When confronted with changing environmental conditions, bacteria and other microorganisms have a remarkable capacity to adapt their functioning. The responses of bacteria to changes in their environment are controlled on the molecular level by large and complex networks of biochemical interactions involving genes, mRNAs, proteins, and metabolites. The study of bacterial regulatory networks requires experimental tools for mapping the interaction structure of the networks and measuring the dynamics of cellular processes. In addition, when dealing with such large and complex systems, we need mathematical modeling and computer simulation to integrate available biological data, and understand and predict the dynamics of the system under various physiological and genetic perturbations. The analysis of living systems through the combined application of experimental and computational methods has gathered momentum in recent years under the name of systems biology.
The first aim of the IBIS project-team is to apply such a systems-biology approach to gain a deeper understanding, on the mechanistic level, of the strategies that bacteria have developed to respond to changes in their environment.
The aims of IBIS raise new questions on the interface of biology, applied mathematics, and computer science. In particular, the following objectives have structured the work of the project-team: (1) the analysis of the qualitative dynamics of gene regulatory networks, (2) the inference of gene regulatory networks from time-series data, (3) the analysis of integrated metabolic and regulatory networks, and (4) natural and engineered control of regulatory networks. Although these axes cover most of the work carried out in IBIS, some members have also made contributions to research projects on different topics. Since this usually represents a minor proportion of the overall research effort of the project-team, we will not describe this work in detail in the activity report. The publications resulting from these side-tracks have been included in the bibliography.
The challenges of the research programme of the IBIS team require
a wide range of competences on the interface of (experimental)
biology, applied mathematics, and computer science (Figure ). Since no
single person can be expected to possess all of these competences,
the international trend in systems biology is to join researchers
from different disciplines into a single group. In line with this
development, the IBIS team is a merger of a microbiology and
molecular genetics group on the one hand, and a bioinformatics and
mathematical biology group on the other hand. In particular, the
IBIS team is composed of members of the group of Johannes
Geiselmann, formerly at the Laboratoire Adaptation et Pathogénicité
des Microorganismes of the Univ Joseph Fourier (UJF,
CNRS UMR 5163), and since September 2014 at the Laboratoire Interdisciplinaire de Physique (CNRS UMR 5588), and the members of the network modeling and simulation group
formerly part of the HELIX project-team at Inria Grenoble -
Rhône-Alpes, a group coordinated by Hidde de Jong. The
two groups have established a fruitful collaboration, which has
resulted in more than 80 peer-reviewed publications in journals,
conferences, and books since 2000.
Hidde de Jong is the head of the IBIS project-team and Johannes Geiselmann its co-director. The experimental component of IBIS is also part of the Laboratoire Interdisciplinaire de Physique, and Johannes Geiselmann continues to represent this group in the interactions with the laboratory and university administration.
The dynamics of gene regulatory networks can be modeled by means of ordinary differential equations (ODEs), describing the rate of synthesis and degradation of the gene products as well as regulatory interactions between gene products and metabolites. In practice, such models are not easy to construct though, as the parameters are often only constrained to within a range spanning several orders of magnitude for most systems of biological interest. Moreover, the models usually consist of a large number of variables, are strongly nonlinear, and include different time-scales, which makes them difficult to handle both mathematically and computationally. This has motivated the interest in qualitative models which, from incomplete knowledge of the system, are able to provide a coarse-grained picture of its dynamics.
A variety of qualitative modeling formalisms have been introduced over the past decades. Boolean or logical models, which describe gene regulatory and signalling networks as discrete-time finite-state transition systems, are probably most widely used. The dynamics of these systems are governed by logical functions representing the regulatory interactions between the genes and other components of the system. IBIS has focused on a related, hybrid formalism that embeds the logical functions describing regulatory interactions into an ODE formalism, giving rise to so-called piecewise-linear differential equations (PLDEs, Figure ). The use of logical functions allows the qualitative dynamics of the PLDE models to be analyzed, even in high-dimensional systems. In particular, the qualitative dynamics can be represented by means of a so-called state transition graph, where the states correspond to (hyperrectangular) regions in the state space and transitions between states arise from solutions entering one region from another.
First proposed by Leon Glass and Stuart Kauffman in the early seventies, the mathematical analysis of PLDE models has been the subject of active research for more than four decades. IBIS has made contributions on the mathematical level, in collaboration with the BIOCORE and BIPOP project-teams, notably for solving problems induced by discontinuities in the dynamics of the system at the boundaries between regions, where the logical functions may abruptly switch from one discrete value to another, corresponding to the (in)activation of a gene. In addition, many efforts have gone into the development of the computer tool Genetic Network Analyzer (GNA) and its applications to the analysis of the qualitative dynamics of a variety of regulatory networks in microorganisms. Some of the methodological work underlying GNA, notably the development of analysis tools based on temporal logics and model checking, which was carried out with the Inria project-teams CONVEX (ex-VASY) and POP-ART, has implications beyond PLDE models as they apply to logical and other qualitative models as well.
Measurements of the transcriptome of a bacterial cell by means of DNA microarrays, RNA sequencing, and other technologies have yielded huge amounts of data on the state of the transcriptional program in different growth conditions and genetic backgrounds, across different time-points in an experiment. The information on the time-varying state of the cell thus obtained has fueled the development of methods for inferring regulatory interactions between genes. In essence, these methods try to explain the observed variation in the activity of one gene in terms of the variation in activity of other genes. A large number of inference methods have been proposed in the literature and have been successful in a variety of applications, although a number of difficult problems remain.
Current reporter gene technologies, based on Green Fluorescent Proteins (GFPs) and other fluorescent and luminescent reporter proteins, provide an excellent means to measure the activity of a gene in vivo and in real time (Figure ). The underlying principle of the technology is to fuse the promoter region and possibly (part of) the coding region of a gene of interest to a reporter gene. The expression of the reporter gene generates a visible signal (fluorescence or luminescence) that is easy to capture and reflects the expression of a gene of interest. The interest of the reporter systems is further enhanced when they are applied in mutant strains or combined with expression vectors that allow the controlled induction of any particular gene, or the degradation of its product, at a precise moment during the time-course of the experiment. This makes it possible to perturb the network dynamics in a variety of ways, thus obtaining precious information for network inference.
The specific niche of IBIS in the field of network inference has been the development and application of genome engineering techniques for constructing the reporter and perturbation systems described above, as well as the use of reporter gene data for the reconstruction of gene regulation functions. We have developed an experimental pipeline that resolves most technical difficulties in the generation of reproducible time-series measurements on the population level. The pipeline comes with data analysis software that converts the primary data into measurements of time-varying promoter activities. In addition, for measuring gene expression on the single-cell level by means of microfluidics and time-lapse fluorescence microscopy, we have established collaborations with groups in Grenoble and Paris. The data thus obtained can be exploited for the structural and parametric identification of gene regulatory networks, for which methods with a solid mathematical foundation are developed, in collaboration with colleagues at ETH Zürich and EPF Lausanne (Switzerland). The vertical integration of the network inference process, from the construction of the biological material to the data analysis and inference methods, has the advantage that it allows the experimental design to be precisely tuned to the identification requirements.
The response of bacteria to changes in their environment involves responses on several different levels, from the redistribution of metabolic fluxes and the adjustment of metabolic pools to changes in gene expression. In order to fully understand the mechanisms driving the adaptive response of bacteria, as mentioned above, we need to analyze the interactions between metabolism and gene expression. While often studied in isolation, gene regulatory networks and metabolic networks are closely intertwined. Genes code for enzymes which control metabolic fluxes, while the accumulation or depletion of metabolites may affect the activity of transcription factors and thus the expression of enzyme-encoding genes.
The fundamental principles underlying the interactions between gene expressions and metabolism are far from being understood today. From a biological point of view, the problem is quite challenging, as metabolism and gene expression are dynamic processes evolving on different time-scales and governed by different types of kinetics. Moreover, gene expression and metabolism are measured by different experimental methods generating heterogeneous, and often noisy and incomplete data sets. From a modeling point of view, difficult methodological problems concerned with the reduction and calibration of complex nonlinear models need to be addressed.
Most of the work carried out within the IBIS project-team specifically addressed the analysis of integrated metabolic and gene regulatory networks in the context of E. coli carbon metabolism (Figure ). While an enormous amount of data has accumulated on this model system, the complexity of the regulatory mechanisms and the difficulty to precisely control experimental conditions during growth transitions leave many essential questions open, such as the physiological role and the relative importance of mechanisms on different levels of regulation (transcription factors, metabolic effectors, global physiological parameters,
]
The adaptation of bacterial physiology to changes in the environment, involving changes in the growth rate and a reorganization of gene expression, is fundamentally a resource allocation problem. It notably poses the question how microorganisms redistribute their protein synthesis capacity over different cellular functions when confronted with an environmental challenge. Assuming that resource allocation in microorganisms has been optimized through evolution, for example to allow maximal growth in a variety of environments, this question can be fruitfully formulated as an optimal control problem. We have developed such an optimal control perspective, focusing on the dynamical adaptation of growth and gene expression in response to envrionmental changes, in close collaboration with the BIOCORE project-team.
A complementary perspective consists in the use of control-theoretical approaches to modify the functioning of a bacterial cell towards a user-defined objective, by rewiring and selectively perturbing its regulatory networks. The question how regulatory networks in microorganisms can be externally controlled using engineering approaches has a long history in biotechnology and is receiving much attention in the emerging field of synthetic biology. Within a number of on-going projects, IBIS is focusing on two different questions. The first concerns the development of open-loop and closed-loop growth-rate controllers of bacterial cells for both fundamental research and biotechnological applications (Figure ). Second, we are working on the development of methods for the real-time control of the expression of heterologous proteins in communities of interacting bacterial populations. The above projects involve collaborations with, among others, the Inria project-teams LIFEWARE (INBIO), BIOCORE, and McTAO as well as with a biophysics group at Univ Paris Descartes and a mathematical modeling group at INRA Jouy-en-Josas.
A publication on the use of mixed-effects models for the analysis of the inheritance and variability of gene expression parameters along lineage trees was published in a special issue of Bioinformatics and presented at the major bioinformatics conference ISMB/ECCB 2020. A publication in BMC Bioinformatics accompanied the release of the new version of the web application WellInverter for the analysis of fluorescent reporter gene data. IBIS member Michel Page launched his start-up ProLeads (https://
Keywords: Bioinformatics - Statistics - Data visualization - Data modeling
Scientific Description: WellFARE is a Python library implementing linear inversion methods for the reconstruction of gene expression profiles from fluorescent or luminescent reporter gene data. WellFARE form the computational core of the WellInverter web application.
Functional Description: As input, WellFARE reads the primary data file produced by a 96-well microplate reader, containing time-series measurements of the absorbance (optical density) as well as the fluorescence and luminescence intensities in each well (if available). Various functions exist to analyze the data, in particular for detecting outliers, subtracting background, estimating growth rates, promoter activities and protein concentrations, visualizing expression profiles, synchronizing replicate profiles, etc. WellFARE is the computational core of the web application WellInverter.
News Of The Year: Publication in BMC Bioinformatics describing the new version of WellFARE
Participants: Delphine Ropers, Hans Geiselmann, Hidde de Jong, Michel Page, Valentin Zulkower and Yannick Martin
Partner: UGA
Contact: Hidde de Jong
Publication: Robust reconstruction of gene expression profiles from reporter gene data using linear inversion
Keywords: Bioinformatics - Statistics - Data visualization - Data modeling
Scientific Description: WellInverter is a web application that implements linear inversion methods for the reconstruction of gene expression profiles from fluorescent or luminescent reporter gene data. WellInverter makes the methods available to a broad audience of biologists and bioinformaticians. In particular, we have put in place a parallel computing architecture with a load balancer to distribute the analysis queries over several back-end servers, redesigned the graphical user interface, and developed a plug-in system for defining high-level routines for parsing data files produced by microplate readers from different manufacturers.
Functional Description: As input, WellInverter reads the primary data file produced by a 96-well microplate reader, containing time-series measurements of the absorbance (optical density) as well as the fluorescence and luminescence intensities in each well (if available). Various modules exist to analyze the data, in particular for detecting outliers, subtracting background, estimating growth rates, promoter activities and protein concentrations, visualizing expression profiles, synchronizing replicate profiles, etc. The computational core of the web application consists of the Python library WellFARE.
News Of The Year: Deployment of WellInverter on an Inria server and on the new cloud of the French Institute for Bioinformatics (see the web page for details). Publication in BMC Bioinformatics describing the new version of the application.
Participants: Delphine Ropers, Hans Geiselmann, Hidde de Jong, Johannes Geiselmann, Michel Page, Valentin Zulkower and Yannick Martin
Partner: UGA
Contact: Hidde de Jong
Publication: Robust reconstruction of gene expression profiles from reporter gene data using linear inversion
Genetic Network Analyzer
Keywords: Model Checking - Bioinformatics - Gene regulatory networks - Qualitative simulation
Scientific Description: Genetic Network Analyzer (GNA) is the implementation of methods for the qualitative modeling and simulation of gene regulatory networks developed in the IBIS project-team.
Functional Description: The input of GNA consists of a model of the regulatory network in the form of a system of piecewise-linear differential equations (PLDEs), supplemented by inequality constraints on the parameters and initial conditions. From this information, GNA generates a state transition graph summarizing the qualitative dynamics of the system. In order to analyze large graphs, GNA allows the user to specify properties of the qualitative dynamics of a network in temporal logic, using high-level query templates, and to verify these properties on the state transition graph by means of standard model-checking tools, either locally installed or accessible through a remote web server.
Release Functional Description: (1) it supports the editing and visualization of regulatory networks, in an SBGN-compatible format, (2) it semi-automatically generates a prototype model from the network structure, thus accelerating the modeling process, and (3) it allows models to be exported in the SBML Qual standard.
News Of The Year: New mode of distribution from the IBIS web site. Tutorial on the use of the model formalism for analyzing synthetic genetic circuits.
Participants: Hidde de Jong, Michel Page and Delphine Ropers
Partner: UGA
Contact: Hidde de Jong
Publications: Genetic Network Analyzer: A Tool for the Qualitative Modeling and Simulation of Bacterial Regulatory Networks - Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens
The use of fluorescent and luminescent reporter genes allows
real-time monitoring of gene expression, both at the level of
individual cells and cell populations
(Section ). Over the
years, many useful resources have appeared, such as libraries of reporter strains for model organisms
and computer tools for designing reporter plasmids. Moreover, the widespread adoption of
thermostated microplate readers in experimental laboratories has made it possible to automate and
multiplex reporter gene assays on the population level. This has resulted in large time-series data
sets, typically comprising
In the past few years we developed novel methods for the analysis of reporter gene data obtained in microplate experiments, based on the use of regularized linear inversion. This allows a range of estimation problems to be solved, notably the inference of growth rate, promoter activity, and protein concentration profiles. The linear inversion methods, published in Bioinformatics in 2015 , have been implemented in the Python package WellFARE and integrated in the web application WellInverter. Funded by a grant from the Institut Français de Bioinformatique (IFB), we improved WellInverter by developing a parallel computational architecture with a load balancer to distribute the analysis queries over several back-end servers, a new graphical user interface, and a plug-in system for defining high-level routines for parsing data files produced by microplate readers from different manufacturers. This has resulted in a scalable and user-friendly web service providing a guaranteed quality of service, in terms of availability and response time. The web service has been redeployed on the new IFB cloud and on an Inria server, accompanied by extensive user documentation, online help, and a tutorial. An article on WellInverter, illustrating the use of the tool by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations, was published in BMC Bioinformatics this year . We notably show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene.
At the single-cell level, the processes that govern single-cell dynamics in general and gene expression in particular are better described by stochastic models rather than the deterministic models underlying the linear inversion methods discussed in Section . Modern techniques for the real-time monitoring of gene expression in single cells enable one to apply stochastic modelling to study the origins and consequences of random noise in response to various environmental stresses, and the emergence of phenotypic variability. The potential impact of single-cell stochastic analysis and modelling ranges from a better comprehension of the biochemical regulatory mechanisms underlying cellular phenotypes to the development of new strategies for the (computer assisted or genetically engineered) control of cell populations and even of single cells.
Work in IBIS on gene expression and interaction dynamics at the level of individual cells is addressed in terms of identification of parametric intrinsic noise models, on the one hand, and the nonparametric inference of gene expression statistics, on the other hand, from population snapshot data. Along with modelling and inference, identifiability analysis is dedicated special attention. The investigation of the problem of reconstructing promoter activity statistics from reporter gene population snapshot data has led to a full-blown spectral analysis and reconstruction method for reporter gene systems. In the context of the ANR project MEMIP (Section ), we have characterized reporter systems as noisy linear systems operating on a stochastic input (promoter activity), and developed an inversion method for estimation of promoter activation statistics from reporter population snapshots. The method has been demonstrated on simulated data. Theoretical as well as simulation resuts have been published in Automatica this year , and will be the object of application to real data.
One of the key limitations of the method is the assumption of stationary promoter activation statistics. In the context of controlled gene expression processes, this may hamper applicability of the method. In response to this, an extension of the method for so-called modulated processes (stationary processes reshaped by a time-varying control input), has been developed and demonstrated on simulations of controlled gene expression. Results were submitted for possible presentation and publication in the proceedings of the IFAC world congress 2020.
The investigation of cellular populations at the single-cell level has led to the discovery of important phenomena, such as the co-occurrence of different phenotypes in an isogenic population. Novel experimental techniques, such as time-lapse fluorescence microscopy combined with the use of microfluidic devices (Section ), enable one to take the investigation further by providing time-course profiles of the dynamics of individual cells over entire lineage trees. The development of models that take into account the genealogy of individual cells is an important step in the study of inheritance in bacterial population. As a prerequisite, the efficient analysis of single-cell data relies on the mathematical analysis of those models.
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 concentration of a specific protein inside the cell. The dependence of bacterial phenotypes like cell division times or ageing on such characteristics has been investigated by Aline Marguet using mathematical analysis of the underlying processes. To understand the long-time behavior of structured branching populations, the process describing the trait of a typical individual along its ancestral lineage, called auxiliary process and its asymptotic behavior play a key role. In a publication in ESAIM: Probability and Statistics that appeared this year , we proved that the empirical measure of the structured branching process converges to the mean value of this auxiliary process. The approach relies on ergodicity arguments for the time-inhomogeneous auxiliary Markov process. The novelty compared to existing spectral methods is that our method allows to consider processes with time-varying rates for the modeling of changing environments. For example, we studied the case of a size-structured population in a varying environment and proved the convergence of the empirical measure in this specific case.
In collaboration with Charline Smadi (IRSTEA Grenoble), Aline Marguet also investigated the long-time behavior of a general class of branching Markov processes. This work, which has been submitted for publication , aims at understanding the link between the dynamic of the trait and the dynamic of the population. In the case of a trait modelling the proliferation of a parasite infection in a cellular population, we exhibit conditions on the dynamics of the parasites to survive in the population, despite the cellular divisions that dilute the number of parasites in each cell.
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 INRA 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 was submitted for publication this year .
As explained in the previous section, recent technological developments have made it possible to obtain time-course single-cell measurements of gene expression as well as the associated lineage information. However, most of the existing methods for the identification of mathematical models of gene expression are not well-suited to single-cell data and 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 will allow in particular for the investigation of inheritance of traits in bacterial populations.
In the framework of structured branching processes, we studied the statistical reconstruction of parameters. We considered the problem of estimating the division rate from the observations of the trait of the cells at birth. Previous works on the subject considered deterministic dynamics for the evolution of the trait. In collaboration with Marc Hoffmann (Université Paris Dauphine), Aline Marguet investigated the case of a trait evolving according to a diffusion process. The study of the asymptotic behavior of the tagged-chain, corresponding to the trait of a uniformly chosen individual, allowed us to prove the convergence of the empirical measure of the branching process, and the asymptotic minmax efficiency of nonparametric estimators for the density of the transition kernel and the invariant measure of the tagged-chain. For the estimation of the division rate, we proved in a parametric framework the asymptotic efficiency of a standard maximum likelihood proxy estimation. Finally, we demonstrate the validity of our approach on simulated datasets. The results of this work were published in Stochastic Processes and their Applications .
Along the same lines, modelling and identification of gene expression models with mother-daughter inheritance are being investigated in the context of the ANR project MEMIP. Starting from an earlier work of the group , Eugenio Cinquemani, Marc Lavielle (XPOP, Inria Saclay–Île-de-France) and Aline Marguet developed a new model and a method for inference from data for gene expression along tree where the kinetic expression parameters are assumed to be inherited from the mother cell in an autoregressive way. This model generalizes the state-of-the-art mixed-effect models to the case of lineage trees. We implemented the inference procedure in Julia and proved that it provides unbiased estimates of the parameters. The application to the data of osmotic shock response by yeast show that the correlation between the parameter of a cell and its daughter is of 0.6 according to our model, leading to new biological questions such as the understanding of the origin of this inheritance. The results of this study were presented at the major bioinformatics conference ISMB/ECCB 2020 and published in the associated special issue of Bioinformatics .
The ability to rapidly respond to changing nutrient availability is crucial for E. coli to survive in many environments including the gut. Reorganization of gene expression is the first step for bacteria to adjust their metabolism accordingly. It involves fine-tuning of both transcription and mRNA stability by dedicated regulatory interactions. While transcriptional regulation has been largely studied, the role of mRNA stability during a metabolic switch is poorly understood.
This question was addressed in the framework of the PhD thesis of Manon Morin funded by an INRA-Inria grant. Using combined genome-wide transcriptome and mRNA decay analyses, Manon Morin, Delphine Ropers and colleagues from the Toulouse Biotechnology Institute (ex-LISBP, INRA/INSA Toulouse) investigated the role of mRNA stability in the response of E. coli to nutrient changes. They demonstrated that transcript stability increases along metabolic transitions representative of the carbon source fluctuations, the glucose-acetate-starvation transition , . Most of the stabilization occurs at glucose-acetate transition when glucose is exhausted. Stabilized mRNAs remain stable during acetate consumption and carbon starvation. Meanwhile, expression of most genes is downregulated. Metabolic control analysis showed that most of gene expression regulation is driven by changes in transcription. Post-transcriptional regulations appear to be important for genes involved in bacterial response to nutrient starvation. These results have been further developed in a paper recently submitted to a biology journal.
The observation of a global stabilization of cellular mRNAs during adaptation to carbon source depletion raises questions about the regulatory mechanisms at work. Known regulators of mRNA stability such as the protein Hfq, the carbon storage regulator Csr, and several small regulatory RNAs, specifically target mRNAs. Are these regulatory mechanisms sufficient to explain the systematic adjustment of mRNA half-lives? The collaboration with Muriel Cocaign-Bousquet and colleagues from the Toulouse Biotechnology Institute has been pursued to answer these questions, in the context of the PhD thesis of Thibault Etienne, funded by an INRA-Inria PhD grant. The objective is to develop models able to explain how cells coordinate their physiology and the functioning of the degradation machinery following environmental changes. In a paper submitted this year, Thibault Etienne, Delphine Ropers and Muriel Cocaign-Bousquet investigate the possibility that competition between mRNAs for their binding to the degradation machinery is an important mechanism for the regulation of mRNA half-lives. They develop a mathematical model of mRNA degradation and assess the role of competitive effects on mRNA degradation kinetics by numerical simulation and sensitivity analysis. Competition appears to globally increase the stability of cellular mRNAs and to amplify the effect of post-transcriptional regulation. In a follow-up study, 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 four different environmental conditions.
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 foundation result for growth control in bacteria , 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.
The experimental work underlying the growth switch has been continued in several directions in the context of the Maximic project by Célia Boyat. Moreover, in collaboration with colleagues from the BIOCORE project-team, we have formulated the maximization of metabolite production by means of the growth switch as a resource reallocation problem that can be analyzed by means of the self-replicator models of bacterial growth in combination with methods from optimal control theory. In a paper published in the Journal of Mathematical Biology this year , we study various optimal control problems by means of a combination of analytical and computational techniques. We show that the optimal solutions for biomass maximization and product maximization are very similar in the case of unlimited nutrient supply, but diverge when nutrients are limited. Moreover, external growth control overrides natural feedback growth control and leads to an optimal scheme consisting of a first phase of growth maximization followed by a second phase of product maximization. This two-phase scheme agrees with strategies that have been proposed in metabolic engineering. More generally, this work shows the potential of optimal control theory for better understanding and improving biotechnological production processes. Extensions concerning the effect on growth and bioproduction of the (biological or technological) costs associated with discontinuous control strategies, and of the time allotted to optimal substrate utilization, were presented at the European Control Conference (ECC 2019) in Naples this year and published in the proceedings .
High concentrations of organic acids such as acetate inhibit growth of Escherichia coli and other bacteria. This phenomenon is of interest for understanding bacterial physiology but is also of practical relevance. Growth inhibition by organic acids underlies food preservation and causes problems during high-density fermentation in biotechnology. The development of new approaches for the relief of growth inhibition by acetate during high-density fermentation of E. coli is one of the motivating assumptions for the work of IBIS in the IPL project COSY (Sections and below).
What causes growth inhibition by acetate? Classical explanations invoke the uncoupling effect of acetate and the establishment of an anion imbalance. During his PhD thesis, Stéphane Pinhal investigated an alternative hypothesis: the perturbation of acetate metabolism due to the inflow of excess acetate. In an experimental and modelling study published in the Journal of Bacteriology , Stéphane Pinhal, Delphine Ropers, Hans Geiselmann, and Hidde de Jong developed a set of isogenic strains that remove different parts of the metabolic network involved in acetate metabolism. Analysis of these strains revealed that the inflow of acetate accounts for 20% of the growth-inhibitory effect through a modification of the acetyl phosphate concentration. While the study does not provide a definite answer to the question of what accounts for the remaining 80% of the reduction in growth rate, some of the observations argue against a prominent role of uncoupling in growth inhibition by acetate in the conditions tested.
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 biootechnological processes. Within the IPL COSY (Section ), we picked up the challenge to design and investigate the dynamics of synthetically engineered microbial communities with a consortium of Inria partners. In IBIS, in particular, we are addressing the design of a bacterial community of two E.coli strains, mimicking mutualistic relationships found in nature, and with the potential to outperform a single producer strain in the production of a heterologous protein. During the post-doctoral stay of Marco Mauri, 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. The results are presented in a paper submitted for publication this year and will be tested experimentally in the framework of the recently-started PhD project of Maaike Sangster. Analysis of optimal community control problems as well as design and deployment of optimal control strategies will follow in synergy with other IPL COSY partners.
Small non-coding RNAs (sRNAs) regulate numerous cellular processes in all domains of life. Several approaches have been developed to identify them from RNA-seq data, which are efficient for eukaryotic sRNAs but remain inaccurate for the longer and highly structured bacterial sRNAs. Together with colleagues from INSA de Lyon, Stéphan Lacour developed APERO, a new algorithm to detect small transcripts from paired-end bacterial RNA-seq data. This algorithm is based on a novel approach, which does not start from the read coverage distribution, but analyzes boundaries of individual sequenced fragments to infer the 5′ and 3′ ends of all transcripts. Validation of the algorithm on Escherichia coli and Salmonella enterica datasets, based on experimentally validated sRNAs, showed it to outperform all existing methods in terms of sRNA detection and boundary precision. Moreover, APERO was able to identify the small transcript repertoire of Dickeya dadantii including putative intergenic RNAs, 5′ UTR or 3′ UTR-derived RNA products and antisense RNAs. This work was published in Nucleic Acids Research this year . APERO is freely available as an open source R package (https://
Project name | MuSE: MUlti-Omics and Metabolic models integration to study growth transition in Escherichia coli |
Coordinator | D. Ropers |
IBIS participants | D. Ropers, T. Etienne |
Type | IXXI/BioSyl project (2018-2020) |
Web page |
http:// |
Project name | RNAfluo: Quantification d'ARN régulateurs in vivo |
Coordinator | S. Lacour |
IBIS participants | S. Lacour |
Type | AGIR project Univ Grenoble Alpes (2016-2019) |
Project name | MEMIP – Modèles à effets mixtes de processus intracellulaires : méthodes, outils et applications |
Coordinator | G. Batt |
IBIS participants | E. Cinquemani, A. Marguet, D. Ropers |
Type | ANR project (2016-2020) |
Project name | ENZINVIVO – Détermination in vivo des paramètres enzymatiques dans une voie métabolique synthétique |
Coordinator | G. Truan |
IBIS participants | J. Geiselmann, H. de Jong |
Type | ANR project (2016-2020) |
Project name | MAXIMIC: Optimal control of microbial cells by natural and synthetic strategies |
Coordinator | H. de Jong |
IBIS participants | C. Boyat, E. Cinquemani, J. Geiselmann, H. de Jong, A. Pavlou, C. Pinel, D. Ropers |
Type | ANR project (2017-2021) |
Web page |
https:// |
Project name | RIBECO (RIBonucleotide ECOnomy): Engineering RNA life cycle to optimize economy of microbial energy |
Coordinator | M. Cocaign-Bousquet |
IBIS participants | E. Cinquemani, T. Etienne, D. Ropers |
Type | ANR project (2018-2022) |
Web page |
https:// |
Project name | COSY: real-time COntrol of SYnthetic microbial communities |
Coordinator | E. Cinquemani |
IBIS participants | E. Cinquemani, H. de Jong, J. Geiselmann, M. Mauri, T. Muszbek, C. Pinel, D. Ropers, M. Sangster |
Type | Inria Project Lab (2017-2021) |
Web page |
https:// |
Project name | OPTICO : OPTImal COntrol software for microbial communities in a system of minibioreactors |
Coordinator | E. Cinquemani |
IBIS participants | E. Cinquemani, H. de Jong, J. Geiselmann, T. Muszbek |
Type | Inria ADT (2019-2021) |
Project name | AlgeaInSilico: Prédire et optimiser la productivité des microalgues en fonction de leur milieu de croissance |
Coordinator | O. Bernard |
IBIS participants | H. de Jong |
Type | Inria Project Lab (2015-2019) |
Web page |
https:// |
Project name | Analyse intégrative de la coordination entre stabilité des ARNm et physiologie cellulaire chez Escherichia coli |
Coordinators | D. Ropers, M. Cocaign-Bousquet (Inra, LISBP) |
IBIS participants | T. Etienne, D. Ropers |
Type | Contrat Jeune Scientifique Inra-Inria (2016-2019) |
Tomas Gedeon, professor in Mathematics at Montana State University (USA), visited the IBIS project-team during two months (May-July 2019) to work on modeling and analysis of resource allocation in microorganisms. His stay at Inria was funded by the Visiting researcher program of the research center Grenoble - Rhône-Alpes.
Emmanouil Sideris, enrolled in the MSc program in Computer Science at the University of Patras (Greece), did a Master internship with Eugenio Cinquemani.
IBIS members | Conference, workshop, school | Date |
Hidde de Jong | CompSysBio: Advanced Lecture Course on Computational Systems Biology, Aussois | Apr 2019 |
Aline Marguet | Biohasard: Stochastic models for biology, Grenoble | Jun 2020 |
Delphine Ropers | Séminaire de Modélisation du Vivant (SeMoVi), Lyon and Grenoble | 2019 |
IBIS member | Conference, workshop, school | Role |
Eugenio Cinquemani | European Control Conference (ECC 2019 and 2020) | Associate editor |
IBIS member | Conference, workshop, program |
Eugenio Cinquemani | ECC 2019 and 2020, CMSB 2018 and 2019, HSB 2019 and 2020, SASB 2019, IEEE CIBCB 2019 |
Hidde de Jong | CMSB 2019, FOSBE 2019, HSB 2019 and 2020 |
Delphine Ropers | CSBio 2019 |
IBIS member | Journal |
Johannes Geiselmann | Frontiers in Microbiology (review editor) |
Hidde de Jong | Journal of Mathematical Biology |
Hidde de Jong | Biosystems (reviews editor) |
Hidde de Jong | ACM/IEEE Transactions on Computational Biology and Bioinformatics |
IBIS member | Organism | Role |
Johannes Geiselmann | INRA | Member scientific advisory committee Microbiologie, Adaptation, Pathogénie |
Johannes Geiselmann | UMR5240 CNRS-UCBL-INSA-BayerCropScience | Member scientific council |
Hidde de Jong | Microbiology and Food Chain Department, Inra | Member scientific council |
Hidde de Jong | Univ Grenoble Alpes | Member scientific council of Pôle MSTIC |
Hidde de Jong | International Human Frontier Science Program (HFSP) | Member grant selection committee |
Delphine Ropers | INRA-Inria | Member selection committee PhD grants |
Delphine Ropers | Elixir | Member of Microbial Biotechnology Community |
Eugenio Cinquemani
Title | Event and location | Date |
Estimation and control of microbial dynamics: From single-cell to microbial communities | Presentation at Inria-KAIST workshop on Applied mathematics, Paris | Mar 2019 |
Optimal control of bacterial growth for metabolite production: The role of timing and costs of control | Presentation at ECC 2019, Naples (Italy) | Jun 2019 |
Enhanced production of heterologous proteins by a synthetic microbial consortium: Conditions and tradeoffs | Invited talk at Workshop Advanced Physics for Medicine, CNR, Rome (Italy) | Sep 2019 |
Hidde de Jong
Title | Event and location | Date |
Integrated models of the cell: metabolism, gene expression, signalling | Tutorial at CompSysBio: Advanced Lecture Course on Computational Systems Biology, Aussois | Apr 2019 |
Optimal control of bacterial growth: biotechnological applications | Invited talk at Workshop New Vistas in Computational Systems and Synthetic Biology, Vigo (Spain) | May 2019 |
Optimal control of microbial growth and bioproduction | Invited talk at SYMER workshop, Global Challenge Week, Univ Grenoble Alpes, Grenoble | Jun 2019 |
Reengineering bacterial metabolism using synthetic biology and optimal control theory | Invited talk in special session on Predictive approaches for biological systems engineering, JOBIM 2019, Nantes | Jul 2019 |
Production of heterologous proteins by a synthetic microbial consortium: conditions and tradeoffs | Invited talk at Biocore seminar, Peyresq | Sep 2019 |
Stéphan Lacour
Title | Event and location | Date |
New insights into the regulation of curli production by E. coli | BactoGre workshop, Grenoble | Avr 2019 |
Aline Marguet
Title | Event and location | Date |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Invited talk at École de printemps de la chaire MMB, Aussois | May 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Presentation at ISMB/ECCB conference, Basel (Switzerland) | Jul 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Invited talk at Biohasard 2019 workshop, Rennes | Aug 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Presentation at Journée Inria-Bio, Lyon | Oct 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Seminar Master Mathématiques pour les Sciences du Vivant, Ecole Polytechnique, Palaiseau | Oct 2019 |
Modelling and statistics of branching processes | Workshop on Growth and division in mathematics and medicine, London (UK) | Nov 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Seminar équipe-projet DRACULA, Lyon | Nov 2019 |
Inheritance and variability of kinetic gene expression parameters in microbial cells: Modelling and inference from lineage tree data | Seminar unité MaIage, Jouy-en-Josas | Dec 2019 |
Long time behavior of a general class of branching Markov processes | Seminar Institut Fourier, Grenoble | Dec 2019 |
Marco Mauri
Title | Event and location | Date |
Thibault Etienne
Title | Event and location | Date |
Regulatory mechanisms underlying coordination of mRNA degradation and cell physiology in Escherichia coli | Seminar during Journée annuelle du LIPhy, Grenoble | Jun 2019 |
Large scale modelling of mRNA decay in Escherichia coli | Seminar during Journée InriaBio, ENS de Lyon | Oct 2019 |
Antrea Pavlou
Title | Event and location | Date |
Experimental and computational analysis of bacterial self-replicators | Poster at CompSysBio: Advanced Lecture Course on Computational Systems Biology, Aussois | Apr 2019 |
Experimental and computational analysis of bacterial self-replicators | Seminar for GdT MathBio, Grenoble | Nov 2019 |
IBIS member | Committee | Role |
Eugenio Cinquemani | Inria Grenoble - Rhône-Alpes | Member Comité des Emplois Scientifiques (CES) |
Eugenio Cinquemani | Inria Grenoble - Rhône-Alpes | Member Comité des Utilisateurs des Moyens Informatiques (CUMI) |
Eugenio Cinquemani | Inria | Member Comité Administrative Paritaire (CAP) |
Eugenio Cinquemani | Inria Grenoble - Rhône-Alpes | Member Comité Développement Technologique (CDT) |
Hidde de Jong | Inria Grenoble - Rhône-Alpes | Member scientific council (COS) |
Hidde de Jong | Inria | Member working group on International Relations of Conseil d'Orientation Scientifique et Technique (COST) |
Delphine Ropers | Inria | Member of Commission d'évaluation d'Inria |
Delphine Ropers | Inria Grenoble - Rhône-Alpes | Référente chercheurs |
Delphine Ropers | Inria Grenoble - Rhône-Alpes | Co-coordinator of mentoring program |
IBIS member | Organism | Recruitment |
Johannes Geiselmann | University of Barcelona | Assistant professor |
Delphine Ropers | Inria Bordeaux Sud-Ouest | Chargés de recherche (jury d'admissibilité) |
Delphine Ropers | Inria national | Chargés de recherche (jury d'admissibilité) |
Delphine Ropers | Univ de Rennes | Assistant professor |
Four members of the IBIS team are either full professor or associate professor at Univ Grenoble Alpes. They therefore have a full teaching service (at least 192 hours per year) and administrative duties related to the organization and evaluation of the university course programs on all levels (from BSc to PhD). Besides the full-time academic staff in IBIS, the following people have contributed to courses last year.
Eugenio Cinquemani
Course: Stochastic modelling of gene regulatory networks, M2, BIM, INSA de Lyon (4 h)
Course: Statistics for systems biology, M1, Master Approches Interdisciplinaires du Vivant, CRI/Univ Paris Descartes (20 h, also in charge of 20 h of practicals)
Course: Modelling and identification of metabolic networks, M1, Phelma, INP Grenoble (4 h)
Hidde de Jong
Course and practicals: Modeling and simulation of gene regulatory networks, M2, BIM, INSA de Lyon (20 h)
Aline Marguet
Practicals: Biostatistics, M2, Univ Grenoble Alpes (24 h)
Delphine Ropers
Course and practicals: Modelling in systems biology, M1, Phelma, INP Grenoble (16 h)
Course and practicals: Cell systems biology and modelling cell functions, M1, Master ingéniérie de la santé, Univ Grenoble Alpes (14 h)
Thibault Etienne
Course: Construction et analyse de plasmides in silico, M1, Master ingéniérie de la santé, Univ Grenoble Alpes (4 h)
Antrea Pavlou
Practicals: Cellular biology, L1, Chemistry and biochemistry, Univ Grenoble Alpes (28 h)
HdR: Eugenio Cinquemani, Identification, estimation and control of gene expression and metabolic network dynamics , Univ Grenoble Alpes, Nov 2019
PhD in progress: Thibault Etienne, Analyse intégrative de la coordination entre stabilité des ARNm et physiologie cellulaire chez Escherichia coli. Supervisors: Delphine Ropers and Muriel Cocaign-Bousquet (INRA Toulouse)
PhD in progress: Joël Espel, RNA engineering: Design of the dynamical folding of RNA and of RNA switches. Supervisors: Alexandre Dawid (Univ Grenoble Alpes) and Johannes Geiselmann
PhD in progress: Antrea Pavlou, Experimental and computational analysis of bacterial self-replicators. Supervisors: Hidde de Jong and Johannes Geiselmann
PhD in progress: Maaike Sangster, Development, characterization and control of E. coli communities on an automated experimental platform. Supervisors: Eugenio Cinquemani and Johannes Geiselmann
PhD thesis committees
IBIS member | Role | PhD student | University | Date |
Johannes Geiselmann | Président | Vanni Petrolli | Univ Grenoble Alpes | Nov 2019 |
Hidde de Jong | Rapporteur | Pauline Trébulle | AgroParisTech | Oct 2019 |
Hidde de Jong | Rapporteur | Firas Hammami | Univ d’Aix-Marseille | Dec 2019 |
Delphine Ropers | Examinateur | Marianyela Petrizzelli | Univ Paris Sud | Jul 2019 |
Delphine Ropers | Examinateur | Ronan Duchesne | Univ de Lyon | Dec 2019 |
Habilitation (HDR) committees
IBIS member | Role | HDR candidate | University | Date |
Johannes Geiselmann | Président | Marianne Weidenhaupt | Univ Grenoble Alpes | Jan 2019 |
Hidde de Jong | Président | Arnaud Tonnelier | Univ Grenoble Alpes | Jun 2019 |
Hidde de Jong | Rapporteur | Stefan Müller | University of Vienna | Sep 2019 |
Hidde de Jong | Examinateur | Eugenio Cinquemani | Univ Grenoble Alpes | Nov 2019 |
PhD advisory committees
IBIS member | PhD student | University |
Eugenio Cinquemani | Arthur Carcano | Institut Pasteur/Inria |
Eugenio Cinquemani | Alexey Koshkin | ENS de Lyon |
Johannes Geiselmann | Alain Lombard | Univ Grenoble Alpes |
Johannes Geiselmann | Shiny Martis | INSA de Lyon |
Hidde de Jong | Charlotte Coton | INRA Moulon |
Hidde de Jong | Kapil Newar | Univ Grenoble Alpes |
Hidde de Jong | Agustín Yabo | Univ Nice–Sophia-Antipolis |
Stéphan Lacour | Julien Trouillon | CEA Grenoble |
Stéphan Lacour | Camille Brunet | CHU Grenoble |
Stéphan Lacour | Raphael Fourquet | INSA de Lyon/Univ de Lyon |
Stéphan Lacour | Kevin Pick | Univ de Lyon |
Delphine Ropers | Manon Barthe | Univ Paul Sabatier, Toulouse |
Delphine Ropers | Charlotte Roux | Univ Paul Sabatier, Toulouse |
Delphine Ropers | Irene Ziska | Univ de Lyon |
Yves Markowicz is director of the BSc department at Univ Grenoble Alpes.
Michel Page is coordinator of the master Systèmes d'information et d'organisation at the Institut d'Adminstration des Entreprises (IAE), Univ Grenoble Alpes.
Eugenio Cinquemani organizes a module on statistics in systems biology at CRI/Univ Paris Descartes.
Delphine Ropers organizes a module on the mathematical modeling of biological systems at PHELMA, INP Grenoble.
Hidde de Jong organizes with Daniel Kahn a module on the modeling of genetic and metabolic networks at INSA de Lyon.
Delphine Ropers participated in the colloquium FIGAS 2019 - Un rêve pour les filles et les garçons : la Science (Nov 2019). She was moderator of the session "Stéréotypes dans le numérique", and co-presented with Thierry Vieville "Mais comment éduquer les garçons à l'équité des genres au niveau informatique et numérique ?".