration.

In collaboration with the BIOCORE project-team and Tomas Gedeon, invited professor from Montana State University in our team in 2019, we developed a coarse-grained resource allocation model to account for the observation that different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We used our model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for this rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. The model thus provides a fundamental understanding of quantitative bounds on rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology. An article based on this work has been published in the journal eLife 14.

While these studies focus on steady-state or balanced growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. This requires microbial cells to continually adapt their functioning and the allocation of resources to gene expression and different metabolic functions. In the framework of the PhD thesis of Antrea Pavlou, we studied resource allocation on the experimental level using a combination of fluorescent reporter genes, time-lapse fluorescence microscopy and statistical inference techniques. The results of this study are currently being prepared for publication.

8.2 Community text book: Economic Principles in Cell Biology

Participant: H. de Jong.

The Economic Cell Collective is a group of researchers, informally coordinated by Wolfram Liebermeister (INRAE), with an interest in the analysis of the functioning of (microbial) cells from an economic perspective. In particular, the growth of microorganisms is seen as an optimization problem consisting in the allocation of limiting resources to cellular functions so as to maximize growth rate or another fitness criterion. One of the most visible outputs of the collective is a free, on-line textbook distributed via a dedicated website and Zenodo. The first version of the book was released in July 2023; updated versions are released every three months. H. de Jong participated in the writing of two chapters of the book. A first chapter deals with coarse-grained models of cellular growth and how they are able to reproduce well-known empirical growth laws 18. A second chapter deals with the use of dynamic optimization to predict the adaptation of microbial physiology to changes in the environment 19.

8.3 Biotechnological applications of bacterial growth control

Participants: H. de Jong, J. Geiselmann, T. Clavier.

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. In recent years we obtained a foundational result for growth control in bacteria 7, in that we engineered an 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, putting the yield very close to the theoretical maximum.

In the framework of the PhD thesis of Thibault Clavier, we addressed one of the limits of the growth switch that is inherent to the construction of synthetic networks in living microorganisms, namely that the latter evolve over time under the pressure of natural selection. In the case of the growth switch, the selection pressure is particularly high, since any spontaneously arising mutations disabling the inhibition of the expression of the RNA polymerase subunits will cause the population of growth-arrested cells to be taken over by cells that have resumed growth (at the expense of metabolite production). We improved the genetic stability of the growth switch by means of a redundant control mechanism of RNA polymerase expression, reducing the escape frequency to less than one in ${10}^9$ cells. We deposited a patent of this invention. An article describing the results obtained with the extended growth switch has been submitted. Moreover, this work is the subject of the start-up project Switch2Prod, led by Thibault Clavier.

8.4 Synthetic microbial communities for bioproduction processes: modelling, analysis and real-time monitoring

Participants: S. Arias, R. Asswad, E. Boucher, E. Cinquemani, T. Clavier, H. de Jong, J. Geiselmann, L. Léau-Mercier, M. Sangster.

Modelling, analysis and control of microbial community dynamics is a fast-developing subject with great potential implications in the understanding of natural processes and the enhancement of biotechnological processes. Within the now-ended project IPL COSY, we picked up the challenge to design and investigate the dynamics of synthetically engineered microbial communities with a consortium of Inria partners, and to test control strategies in vivo.

In MICROCOSME, in particular, we have addressed the design of a bacterial community of two E. coli strains, mimicking mutualistic relationships found in nature, and with the potential to outperform a producer strain working in isolation in the production of a heterologous protein. We developed an ODE model of the key growth phenotypes of the community and their interactions, calibrated the model on literature data, and analysed the model for an in-depth understanding of the conditions supporting coexistence and of the tradeoffs encountered in this production process 10. With the work of Maaike Sangster, who defended her Ph.D. in May 2023, we have bioengineered one version of the consortium, characterized individual species in batch as well as explored the consortium growth in continuous-flow experiments on an automated platform. Somewhat unexpected results have been obtained experimentally, which led to advancements on the modelling and analysis of E. coli overflow metabolism in the context of microbial consortia, as included in the thesis 21, and gave us new indications for modifications of the consortium that should enable co-existence. The finalization of this work by a joint effort of several team members is expected to lead to a paper submission in the near future. As a subsequent challenge, feedback control of the community for stabilization of biomasses to desirable bioproduction regimes remains in the aims of this research direction.

In parallel, we have pushed further the investigation of the synthetic community design for bioproduction optimization. In collaboration with BIOCORE, we considered a generalized version of the synthetic consortium proposed in 10 where both species of the consortium can synthesize a product of interest. In addition to a theoretical analysis of equilibria and coexistence regimes, we showed that the generalized design can indeed outperform our original consortium design based on a single producer species, thus shifting the interest from the commonly evoked concept of division of labor (different tasks for different species) to distribution of labor (same task across different species). This work is now published in 16.

The ANR Ctrl-AB puts related concepts to work for the synergistic growth of E. coli and microalgae. As per project goals, vitamin synthesis by modified E. coli strains represents a leverage to control vitamin-dependent growth of suitable algal strains, with the potential to robustify and maximize algal lipid synthesis. On the experimental side, we advanced on the design and synthesis of the bacterial strain in the consortium. Two alternative strategies for controlling bacterial vitamin synthesis via optogenetics are being explored and, together with the lab advancements on optogenetic systems and experimental equipment, will be tested next. On the methodological side, in collaboration with BIOCORE, modelling, analysis and control problems are being addressed with the Ph.D. thesis of Rand Asswad, started in 2022. Toward model-based feedback control of microbial communities, we focused so far on the problem of real-time state estimation of microbial growth in a bioreactor. In connection with a simple algal-bacterial growth model established within Ctrl-AB, we developed, theoretically analyzed and demonstrated in simulation estimation methods for a single species that can be generalized to co-cultures. The work resulted so far in a conference submission, currently under review. It will be extended to co-cultures and integrated into the upcoming work on control of algal-bacterial growth and interaction dynamics in a bioreactor.

8.5 Modelling of carbon and mRNA metabolism in microorganisms

Participants: A. Belcour, I. Cancino-Aguirre, E. Cinquemani, M. Cocaign-Bousquet, H. de Jong, D. Ropers.

The ability to rapidly respond to changing nutrient availability is crucial for microorganisms to survive in many environments. The first step in adjusting metabolism is to reorganise gene expression. It involves fine-tuning of both transcription and mRNA stability by dedicated regulatory interactions. While transcriptional regulation has been extensively studied, the role of mRNA stability during a metabolic switch is poorly understood and often overlooked, as shown by our recent compilation of literature evidence in E. coli, in collaboration with the team of Muriel Cocaign-Bousquet at the Toulouse Biotechnology Institute 28. We also addressed this question in 12, where we combined genome-wide transcriptome and mRNA decay analyses to investigate the role of mRNA stability in the response of E. coli to nutrient changes. We demonstrated that while transcription of most genes is down-regulated when glucose is exhausted, concomitant mRNA stabilization of many mRNAs occurs and leads to the upregulation of genes involved in responses to nutrient changes and stresses. The observation of a global stabilization of cellular mRNAs during adaptation to carbon source depletion raises questions about the regulatory mechanisms at work. Are these regulatory mechanisms sufficient to explain the systematic adjustment of mRNA half-lives?

In a follow-up study, we developed a kinetic model of mRNA degradation that allows to propose hypotheses on the regulatory mechanisms at work to adjust mRNA stability to environmental conditions 4. From a practical point of view, this amounts to infer model parameters from high-throughput biological datasets. In the framework of the ANR project RIBECO (Section 9), we have developed a nonlinear mixed-effects modelling approach for parameter estimation of large-scale mechanistic models from time-series transcriptomics data, which enables biological interpretation of microarray and RNA-Seq gene expression profiles. When integrated in a model describing the degradation kinetics of 4254 mRNAs in E. coli cells, the data allowed to identify a new post-transcriptional regulatory mechanism and its targets. The modelling results are being prepared for publication, and their experimental demonstration will be at the heart of the newly accepted ANR RECOM project, in which the Toulouse Biotechnology Institute and MICROCOSME are partners.

Genome-scale reconstructions of cellular metabolism, such as those used in 12, are often available in public databases for well-studied microorganisms, but this is not the case for poorly studied species. As part of I. Cancino-Aguirre's PhD thesis co-advised by D. Ropers and H. de Jong, Arnaud Belcour's postdoc, and the associate-team GERM (Section 9), we aim to develop such reconstructions from genome sequences for the Mycobacterium genus. They will then be combined with growth kinetics data to analyse how differences in carbon metabolism account for the variability in growth rates of mycobacterial species, which include both dangerous pathogens and non-pathogenic bacteria. Inferring metabolic function from sequenced genomes is a difficult problem, but even more so when dealing with microbial communities in natural environments. In a new collaboration with BRGM and the European project HyLife (Section 9), Arnaud Belcour, Hidde de Jong and Delphine Ropers are beginning to address this issue, by using marker gene sequence data to characterise the metabolic activity of underground microbial communities, which may influence the quality of gas storage in underground reservoirs.

8.6 Inference of parameters on lineage trees

Participants: E. Cinquemani, C. Fonte Sanchez, A. Marguet, E. Reginato.

Recent technological developments have made it possible to obtain single-cell measurements of gene expression and, in some cases, the associated lineage information. However, most of the existing methods for the identification of mathematical models of gene expression do not account for the fact that cells undergo divisions and are related to one another through parental relationships. Most methods developed for single-cell data make the simplifying assumptions that cells in a population are independent, thus ignoring cell lineages. The development of statistical tools taking into account the correlations between individual cells is needed in particular to enable the investigation of inheritance of traits in bacterial populations.

With the near-ending Ph.D. project of E. Reginato, we have advanced in the study of tree-structured single-cell gene expression models with mother-daughter inheritance that we had started in a previous publication 9. Contrary to this previous work, where model inference methods from single-cell gene expression data were developed assuming knowledge of the lineage of the observed cells, the thesis project has been focused on the case where lineage information is not available. We notably explored how well inheritance model parameters can be inferred depending on absence or presence of dynamics in the mean and variance data. In relation with certain literature datasets obtained by videomicroscopy, we developed statistically exact maximum-likelihood estimation methods leveraging correlation of empirical means along generations, and approximate methods also exploiting variance data that we showed to perform well even in absence of transient mean dynamics. Results constitute the object of a conference submission currently under review. Demonstration on the reference datasets is being finalized.

While the above modelling approach to mother-daughter inheritance is of statistical nature, it can be related with mechanisms into play at cell division. For this, within the same Ph.D., we started looking at the regulation of the repartition of multicopy resistance plasmids at cell division and its impact on population growth in selective media. We have developed and compared several individual-based models, with first results on the interplay between plasmid repartition statistics and stochastic cell division modelling in the understanding of the emerging population dynamics. This will be completed and analysed using real data in a future publication. Related to the above studies, Claudia Fonte Sanchez's postdoc in project AnaComBa is looking at individual-based models of population growth and gene expression, and investigating the relation between growth-rate variability across cells and gene expression distribution in population-snapshot data. Theoretical analysis is ongoing and will be subject to publication and subsequent integration with experimental investigations.

8.7 Mathematical analysis of structured branching populations

Participants: C. Fonte-Sanchez, Ch. Medous, A. Marguet.

The investigation of cellular populations at a single-cell level has already led to the discovery of important phenomena, such as the occurrence of different phenotypes in an isogenic population. Nowadays, several experimental techniques, such as microscopy combined with the use of microfluidic devices, enable one to take investigation further by providing time-profiles of the dynamics of individual cells over entire lineage trees. The development of models that take into account the genealogy is an important step in the study of inheritance in bacterial population. In particular, their mathematical analysis is essential for the efficient analysis of single cell data.

Structured branching processes allows for the study of populations, where the lifecycle of each cell is governed by a given characteristic or trait, such as the internal concentration of proteins. The dependence on this characteristic of cellular mechanisms, like division or ageing, has been explored by Aline Marguet via the mathematical analysis of those processes. In collaboration with Charline Smadi (INRAE Grenoble), Aline Marguet investigated the long-time behavior of a parasite infection in a cell population. In this work, accepted for publication in Stochastic Processes and their Applications15, the dynamics of the cell population is modelled using a structured branching process, where the cell cycle depends on the dynamics of the parasites contained in the cell. The results obtained focus on the asymptotic behaviour of the intracellular quantity of parasites. Based on criteria that depend on the comparison between the growth rate of the cell population and the parameters associated with the parasite dynamics, containment or explosion of the infection is established. The strategy of proof relies on the study of auxiliary processes that describe the level of infection of a typical cell in the population, using coupling techniques.

The fate of the infection appears to be very sensitive to the law of parasite sharing between the two daughter cells as they divide. In particular, the effect of various sharing strategies in the case of a constant division rate has been considered in a follow-up work. A perfect symmetric sharing of the parasites between the two daughter cells has been proven to be the worst strategy for the survival of the cell population. Stochastic and deterministic strategies have also been compared, highlighting that variability favors survival. This paper is under revision for Journal of Mathematical Biology25.

Spinal processes and many-to-one formulas have proved very useful for the study of complex structured branching processes, as they allow to reduce the problem to the study of a simpler lineage process. In the context of the project AnaComBa, for the study of microbial communities, such tools appear to be needed for structured branching processes with interactions. In a recently submitted paper 26, Charles Medous developed a spinal construction and established a Girsanov-type result for branching processes describing structured, interacting populations in continuous time, where the dynamics of each individual can be influenced by the entire population. He also derived a modified continuous-time version of the Kesten-Stigum theorem that incorporates interactions, and proposed an alternative simulation approach for stochastic size-dependent populations using appropriate spine constructions.

The study of the asymptotic behavior of general semigroups is important for several aspects of branching processes, especially to prove the efficiency of statistical procedures. In this context, Claudia Fonte Sanchez, in collaboration with Pierre Gabriel (Université de Tours) et Stéphane Mischler (Université Paris-Dauphine) revisited the Krein-Rutman theory for semigroups of positive operators and provided some very general, efficient and practical results with constructive estimates on the existence of a solution to the first eigentriplet problem, the geometry of the principal eigenvalue problem, and the asymptotic stability of the first eigenvector with possible constructive rate of convergence 24.

9.1 International initiatives

9.2 European initiatives

9.3 National initiatives

The following two projects have just been accepted and will start in spring 2024.

In addition to the above projects, A. Marguet contributes to the ANR JCJC NOLO of Bertrand Cloez (INRAE Montpellier) on non-local branching processes.

9.4 Regional initiatives

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 articleV.Valentina Baldazzi, D.Delphine Ropers, J.-L.Jean-Luc Gouzé, T.Tomas Gedeon and H.Hidde de Jong. Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains.eLife12May 2023, 1-29HALDOI
• 2 articleE.Eugenio Cinquemani, V.Valerie Laroute, M.Muriel Bousquet, H.Hidde De Jong and D.Delphine Ropers. Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data.Bioinformatics33142017, i301-i310HALDOI
• 3 articleE.Eugenio Cinquemani. Stochastic reaction networks with input processes: Analysis and application to gene expression inference.Automatica1012019, 150-156HALDOI
• 4 articleT.Thibault Etienne, M.Muriel Cocaign-Bousquet and D.Delphine Ropers. Competitive effects in bacterial mRNA decay.Journal of Theoretical Biology504November 2020HALDOIback to text
• 5 articleN.Nils Giordano, F.Francis Mairet, J.-L.Jean-Luc Gouzé, J.Johannes Geiselmann and H.Hidde De Jong. Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies.PLoS Computational Biology123March 2016, e1004802HALDOI
• 6 articleM.Marc Hoffmann and A.Aline Marguet. Statistical estimation in a randomly structured branching population.Stochastic Processes and their Applications129122019, 5236-5277HALDOI
• 7 articleJ.Jérôme Izard, C.Cindy Gomez-Balderas, D.Delphine Ropers, S.Stephan Lacour, X.Xiaohu Song, Y.Yifan Yang, A. B.Ariel B. Lindner, J.Johannes Geiselmann and H.Hidde De Jong. A synthetic growth switch based on controlled expression of RNA polymerase.Molecular Systems Biology1111November 2015, 840HALback to textback to text
• 8 articleA.Artémis Llamosi, A.Andres Gonzalez, C.Cristian Versari, E.Eugenio Cinquemani, G.Giancarlo Ferrari-Trecate, P.Pascal Hersen and G.Gregory Batt. What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast.PLoS Computational Biology122February 2016, e1004706HALDOI
• 9 articleA.Aline Marguet, M.Marc Lavielle and E.Eugenio Cinquemani. Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data.Bioinformatics35142019, i586-i595HALDOIback to text
• 10 articleM.Marco Mauri, J.-L.Jean-Luc Gouzé, H.Hidde De Jong and E.Eugenio Cinquemani. Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs.PLoS Computational Biology1642020, e1007795HALDOIback to textback to text
• 11 articleM.Manon Morin, D.Delphine Ropers, E.Eugenio Cinquemani, J.-C.Jean-Charles Portais, B.Brice Enjalbert and M.Muriel Cocaign-Bousquet. The Csr System Regulates Escherichia coli Fitness by Controlling Glycogen Accumulation and Energy Levels.mBio85October 2017, 1-14HALDOI
• 12 articleM.Manon Morin, D.Delphine Ropers, F.Fabien Letisse, S.Sandrine Laguerre, J.-C.Jean-Charles Portais, M.Muriel Cocaign-Bousquet and B.Brice Enjalbert. The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli.Molecular Microbiology1004May 2016, 686-700HALDOIback to textback to text
• 13 articleS.Stéphane Pinhal, D.Delphine Ropers, J.Johannes Geiselmann and H.Hidde De Jong. Acetate metabolism and the inhibition of bacterial growth by acetate.Journal of Bacteriology20113July 2019, 147 - 166HALDOI

11.2 Publications of the year

11.3 Cited publications

• 27 articleD.Delphine Ropers, Y.Yohann Couté, L.Laëtitia Faure, S.Sabrina Ferré, D.Delphine Labourdette, A.Arieta Shabani, L.Lidwine Trouilh, P.Perrine Vasseur, G.Gwénaëlle Corre, M.Myriam Ferro, M.-A.Marie-Ange Teste, J.Johannes Geiselmann and H.Hidde de Jong. Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application.ACS Synthetic Biology1011November 2021, 2910-2926HALDOIback to text
• 28 articleC.Charlotte Roux, T.Thibault Etienne, E.Eliane Hajnsdorf, D.Delphine Ropers, A. J.Agamemnon J. Carpousis, M.Muriel Cocaign-Bousquet and L.Laurence Girbal. The essential role of mRNA degradation in understanding and engineering E. coli metabolism.Biotechnology Advances2022, 1-13HALDOIback to text