RESEARCH CENTRE

Inria Centre
at Université Grenoble Alpes

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Grenoble Alpes

Project-Team

MICROCOSME

Analysis, engineering, and control of
microorganisms

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team MICROCOSME

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Genome-scale analysis of microbial physiology . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Natural and engineered resource allocation strategies in microorganisms . . . . . . . . . . .
3.3 Variability and robustness of microbial adaptation . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Analysis and control of microbial communities . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Biotechnology and bioeconomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

6 Highlights of the year

7 New software, platforms, open data

7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 ODIN+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.2 GNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.3 WellInverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.4 WellFARE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2.1 Automated mini-bioreactor platform for (dynamical) monitoring and control of
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

microbial cultures

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8 New results

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8.1 Resource allocation models of bacterial growth . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.2 Community text book: Economic Principles in Cell Biology . . . . . . . . . . . . . . . . . . . 10
8.3 Biotechnological applications of bacterial growth control
. . . . . . . . . . . . . . . . . . . . 11
8.4 Synthetic microbial communities for bioproduction processes: modelling, analysis and

real-time monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.5 Modelling of carbon and mRNA metabolism in microorganisms . . . . . . . . . . . . . . . . 12
8.6 Inference of parameters on lineage trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.7 Mathematical analysis of structured branching populations . . . . . . . . . . . . . . . . . . . 14

9 Partnerships and cooperations

15
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework

9.1.2 Informal international partners
9.1.3 Visits to international teams

of an Inria International Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
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9.2.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
9.4 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

9.2 European initiatives

10 Dissemination

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10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.3 Journal
10.1.4 Invited talks and seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.5 Scientific evaluation and expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.1.7 Recruitment committees
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.2.4 Teaching administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3.1 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

11 Scientific production

22
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
11.3 Cited publications

Project MICROCOSME

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Project-Team MICROCOSME

Creation of the Project-Team: 2021 October 01

Keywords

Computer sciences and digital sciences

A3.1.1. – Modeling, representation

A3.4.5. – Bayesian methods

A6.1.1. – Continuous Modeling (PDE, ODE)

A6.1.2. – Stochastic Modeling

A6.2.1. – Numerical analysis of PDE and ODE

A6.2.4. – Statistical methods

A6.3.1. – Inverse problems

A6.3.2. – Data assimilation

A6.3.3. – Data processing

A6.4.1. – Deterministic control

Other research topics and application domains

B1. – Life sciences

B1.1.2. – Molecular and cellular biology

B1.1.4. – Genetics and genomics

B1.1.7. – Bioinformatics

B1.1.8. – Mathematical biology

B1.1.10. – Systems and synthetic biology

B2.2.4. – Infectious diseases, Virology

B4.3.1. – Biofuels

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Delphine Ropers [Team leader, INRIA, Senior Researcher, Professeur attaché à l’UGA, HDR]

• Eugenio Cinquemani [INRIA, Senior Researcher, HDR]

• Aline Marguet [INRIA, Researcher]

• Hidde de Jong [INRIA, Senior Researcher, HDR]

Faculty Member

• Johannes Geiselmann [UGA, Professor]

Post-Doctoral Fellows

• Arnaud Belcour [INRIA, Post-Doctoral Fellow, from Sep 2023]

• Claudia Fonte Sanchez [UGA, Post-Doctoral Fellow, from May 2023]

PhD Students

• Rand Asswad [INRIA]

• Ignacia Cancino Aguirre [INRIA]

• Thibault Clavier [UGA]

• Charles Medous [UGA]

• Emrys Reginato [INRIA]

• Maaike Sangster [INRIA, until Feb 2023]

Technical Staff

• Soraya Arias [INRIA, Engineer]

• Ludovic Leau-Mercier [INRIA, Engineer]

Interns and Apprentices

• Eugene Ferragu [INRIA, Intern, from Oct 2023]

• Gwendal Le Guennec [ENS DE LYON, Intern, from Jun 2023 until Jul 2023]

Administrative Assistant

• Diane Courtiol [INRIA]

Visiting Scientist

• Antrea Pavlou [UNIV ZURICH]

External Collaborators

• Muriel Cocaign-Bousquet [INRAE, Toulouse Biotechnology Institute, HDR]

• Thibault Etienne [Gencovery, until Mar 2023]

Project MICROCOSME

3

Figure 1: Microscopy image of Escherichia coli bacteria growing on a solid nutrient medium. Some
bacteria have grown in the form of a hot air balloon (top) which, by colonizing the surface, will soon fuse
with a second, bigger colony (bottom). The bacteria are rod shaped, 2 µm long, and divide every 20 minutes
in the conditions in which the picture was taken. Credit: Antrea Pavlou, December 2020.

2 Overall objectives

MICROCOSME combines computational and experimental approaches for the analysis, engineering,
and control of the growth of microorganisms. Understanding and controlling the dynamics of bacterial
growth is vitally important in health, medicine, biotechnology, and food industries, for instance to halt
the growth of pathogens or stimulate the growth of probiotics or industrial microorganisms.

We develop multiscale models of growth, where the macroscopic observable, growth of a microbial
population or community, depends on various metabolic pathways and regulatory mechanisms operat-
ing at microscopic scales within the cells. We use our (deterministic or stochastic) models to interpret
experimental data or to infer the underlying growth processes from the data. This requires developing
a platform for the automation of experiments, as well as methods and software for model estimation
and data analysis. The analysis of microbial growth calls for new methodologies at the interface of
microbiology, control theory, applied mathematics, computer science, biophysics, and molecular biology,
which also leads to contributions in all of these fields. Our workhorse for the realization of this research
program is the bacterium Escherichia coli pictured in Figure 1. Part of the microbiota of the human
gut, E. coli is the model organism par excellence in microbiology and a popular platform for bio-based
chemical production. We intend to extend approaches developed in-house for this specific microbe to
other microorganisms including pathogens.

MICROCOSME has been created on October 1st, 2021. A recomposition and follow-up of former
IBIS project-team, MICROCOSME unites researchers from Inria and the Laboratoire Interdisciplinaire de
Physique (CNRS UMR 5588) at Université Grenoble Alpes.

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Inria Annual Report 2023

3 Research program

The research program of MICROCOSME is articulated around four research axes combining theory and
experiments, which are illustrated in Figure 2 and detailed below.

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 devel-
oping a platform for feedback control experiments allowing the real-time monitoring, data processing,
evaluation, and application of control laws.

Project MICROCOSME

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Figure 2: Research axes and methods in the MICROCOSME project-team. The first axis is dedicated to a
genome-scale understanding of microbial physiology through model-based analysis of high-throughput
data. This allows us to comprehend how cells adjust growth processes to environmental perturbations. This
coordination reflects strategies evolved by microorganisms to allocate their resources to different cellular
functions and (optimally) grow in their environment. The study of these natural strategies and their
re-engineering is the focus of Research axis 2 which views cells as self-replicators that can be described using
coarse-grained models and analysed by means of optimal and feedback control theory. In Research axis 3,
we adopt a different angle by analysing the variability and robustness of microbial growth. In particular,
we shift from deterministic to stochastic models, using data on the level of single cells in a population
rather than averaged over all cells in the population. In Research axis 4, a different type of variability is
considered, namely heterogeneity within communities consisting of different microbial species and how the
community can be controlled for biotechnological applications. Research carried out in the four research
axes relies on the methodological resources shown in the pie chart at the centre of the figure.

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Inria Annual Report 2023

4 Application domains

The research agenda of MICROCOSME is interdisciplinary in nature, driven by fundamental questions in
biology, which we address by a combination of mathematical, computational, and experimental tools.
This enables us to develop and share with partners a know-how useful to address challenging problems
in health, bioeconomy, and biotechnology.

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][27]. 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 tubercu-
losis 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.

5 Social and environmental responsibility

Several of our research activities have a direct societal impact. Our work on Mycobacteria addresses
important questions of public health, while the project on the degradation and valorisation of vegetal
waste meet European efforts in Circular Bioeconomy to replace fossil feedstock with renewable resources.
Our recently accepted Clean Energy Transition Partnership project will allow us to address the issue of
microbial risks associated with underground gas storage, as part of Europe’s efforts to develop innovative
energy system solutions towards net-zero by 2050.

6 Highlights of the year

In a recent paper published in eLife [14], we developed a coarse-grained resource allocation model to
investigate the quantitative relationship between growth rate and growth yield in different strains of the
bacterium Escherichia coli. The model, which has been validated using a large number of datasets, shows
that there is no simple correlation between growth rate and growth yield, as is usually assumed.

A recent collaboration with BRGM has led MICROCOSME to explore new avenues in environmental
microbiology as part of the European HyLife project on microbial risks associated with hydrogen under-
ground storage.

Project MICROCOSME

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7 New software, platforms, open data

7.1 New software

7.1.1 ODIN+

Name: Platform for advanced monitoring, control and optimisation of bioprocesses

Keywords: Systems Biology, Biotechnology, Automatic control, Monitoring

Functional Description: This application proposes a framework for on-line supervision of bioreactors.
It gathers the data sampled from different on-line and off-line sensors. ODIN+ is a distributed
platform, enabling remote monitoring as well as remote data acquisition.More originally, it enables
researchers and industrials to easily develop and deploy advanced control algorithms, optimisation
strategies, together with estimates of state variables or process state. It also contains a process
simulator which can be harnessed for experimentation and training purposes. It is modular in
order to adapt to any plant and to run most of the algorithms, and it can handle the high level of
uncertainties that characterises the biological processes. The architecture is based on Erlang, and
communication between modules through a MQTT Broker with Python for running the algorithms.
ODIN+ is developed in collaboration with the INRIA MICROCOSME research team.

Authors: Nicolas Niclausse, Nicolas Chleq, Jean-Luc Szpyrka, Pierre Fernique, Thibaud Kloczko, Tamas

Muszbek, Amine Lahouel, Olivier Bernard, Eugenio Cinquemani

Contact: Olivier Bernard

7.1.2 GNA

Name: 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 (now
MICROCOSME) 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 Contributions: (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 struc-
ture, thus accelerating the modeling process, and (3) it allows models to be exported in the SBML
Qual standard.

Publications: hal-01417975, hal-03094873, hal-00762122

Contact: Hidde de Jong

Participants: Hidde de Jong, Delphine Ropers

Partner: UGA

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Inria Annual Report 2023

7.1.3 WellInverter

Name: WellInverter

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 micro-
plate 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.

URL: https://team.inria.fr/ibis/wellinverter/

Publications: hal-01217800, hal-02195461

Contact: Hidde de Jong

Participants: Delphine Ropers, Hidde de Jong, Johannes Geiselmann

Partner: UGA

7.1.4 WellFARE

Name: WellFARE

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.

URL: https://github.com/ibis-inria/wellfare

Publication: hal-01217800

Contact: Hidde de Jong

Participants: Delphine Ropers, Johannes Geiselmann, Hidde de Jong

Partner: UGA

Project MICROCOSME

7.2 New platforms

9

Participants:

Soraya Arias, Eugenio Cinquemani,
Ludovic Léau-Mercier.

Johannes Geiselmann,

7.2.1 Automated mini-bioreactor platform for (dynamical) monitoring and control of microbial

cultures

Advanced dynamical experiments with microbial cultures require regular, complex measurement opera-
tions over several days or weeks. Manual execution by human operators is error-prone and exposed to
weak reproducibility, besides being a poor utilisation of human resources. Reactive control experiments,
in addition, necessitate online calculation of control actions in response to all acquired measurements.
MICROCOSME is actively developing an automated platform for automated monitoring and reactive
control experiments on microbial cultures. The platform consists of a system of mini-bioreactors connec-
ted to nutrient sources and measurement devices via a pump-based fluidic network, and it also supports
optogenetic control. Computer-operated by software ODIN+ (Section 7.1) as well as via platform-specific
software developments, it enables automated monitoring and online data processing, as already achieved
in week-long experiments over several bioreactors [21], and it will be exploited for feedback control
experiments as part of the ongoing (Section 9) and future research projects of the group.

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Inria Annual Report 2023

8 New results

8.1 Resource allocation models of bacterial growth

Participants:

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

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

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

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

8.2 Community text book: Economic Principles in Cell Biology

Participant: H. de Jong.

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

Project MICROCOSME

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8.3 Biotechnological applications of bacterial growth control

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

The ability to experimentally control the growth rate is crucial for studying bacterial physiology. It is
also of central importance for applications in biotechnology, where often the goal is to limit or even arrest
growth. Growth-arrested cells with a functional metabolism open the possibility to channel resources into
the production of a desired metabolite, instead of wasting nutrients on biomass production. In recent
years we obtained a foundational result for growth control in bacteria [7], in that we engineered an E. coli
strain where the transcription of a key component of the gene expression machinery, RNA polymerase, is
under the control of an inducible promoter. By changing the inducer concentration in the medium, we
can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and
the maximal growth rate supported by the medium. The publication also presented a biotechnological
application of the synthetic growth switch in which both the wild-type E. coli strain and our modified
strain were endowed with the capacity to produce glycerol when growing on glucose. Cells in which
growth has been switched off continue to be metabolically active and harness the energy gain to produce
glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression, putting
the yield very close to the theoretical maximum.

In the framework of the PhD thesis of Thibault Clavier, we addressed one of the limits of the growth
switch that is inherent to the construction of synthetic networks in living microorganisms, namely that
the latter evolve over time under the pressure of natural selection. In the case of the growth switch, the
selection pressure is particularly high, since any spontaneously arising mutations disabling the inhibition
of the expression of the RNA polymerase subunits will cause the population of growth-arrested cells to be
taken over by cells that have resumed growth (at the expense of metabolite production). We improved the
genetic stability of the growth switch by means of a redundant control mechanism of RNA polymerase
expression, reducing the escape frequency to less than one in 109 cells. We deposited a patent of this
invention. An article describing the results obtained with the extended growth switch has been submitted.
Moreover, this work is the subject of the start-up project Switch2Prod, led by Thibault Clavier.

8.4 Synthetic microbial communities for bioproduction processes: modelling, ana-

lysis and real-time monitoring

Participants:

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

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

In MICROCOSME, in particular, we have addressed the design of a bacterial community of two E.
coli strains, mimicking mutualistic relationships found in nature, and with the potential to outperform
a producer strain working in isolation in the production of a heterologous protein. We developed an
ODE model of the key growth phenotypes of the community and their interactions, calibrated the model
on literature data, and analysed the model for an in-depth understanding of the conditions supporting
coexistence and of the tradeoffs encountered in this production process [10]. With the work of Maaike
Sangster, who defended her Ph.D. in May 2023, we have bioengineered one version of the consortium,
characterized individual species in batch as well as explored the consortium growth in continuous-flow
experiments on an automated platform. Somewhat unexpected results have been obtained experiment-
ally, which led to advancements on the modelling and analysis of E. coli overflow metabolism in the
context of microbial consortia, as included in the thesis [21], and gave us new indications for modifica-
tions of the consortium that should enable co-existence. The finalization of this work by a joint effort

12

Inria Annual Report 2023

of several team members is expected to lead to a paper submission in the near future. As a subsequent
challenge, feedback control of the community for stabilization of biomasses to desirable bioproduction
regimes remains in the aims of this research direction.

In parallel, we have pushed further the investigation of the synthetic community design for bioproduc-
tion optimization. In collaboration with BIOCORE, we considered a generalized version of the synthetic
consortium proposed in [10] where both species of the consortium can synthesize a product of interest.
In addition to a theoretical analysis of equilibria and coexistence regimes, we showed that the generalized
design can indeed outperform our original consortium design based on a single producer species, thus
shifting the interest from the commonly evoked concept of division of labor (different tasks for different
species) to distribution of labor (same task across different species). This work is now published in [16].
The ANR Ctrl-AB puts related concepts to work for the synergistic growth of E. coli and microalgae. As
per project goals, vitamin synthesis by modified E. coli strains represents a leverage to control vitamin-
dependent growth of suitable algal strains, with the potential to robustify and maximize algal lipid
synthesis. On the experimental side, we advanced on the design and synthesis of the bacterial strain in
the consortium. Two alternative strategies for controlling bacterial vitamin synthesis via optogenetics
are being explored and, together with the lab advancements on optogenetic systems and experimental
equipment, will be tested next. On the methodological side, in collaboration with BIOCORE, modelling,
analysis and control problems are being addressed with the Ph.D. thesis of Rand Asswad, started in 2022.
Toward model-based feedback control of microbial communities, we focused so far on the problem of
real-time state estimation of microbial growth in a bioreactor. In connection with a simple algal-bacterial
growth model established within Ctrl-AB, we developed, theoretically analyzed and demonstrated in
simulation estimation methods for a single species that can be generalized to co-cultures. The work
resulted so far in a conference submission, currently under review. It will be extended to co-cultures and
integrated into the upcoming work on control of algal-bacterial growth and interaction dynamics in a
bioreactor.

8.5 Modelling of carbon and mRNA metabolism in microorganisms

Participants:

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

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

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

Project MICROCOSME

13

will be at the heart of the newly accepted ANR RECOM project, in which the Toulouse Biotechnology
Institute and MICROCOSME are partners.

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

8.6 Inference of parameters on lineage trees

Participants:

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

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

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

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

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Inria Annual Report 2023

8.7 Mathematical analysis of structured branching populations

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

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

Structured branching processes allows for the study of populations, where the lifecycle of each cell is
governed by a given characteristic or trait, such as the internal concentration of proteins. The dependence
on this characteristic of cellular mechanisms, like division or ageing, has been explored by Aline Marguet
via the mathematical analysis of those processes. In collaboration with Charline Smadi (INRAE Grenoble),
Aline Marguet investigated the long-time behavior of a parasite infection in a cell population. In this
work, accepted for publication in Stochastic Processes and their Applications [15], the dynamics of the
cell population is modelled using a structured branching process, where the cell cycle depends on the
dynamics of the parasites contained in the cell. The results obtained focus on the asymptotic behaviour
of the intracellular quantity of parasites. Based on criteria that depend on the comparison between the
growth rate of the cell population and the parameters associated with the parasite dynamics, containment
or explosion of the infection is established. The strategy of proof relies on the study of auxiliary processes
that describe the level of infection of a typical cell in the population, using coupling techniques.

The fate of the infection appears to be very sensitive to the law of parasite sharing between the two
daughter cells as they divide. In particular, the effect of various sharing strategies in the case of a constant
division rate has been considered in a follow-up work. A perfect symmetric sharing of the parasites
between the two daughter cells has been proven to be the worst strategy for the survival of the cell
population. Stochastic and deterministic strategies have also been compared, highlighting that variability
favors survival. This paper is under revision for Journal of Mathematical Biology [25].

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

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

Project MICROCOSME

15

9 Partnerships and cooperations

9.1 International initiatives

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria

International Program

GERM

Participants:

A. Belcour, I. Cancino-Aguirre, H. de Jong, D. Ropers.

Title: Growth-rate control in mycobacteria: Computational exploration of metabolic strategies

Partner Institution(s)

• Francis Crick Institute (United Kingdom)

Date/Duration: 2022-2024

Additionnal info/keywords: Numerous Mycobacterium species pose serious threats to human and an-
imal health. Genome-scale mathematical models of Mycobacterium metabolism are promising
avenues to uncover bottlenecks explaining the growth-rate variability and pathogenicity observed
across the genus. We employ these models to integrate and analyze diverse types of experimental
data, including measurements of doubling times and metabolite concentrations. The results will
allow us to formulate hypotheses on the molecular mechanisms responsible for growth-rate variab-
ility and pathogenicity observed across mycobacteria. The hypotheses will be tested by targeted
experiments.

9.1.2 Informal international partners

H. de Jong and D. Ropers collaborate with T. Gedeon, former invited researcher in our former team IBIS,
on research allocation strategies in microorganisms. The collaboration has already resulted in a paper
published this year in eLife [14].

9.1.3 Visits to international teams

Research stays abroad

I. Cancino Aguirre

Visited institution: Francis Crick Institute

Country: United Kingdom

Dates: 20/08/2023 - 23/09/2023

Context of the visit: visit to the Carvalho’s laboratory in the context of the associate-team GERM

Mobility program/type of mobility: research stay

9.2 European initiatives

9.2.1 Horizon Europe

Project name
Coordinator
MICROCOSME participants
Type
Web page

HyLife: Optimal control of microbial cells by natural and synthetic strategies
N. Dopffel (NORCE, Norway)
A. Belcour, H. de Jong, D. Ropers
Clean Energy Transition Co-funded Partnership (2023-2026)
Link to project description

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Inria Annual Report 2023

9.3 National initiatives

Project name
Coordinator
MICROCOSME participants
Type
Web page

Project name

Coordinator
MICROCOSME participants
Type
Web page

Project name
Coordinator
MICROCOSME participants

Type

Project name

Coordinator
MICROCOSME participants
Type

MAXIMIC: Optimal control of microbial cells by natural and synthetic strategies
H. de Jong
E. Cinquemani, T. Clavier, J. Geiselmann, H. de Jong, A. Pavlou, D. Ropers
ANR project (2017-2023)
Link to project description

RIBECO (RIBonucleotide ECOnomy): Engineering RNA life cycle to optimize economy
of microbial energy
M. Cocaign-Bousquet
E. Cinquemani, M. Cocaign-Bousquet, D. Ropers
ANR project (2018-2023)
Link to project description

Ctrl-AB : Optimization and control of the productivity of an algal-bacterial consortium
J.-L. Gouzé
R. Asswad, S. Arias, E. Boucher, E. Cinquemani, Th. Clavier, H. de Jong, J. Geiselmann,
L. Léau-Mercier, A. Marguet, M. Sangster
ANR project (2020-2025)

PlugNBio: A plug-and-play platform for reproducible microbial culture control exper-
iments
E. Cinquemani
S. Arias, E. Boucher, E. Cinquemani, J. Geiselmann, L. Léau-Mercier
Inria ADT (2022-2024)

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

Project name

Coordinator
MICROCOSME participants
Type
Web page

Project name

Coordinator
MICROCOSME participants
Type
Web page

ARBOREAL: Branching resource allocation processes for the analysis and inference of
phenotypic growth variability
A. Marguet
E. Cinquemani, J. Geiselmann, H. de Jong, A. Marguet
ANR project (2024-2029)

RECOM: Competition of RNAs for RNase E, a mechanism regulating their degradation
and the energy and carbon metabolism in the cell
M. Cocaign-Bousquet
E. Cinquemani, M. Cocaign-Bousquet, D. Ropers
ANR project (2023-2027)

In addition to the above projects, A. Marguet contributes to the ANR JCJC NOLO of Bertrand Cloez

(INRAE Montpellier) on non-local branching processes.

9.4 Regional initiatives

Project name
Coordinator
MICROCOSME participants
Type
Web page

Project name
Coordinator
MICROCOSME participants
Type

ATLAS: Analysis of brain energy metabolism in the context of Parkinson’s disease
F. Fauvelle (Grenoble Institute of Neurosciences)
D. Ropers
IXXI/BioSyl project (2022 – 2024)
Link to project description

AnaComBa: Analyse de Communautés Bactériennes : modélisation stochastique
A.Marguet & L. Coquille
E. Cinquemani, C. Fonte-Sanchez, A.Marguet, C.Medous
Equipe-Action du LABEX Persyval (2021 – 2024)

Project MICROCOSME

17

10 Dissemination

Participants:

R. Asswad, I. Cancino Aguirre, E. Cinquemani, Th. Clavier, M. Cocaign-
Bousquet, H. de Jong, J. Geiselmann, A. Marguet, Ch. Medous, D. Rop-
ers.

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

Member of organizing committees

MICROCOSME members
Ignacia Cancino Aguirre
Hidde de Jong

A. Marguet
A. Marguet

Conference, workshop, school
Inria PhD seminar, Montbonnot
Summer school on Economic Principles in Cell
Physiology, Paris
Conférence du GDR Branchement, Toulouse
GDT Math-Bio, Lyon-Grenoble

Date
2023
Jul 2023

Nov 2023
2023

10.1.2 Scientific events: selection

Chair of conference program committees

MICROCOSME member
Eugenio Cinquemani

Eugenio Cinquemani

Conference, workshop, school
European Control Conference (ECC 2023,
ECC2024)
Special Issue of the 19th International Confer-
ence on Computational Methods in Systems
Biology. BMC Bioinformatics Supplements,
24(1) [20]

Role
Associate editor

Supplement
(with Loïc Paulevé)

editor

Member of conference program committees

MICROCOSME member
Eugenio Cinquemani

Conference, workshop, program
ECC 2023

10.1.3 Journal

Member of editorial boards

MICROCOSME member
Hidde de Jong
Hidde de Jong
Johannes Geiselmann

Journal
Journal of Mathematical Biology
Biosystems (reviews editor)
Frontiers in Microbiology (review editor)

10.1.4 Invited talks and seminars

Ignacia Cancino Aguirre

Title
Analysis of mycobacterial metabolic variability
using genome-scale metabolic models

Event and location
Advanced School on Quantitative Principles in Mi-
crobial Physiology: from Single Cells to Cell Com-
munities, ICTP Trieste, Italy

Date
Oct 2023

Eugenio Cinquemani

Title
Power spectral analysis for the optimal design
of gene reporter systems

Thibault Clavier

Back and forth between growth and biosyn-
thesis : a bacterial metabolism switch system
to improve bioproduction

Event and location
GdT MathBio seminars, Grenoble, France

Date
Nov 2023

8th Bioproduction Congress (BIOPC2023), Lyon,
France

Oct 2023

18

Hidde de Jong

Inria Annual Report 2023

Title
Enhanced production of heterologous proteins
in synthetic microbial consortia
Modelling integrated networks of metabolism
and gene expression

Event and location
Invited talk at summer school Design and Control
of Microbial Communities, Leuven, Belgium
Blackboard session at summer school Design and
Control of Microbial Communities, Leuven, Bel-
gium

Date
Sep 2023

Sep 2023

Aline Marguet

Title
Spread of parasites affecting death and division
rates in a cell population
Parasite infection in a cell population

Modélisation de la prolifération de parasites
dans une population de cellules
Parasite infection in a cell population

Charles Medous
Title
Epine pour des populations branchantes avec
interactions et application à un processus de
Yule
Spinal constructions for continuous-space
branching processes
Mathematical modeling of interacting com-
munities
Construction d’épines pour des populations
stochastiques d’individus en interactions

Delphine Ropers

Event and location
Informs Applied Probability Society Conference
2023, Nancy, France
"Biology meets math: building a better under-
standing of host-parasite dynamics in the wild",
Montpellier
Journées Math-Bio-Santé 2023, Marne-la-Vallée,
France
Workshop EveREvol, Grenoble, France

Date
Jun 2023

Sep 2023

Nov 2023

Dec 2023

Event and location
Ecole d’été de la chaire MMB, Aussois, France

Date
Jun 2023

Journées Jeunes Probabilistes et Statisticiens,
Oléron, France
Invited talk at Séminaire de l’unité Evolution, Eco-
logie et Paléontologie, Lille, France
Invited talk at Journée du Groupe de travail Math-
Bio Sud-Est, Lyon

Oct 2023

Dec 2023

Dec 2023

Title
A short introduction to bioinformatics

Event and location
Invited talk at Séminaire du département
d’informatique de l’ENS Lyon, Le Pleynet, France
Mathematical modelling of energy metabolism Invited talk, Grenoble Institute of Neurosciences,

Date
Jan 2023

Jan 2023

Doing a PhD, good practice and pitfalls to
avoid

Grenoble, France
Matinée des doctorants, Centre Inria de
l’Université Grenoble Alpes

Oct 2023

10.1.5 Scientific evaluation and expertise

MICROCOSME member
Johannes Geiselmann

Hidde de Jong

Hidde de Jong

Organism
UMR5240 CNRS-UCBL-INSA-
BayerCropScience
Microbiology and Food Chain
Department, INRAE
Univ Grenoble Alpes

Hidde de Jong

Univ Grenoble Alpes

Hidde de Jong

Univ Grenoble Alpes

Hidde de Jong

Univ Grenoble Alpes

Delphine Ropers

National Science Centre, Po-
land

Role
Member scientific council

Member scientific council

Member scientific council of
Pôle MSTIC
Member of Vice-Présidence
Recherche et Innovation élar-
gie
Member of Collège des Ecoles
Doctorales
Member advisory board of
LabEX PERSYVAL 3
Member of Expert Panel LS2

Project MICROCOSME

19

10.1.6 Research administration

MICROCOSME member
Ignacia Cancino Aguirre
Eugenio Cinquemani

Committee
Inria - Univ. Grenoble Alpes
Inria - Univ. Grenoble Alpes

Eugenio Cinquemani

Inria - Univ. Grenoble Alpes

Eugenio Cinquemani

Inria - Univ. Grenoble Alpes

Hidde de Jong
Hidde de Jong

Inria - Univ Grenoble Alpes
Inria - Univ Grenoble Alpes

Hidde de Jong

Inria - Univ Grenoble Alpes

Hidde de Jong

Inria - Univ Grenoble Alpes

Hidde de Jong

Inria - Univ Grenoble Alpes

Hidde de Jong

Inria - Univ. Grenoble Alpes

Hidde de Jong

Inria

Aline Marguet
Aline Marguet

Inria - Univ. Grenoble Alpes
Inria - Univ. Grenoble Alpes

Delphine Ropers

Inria - Univ. Grenoble Alpes

10.1.7 Recruitment committees

Role
Member Comité du centre
Member Comité des Emplois
Scientifiques (CES)
Member Comité des Util-
isateurs des Moyens Inform-
atiques (CUMI)
Member Comité Développe-
ment Technologique (CDT)
Member Direction
President Comité des Equipes
Projets (CEP)
Member Comité des Emplois
Scientifiques (CES)
Member Comité Développe-
ment Technologique (CDT)
Member Comité des Etudes
Doctorales
President scientific council
(COS)
Member
d’évaluation (CE)
Member Comité du centre
Member Comité des études
doctorales
Référente chercheurs

Commission

MICROCOSME mem-
ber
Hidde de Jong
Hidde de Jong
Hidde de Jong
Delphine Ropers
Delphine Ropers
Delphine Ropers
Delphine Ropers

Organism

Recruitment

Inria national
Inria
Inria - Univ Grenoble Alpes
Inria - Univ Grenoble Alpes
INSA Lyon
Université Toulouse III
Inria - Université Côte d’Azur

CRHC (jury de promotion)
DR2 (jury d’admissibilité)
ISFP (jury d’admission)
CRCN/IFSP (jury d’admissibilité)
Selection committee Professor
Selection committee Professor
CRCN/IFSP 2024
d’admissibilité)

(Présidente du jury

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

D. Ropers received the title of Full Professor ("Professeur attaché") at Univ Grenoble Alpes for 3 years
(2023 - 2026) in recognition of her teaching activity.

J. Geiselmann is full professor at Univ Grenoble Alpes. He therefore has 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.

The following people have also contributed to courses last year.

Rand Asswad

• Practicals: Introduction to applied mathematics - image processing, L1, Computer Science (18 h)

• Course and practicals: Introduction to scientific programming, L1, Computer Science (24 h)

Ignacia Cancino Aguirre

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Inria Annual Report 2023

• Practicals: Cellular biology, L2, Life Sciences, Univ Grenoble Alpes (10 h)

• Practicals: Biotechnology of membrane and cell systems, M1, TD, Univ Grenoble Alpes (10 h)

Eugenio Cinquemani

• Course: Statistics for systems biology, M1, Master AIRE (Interdisciplinary Approaches in Research
and Education)/Learning planet institute, Université Paris Cité (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 (25 h)

Aline Marguet

• Practicals: Biostatistics, M2, Univ Grenoble Alpes (27 h)

Charles Medous

• Practicals: Introduction to applied mathematics - image processing, L1, Computer Science (36

hours)

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 (24 h)

• Course: Modelling and simulation of genetic regulatory networks, M2, INSA de Toulouse (4 h)

• Course: Metabolic modelling with omics data, M2, IA4 Health International master course, Univ

Grenoble Alpes (6 h)

10.2.2 Supervision

• PhD in progress: Rand Asswad, Development of control strategies for synthetic microbial consortia.

Supervisors: Eugenio Cinquemani and Jean-Luc Gouzé (Inria - Univ Côte d’Azur)

• PhD in progress: Ignacia Cancino Aguirre, Computational analysis of metabolic strategies in

pathogenic bacteria. Supervisors: Delphine Ropers and Hidde de Jong

• PhD in progress: Thibault Clavier Genetic growth control to maximize the bioproduction in E. coli.

Supervisors: Johannes Geiselmann and Hidde de Jong

• PhD in progress: Charles Medous, Analysis of bacterial communities: stochastic modelling. Super-
visors: Loren Coquille (Institut Fourier, Grenoble), Aline Marguet, Charline Smadi (Inrae Grenoble)

• PhD in progress: Emrys Reginato, Development, analysis, and inference of stochastic models of
gene expression in growing cell populations. Supervisors: Eugenio Cinquemani and Aline Marguet

• PhD completed: Maaike Sangster, Development, characterization and control of E. coli communit-
ies on an automated experimental platform. Supervisors: Eugenio Cinquemani and Johannes
Geiselmann

Project MICROCOSME

21

10.2.3 Juries

PhD thesis committees

MICROCOSME member
Eugenio Cinquemani

Eugenio Cinquemani
Hidde de Jong
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann
Johannes Geiselmann

Delphine Ropers
Delphine Ropers

Role
Maaike Sang-
ster
Marielle Péré
Rapporteur
Examinateur
Rapporteur
Examinateur
Directeur
Rapporteur
Examinateur
Examinateur
Rapporteur
Examinateur

Rapporteur
Rapporteur

PhD student
Co-directeur

University
Univ Grenoble Alpes

Rapporteur
Leo Diaz
Clément Caffaratti
Dimitrije Milunov
Sara Badawi
Maaike Sangster
Lexuan Liu
Victor Simon
Agate Nidriche
Yuliia Shymko
Clémence Dupont
Thibert
Maxime Mahout
Matteo Bouvier

Univ Côte d’Azur
Univ Melbourne, Australia
Univ Grenoble Alpes
Univ Paris Cité
Univ Grenoble Alpes
Univ Grenoble Alpes
Sorbonne Université
Univ Grenoble Alpes
Univ Grenoble Alpes
Univ Paris-Saclay
Univ Grenoble Alpes

Univ Paris-Saclay
ENS Lyon

Date
May 2023

Jul 2023
Jul 2023
Jan 2023
Feb 2023
Mar 2023
May 2023
Sept 2023
Oct 2023
Dec 2023
Dec 2023
Dec 2023

Nov 2023
Dec 2023

Habilitation (HDR) committees

MICROCOSME member
Eugenio Cinquemani

Role
Paolo Ballarini

HDR candidate
Rapporteur

PhD advisory committees

MICROCOSME mem-
ber
Eugenio Cinquemani
Johannes Geiselmann
Delphine Ropers

PhD student

University

Marielle Peré
Clément Caffaratti
Paul Ahavi

Univ Côte d’Azur
Univ Grenoble Alpes
Univ Paris Saclay

10.2.4 Teaching administration

University
CentraleSupélec, Université
Paris-Saclay

Date
Jun 2023

• Eugenio Cinquemani organizes a module on statistics in systems biology at Learning planet insti-

tute, Université Paris Cité.

• Delphine Ropers organizes a module on the mathematical modelling of biological systems at

PHELMA, INP Grenoble.

• Hidde de Jong organizes a module on the modelling of genetic and metabolic networks at INSA de

Lyon.

10.3 Popularization

10.3.1 Interventions

Charles Medous
Title
Mathematical games for children from
primary (CE2) to high school

Event and location
Club maths "Les maths Autrement", Institut
Fourier, Univ Grenoble Alpes

Aline Marguet
Title
Modélisation mathématique des popu-
lations de cellules

Modélisation mathématique des popu-
lations de cellules

Event and location
Invited talk at the "cérémonie de remise
de prix des Olympiades de Mathématiques",
Académie de Grenoble
Seminar to high school students (2de), Fête de
la Science, Montbonnot

Date
Organisation
and animation
of 2 sessions of
2 hours/month

Date
May 2023

22

Inria Annual Report 2023

11 Scientific production

11.1 Major publications

[1] V. Baldazzi, D. Ropers, J.-L. Gouzé, T. Gedeon and H. de Jong. ‘Resource allocation accounts for
the large variability of rate-yield phenotypes across bacterial strains’. In: eLife 12 (31st May 2023),
pp. 1–29. DOI: 10.7554/eLife.79815. URL: https://hal.inrae.fr/hal-04145943.

[2] E. Cinquemani. ‘Stochastic reaction networks with input processes: Analysis and application to
gene expression inference’. In: Automatica 101 (2019), pp. 150–156. DOI: 10.1016/j.automatica
.2018.11.047. URL: https://hal.inria.fr/hal-01925923.

[3] E. Cinquemani, V. Laroute, M. Bousquet, H. De Jong and D. Ropers. ‘Estimation of time-varying
growth, uptake and excretion rates from dynamic metabolomics data’. In: Bioinformatics 33.14
(2017), pp. i301–i310. DOI: 10.1093/bioinformatics/btx250. URL: https://hal.archives-o
uvertes.fr/hal-01607919.

[4] T. Etienne, M. Cocaign-Bousquet and D. Ropers. ‘Competitive effects in bacterial mRNA decay’.
In: Journal of Theoretical Biology 504 (Nov. 2020). DOI: 10.1016/j.jtbi.2020.110333. URL:
https://hal.inria.fr/hal-02967513.

[5] N. Giordano, F. Mairet, J.-L. Gouzé, J. Geiselmann and H. De Jong. ‘Dynamical allocation of cellular
resources as an optimal control problem: Novel insights into microbial growth strategies’. In: PLoS
Computational Biology 12.3 (9th Mar. 2016), e1004802. DOI: 10.1371/journal.pcbi.1004802.
URL: https://hal.inria.fr/hal-01332394.

[6] M. Hoffmann and A. Marguet. ‘Statistical estimation in a randomly structured branching popula-
tion’. In: Stochastic Processes and their Applications 129.12 (2019), pp. 5236–5277. DOI: 10.1016/j
.spa.2019.02.015. URL: https://hal.archives-ouvertes.fr/hal-01662203.

[7]

J. Izard, C. Gomez-Balderas, D. Ropers, S. Lacour, X. Song, Y. Yang, A. B. Lindner, J. Geiselmann
and H. De Jong. ‘A synthetic growth switch based on controlled expression of RNA polymerase’. In:
Molecular Systems Biology 11.11 (23rd Nov. 2015), p. 840. URL: https://hal.inria.fr/hal-012
47993.

[8] A. Llamosi, A. Gonzalez, C. Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen and G. Batt. ‘What
population reveals about individual cell identity: Single-cell parameter estimation of models of
gene expression in yeast’. In: PLoS Computational Biology 12.2 (9th Feb. 2016), e1004706. DOI:
10.1371/journal.pcbi.1004706. URL: https://hal.inria.fr/hal-01248298.

[9] A. Marguet, M. Lavielle and E. Cinquemani. ‘Inheritance and variability of kinetic gene expression
parameters in microbial cells: modeling and inference from lineage tree data’. In: Bioinformatics
35.14 (2019), pp. i586–i595. DOI: 10.1093/bioinformatics/btz378. URL: https://hal.archi
ves-ouvertes.fr/hal-02317115.

[10] M. Mauri, J.-L. Gouzé, H. De Jong and E. Cinquemani. ‘Enhanced production of heterologous
proteins by a synthetic microbial community: Conditions and trade-offs’. In: PLoS Computational
Biology 16.4 (2020), e1007795. DOI: 10.1371/journal.pcbi.1007795. URL: https://hal.sorb
onne-universite.fr/hal-02640446.

[11] M. Morin, D. Ropers, E. Cinquemani, J.-C. Portais, B. Enjalbert and M. Cocaign-Bousquet. ‘The
Csr System Regulates Escherichia coli Fitness by Controlling Glycogen Accumulation and Energy
Levels’. In: mBio 8.5 (31st Oct. 2017), pp. 1–14. DOI: 10.1128/mBio.01628-17. URL: https://hal
.inria.fr/hal-01672038.

[12] M. Morin, D. Ropers, F. Letisse, S. Laguerre, J.-C. Portais, M. Cocaign-Bousquet and B. Enjalbert.
‘The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper
glycolysis of Escherichia coli’. In: Molecular Microbiology 100.4 (May 2016), pp. 686–700. DOI:
10.1111/mmi.13343. URL: https://hal.archives-ouvertes.fr/hal-02147255.

[13] S. Pinhal, D. Ropers, J. Geiselmann and H. De Jong. ‘Acetate metabolism and the inhibition of
bacterial growth by acetate’. In: Journal of Bacteriology 201.13 (1st July 2019), pp. 147–166. DOI:
10.1128/JB.00147-19. URL: https://hal.inria.fr/hal-02195459.

Project MICROCOSME

23

11.2 Publications of the year

International journals

[14] V. Baldazzi, D. Ropers, J.-L. Gouzé, T. Gedeon and H. de Jong. ‘Resource allocation accounts for
the large variability of rate-yield phenotypes across bacterial strains’. In: eLife 12 (31st May 2023),
pp. 1–29. DOI: 10.7554/eLife.79815. URL: https://hal.inrae.fr/hal-04145943.

[15] A. Marguet and C. Smadi. ‘Spread of parasites affecting death and division rates in a cell population’.
In: Stochastic Processes and their Applications 168 (Feb. 2024), p. 104262. DOI: 10.1016/j.spa.20
23.104262. URL: https://hal.science/hal-03854347.

[16] C. Martinez, E. Cinquemani, H. de Jong and J.-L. Gouzé. ‘Optimal protein production by a synthetic
microbial consortium: Coexistence, distribution of labor, and syntrophy’. In: Journal of Mathemat-
ical Biology 87 (2023), pp. 1–37. DOI: 10.1007/s00285-023-01935-3. URL: https://inria.hal
.science/hal-03525249.

Invited conferences

[17] V. Baldazzi, D. Ropers, J.-L. Gouzé, T. Gedeon and H. de Jong. ‘Resource allocation strategies behind
rate-yield phenotypes in E.coli’. In: 2023 - Workshop on Advanced School on Quantitative Principles
in Microbial Physiology: from Single Cells to Cell Communities. Trieste, Italy, 31st July 2023, p. 1.
URL: https://hal.inrae.fr/hal-04216644.

Scientific book chapters

[18] O. Golan, H. Hindley, H. de Jong, M. Köbis, E. Pascual Garcia and A. Weiße. ‘Models of growing
cells’. In: Economic Principles in Cell Biology. Zenodo, 2023, pp. 117–129. DOI: 10.5281/zenodo.8
156979. URL: https://inria.hal.science/hal-04383889.

[19] H. de Jong, D. Giannari, D. A. Oyarzún, S. Waldherr and A. G. Yabo. ‘Optimal cell behavior in
time’. In: Economic Principles in Cell Biology. Zenodo, 2023. DOI: 10.5281/zenodo.8157066. URL:
https://inria.hal.science/hal-04383880.

Edition (books, proceedings, special issue of a journal)

[20]

Special Issue of the 19th International Conference on Computational Methods in Systems Biology.
BMC Bioinformatics Supplements 24.1 (23rd May 2023). URL: https://inria.hal.science/ha
l-04372213.

Doctoral dissertations and habilitation theses

[21] M. F. Sangster. ‘Development, characterization and control of E. coli communities on an automated
experimental platform’. Université Grenoble Alpes, 11th May 2023. URL: https://inria.hal.sc
ience/tel-04380648.

Reports & preprints

[22] F. Blanqui, A. Canteaut, H. de Jong, S. Imperiale, N. Mitton, G. Pallez, X. Pennec, X. Rival and B.
Thirion. Recommandations sur les « éditeurs de la zone grise ». Inria, 25th Jan. 2023, pp. 1–3. URL:
https://inria.hal.science/hal-04001505.

[23] F. Blanqui, A. Canteaut, H. D. Jong, S. Imperiale, N. Mitton, G. Pallez, X. Pennec, X. Rival and B.
Thirion. Recommendations on "Grey-Zone Publishers": Recommendations from the Inria Evaluation
Committee, translated from h t t p s : // h a l . i n r i a . f r / h a l -0 4 0 0 1 5 0 5 . Inria, 25th Jan.
2023, pp. 1–3. URL: https://inria.hal.science/hal-04201298.

[24] C. Fonte Sánchez, P. Gabriel and S. Mischler. On the Krein-Rutman theorem and beyond. 10th Jan.

2024. URL: https://hal.science/hal-04093201.

24

Inria Annual Report 2023

[25] A. Marguet and C. Smadi. Parasite infection in a cell population: role of the partitioning kernel.

20th Dec. 2023. URL: https://hal.science/hal-04095455.

[26] C. Medous. Spinal constructions for continuous type-space branching processes with interactions.

9th Aug. 2023. URL: https://hal.science/hal-04179190.

11.3 Cited publications

[27] D. Ropers, Y. Couté, L. Faure, S. Ferré, D. Labourdette, A. Shabani, L. Trouilh, P. Vasseur, G. Corre,
M. Ferro, M.-A. Teste, J. Geiselmann and H. de Jong. ‘Multiomics Study of Bacterial Growth Arrest
in a Synthetic Biology Application’. In: ACS Synthetic Biology 10.11 (Nov. 2021), pp. 2910–2926. DOI:
10.1021/acssynbio.1c00115. URL: https://hal.inria.fr/hal-03516727.

[28] C. Roux, T. Etienne, E. Hajnsdorf, D. Ropers, A. J. Carpousis, M. Cocaign-Bousquet and L. Girbal.
‘The essential role of mRNA degradation in understanding and engineering E. coli metabolism’.
In: Biotechnology Advances (2022), pp. 1–13. DOI: 10.1016/j.biotechadv.2021.107805. URL:
https://hal.science/hal-03325744.
<scratchpad>
- Main topic: Research Centre MICROCOSME at Université Grenoble Alpes
- Key points:
  - Combining computational and experimental approaches for microbial growth analysis
  - Focusing on understanding and controlling microbial growth for health, bioeconomy, and biotechnology
  - Developing multiscale models of growth and using genome-scale analysis and high-throughput data
  - Exploring natural and engineered resource allocation strategies, variability, and robustness of microbial adaptation
  - Analyzing and controlling microbial communities for bioproduction processes
- Structure: The document is divided into sections, including activity report, project-team, contents, and application domains
- Potential artifacts to ignore: Web-related elements such as links, HTML tags, and other web-specific content
</scratchpad>

<final_summary>
The MICROCOSME research centre at Université Grenoble Alpes focuses on combining computational and experimental approaches to understand and control microbial growth for various applications in health, bioeconomy, and biotechnology. The centre develops multiscale models of growth and uses genome-scale analysis and high-throughput data to explore natural and engineered resource allocation strategies, variability, and robustness of microbial adaptation. By analyzing and controlling microbial communities, MICROCOSME aims to improve bioproduction processes and address challenges in health, bioeconomy, and biotechnology. The centre's research encompasses various aspects, including genome-scale analysis, high-throughput data, and microbial growth control, with the ultimate goal of developing innovative solutions for real-world problems.
The MICROCOSME research centre at Université Grenoble Alpes focuses on combining computational and experimental approaches to understand and control microbial growth for various applications in health, bioeconomy, and biotechnology. The centre develops multiscale models of growth and uses genome-scale analysis and high-throughput data to explore natural and engineered resource allocation strategies, variability, and robustness of microbial adaptation. By analyzing and controlling microbial communities, MICROCOSME aims to improve bioproduction processes and address challenges in health, bioeconomy, and biotechnology. The centre's research encompasses various aspects, including genome-scale analysis, high-throughput data, and microbial growth control, with the ultimate goal of developing innovative solutions for real-world problems.