2025Activity report​​​‌Project-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) 7, 35‌. 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 Latest software developments​

7.2 New​​​‌ platforms

Participants: Soraya Arias​, Eugenio Cinquemani,​‌ Johannes Geiselmann.

7.3​​ Open data

8.1 Quantifying​​ bacterial resource allocation on​​​‌ the single-cell level

Participants:‌ E. Cinquemani, H.‌​‌ de Jong, J.​​ Geiselmann, A. Marguet​​​‌, A. Schiavone.‌

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 34 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 Communications17​​, 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. The paper​​​‌ was selected as an​ Editor's Highlight in Nature​‌ Communications, in the​​ category Microbiology and infectious​​​‌ diseases. In follow-up​ work, in the context​‌ of the internship of​​ Amelio Schiavone , we​​​‌ have started to explore​ resource allocation models of​‌ the growth of individual​​ cells to account for​​ the experimental findings.

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​​ 30, we managed​​​‌ to improve the genetic‌ stability of the growth‌​‌ switch, by means of​​ a redundant control mechanism​​​‌ of RNA polymerase expression.‌ This reduces the occurrence‌​‌ of any spontaneously arising​​ mutations disabling the growth​​​‌ switch to less than‌ one in ${10}^{\mathrm{9‌‌}}$ cells 31. A​​ patent describing this improvement​​​‌ of the approach has‌ been filed and is‌​‌ under review. The transfer​​ of the approach is​​​‌ further explored in collaboration‌ with SATT Linksium, in‌​‌ the framework of the​​ maturation project Switch2Prod that​​​‌ will start in January‌ 2026 (Section 9.2).‌​‌ Thibault Clavier is the​​ leader of this project,​​​‌ aiming at the scale-up‌ of the approach and‌​‌ its application to a​​ broader range of molecules​​​‌ of biotechnological interest.

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, J.‌ Mokuinema Wawina.

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. With‌ a series of collaborative‌​‌ projects, including project CtrlAB​​ that ended in September​​​‌ 2025 (Section 10),‌ we picked up the‌​‌ challenge to design and​​ investigate the dynamics of​​​‌ synthetically engineered microbial communities,‌ toward the development and‌​‌ in vivo testing of​​​‌ optimal control strategies.

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. The​‌ engineering of the consortium​​ and its experimental characterization​​​‌ revealed a more complex​ picture than expected 37​‌. The experimental scrutiny​​ of new modelling hypotheses​​​‌ and the achievement of​ stable coexistence with the​‌ studied consortium are being​​ addressed with an effort​​​‌ that involves several team​ members and that constitutes​‌ the subject of a​​ paper in preparation.

In​​​‌ the context of project​ CtrlAB, modelling, analysis and​‌ control problems for an​​ algal-bacterial consortium are addressed​​​‌ with the PhD thesis​ of Rand Asswad. The​‌ consortium consists of bacteria​​ synthesizing vitamins that algae​​​‌ need for their growth,​ and an optogenetic control​‌ driving bacterial resource reallocation​​ from their own growth​​​‌ to vitamin synthesis. Lipid​ content of algae is​‌ of interest for a​​ wide range of bioproduction​​​‌ applications, thus the objective​ of maximizing algal biosynthesis.​‌ In a continuous-flow bioreactor​​ setup, optogenetic and dilution​​​‌ rate control gives rise​ to nontrivial tradeoffs. Taking​‌ further previously published results,​​ in 19, we​​​‌ extended the optmization study​ from a pure productivity​‌ objective to a broader​​ set of criteria. We​​​‌ notably formulated and studied​ a Pareto-optimality problem directly​‌ related with a class​​ of scalar economic-type objectives​​​‌ balancing productivity with process​ cost. Results have been​‌ presented at the 64th​​ IEEE Conference on Decision​​​‌ and Control (CDC 2025)​ 19. In parallel,​‌ we extended previous results​​ concerning the optimization of​​​‌ process productivity. We notably​ showed that pseudo-periodic control​‌ profiles naturally emerge as​​ optimal strategies from the​​​‌ concerted action of optogenetic​ and dilution rate control​‌ inputs, while periodicity is​​ lost if only one​​​‌ control input is available.​ This and other results​‌ (precise quantification of the​​ overyielding, limiting behaviours etc.)​​​‌ are the subject of​ a journal paper submitted​‌ for review.

8.4 Modelling​​ and inference of cellular​​​‌ metabolism

Participants: Y. Agbedoga​, M. Baumgärtner,​‌ A. Belcour, I.​​ Cancino-Aguirre, M. Cocaign-Bousquet​​​‌, H. de Jong​, N. Mogharbel,​‌ D. Ropers, B.-V.​​ Tan.

Microorganisms are​​​‌ regularly exposed to environmental​ perturbations. In order to​‌ thrive in new environments,​​ they must adapt their​​​‌ microbial metabolism. 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​​​‌ the recently defended PhD​ thesis of Ignacia Cancino​‌ Aguirre22, co-advised​​ by Delphine Ropers and​​ Hidde de Jong ,​​​‌ we develop such reconstructions‌ from genome sequences for‌​‌ the Mycobacterium genus. This​​ allows 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 validated​​​‌ experimentally by our partner,‌ L. Sorio de Carvalho‌​‌ (The Herbert Wertheim UF​​ Scripps Institute for Biomedical​​​‌ Innovation & Technology, Florida,‌ USA), and 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 10),‌​‌ 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 presented‌​‌ at the main bioinformatics​​ conference, ISMB/ECCB 2025, and​​​‌ published in the Bioinformatics‌ journal 16 (Section 6‌​‌). Tabigecy uses the​​ tool EsMeCaTa to infer​​​‌ consensus proteomes and metabolic‌ functions from taxonomic affiliations.‌​‌ EsMeCaTa is described in​​ a paper that is​​​‌ currently under submission 24‌. 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).​​​‌

A. Belcour and M.‌ Baumgärtner are currently extending‌​‌ the pipeline Tabigecy in​​ order to apply it​​​‌ to subsurface microbial communities.‌ The aim is to‌​‌ characterise their metabolic activity​​ and any potential detrimental​​​‌ effects on gas storage‌ in underground reservoirs. The‌​‌ first results obtained from​​ samples taken from 19​​​‌ European underground reservoirs are‌ described in a paper‌​‌ recently submitted for publication​​ 26. This study​​​‌ shows that geochemical conditions‌ and anthropogenic disturbances play‌​‌ a key role in​​ the structure and functional​​​‌ potential of deep subsurface‌ microbial communities. As outlined‌​‌ in the proceedings of​​ the Global Energy Transition​​​‌ Conference & Exhibition (EAGE‌ GET 2026, 20),‌​‌ some of these communities​​ can consume hydrogen in​​​‌ a laboratory setting. Further‌ experimental and modelling work‌​‌ is underway to characterise​​ the microbial interactions. Another​​​‌ work by A. Belcour‌ and collaborators studied the‌​‌ interactions between microbial groups​​ in the context of​​​‌ the holobiont formed by‌ the brown algae Ascophyllum‌​‌ nodosum and its microbiota​​ 18.

These studies​​​‌ all predict the potential‌ of microbes to carry‌​‌ out certain metabolic functions.​​ However, the actual realisation​​​‌ of these functions relies‌ on the gene expression‌​‌ program. Our previous research​​ has demonstrated the significance​​​‌ of regulating mRNA stability‌ in adapting metabolism to‌​‌ environmental changes 12,​​ 13, 33,​​​‌ 36. As part‌ of Y. Agbedoga 's‌​‌ PhD thesis supervised by​​​‌ D. Ropers and M.​ Cocaign-Bousquet and the ANR​‌ project RECOM (see Section​​ 10), and building​​​‌ on previous modelling work​ 4, mathematical and​‌ statistical modelling approaches are​​ being developed to infer​​​‌ the regulatory mechanisms underlying​ the genome-wide control of​‌ mRNA stability, based on​​ both high- and low-throughput​​​‌ biological data. A paper​ describing these results is​‌ in preparation.

8.5 Inference​​ of parameters on lineage​​​‌ trees

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

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 to enable​​​‌ the investigation of inheritance​ of traits and of​‌ emerging dynamics in bacterial​​ populations.

With the PhD​​​‌ thesis of E. Reginato,​ defended in November 2025,​‌ we have advanced on​​ the analysis and inference​​​‌ of tree-structured single-cell gene​ expression models with mother-daughter​‌ inheritance that we had​​ started in a previous​​​‌ publication 10. We​ addressed inference from single-cell​‌ gene expression data in​​ the case where lineage​​​‌ information is not available.​ We notably explored how​‌ well inheritance parameters can​​ be inferred depending on​​​‌ absence or presence of​ dynamics in the mean​‌ and variance data. In​​ relation with certain literature​​​‌ datasets from videomicroscopy, we​ developed statistically exact maximum-likelihood​‌ estimation methods leveraging correlation​​ of empirical means along​​​‌ generations, and approximate methods​ also exploiting variance data.​‌ Methods, simulation-based performance analysis​​ as well as demonstration​​​‌ of application to the​ reference dataset are presented​‌ as a first chapter​​ of the thesis manuscript​​​‌ 23.

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 thesis, we​​​‌ looked 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.​ Simulation results, as well​‌ as semi-quantitative comparison with​​ literature datasets from plasmid-mediated​​​‌ yeast growth in selective​ media, are discussed as​‌ another chapter of 23​​.

Related to the​​​‌ above studies, with the​ now-ended postdoc of Claudia​‌ Fonte Sanchez (funded in​​ 2025 on project IMOCEP,​​​‌ PEPR MathVives; Section 10​), we looked at​‌ individual-based models of population​​ growth and gene expression​​​‌ under different promoter regulatory​ mechanisms, and the inference​‌ of the regulatory function​​ from population-snapshot data. Our​​ results on the nonparametric​​​‌ statistical estimation of these‌ regulatory functions from stationary‌​‌ distributions constitute the material​​ of a paper in​​​‌ preparation for journal submission.‌

8.6 Mathematical analysis of‌​‌ structured branching populations

Participants:​​ E. Ferragu, C.​​​‌ Fonte-Sanchez, A. Marguet‌.

The investigation of‌​‌ cellular populations at the​​ 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. 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 now ended AnaComBa project,‌​‌ 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 2024 32.‌​‌ 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. In collaboration with​​ Charline Smadi (INRAE Grenoble)​​​‌ 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 under revision‌​‌ for Stochastic Processes and​​ their Applications25.​​​‌

Interacting systems of particles‌ are also used for‌​‌ the modelisation of populations​​ of neurons. In collaboration​​​‌ with Marc Hoffmann (Université‌ Paris-Dauphine), Claudia Fonte Sanchez‌​‌ proved theoretical properties of​​ such systems, by controling​​​‌ the fluctuations of the‌ empirical measure of the‌​‌ system around the solution​​ of the corresponding Vlasov-Fokker-Planck​​​‌ equation. They also studied‌ the nonparametric statistical estimation‌​‌ of the classical solution​​ of Vlasov-Fokker-Planck equation from​​​‌ the observation of the‌ empirical measure and proved‌​‌ an oracle inequality using​​ the Goldenshluger-Lepski methodology. Finally,​​​‌ they derived moment estimators‌ for the FitzHugh-Nagumo model‌​‌ for populations of neurons​​ 28.

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​ 27. This work​‌ has been accepted for​​ publication in Memoirs of​​​‌ the European Mathematical Society.​

9.1​​ Bilateral contracts with industry​​​‌

Amélie Caddeo is a​ CIFRE PhD student at​‌ the Toulouse-based bioinformatics company​​ iMEAN and the Institute​​​‌ of Research in Horticulture​ and Seeds at INRAE​‌ in Angers. The MICROCOSME​​ team is currently hosting​​​‌ her for the final​ year of her doctoral​‌ research, which focuses on​​ the mathematical modelling of​​​‌ the seed microbiome.

9.2​ Maturation project: Switch2Prod

Participants:​‌ Th. Clavier, H.​​ de Jong, J.​​​‌ Geiselmann.

Thibault Clavier​ , former PhD student​‌ in MICROCOSME, has received​​ support from SATT Linksium​​​‌ for the technology transfer​ project Switch2Prod (2026-2027). The​‌ project, coordinated by Hidde​​ de Jong and led​​​‌ by Thibault Clavier ,​ aims at improving the​‌ bioproduction performance of microorganisms​​ through dynamic control of​​​‌ their growth. It relies​ on the development of​‌ a genetic switch regulating​​ resource allocation to maximise​​​‌ either biomass or the​ synthesis of molecules of​‌ interest (see Section 8.2​​ for more details).

10.1​ International initiatives

10.2 European initiatives​‌

10.3 National​​ initiatives

10.4 Regional initiatives

The‌ following project has just‌​‌ been accepted and will​​ start in 2026:

11.1​​​‌ Promoting scientific activities

11.2 Teaching​​ - Supervision - Juries​​​‌ - Educational and pedagogical‌ outreach

12.1 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, 1-29HAL​DOI
• 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​‌.Bioinformatics3314​​2017, i301-i310HAL​​​‌DOI
• 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 2020​‌HALDOIback 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 Biology​‌123March 2016​​, e1004802HALDOI​​​‌
• 6 articleM.Marc​ Hoffmann and A.Aline​‌ Marguet. Statistical estimation​​ in a randomly structured​​​‌ branching population.Stochastic​ Processes and their Applications​‌129122019,​​ 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 Biology11​‌11November 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 Applications7​​142021HALDOI​​​‌
• 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-i595HAL‌​‌DOIback 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 Biology​​​‌1642020,‌ e1007795HALDOIback‌​‌ to text
• 12 article​​M.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​​back to text
• 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, 686-700HAL‌​‌DOIback to text​​back 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, 285‌​‌HALDOI
• 15 article​​S.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 Bacteriology201​​13July 2019,​​​‌ 147 - 166HAL‌DOI

12.2 Publications of‌​‌ the year

12.3 Cited​​ publications

• 29 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, 1-29‌​‌HALDOIback to​​ text
• 30 phdthesisT.​​​‌Thibault Clavier. Contrôle‌ génétique de la croissance‌​‌ et des conditions de​​ culture pour maximiser la​​​‌ production biotechnologique chez E.‌ coli.Université Grenoble‌​‌ AlpesMarch 2024HAL​​back to textback​​​‌ to text
• 31 article‌T.Thibault Clavier,‌​‌ C.Corinne Pinel,​​ H.Hidde de Jong​​​‌ and J.Johannes Geiselmann‌. Improving the genetic‌​‌ stability of bacterial growth​​ control for long-term bioproduction​​​‌.Biotechnology and Bioengineering‌1219June 2024‌​‌, 2808-2819HALDOI​​back to text
• 32​​​‌ articleC.Charles Medous‌. Spinal constructions for‌​‌ continuous type-space branching processes​​ with interactions.Electronic​​​‌ Journal of Probability29‌January 2024, 1-46‌​‌HALDOIback to​​ text
• 33 articleM.​​​‌Manon Morin, B.‌Brice Enjalbert, D.‌​‌Delphine Ropers, L.​​​‌Laurence Girbal and M.​Muriel Cocaign-Bousquet. Genomewide​‌ Stabilization of mRNA during​​ a ''Feast-to- Famine'' Growth​​​‌ Transition in Escherichia coli​.MSphere53​‌June 2020HALDOI​​back to text
• 34​​​‌ phdthesisA.Antrea Pavlou​. Quantification of bacterial​‌ resource allocation in changing​​ environments on the single-cell​​​‌ level.Université Grenoble​ Alpes [2020-....]July 2022​‌HALback to text​​
• 35 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 Biology1011​November 2021, 2910-2926​‌HALDOIback to​​ text
• 36 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, 1-13​​​‌HALDOIback to​ text
• 37 phdthesisM.​‌ F.Maaike Fonsine Sangster​​. Development, characterization and​​​‌ control of E. coli​ communities on an automated​‌ experimental platform.Université​​ Grenoble AlpesMay 2023​​​‌HALback to text​back to text