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.

Show image description

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) 737. 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 transformation of vegetal waste into value-added product.

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.

4.3 Environmental microbiology

Microbial subsurface ecology is poorly characterised. Current knowledge suggests that the subsurface is rich in microbial biodiversity, whose metabolic activity influences global biogeochemical cycles (e.g. carbon and nitrogen cycles). The metabolic potential of subsurface microbes can be inferred from high-throughput sequencing data, but this remains a difficult bioinformatics problem. We have started to extend our analyses of metabolism to the prediction of biogeochemical cycles from metabarcoding data. The approaches developed should help us to assess the microbial risk of underground hydrogen storage as part of our collaboration with BRGM and our partners in the European HyLife project.

7.1 New software

7.2 New platforms

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

7.3 Open data

8.1 Quantifying bacterial resource allocation on the single-cell level

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

Microbial growth involves the conversion of nutrients from the environment into biomass. The main component of biomass are proteins, which also play a major role in the synthesis of new biomass by functioning as enzymes in metabolism and by constituting the molecular machinery responsible for the synthesis of proteins and other macromolecules. Microbial growth therefore requires the coordinated investment of cellular resources in different categories of proteins. Ribosomes are probably the most important protein category for two reasons. First, they are responsible for the synthesis of all proteins in the cell. Second, they are themselves very costly to make: ribosomes constitute up to 40-50% of the total protein mass in Escherichia coli.

Very few studies have addressed the quantification of ribosomal resource allocation on the single-cell level. How do the resources allocated to the synthesis of ribosomal proteins, both during balanced and unbalanced growth, vary over the individual cells of an isogenic population? In order to answer this question, in the framework of the PhD thesis of Antrea Pavlou 36 and the ANR project Maximic (2017-2023), we constructed chromosomal reporter systems for monitoring ribosome expression in the model organism Escherichia coli. We measured the ribosome concentration in individual cells growing on a rich or a poor carbon source, as well as changes in ribosome concentration during upshifts and downshifts between these carbon sources, over extended periods of time (>80 generations). Moreover, we developed a method for the statistical inference of time-varying ribosome synthesis rates from the single-cell, time-course data thus acquired.

We found that, during balanced growth in a given medium, the bacteria display a wide variety of ribosome concentrations that are only weakly correlated with the single-cell growth rate. This would not be expected if bacteria had optimized costly ribosome expression to precisely match the protein synthesis rate required for a certain growth rate. During the upshift from a poor to a rich carbon source, we observed that cells with a higher pre-shift ribosome concentration more rapidly adapt their ribosome synthesis rate, but also the ribosome synthesis activity and the growth rate, to the new environment. We remark that these observations are consistent with the existence of a variable ribosome reserve which the bacterial cells may exploit to speed up adaptation to sudden changes in the environment.

In this study published in Nature Communications20, we thus quantified, using a combination of reporter genes and statistical inference algorithms, dynamic investment in ribosomes on the single-cell level. The results reveal a surprising variability in the allocation of resources to ribosomes, the most costly molecular machine in bacterial cells, during both balanced and unbalanced growth. This raises fundamental questions on the role of the variability of ribosome concentrations in shaping the growth of a bacterial population and its adaptation to changing environments. Given the importance of growth and adaptation in biomedical and biotechnological applications, we expect our findings to have practical implications as well.

8.2 Biotechnological applications of bacterial growth control

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

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, defended in March 2024 28, 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 was published in Biotechnology and Bioengineering 16. Moreover, this work is the subject of the start-up project Switch2Prod, led by Thibault Clavier and to be submitted to the SATT Linksium.

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

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

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, 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 previously developed an ODE model of the consortium, and analysed the model to characterize the conditions supporting coexistence and the tradeoffs involved in the production process 11, 35. The engineering of the consortium and its experimental characterization revealed a more complex picture as witnessed by partially unexpected results 38, which led us to formulate new hypotheses on the interactions among species and their intertwining with the E. coli overflow metabolism. The experimental scrutiny of these hypotheses and the achievement of stable coexistence with the studied consortium are object of an ongoing effort that involves several team members and that will be made value of by a journal paper submission. A subsequent aim for this research direction is the feedback control of the community for the robust stabilization of biomasses into the most desirable bioproduction regimes.

On the methodological side, in collaboration with BIOCORE, modelling, analysis and control problems are being addressed with the Ph.D. thesis of Rand Asswad. A simple ODE model of the above algal-bacterial consortium was developed based on literature knowledge, also integrating data from the partners for algal growth parameters. With reference to this model, toward model-based feedback control of the consortium, we first focused on the problem of real-time state estimation of microbial growth in a bioreactor. Several different Kalman-based approaches, tackling the challenge of the model nonlinearity by suitable “quasi-linear” problem reformulations, have been proposed in a paper that has been presented at and published as part of the proceedings of the 22nd European Control Conference (ECC 2024, Stockholm, Sweden) 24. Focused on a single species, the methods will be generalized and integrated into adaptive feedback control strategies currently being explored. We next focused on the problem of optimal productivity of algal biomass. We explored optimal control problems from a static and a dynamic viewpoint, and obtained intriguing results on the convexity of the static problem, and on the spontaneous onset of oscillatory control laws in the dynamic case leading to over-yielding. These results were presented at and published in the proceedings of the 63rd IEEE Conference on Decision and Control (CDC 2024, Milan, Italy) 25. A more complete set of results and a broader investigation are being arranged in the form of a journal paper to be submitted in the near future.

8.4 Modelling and inference of cellular metabolism

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

Microorganisms are regularly exposed to environmental perturbations. This requires an adaptation of the microbial metabolism to thrive in new environments. In order to study the mechanisms of metabolic adaptation, we use genome-scale reconstructions of cellular metabolism, such as in 13. These networks are reconstructed using genomic annotations, in which genes encoding enzymes are linked to metabolic reactions. Such reconstructions are often available in public databases for well-studied microorganisms, but this is not the case for poorly studied species. As part of Ignacia Cancino Aguirre 's PhD thesis co-advised by Delphine Ropers and Hidde de Jong and the associate-team GERM (Section 9), we aim to develop such reconstructions from genome sequences for the Mycobacterium genus. This will allow us 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. The results of this study are currently being prepared for publication.

Inferring metabolic function from sequenced genomes is a difficult problem, but even more so when dealing with microbial communities in natural environments. In collaboration with BRGM, the French geological survey, NORCE (Norway) and Isodetect Gmbh (Germany) in the framework of the European project HyLife (Section 9), Arnaud Belcour , Hidde de Jong , and Delphine Ropers have begun to address this issue. They developed a bioinformatics pipeline, Tabigecy (7.1.5), to use metabarcoding data in order to predict metabolic functions that make up biogeochemical cycles. A paper describing the approach has been submitted for publication. Tabigecy uses the tool EsMeCaTa to infer consensus proteomes and metabolic functions from taxonomic affiliations. EsMeCaTa is described in a recently submitted paper 30. This software can be time-consuming to run, which prompted us to develop a precomputed database of EsMeCaTa (Section 7.3) using the Gricad infrastructure supported by the Grenoble research community, in collaboration with Loris Mégy (Gricad, Inria, CNRS, Université Grenoble Alpes, Grenoble INP). As described in the proceedings of the Solution Mining Research Institute Spring conference (SMRI 2024, 23) and of the annual conference of the European Association of Geoscientists & Engineers (85th EAGE, 26), the next step will be to apply the pipeline Tabigecy to subsurface microbial communities to characterise their metabolic activity and possible detrimental effects on gas storage in underground reservoirs.

The functional predictions returned by the EsMeCaTa tool open up the possibility of classifying metabolic functions. Arnaud Belcour studied this question in collaboration with the Inria project-team DYLISS, the laboratory NuMeCan (INRAe, INSERM) and CHU Rennes. They developed an automated pipeline called SPARTA (Shifting Paradigms to Annotation Representation from Taxonomy to identify Archetypes) to discriminate between unhealthy and control samples in the study of the gut microbiome 21. An evolutionary perspective on metabolism was then adopted in a recent study 22. In this paper, Arnaud Belcour and his co-authors analysed possible mechanisms leading to metabolic plasticity in plant and algal lipid biosynthesis pathways.

8.5 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 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 10. 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 have been presented at and published in the proceedings of the 22nd European Control Conference (ECC 2024, Stockholm, Sweden) 27. Demonstration on the reference datasets proved promising, however adaptations will be necessary for best exploiting real datasets.

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, and obtained a first understanding of the role of plasmid repartition statistics and stochastic cell division in the emerging population dynamics. The study is being taken further with thorough simulation-based and mathematical characterization of the individual-based population model dynamics across selective and non-selective media, and comparison with real data.

Related to the above studies, Claudia Fonte Sanchez 's postdoc across projects AnaComBa (Persyval) and IMOCEP (PEPR MathVives; Section 9) is looking at individual-based models of population growth and gene expression, and the investigation of the relation between growth-rate variability across cells and gene expression distribution in population-snapshot data. A first set of results on the nonparametric statistical estimation of single-cell protein synthesis kernels from stationary distributions is being arranged in the form of a paper, for journal submission in the near future. Model-based analysis of population dynamics and identifiability of protein synthesis, cell division and repartition kernels from transient snapshot data will further be addressed, along with comparison with experimental data, for a possible second publication from this activity.

8.6 Mathematical analysis of structured branching populations

Participants: E. Ferragu, 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 allow 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 these 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, published in Stochastic Processes and their Applications 18, the dynamics of the cell population is modeled 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 behavior 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 different sharing strategies on the long-term behavior of the infection in the case of a constant division rate was investigated in a follow-up paper published in the Journal of Mathematical Biology 17. Perfectly symmetric sharing of the parasites between the two daughter cells proved to be the worst strategy for the survival of the cell population. Stochastic and deterministic strategies were also compared, highlighting that variability favors survival. Finally, we proved that there is a partitioning kernel that allows the cell population to survive the infection, regardless of the strength of the infection. The proofs are based on a spinal decomposition, which allows us to study the behavior of the whole population through the fate of a well-chosen lineage.

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 (Section 9), for the study of microbial communities, such tools appear to be needed for structured branching processes with interactions and were developed by Charles Medous during his PhD, defended in December 2024 29. Charles Medous established a spinal construction and 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. An article based on this work has been published in the Electronic Journal of Probability 19. In collaboration with Charline Smadi and Sylvain Billiard (Université de Lille), Charles Medous also extended the spinal construction to diffusive population to study the role of environmental noise in growing colonies. They have proved that the repartition of the population depends crucially on the comparison between the individual and the environmental noise. The results of this study are currently being prepared for publication. Charles Medous also used spine techniques to propose an exact simulation algorithm for individual-based population models. He provided many applications of this algorithm as well as its Python implementation. A paper on this new simulation algorithm is also in preparation.

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 31.

9.1 International initiatives

9.2 European initiatives

9.3 National initiatives

The following project has just been accepted and will start in spring 2025:

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

Hidde de Jong supervised a one-week lab internship of two highschool students participating in the U-Talent program of Utrecht University (the Netherlands). Delphine Ropers took part in the "Journée Filles, Math et Info" at ENSIMAG. The aim of this event is to make high-school girls aware of careers in computer science and mathematics.

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 2020HALDOI
• 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 articleF.Francis Mairet, J.-L.Jean-Luc Gouzé and H.Hidde De Jong. Optimal proteome allocation and the temperature dependence of microbial growth laws.npj Systems Biology and Applications7142021HALDOI
• 10 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
• 11 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 text
• 12 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
• 13 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 text
• 14 articleA.Antrea Pavlou, E.Eugenio Cinquemani, C.Corinne Pinel, N.Nils Giordano, M.Mathilde Van Melle-Gateau, I.Irina Mihalcescu, J.Johannes Geiselmann and H.Hidde de Jong. Single-cell data reveal heterogeneity of investment in ribosomes across a bacterial population.Nature Communications161January 2025, 285HALDOI
• 15 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

• 32 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, 1-29HALDOIback to text
• 33 miscE.Eugenio Cinquemani and L.Lo\"ic Paulevé. Special Issue of the 19th International Conference on Computational Methods in Systems Biology.May 2023HALback to text
• 34 phdthesisN.Nils Giordano. Microbial growth control in changing environments : Theoretical and experimental study of resource allocation in Escherichia coli.Université Grenoble AlpesMarch 2017HALback to text
• 35 articleC.Carlos Martinez, E.Eugenio Cinquemani, H.Hidde de Jong and J.-L.Jean-Luc Gouzé. Optimal protein production by a synthetic microbial consortium: Coexistence, distribution of labor, and syntrophy.Journal of Mathematical Biology8712023, 1-37HALDOIback to text
• 36 phdthesisA.Antrea Pavlou. Quantification of bacterial resource allocation in changing environments on the single-cell level.Université Grenoble Alpes [2020-....]July 2022HALback to textback to text
• 37 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
• 38 phdthesisM. F.Maaike Fonsine Sangster. Development, characterization and control of E. coli communities on an automated experimental platform.Université Grenoble AlpesMay 2023HALback to textback to text