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 developments 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) 6. 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

8.1 Analysis of fluorescent reporter genes

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

In the past few years we developed methods for the analysis of reporter gene data obtained in microplate experiments. These methods solve a range of estimation problems, notably the inference of growth rate, promoter activity, and protein concentration profiles. In the framework of her PhD thesis, Antrea Pavlou has continued this work by addressing a major issue in the interpretation of fluorescent data, namely that the inference of promoter activities is strongly dependent on the maturation properties of fluorescent proteins. We have therefore proposed an approach for incorporating maturation dynamics in the reconstruction of promoter activities. Our approach consists in developing and calibrating mechanistic maturation models for distinct fluorescent proteins. These models are then used alongside a Bayesian approach to estimate promoter activities from fluorescence data. We demonstrated by means of targeted experiments in Escherichia coli that our approach provides robust estimates, and that accounting for maturation is, in many cases, essential for the interpretation of gene expression data. This work has been submitted for publication.

8.2 Resource allocation models of bacterial growth

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

In recent years, in the framework of the PhD thesis of Nils Giordano, we extended the study of microbial growth strategies to dynamical environments, using a self-replicator model. In collaboration with the BIOCORE project-team, we formulated dynamical growth maximization as an optimal control problem that can be solved using Pontryagin’s Maximum Principle and we compared the theoretical results thus obtained with different possible implementations of growth control in bacterial cells 4. The experimental validation of some of these results are currently being carried out by Antrea Pavlou in the framework of her PhD project, funded by the ANR project Maximic (Section 9).

Also within the context of Maximic, we have worked on several extensions of the original self-replicator model. In collaboration with the BIOCORE and McTAO project-teams, we developed a model that accounts for additional classes of proteins, in particular a class of growth-rate-independent housekeeping proteins in addition to ribosomes and enzymes. Moreover, the model takes into account that the protein synthesis rate does not linearly depend on the total ribosome concentration, but only on its growth-rate-dependent fraction. In a paper accepted for publication in SIAM Journal on Applied Dynamical Systems 20, we perform an optimal control analysis of the extended model, using a combination of analytical and numerical tools. Our major conclusion is that, while the predicted dynamics of the system does not significantly change in comparison with the original model, the extended model more closely adheres to the experimental data. Moreover, it is capable of predicting the effects of metabolic load due to heterologous protein expression on the growth-rate dynamics.

In another follow-up work, carried out in collaboration with Francis Mairet (Ifremer Nantes) and Jean-Luc Gouzé (BIOCORE), we studied the effect of temperature on microbial growth, in particular the role of proteome allocation in bringing about temperature-induced changes. To tackle this problem, we propose an extended coarse-grained model of microbial growth that includes the processes of temperature-sensitive protein unfolding and chaperone-assisted (re)folding. We determine the proteome sector allocation that maximizes balanced growth rate as a function of nutrient limitation and temperature. Calibrated with quantitative proteomic data for Escherichia coli, the model allows us to bring out general principles of temperature-dependent proteome allocation and growth laws. The same activation energy for enzymes and ribosomes leads to an Arrhenius increase in growth rate at constant proteome composition over a large range of temperatures, whereas at extreme temperatures resources are diverted away from growth to chaperone-mediated stress responses. Our approach points at risks and possible remedies for the use of ribosome content to characterize complex ecosystems with temperature gradients. A paper based on this work was published in the journal npj Systems Biology and Applications this year 15.

8.3 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 6, 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 an article published in ACS Synthetic Biology 18, we investigated the scalability of the growth switch in culture conditions that are closer to standard process conditions in biotechnology. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

8.4 Analysis of synthetic microbial communities for bioproduction processes: real-time monitoring and optimality

Participants: E. Cinquemani, H. de Jong, J. Geiselmann, T. Muszbek, G. Roudaut, 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 (Section 9), 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 IBIS/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. The synthetic consortium is now at an advanced bioengineering stage. Ongoing model re-calibration based on our own data, including experiments on our automated platform (Section 3), now allows us to implement feedback control strategies for stabilization of biomasses to desirable bioproduction regimes. This work is currently being carried out by Maaike Sangster in the framework of her PhD thesis.

The developments in 10 have spurred a number of novel questions with theoretical and practical relevance. Model-based feedback control of microbial communities requires as a first step to be able to estimate the full system state in real time (abundance of the different species, but also current metabolite concentrations in the bioreactor). This is a nontrivial problem since typically only partial, indirect state measurements are available. In a publication in the International Journal of Robust and Nonlinear Control 14, in collaboration with VALSE, we have addressed observability for the (nonlinear dynamical) model in 10 and the development of real-time estimators under the realistic assumption that only biomass abundance is monitored, and that the contribution from different species is discerned via fluorescent reporters. We have notably shown that, while total biomass measurements may suffice to estimate individual biomasses with a perfectly known model, the simultaneous use of the reporter measurements allows one to do so in absence of specific knowledge of the interactions among species, and is essential for estimating substrate concentrations.

In parallel, we have pushed further the investigation of the synthetic community design for bioproduction optimization. In our previous work 10, bioproduction is accomplished by one species, while the second species grows on byproducts excreted by the first and eventually impairing the growth of the latter. Although this helps growth of the producing species, we investigated whether a modification of the second species so as to exploit the byproducts of the first species for additional bioproduction could improve the performance of the consortium as a whole. In work in collaboration with BIOCORE that we just submitted 25, in addition to a theoretical analysis of equilibria and coexistence regimes for this generalized community, we demonstrate that a second producing species can indeed outperform our original consortium design, 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). The ANR Ctrl-AB puts these concepts to work for the synergistic growth of E. coli and microalgea (Section 9).

8.5 Modelling and inference of mRNA degradation

Participants: A. Caddeo, M. Cocaign-Bousquet, T. Etienne, O. Feudjio, D. Ropers.

Metabolic engineering strategies are crucial for the development of bacterial cell factories with improved performance. Until now, metabolic networks capable of (optimally) producing a certain compound of interest have been designed based on approaches integrating large-scale data on the steady-state concentrations of mRNA, protein and metabolites, sometimes with dynamic data on fluxes, but rarely with any information on mRNA degradation.

In a review published in Biotechnology Advances with colleagues at the Toulouse Biotechnology Institute and IBPC Paris 19, Thibault Etienne, Delphine Ropers, and Muriel Cocaign-Bousquet have compiled evidence that mRNA degradation is a key regulatory level in E. coli that metabolic engineering strategies should take into account. The degradation of mRNAs interacts with transcription and translation, two other gene expression processes, while mRNA degradation and cellular metabolism are intertwined: metabolic activity can be controlled by changes in mRNA degradation and in return, the activity of the mRNA degradation machinery is controlled by metabolic factors. This calls for mathematical models describing the crosstalk between mRNA degradation dynamics and other cellular processes that can be used to design novel mRNA degradation-based metabolic engineering strategies in biotechnology. While some efforts in this direction have started, model integration of different biological processus remains challenging. In the framework of the ANR project RIBECO (Section 9), Thibault Etienne, Muriel Cocaign-Bousquet and Delphine Ropers have developed a model of mRNA degradation that allows to analyse the regulation of mRNA degradation 3. The model is currently being used to interpret large data sets corresponding to the degradation kinetics of 4254 mRNAs in E. coli cells growing in different environmental and genetic backgrounds.

8.6 Inference of parameters on lineage trees

Participants: E. Cinquemani, A. Marguet, E. Reginato.

Modelling and identification of gene expression models with mother-daughter inheritance have been investigated previously through a collaboration among Eugenio Cinquemani, Marc Lavielle (XPOP, Inria Saclay–Île-de-France) and Aline Marguet, which led to a publication in Bioinformatics 9. Here, a novel method generalizing the Mixed-Effects (ME) paradigm to tree-structured populations, called ARME, was developed for the inference of an Auto-Regressive (AR) inheritance model of kinetic gene expression parameters. The method assumes deterministic gene expression dynamics, which constitutes a limitation for systems with strong gene expression noise. Work this year addressed generalization of the method to stochastic gene expression dynamics. Focusing on noisy promoter activation dynamics in response to an environmental stimulus common to all cells, we considered a model where promoter activation is described by an Ornstein-Uhlenbeck process, and extended the ARME approach so as to cope with this additional noise source via Kalman filtering techniques. We demonstrated in simulation that the new method ourperforms its predecessor in case of significant promoter noise. The work was presented at the international IFAC conference SysId 2021 and published in the conference proceedings 16. Further exploration of the subject, including inference of inheritance dynamics from different types of data, constitutes the subject of the Ph.D. project of Emrys Reginato, started in the fall of 2020.

8.7 Mathematical analysis of structured branching populations

Participant: A. Marguet.

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, such as division or ageing, has been investigated 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 continuous-state nonlinear branching processes with catastrophes. This work, which has been published in the Electronic Journal of Probability 17, is motivated by the study of parasite infection in a cell line. The continuous-state branching processes model the dynamic of the parasites inside a cell and the catastrophes model cellular divisions that dilute the number of parasites in the cell. We pinpoint conditions on the dynamics of the parasites to explode or get extinct in a cell line. Combining some results of 17 and the Many-to-one formula proved in 27 that characterizes the dynamic of a typical individual, we investigated the long-time behaviour of an infected cell population. In this companion work, submitted for publication 26, we model the dynamic of the cell population using a structured branching process where the cell cycle depends on the dynamics of the parasites contained in the cell. We investigate the role of different sharing strategies at division on the fate of the cell population and give conditions for recovery or proliferation of the infection. In particular, we proved that for a constant division rate, according to our model, the more asymetric the repartition of the parasites at division, the more the cell population can survive strong parasite infections.

The study of the asymptotic behavior of general non-conservative semigroups is important for several aspects of branching processes, especially to prove the efficiency of statistical procedures. Vincent Bansaye from École Polytechnique, Bertrand Cloez from INRAE Montpellier, Pierre Gabriel from Université Versailles Saint-Quentin, and Aline Marguet, obtained necessary and sufficient conditions for uniform exponential contraction in weighted total variation norm of non-conservative semigroups. It ensures the existence of Perron eigenelements and provides quantitative estimates of spectral gaps, complementing Krein-Rutman theorems and generalizing recent results relying on probabilistic approaches. This work has been submitted for publication 24.

8.8 Qualitative modelling of synthetic gene circuits

Participant: H. de Jong.

Qualitative modelling approaches are promising and still underexploited tools for the analysis and design of synthetic gene circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. In a book chapter for an edited volume 21, co-written with Madalena Chaves (BIOCORE), we review qualitative modelling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, the discrete representations of the network dynamics that are generated in both modelling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modelling approaches to rapidly scan a space of design alternatives.

9.1 International initiatives

H. de Jong and D. Ropers collaborate with T. Gedeon (Montana State University), former invited researcher in our former team IBIS, on research allocation strategies in microorganisms.

I. Cancino Aguirre, H. de Jong and D. Ropers have started a collaboration with L. Carvalho (Francis Crick Institute, London) on the molecular control of the growth and pathogenicity of mycobacterium species.

9.2 National initiatives

In addition to the above projects, A. Marguet contributes to the ANR JCJC NOLO of Bertrand Cloez (INRAE Montpellier) on non-local branching processes. J. Geiselmann and H. de Jong work with P. Hersen (Unité Physico-chimique, Institut Curie) on growth control in microorganisms.

9.3 Regional initiatives

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

11.1 Major publications

• 1 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-i310
  HALDOI
• 2 articleE.Eugenio Cinquemani. Stochastic reaction networks with input processes: Analysis and application to gene expression inference.Automatica1012019, 150-156
  HALDOI
• 3 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
• 4 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, e1004802
  HALDOIback to text
• 5 articleM.Marc Hoffmann and A.Aline Marguet. Statistical estimation in a randomly structured branching population.Stochastic Processes and their Applications129122019, 5236-5277
  HALDOI
• 6 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, 840
  HALback to textback to text
• 7 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, e1004706
  HALDOI
• 8 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 Applications7142021
  HALDOI
• 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-i595
  HALDOIback 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, e1007795
  HALDOIback to textback to textback 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, 1-14
  HALDOI
• 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, 686-700
  HALDOI
• 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 - 166
  HALDOI

11.2 Publications of the year

11.3 Cited publications

• 26 unpublishedA.Aline Marguet and C.Charline Smadi. Parasite infection in a cell population with deaths. 2020, Preprint
  HALback to text
• 27 articleA.Aline Marguet. Uniform sampling in a structured branching population.Bernoulli254A2019, 2649-2695
  HALDOIback to text