RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

CNRS, INRAE, Sorbonne Université

Project-Team

BIOCORE

Biological control of artificial ecosystems

IN COLLABORATION WITH: Laboratoire d’océanographie de Villefranche
(LOV)

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team BIOCORE

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1 Mathematical and computational methods
3.2 A methodological approach to biology: from genes to ecosystems . . . . . . . . . . . . . . .

4 Application domains

4.1 Bioenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 CO2 fixation and fluxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Biological control for plants and micro-plants production systems . . . . . . . . . . . . . . .
4.4 Biological depollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

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6 Highlights of the year

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6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

7 New software, platforms, open data

10
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.1 In@lgae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.2 Odin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

8 New results

11
Mathematical methods and methodological approach to biology . . . . . . . . . . . . . . . . . . 11
8.1 Mathematical analysis of biogical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.1 Mathematical study of ecological models . . . . . . . . . . . . . . . . . . . . . . . . . . 11
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1.2 Estimation and control
8.2 Network interactions for cell function and growth . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2.1 Cellular response and cell-to-cell variability . . . . . . . . . . . . . . . . . . . . . . . . 12
8.2.2 Intercellular communication in peripheral clocks . . . . . . . . . . . . . . . . . . . . . 12
8.2.3 Cell economy and control of cell growth . . . . . . . . . . . . . . . . . . . . . . . . . . 12
8.3 Dynamics and control for synthetic biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.3.1 Dynamics in networks of cellular oscillators . . . . . . . . . . . . . . . . . . . . . . . . 13
Fields of application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.4 Bioenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.4.1 Modelling microalgae production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.4.2 Control and Optimization of microalgae production . . . . . . . . . . . . . . . . . . . 15
8.5 Biological depollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.5.1 Control and optimization of bioprocesses for depollution . . . . . . . . . . . . . . . . 15
8.5.2 Coupling microalgae to anaerobic digestion . . . . . . . . . . . . . . . . . . . . . . . . 16
8.6 Life Cycle Assessment
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.7 Modeling and control of cancer cell population dynamics. . . . . . . . . . . . . . . . . . . . . 17
8.8 Optimal bacterial resource allocation for metabolite production. . . . . . . . . . . . . . . . . 17
8.9 Optimizing waste-to-energy systems for profitable energy production.
. . . . . . . . . . . . 17
8.10 Design of ecologically friendly plant production systems . . . . . . . . . . . . . . . . . . . . . 18
8.10.1 Ecophysiological modelling of plant-microbiota interactions . . . . . . . . . . . . . . 18
8.10.2 Epidemiological modelling of plant-enemy interactions . . . . . . . . . . . . . . . . . 18
8.11 Design and control of managed ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.11.1 Design and control of synthetic microbial ecosystems . . . . . . . . . . . . . . . . . . 19
8.11.2 Design of biological control strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.11.3 Sustainable management of plant resistance . . . . . . . . . . . . . . . . . . . . . . . . 20

9 Bilateral contracts and grants with industry

20
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
9.2 Bilateral grants with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

10 Partnerships and cooperations

21
10.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework

10.3 European initiatives

10.2 International research visitors

of an Inria International Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10.4.1 National programmes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
10.4.2 Inria funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
10.4.3 INRAE funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

10.3.1 H2020 projects

11 Dissemination

25
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
11.2.3 Master theses and internships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
11.2.4 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
11.3.1 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
11.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

12 Scientific production

31
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
12.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
12.4 Cited publications

Project BIOCORE

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Project-Team BIOCORE

Creation of the Project-Team: 2011 January 01

Keywords

Computer sciences and digital sciences

A1.5.1. – Systems of systems

A6. – Modeling, simulation and control

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

A6.1.3. – Discrete Modeling (multi-agent, people centered)

A6.1.4. – Multiscale modeling

A6.1.5. – Multiphysics modeling

A6.2.1. – Numerical analysis of PDE and ODE

A6.2.6. – Optimization

A6.4. – Automatic control

A6.4.1. – Deterministic control

A6.4.3. – Observability and Controlability

A6.4.4. – Stability and Stabilization

A6.4.6. – Optimal control

A8.1. – Discrete mathematics, combinatorics

A8.2. – Optimization

A8.7. – Graph theory

A8.11. – Game Theory

A9.2. – Machine learning

Other research topics and application domains

B1.1.7. – Bioinformatics

B1.1.8. – Mathematical biology

B1.1.10. – Systems and synthetic biology

B2.4.1. – Pharmaco kinetics and dynamics

B3.1. – Sustainable development

B3.1.1. – Resource management

B3.4. – Risks

B3.4.1. – Natural risks

B3.4.2. – Industrial risks and waste

B3.4.3. – Pollution

B3.5. – Agronomy

B3.6. – Ecology

B3.6.1. – Biodiversity

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Inria Annual Report 2023

B4.3. – Renewable energy production

B4.3.1. – Biofuels

Project BIOCORE

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1 Team members, visitors, external collaborators

Research Scientists

• Olivier Bernard [Team leader, INRIA, Senior Researcher, HDR]

• Valentina Baldazzi [INRAE, Senior Researcher, until Jun 2023, HDR]

• Pierre Bernhard [INRIA, Emeritus, until Jun 2023, HDR]

• Francesca Casagli [INRIA, Researcher]

• Madalena Chaves [INRIA, Senior Researcher, until Jun 2023, HDR]

• Walid Djema [INRIA, ISFP]

• Jean-Luc Gouzé [INRIA, Senior Researcher, until Jun 2023, HDR]

• Frédéric Grognard [INRIA, Researcher, until Jun 2023, HDR]

• Ludovic Mailleret [INRAE, Researcher, until Jun 2023, HDR]

• Jérémie Roux [CNRS, until Jun 2023]

• Antoine Sciandra [CNRS, Senior Researcher, HDR]

• Suzanne Touzeau [INRAE, Researcher, until Jun 2023]

Faculty Members

• Bastien Polizzi [UNIV FRANCHE-COMTE, Associate Professor Delegation, until Feb 2023]

• Jacques Sepulchre [UNS, until Jun 2023, HDR]

Post-Doctoral Fellows

• Juan Carlos Arceo Luzanilla [INRIA, Post-Doctoral Fellow, until Mar 2023]

• Giada Fiandaca [INRIA, Post-Doctoral Fellow, from Nov 2023]

• Charlotte Gaviard [Inria]

• Emna Krichen [INRIA, Post-Doctoral Fellow]

PhD Students

• Bruno Assis Pessi [INRIA, until Feb 2023]

• Benjamin Bobel [INRIA, from Apr 2023 until Jun 2023]

• Odile Burckard [INRIA, until Jun 2023]

• Marine Courtois [INRAE, until Jun 2023]

• Clotilde Djuikem [UNIV COTE D’AZUR, until Jun 2023]

• Joel Ignacio Fierro Ulloa [INRIA]

• Yan Gao [CENTRALESUPELEC, until Mar 2023]

• Ali Gharib [INRIA]

• Diego Penaranda Sandoval [INRIA]

• Joseph Junior Penlap Tamagoua [INRIA, until Jun 2023]

• Marielle Pere [INRIA, until Apr 2023]

• Romain Ranini [INRIA]

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Technical Staff

Inria Annual Report 2023

• Hubert Bonnefond [INRIA, Engineer, from Sep 2023]

• Yan Gao [INRIA, Engineer, from Apr 2023 until Jul 2023]

• Marielle Pere [Inria Startup Studio, Engineer, from Jul 2023 until Jul 2023]

• Eric Pruvost [Sorbonne Université, Engineer]

• Amélie Talec [CNRS, Engineer]

Interns and Apprentices

• Alice Guillet [INRIA, Intern, from Feb 2023 until Aug 2023]

• Javier Innerarity Imizcoz [INRIA, Intern, from Apr 2023 until Sep 2023]

• Annalisa Reali [INRIA, Intern, from Oct 2023]

• Oscar Rousseau [INRIA, Intern, from Aug 2023 until Sep 2023]

• Morgan Scalabrino [INRIA, Intern, from Jun 2023 until Aug 2023]

• Eliane Younes [INRIA, Intern, from Mar 2023 until Aug 2023]

Administrative Assistant

• Maeva Jeannot [INRIA]

Visiting Scientists

• Jineth Arango Oviedo [PUC - VALPARAISO, until Apr 2023]

• Jérôme Coville [INRAE, until Feb 2023]

• Elisabeth Gualda Alonso [UNIV ALMERIA, from Sep 2023]

• Aurelien Kambeu Youmbi [UNIV DSCHANG CAMEROU, from Feb 2023 until Apr 2023]

• Frank Kemayou Mangwa [UNIV DOUALA, from Feb 2023 until Apr 2023]

• David Morgado Pereira [CENTRALESUPELEC, from Nov 2023]

• Rebecca Nordio [University of Almeria, from Mar 2023 until May 2023]

• Gustavo Ribeiro da Silva [São Paulo State University/Universidade Estadual Paulista UNESP, from

Apr 2023 until May 2023]

• Tehreem Syed [TU Dresden, School of Engineering, until Mar 2023]

External Collaborators

• Samuel Bowong Tsakou [UNIV DOUALA]

• Francis Mairet [IFREMER, HDR]

Project BIOCORE

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2 Overall objectives

BIOCORE is a joint research team between Inria (Inria centre at Université Côte d’Azur), INRAE (ISA -
Institut Sophia Agrobiotech and LBE - Laboratory of Environmental Biotechnology in Narbonne) and Sor-
bonne Université-CNRS (Oceanographic Laboratory of Villefranche-sur-mer - LOV, UMR 7093/ Sorbonne
Université, Villefranche sur Mer, Team: Processes in Pelagic Ecosystems - PEPS).

Sustainable growth of living organisms is one of the major challenges of our time. In order to tackle
it, the development of new technologies is necessary, and many of these new technologies will need to
use modeling and computer tools. BIOCORE contributes to this theme, in the general field of design
and control of artificial ecosystems (or biosystems). Its general goal is to design devices, systems and
processes containing living cells or individuals and performing some tasks to decrease pollution, use of
chemicals, or to produce bioenergy in a sustainable way. We build biological/ecological models in close
collaborations with biologists and bioprocess engineers, and validate them with experimental platforms.
Our activities are structured in three levels: mathematical and computational methods, a methodological
approach to biology, and applications.

Research themes:
Mathematical and computational methods:

• Tools for modeling in biology: model design, validation, parameter identification.

• Mathematical properties of models in biology: mathematical studies of models and of their global

behavior.

• Software sensors for biological systems: using the model and on-line measurements to estimate

the variables that are not measured directly.

• Control, regulation, and optimization for biological systems; design of laws to maintain a variable

at a given level, or to optimize the productivity of the system.

A methodological approach to biology: system study at different scales

• At the intra-individual level: theoretical and experimental study of simple metabolic-genetic

networks, coarse grained models of the internal state.

• At the level of interactions between individuals in the population: individual behavior, resource

allocation.

• At the scale of interaction between populations: interaction between prey and predator populations

in a trophic network or competition between species in a chemostat.

• At the scale of interaction between ecosystems: coupling of two artificial ecosystems as a unique
bioprocess or interactions between an artificial ecosystem and the surrounding natural ecosystem.

Fields of application:

• Bioenergy, in particular the production of lipids (which can be used as biofuel), methane and

hydrogen by microorganisms (with LOV and LBE).

• CO2 fixation by micro-algae, with the aim of capturing industrial CO2 fluxes (with LOV). This theme
can also include artificial ecosystems developed to improve the prediction of carbon fluxes between
the ocean and the atmosphere.

• Design and optimization of ecologically friendly protection methods for plants and micro-plants
artificial production systems (with ISA and LOV). This theme focuses in particular on biological
control programs to control pathogens and pest invasions in crops and bioreactors.

• Biological waste treatment with microorganisms in bioreactors to reduce pollution emission levels

(in collaboration with LBE).

Software development:

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Inria Annual Report 2023

• Software tools for biological modeling and supervision of biological processes.

National, international and industrial relations

• National collaborations: IFREMER (Nantes), INRAE (MISTEA Montpellier, BIOGER Grignon, IAM
Nancy, Agrocampus Ouest, MaIAGE Jouy-en-en-Josas, BioEpAR Nantes), CIRAD Montpellier, In-
stitut Méditerranéen d’Océanologie, LOCEAN (Paris), GIPSA Grenoble, MICROCOSME, ANGE,
MCTAO, and VALSE Inria teams.

• Participation in French groups : ModStatSAP (Modélisation et Statistique en Santé des Animaux
et des Plantes), GDR Invasions Biologiques, BIOSS (Modélisation symbolique des systèmes biolo-
giques)

• Participation to national programmes: ANR projects PhotoBioFilmExplorer, Ctrl-AB, InSync and

Maximic, Plan Cancer Imodrez, UMT Fiorimed, and Labex SIGNALIFE.

• International collaborations: Université de Mons (Belgium), Politecnico de Milano (It), Imperial
College (United Kingdom), Norwegian University of Science and Technology (Norway), Pontificia
Universidad Católica de Valparaíso and Universidad de Chile (Chile), University of Edinburgh (UK),
Universities of Douala, Yaoundé I and Dschang (Cameroon).

3 Research program

3.1 Mathematical and computational methods

BIOCORE’s action is centered on the mathematical modeling of biological systems, more particularly of
artificial ecosystems, that have been built or strongly shaped by man. Indeed, the complexity of such
systems where life plays a central role often makes them impossible to understand, control, or optimize
without such a formalization. Our theoretical framework of choice for that purpose is Control Theory,
whose central concept is “the system”, described by state variables, with inputs (action on the system),
and outputs (the available measurements on the system). In modeling the ecosystems that we consider,
mainly through ordinary differential equations, the state variables are often population, substrate and/or
food densities, whose evolution is influenced by the voluntary or involuntary actions of man (inputs
and disturbances). The outputs will be some product that one can collect from this ecosystem (harvest,
capture, production of a biochemical product, etc.), or some measurements (number of individuals,
concentrations, etc.). Developing a model in biology is however not straightforward: the absence of
rigorous laws as in physics, the presence of numerous populations and inputs in the ecosystems, most
of them being irrelevant to the problem at hand, the uncertainties and noise in experiments or even in
the biological interactions require the development of dedicated techniques to identify and validate the
structure of models from data obtained by or with experimentalists.

Building a model is rarely an objective in itself. Once we have checked that it satisfies some biological
constraints (e.g., densities stay positive) and fitted its parameters to data (requiring tailor-made methods),
we perform a mathematical analysis to check that its behavior is consistent with observations. Again,
specific methods for this analysis need to be developed that take advantage of the structure of the model
(e.g., the interactions are monotone) and that take into account the strong uncertainty that is linked to
life, so that qualitative, rather than quantitative, analysis is often the way to go.

In order to act on the system, which often is the purpose of our modeling approach, we then make use
of two strong points of Control Theory: 1) the development of observers, that estimate the full internal
state of the system from the measurements that we have, and 2) the design of a control law, that imposes
to the system the behavior that we want to achieve, such as the regulation at a set point or optimization
of its functioning. However, due to the peculiar structure and large uncertainties of our models, we need
to develop specific methods. Since actual sensors can be quite costly or simply do not exist, a large part
of the internal state often needs to be re-constructed from the measurements and one of the methods we
developed consists in integrating the large uncertainties by assuming that some parameters or inputs

Project BIOCORE

7

belong to given intervals. We then developed robust observers that asymptotically estimate intervals
for the state variables [78]. Using the directly measured variables and those that have been obtained
through such, or other, observers, we then develop control methods that take advantage of the system
structure (linked to competition or predation relationships between species in bioreactors or in the
trophic networks created or modified by biological control).

3.2 A methodological approach to biology: from genes to ecosystems

One of the objectives of BIOCORE is to develop a methodology that leads to the integration of the different
biological levels in our modeling approach: from the biochemical reactions to ecosystems. The regulatory
pathways at the cellular level are at the basis of the behavior of the individual organism but, conversely, the
external stresses perceived by the individual or population will also influence the intracellular pathways.
In a modern “systems biology” view, the dynamics of the whole biosystem/ecosystem emerge from
the interconnections among its components, cellular pathways/individual organisms/population. The
different scales of size and time that exist at each level will also play an important role in the behavior
of the biosystem/ecosystem. We intend to develop methods to understand the mechanisms at play at
each level, from cellular pathways to individual organisms and populations; we assess and model the
interconnections and influence between two scale levels (e.g., metabolic and genetic; individual organism
and population); we explore the possible regulatory and control pathways between two levels; we aim at
reducing the size of these large models, in order to isolate subsystems of the main players involved in
specific dynamical behaviors.

We develop a theoretical approach of biology by simultaneously considering different levels of de-
scription and by linking them, either bottom up (scale transfer) or top down (model reduction). These
approaches are used on modeling and analysis of the dynamics of populations of organisms; modeling
and analysis of small artificial biological systems using methods of systems biology; control and design of
artificial and synthetic biological systems, especially through the coupling of systems.

The goal of this multi-level approach is to be able to design or control the cell or individuals in order
to optimize some production or behavior at higher level: for example, control the growth of microalgae
via their genetic or metabolic networks, in order to optimize the production of lipids for bioenergy at the
photobioreactor level.

4 Application domains

4.1 Bioenergy

Finding sources of renewable energy is a key challenge for our society. We contribute to this topic through
two main domains for which a strong and acknowledged expertise has been acquired over the years. First,
we consider anaerobic digesters, the field of expertise of the members of the team at the Laboratory of
Environmental Biotechnology (LBE), for the production of methane and/or biohydrogen from organic
wastes. The main difficulty is to make these processes more reliable and exploit more efficiently the
produced biogas by regulating both its quality and quantity despite high variability in the influent wastes.
One of the specific applications that needs to be tackled is the production of biogas in a plant when the
incoming organic waste results from the mixing of a finite number of substrates. The development of
control laws that optimize the input mix of the substrates as a function of the actual state of the system is
a key challenge for the viability of this industry.

The second topic consists in growing microalgae, the field of expertise of the members of the team
at the Oceanographic Laboratory of Villefranche-sur-Mer (LOV), to produce biofuel. These microor-
ganisms can synthesize lipids with a much higher productivity than terrestrial oleaginous species. The
difficulty is to better understand the involved processes, which are mainly transient, to stimulate and
optimize them on the basis of modeling and control strategies. Predicting and optimizing the productivity
reached by these promising systems in conditions where light received by each cell is strongly related to
hydrodynamics, is a crucial challenge.

Finally, for the energy balance of the process, it is important to couple microalgae and anaerobic
digestion to optimize the solar energy that can be recovered from microalgae, as was explored within the
ANR Symbiose project (2009-2012) [4].

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Inria Annual Report 2023

4.2 CO2 fixation and fluxes

Phytoplanktonic species, which assimilate CO2 during photosynthesis, have received a lot of attention in
the last years. Microalgal based processes have been developed in order to mitigate industrial CO2. As for
biofuel productions, many problems arise when dealing with microalgae which are more complex than
bacteria or yeasts. Several models have been developed within our team to predict the CO2 uptake in
conditions of variable light and nitrogen availability. The first modeling challenge in that context consists
in taking temperature effects and light gradient into account.

The second challenge consists in exploiting the microalgal bioreactors which have been developed
in the framework of the quantification of carbon fluxes between ocean and atmospheres. The SEMPO
platform (simulator of variable environment computer controlled), developed within the LOV team, has
been designed to reproduce natural conditions that can take place in the sea and to accurately measure
the cells behavior. This platform, for which our team has developed models and control methods over the
years, is an original and unique tool to develop relevant models which stay valid in dynamic conditions. It
is worth noting that a better knowledge of the photosynthetic mechanisms and improved photosynthesis
models benefits both thematics: CO2 mitigation and carbon fluxes predictions in the sea.

4.3 Biological control for plants and micro-plants production systems

This research concentrates on the protection of cultures of photosynthetic organisms against their pests
or their competitors. The cultures we study are crop and micro-algae productions. In both cases, the
devices are more or less open to the outside, depending on the application (greenhouse/field, photobi-
oreactor/raceway), so that they may give access to harmful pathogens and invading species. We opt for
protecting the culture through the use of biocontrol in a broad sense.

In crop production, biocontrol is indeed a very promising alternative to reduce pesticide use: it helps
protecting the environment, as well as the health of consumers and producers; it limits the development
of resistance (in comparison to chemicals). The use of biocontrol agents, which are, generically, natural
enemies (predators, parasitoids or pathogens) of crop pests [81], is however not widespread yet because
it often lacks efficiency in real-life crop production systems (while its efficiency in the laboratory is much
higher) and can fail to be economically competitive. Resistant crops are also used instead of pesticides
to control pests and pathogens, but the latter eventually more or less rapidly overcome the resistance,
so these crops need to be replaced by new resistant crops. As resistant genes are a potentially limited
resource, a challenge is to ensure the durability of crop resistance. Our objective is to propose models that
would help to explain which factors are locks that prevent the smooth transition from the laboratory to
the agricultural crop, as well as to develop new methods for the optimal deployment of the pests natural
enemies and of crop resistance.

Microalgae production is faced with exactly the same problems since predators of the produced
microalgae (e.g., zooplankton) or simply other species of microalgae can invade the photobioreactors
and outcompete or eradicate the one that we wish to produce. Methods need therefore to be proposed
for fighting the invading species; this could be done by introducing predators of the pest and so keeping
it under control, or by controlling the conditions of culture in order to reduce the possibility of invasion.
The design of such methods could greatly take advantage of our knowledge developed in crop protection
since the problems and models are related.

4.4 Biological depollution

These works are carried out with the LBE, mainly on anaerobic treatment plants. This process, despite
its strong advantages (methane production and reduced sludge production) can have several locally
stable equilibria. In this sense, proposing reliable strategies to stabilize and optimize this process is a key
issue. Because of the recent (re)development of anaerobic digestion, it is crucial to propose validated
supervision algorithms for this technology. A problem of growing importance is to take benefit of various
waste sources in order to adapt the substrate quality to the bacterial biomass activity and finally optimize
the process. This generates new research topics for designing strategies to manage the fluxes of the
various substrate sources meeting at the same time the depollution norms and providing a biogas of
constant quality. In the past years, we have developed models of increasing complexity. However there is

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a key step that must be considered in the future: how to integrate the knowledge of the metabolisms in
such models which represent the evolution of several hundreds bacterial species? How to improve the
models integrating this two dimensional levels of complexity? With this perspective, we wish to better
represent the competition between the bacterial species, and drive this competition in order to maintain,
in the process, the species with the highest capability to consume the organic pollutant. This approach,
initiated in [83] must be extended from a theoretical point of view and validated experimentally.

5 Social and environmental responsibility

Since its creation, team BIOCORE has been actively engaged in contributing to sustainable growth of
living organisms and the production of bioenergy in a sustainable way. Through our expertise in the
development of new technologies, mathematical models, and computer tools, BIOCORE contributes
to the general field of design and control of artificial ecosystems (or biosystems). The general goal of
BIOCORE (see Section 2) is to design devices, systems and processes containing living cells or individuals
and performing some tasks to decrease pollution, use of chemicals, or to produce bioenergy in a sustain-
able way. We build biological/ecological models in close collaborations with biologists and bioprocess
engineers, and validate them with experimental platforms. Our main applications are:

- Bioenergy, in particular the production of lipids (which can be used as biofuel), methane and

hydrogen by microorganisms (with LOV and LBE).

- CO2 fixation by micro-algae, with the aim of capturing industrial CO2 fluxes (with LOV).

- Design and optimization of ecologically friendly protection methods for plants and micro-plants

artificial production systems (with ISA and LOV).

- Biological waste treatment with microorganisms in bioreactors to reduce pollution emission levels

(in collaboration with LBE).

Some members of our team (O. Bernard and W. Djema) are also participants in the local committee
for sustainable development (CLDD), which was (re-)activated in 2019 for Inria Centre at Université
Côte d’Azur. This committee is active in various ways, and organizes events to introduce, inform, and
familiarize the community to sustainable development questions and actions.

Some Biocore members did presentations and participated in events on sustainable development

(see Section 11.3)

6 Highlights of the year

• The Biocore Team arrived at the end of its life time, and gave rise to the new Macbes team (July

2023) and in 2024 the Greenowl Team should be created.

• Combining algae and bacteria in High Rate Algal/Bacterial Ponds can lead to more efficicent, yet
more complex wastewater remediation and resource recovery. In [5] a modelling and optimization
study is carried out to assess advantage of a solid/liquid separation system. We identified the
conditions maximising the algal productivity and those optimizing the efficiency of nitrogen
recycling. We propose to control alkalinity and water depth to boost the algal productivity without
meeting dangerous conditions favorable to N2O emission.

• Different strains of a microorganism growing in the same environment display a wide variety of
growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that
different resource allocation strategies, corresponding to different compositions of the proteome,
can account for the observed rate-yield variability [2]. Validated by a compiled database of hundreds
of published rate-yield phenotypes of Escherichia coli strains, the model highlights the complex
nature of the relationship between metabolites, growth rate, and yield. An interesting prediction of
our model is that high growth rates are not necessarily accompanied by low growth yields: high-rate,

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high-yield growth of E. coli can be achieved by a higher saturation of enzymes and ribosomes, and
thus by a more efficient utilization of proteomic resources.

6.1 Awards

• F. Casagli was selected to give a Ted-X talk in Cannes, the 24t h of November

• Charlotte Gaviard got the i-PhD Concours d’Innovation grand prize in the category Pharmacy and
Biotechnologies for her project DareWin Evolution, proposing new approaches based on guided
dynamical Darwinian selection to improve microorganisms performances.

• Marielle Péré obtained an i-PhD Concours d’Innovation grand prize for her project CellEmax,
issued from her thesis work, whose goal is to propose rational identification of new targets for
anti-cancer combined treatments.

7 New software, platforms, open data

7.1 New software

7.1.1 In@lgae

Name: Numerical simulator of microalgae based processes

Keywords: Simulation, Microalgae system, Productivity

Functional Description: In@lgae simulates the productivity of a microalgae production system, taking
into account both the process type and its location and time of the year. The process is mainly
defined by its thermal dynamics and by its associated hydrodynamics. For a given microalgal strain,
a set of biological parameters describe the response to nitrogen limitation, temperature and light.
As a result, the biomass production, CO2 and nitrogen fluxes, lipid and sugar accumulation are
predicted.

Release Contributions: The In@lgae platform has been optimised to make it faster. Some of the key
models have been rewritten in C++ to allow a faster computation. Models have been improved to
include, in the growth rate computation, the composition of the light spectrum. The graphical user
interface has been enhanced and several sets of parameters describing different microalgal species
have been stored.

Contact: Olivier Bernard

Participants: Étienne Delclaux, Francis Mairet, Olivier Bernard, Quentin Béchet

7.1.2 Odin

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

Keywords: Bioinformatics, Biotechnology, Monitoring, Automatic control

Scientific Description: ODIN is a distributed application, whose graphical interface can be launched re-
motely through the Internet. The application, developed in Erlang, is architected around an MQTT
broker. It is robust and tolerant to hardware failures in order to avoid that a wrong manipulation
can have harmful consequences on the biotechnological process.

Thus, the implementation of a new algorithm is done by a plugin written in Python language.
Modifying one of these algorithms does not require recompiling the code.

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. In a more original way,

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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 Ibis research team.

URL: https://team.inria.fr/biocore/software/odin/

Contact: Olivier Bernard

Participants: Olivier Bernard, Nicolas Niclausse, Eugenio Cinquemani, Tamas Muszbek, Thibaud Kloczko,
Nicolas Chleq, Jean-Luc Szpyrka, Pierre Fernique, Julia Elizabeth Luna, Come Le Breton, Jonathan
Levy, Amine Lahouel, Tristan Cabel, François Caddet, Erwan Demairy, Riham Nehmeh, Marc Vesin,
Carlos Zubiaga Pena

8 New results

Mathematical methods and methodological approach to biology

8.1 Mathematical analysis of biogical models

8.1.1 Mathematical study of ecological models

Participants:

Frédéric Grognard,
Clotilde Djuikem, Yves Fotso Fotso, Marine Courtois.

Ludovic Mailleret,

Suzanne Touzeau,

Semi-discrete models have shown their relevance in modeling biological phenomena whose nature
presents abrupt changes over time [82]. In plant epidemiology, they can represent seasonality or external
perturbations of natural systems, such as harvest. We developed and analyzed such models in the context
of biological control applied to coffee leaf rust [14] or coffee berry borers [56], and of the sterile insect
technique [46]. Semi-discrete models were central in Frédéric Grognard’s HDR defense [50] and Clotilde
Djuikem’s PhD thesis [48].

In this framework, we studied how recurrent migration events (“pulsed migration”) between different
spatial locations influence the evolution of populations through a population genetics approach [6]. We
evidenced that migration pulsedness affects allele fixation rates in interaction with their selective value,
generally reducing the level of local adaptation as compared to continuous migration. This research
was part of Flora Aubree’s PhD thesis (defended in 2022), and has been performed in collaboration with
Vincent Calcagno (ISA).

8.1.2 Estimation and control

Participants:

Frédéric Grognard, Ludovic Mailleret, Suzanne Touzeau, Yves Fotso
Fotso, Clotilde Djuikem.

Optimal control and optimisation. We developed several approaches to control the evolution of crop
pests. We solved optimal control problems to limit the damages due to coffee berry borers [16] and Coffee
Leaf Rust [77] using the BOCOP software, which is developed by Inria team COMMANDS.

8.2 Network interactions for cell function and growth

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Participants: Odile Burckard, Madalena Chaves, Giada Fiandaca, Marielle Péré,

Jérémie Roux, Jean-Luc Gouzé, Valentina Baldazzi.

8.2.1 Cellular response and cell-to-cell variability

To analyze the considerable amount of data from fate-seq [85], we proposed an ODE model of the molecu-
lar pathways involved in cell death triggered by Tumor necrosis factor-related apoptosis-inducing ligand
(TRAIL) calibrated on single-cell time-trajectories of a fluorescent time-trajectories (FRET) reporter
measuring apoptosis signaling dynamics in clonal HeLa cells [88]. With this model, we constructed a
timeline for the different steps in the regulation of apoptosis and located an initial cell fate decision
just after TRAIL binding [90]. Furthermore, we identified three specific parameter combinations that
can distinguish between drug resistant or sensitive phenotypes. The next step was to combine the ODE
model’s mechanistic features with predictive values for cell decision with machine-learning classifica-
tion models, to determine the drug response of each cell before it commits to an irreversible decision.
Our mechanistic-informed approach, combining an ODE system with machine learning classifiers,
outperformed classic machine learning approaches and enabled the accurate cell response prediction
of otherwise unpredictable cells [89]. This part of the work was in collaboration with Diego Oyarzún
(University of Edinburgh). This methodology is part of Marielle Péré PhD thesis [51], who won a STIC
Doctoral School prize.

In addition, based on her thesis work, Marielle developed her project CellEmax, whose goal is to pro-
pose rational identification of new targets for anti-cancer combined treatments, for which she obtained
an i-PhD Concours d’Innovation grand prize.

8.2.2 Intercellular communication in peripheral clocks

The intercellular interactions between peripheral circadian clocks, located in tissues and organs other
than the suprachiasmatic nuclei of the hypothalamus, are still very poorly understood. To investigate
this question, we performed a theoretical and computational study of the coupling between two or more
clocks, using a reduced model of the mammalian circadian clock previously developed in [1]. Based on
a piecewise linearization of the dynamics of the mutual CLOCK:BMAL1 / PER:CRY inactivation term,
we proposed a segmentation of the circadian cycle into six stages, to help analyze different types of
synchronization between two clocks, including single stage duration, total period, and maximal amp-
litudes. Our model reproduces some recent experimental results on the effects of different regimes of
fasting/feeding alternance in liver circadian clocks of mice [71]. This method helps to further characterize
the synchronization steps between two clocks of distinct (but close) periods. This work was presented
by Odile Burckard at the 48th Congress of the Société Francophone de Chronobiologie [32]. In addition,
through the analytical study of the above piecewise linear model of the clock, we proposed an Algorithm
to generate biologically-consistent circadian oscillators. Our study provides a characterization of the
cycle dynamics in terms of four fundamental threshold parameters and one scaling parameter, shows
robustness of the circadian system and its period, and identifies critical points for correct cycle progres-
sion [60, 53]. This work is in collaboration with F. Delaunay (ANR InSync), and is part of Odile Burckard
PhD thesis.

8.2.3 Cell economy and control of cell growth

Microbial growth consists of the conversion of nutrients from the environment into biomass and small
energy cofactors (ATP, NADH, NADPH, ...) driving biomass synthesis forward. Two macroscopic criteria
for characterizing microbial growth are growth rate and growth yield. The former refers to the rate of
conversion of substrate into biomass, and the latter to the efficiency of the process, that is, the fraction of
substrate taken up by the cells that is converted into biomass.

In the framework of the ANR Maximic project (collab. H. de Jong, MICROCOSME team, and T.
Gedeon, Montana State University), we developed a coarse-grained model of coupled energy and mass
fluxes in microorganisms, based on minimal assumptions, and used it to explore the variability of rate-
yield phenotypes obtained by change in proteome allocation strategy [8]. The model predictions were

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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. An interesting prediction of our model, supported by the experimental data, is that high growth
rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling
high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a
more efficient utilization of proteomic resources.

The model is currently used to investigate the set of adaptation strategies that microbial cells can use
to increase their growth rate or their growth yield in a given environment. Preliminary results suggest
that multiple allocation choices are possible, depending on the initial allocation state of the cell and its
biomass composition.

8.3 Dynamics and control for synthetic biology

Participants: Benjamin Böbel, Madalena Chaves, Javier Innerarity Imizcoz, Jean-

Luc Gouzé.

8.3.1 Dynamics in networks of cellular oscillators

Weak synchronization and convergence in coupled genetic regulatory networks. We consider a general
model of genetic networks and examine two forms of interconnection, either homogeneous or hetero-
geneous coupling, corresponding to coupling functions that are either equal or different from those
governing the individual dynamics. In the case of individual subsystems having unique but different
steady states, we prove that the homogeneous coupled system has a unique globally asymptotically stable
steady state. Moreover, in the case of large coupling strength, we show that under suitable assumptions
the network achieves weak synchronization since individual steady states become arbitrarily close [7]. We
apply the results to the synchronization of damped oscillators and to the control of multistable systems.
Synchronization of circadian clock models. To study coupling and synchronization of two clock
oscillator models, we have used Lyapunov function techniques to bound the difference between the two
oscillators in terms of the difference between their oscillating periods. We have also analyzed the form of
the periodic solutions as a function of some of the parameters, in particular one of the major degradation
rates. This is part of Benjamin Böbel PhD thesis.

Fields of application

8.4 Bioenergy

8.4.1 Modelling microalgae production

Participants: Olivier Bernard, Antoine Sciandra, Walid Djema, Francesca Casagli,
Bruno Assis Pessi, Ignacio Fierro, Ali Gharib, Yan Gao, Emna Krichen,
Jean-Philippe Steyer, Amélie Talec, Eric Pruvost.

Experimental developments in controlled dynamical environments. The experimental Phytopulse
platform made of continuous photobioreactors driven by a set of automaton controlled by the ODIN+
software is a powerful and unique tool which gave rise to a quantity of very original experiments. Such
platform improved knowledge of several biological processes such as lipid accumulation or pigment
dynamics under light fluctuation, nitrogen or temperature stress... In particular, various experiments
were carried out for determining the ability of biofilms to grow with different sources of nitrogen. This

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experimental platform was used to control the long term stress applied to a population of microalgae
using optimal control strategies[37]. This Darwinian selection procedure generated several new strains
with higher lipids or pigments of interests after several months in the so called selectiostats [31]. These
experimental works were carried out in the frameworks of the ISS incubated Darewin project, and in the
pre-maturation Pycoplus project funded by CNRS.

Experimental work was also carried out in collaboration with the Inalve startup with microalgal biofilm
to determine the impact of light and dark sequences on cell growth and photoacclimation [57]. The
architecture of the biofilms was also observed for different species with confocal microscopic techniques
[17, 21].

These works have been carried out in collaboration with A. Talec and E. Pruvost (CNRS/Sorbonne

Université -Oceanographic Laboratory of Villefranche-sur-Mer LOV).

Metabolism of carbon storage and lipid production. A metabolic model has been set up and validated
for the microalgae Chlorella vulgaris, on the basis of the DRUM framework [3] , in order to simulate
autotrophic, heterotrophic and mixotrophic growth, and to determine how to reduce substrate inhibition.
The model was extended to other substrates such as glucose or glycerol. After a calibration phase, the
model was successfully challenged with data from 122 experiments collected from scientific literature in
autotrophic, heterotrophic and mixotrophic conditions [24].

As part of B.A. Pessi PhD thesis, this metabolic modeling framework is extended to describe a co-
culture of bacteria and microalgae. First, by modeling a thiamine auxotroph E. coli to maximize the
production of lactate [52]. Then, we extend the previous developed metabolic model to describe a co-
culture of Chlorella with the bacteria E. coli overproducing the vitamin biotin. We represent the influence
of biotin concentration in the accumulation of lipids. The model results show that a biotin overproducer
E. coli could support the needs of vitamin by Chlorella and favor lipid accumulation.

Modeling the coupling between photosynthesis and hydrodynamics. We consider a coupled
physical-biological model describing growth of microalgae in a raceway pond cultivation process, ac-
counting for hydrodynamics. Our approach combines a biological model (based on the Han model)
and shallow water dynamics equations that model the fluid into the raceway pond. We developped an
optimization procedure dealing with the topography to maximize the biomass production over one lap
or multiple laps with a paddle wheel. The results show that a flat topography is optimal in a periodic
regime. We then studied the influence of mixing, assuming that a mixing device can redistribute the algae
so that they can have access to light [70]. A strategy to optimally mix the algae was derived. It was finally
combined with a non flat topography [69].

Modeling photosynthetic biofilms. Several models have been developed to represent the growth of
microalgae within a biofilm. A first structured physiological model [92] uses mixture theory to represent
the microalgae growth, based on the consideration of intracellular reserves triggering the processes of
growth, respiration and excretion. We consider separately the intracellular storage carbon (lipids and
carbohydrates) and the functional part of microalgae. The model corroborated observations realized at
the LGPM (CentraleSupelec) [57, 21]. Another approach accounts for the dynamics of the light harvesting
systems when cells are submitted to rapid successions of light and dark phases [57]. A simpler model
was developed [18] and used to identify the optimal working mode of a process based on photosynthetic
biofilm growing on a conveyor belt [49]. The model was used to identify the worldwide potential of
microalgal biofilms under different climates [63]. This study was extended considering the impact if the
culture has to be heated in winter.

We studied the Han model for different alternation of high and low light intensities, with various
durations. We showed that there are specific light levels than can enhance the biofilm growth [15]. The
experimental validation of this model, together with the extrapolation of productivities at larger scale
was the topic of the PhD thesis of Yan GAO at CentraleSupelec [57] (directed by F. Lopes and O. Bernard).
Modeling thermal adaptation in microalgae. We studied a broad range of species and their response
to temperature. Algorithms were developped to automatically calibrate the parameters of the CTMI and
the Hinshelwood models[79]. It turns out that the optimal temperature, the minimal and the maximal
temperature for growth are strongly correlated. Relationships between these cardinal temperatures and
key parameters from the environment (sea surface temperature, solar flux, ...) were explored. It turns out
that some of the relationships stay valid also for a broad range of microorganisms such as yeast, bacteria.
Modeling N2O production in algae-bacteria systems. F. Casagli has set up dedicated experiments
guided by modelling, in partnership with F. Béline (OPAALE team, INRAE Rennes), for measuring N2O

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production and emissions in algae-bacteria systems for wastewater remediation. The experimental
results were used for validating experimentally the model predictions [73]. The specific experimental
set-up was financed by the Fondation L’Oréal - UNESCO with the prize For Women in Science and the
"Prime C3" within the RIPEC.

8.4.2 Control and Optimization of microalgae production

Participants: Olivier Bernard, Francesca Casagli, Ignacio Fierro, Ali Gharib, Yan Gao.

Optimization of the microalgae production through light pattern

Dynamic light regimes strongly impact microalgal photosynthesis efficiency. Finding the optimal way
to supply light is then a tricky problem, especially when the growth rate is inhibited by overexposition
to light and, at the same time, there is a lack of light in the deepest part of the culture. We used the
Han model to study the theoretical microalgal growth rate by applying periodically two different light
intensities [15]. For a large light period, we demonstrate that the average photosynthetic rate can be
enhanced.

Microalgal growth is affected by the dynamics of photon harvesting processes, such as photoinhibition
and photoacclimation. Taking into account these phenomena, we propose and solve an optimal control
problem to maximize the production of biomass [37]. Theoretical and numerical properties of the
problem were described, where Turnpike-like properties were observed using Pontryagin’s maximum
principle and the BOCOP software for numerical simulations.

This is the subject of the PhD thesis of Ignacio Fierro in the framework of the ITN Digitalgaesation.
Optimization of the microalgae production through temperature control
Temperature plays a key role in the microalgae dynamics. We developed a heat transfer model
coupled with biological model to simulate the temperature evolution, depending on the meteorology,
and its impact on algal productivity. We propose a general framework to simulate these multiphysics
systems, keeping a balanced degree of complexity between the biological and the physical models [72].
We also considered the influence of a greenhouse on the medium temperature and subsequently on
algal productivity [91] simulating different climatic conditions and the species for which the use of a
greenhouse is beneficial. Based on that developed heat-transfer model, we proposed an auto-adaptive
heat-transfer model[39] that can predict the temperature evolution inside different type of raceway ponds
or other cultivation systems using weather measurements.

A model predictive control algorithm was elaborated based on simple microalgae models coupled
with physical models where culture depth influences thermal inertia. Optimal operation in continuous
mode for outdoor cultivation was determined when allowing variable culture depth. Assuming known
weather forecasts considerably improved the control efficiency. This is the subject of the PhD thesis of Ali
Gharib in the framework of the ITN Digitalgaesation.

8.5 Biological depollution

Participants: Olivier Bernard, Jean-Luc Gouzé, Francesca Casagli, Annalisa Reali,

Morgan Scalabrino.

8.5.1 Control and optimization of bioprocesses for depollution

We consider artificial ecosystems including microalgae, cyanobacteria and bacteria in interaction. The
objective is to more efficiently remove inorganic nitrogen and phosphorus from wastewater, while
producing a microalgal biomass which can be used for biofuel or bioplastic production [87],[25]. We
designed and calibrated a model, that was validated with more than one year of data [75]. The model
analysis revealed that despite pH regulation, a strong limitation for inorganic carbon was found to
hinder the process efficiency and to generate conditions that are favorable for N2O emission. A control
strategy regulating alkalinity turns out to be necessary to enhance the performance and avoid harmful

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Inria Annual Report 2023

emissions [74]. We developed a general framework for simulating biotechnological processes affected
by meteorology, integrating, on top of the core biological model, a heat transfer model and a chemical
sub-model for computing the speciation of all the dissociated chemical molecules [72]. The developed
framework is a powerful tool for advanced control and optimization of environmental processes, which
can guide the scaling-up and management of the most innovative bioprocesses. In this framework was
implemented the ABACO-2 model, in order to couple the biological model with the chemical model
[22]. We explored and quantified, through a modeling study, the advantage of adding a solid/liquid
separation system to uncouple Hydraulic Retention Time (HRT) and Solid Retention Time (SRT), in order
to maximize the algal productivity and the nitrogen recycling [5].

A work was started to simplify these models and enhance their calibration by considering artificial
neural networks, which are integrated in a way that the full model respects some key constraints (positivity,
boundness, ...). A dedicated strategy was developed [34, 33] and successfully applied to experimental data
from two pilot plants. This approach allows a fast calibration strategy for new processes by combining
the hypothesis on mass balance and stroichiometry and the artificial neural networks. Another approach
with neural ODE was also developed for representing microalgae growth in photobioreactors [43, 44].

8.5.2 Coupling microalgae to anaerobic digestion

Participants: Olivier Bernard,

Antoine

Sciandra,

Jean-Philippe

Steyer,

Francesca Casagli, Annalisa Reali.

The coupling between a microalgal-bacteria high rate pond and an anaerobic digester is a promising
alternative for sustainable energy production and wastewater treatment by transforming carbon dioxide
into methane using light energy [87]. The challenge when dealing with anaerobic digestion effluent
for feeding the algae-bacteria process is the potential low alkalinity which can induce a competition
between nitrifying bacteria and algae [11]. We showed that this competition for inorganic carbon can
create conditions favorable for N2O emission. We studied the metabolism of Chlorella vulgaris growing
on fermentation products and developed a metabolic model [24] that was validated with a large number
of experiments covering various working modes (autotrophic, heterotrophic, mixotrophic).

8.6 Life Cycle Assessment

Participants: Olivier Bernard,

Jean-Philippe

Steyer, Diego

Penaranda,

Francesca Casagli, Arnaud Hélias.

Environmental impact assessment.
We have studied the environmental impact of protein production from microalgae in an algal biofilm
process, using a Life Cycle Assesment (LCA) approach and compared it to other sources (fisheries, soy,...).
We have analyzed the updated version of the production system and proposed several optimization
to reduce the environmental impact. This study confirms the interest of microalgae for reducing the
environmental impact [64, 40] compared with other sources of proteins like fishmeal or soy. More
generally, this work proposes a new approach to assess and reduce the environmental footprint of
processes which use non mature technologies [64] (PhD thesis of D. Penaranda in collaboration with M.
Morales from the Norwegian University of Science and Technology).

A study was carried out to assess the environmental benefit of using microalgae-bacteria to process
wastewater, and in particular digestates from anaerobic digestion [87]. Various scenarios were simulated
using the ALBA model [11] assuming an industrial plant located in Rennes (Britany, France) dealing
with an actual climatology and for various conditions of Hydraulic and Solid retention times, alkalinity
addition and biomass valorization for generating energy. In total 72 different scenarios were explored, for
which LCA was carried out. The results were compared to scenarios where the digestates are classically
treated in a wastewater treatment plant. It results that the microalgae approach makes sense from an
environmental point of view [63, 23].

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This work is the result of a collaboration with Arnaud Helias of INRAE-LBE (Laboratory of Environ-

mental Biotechnology, Narbonne).

8.7 Modeling and control of cancer cell population dynamics.

Participants: Walid Djema, Frédéric Grognard, Jérémie Roux, Marielle Péré, Pau-

line Mazel.

Our research continues in the field of modeling and analysis of cancer cell populations, focusing
on enhancing our comprehension of the emergence, development, and treatment improvement of
cancer. We have concentrated on examining the resistance that arises with specific types of therapies. A
novel model, calibrated on clinical data, has been developed to demonstrate how sequential different
treatments can achieve better therapeutic outcomes. Additionally, we have explored the development of
sub-optimal cancer treatment techniques [36], using network representations combined with artificial
intelligence (AI) tools, particularly pathfinding algorithms. This approach aims to determine the most
effective treatment infusion strategies. This advanced application of AI methods intends to refine and
improve treatment protocols, thereby increasing treatment efficacy. Another line of research involves the
application of optimal control to Lotka-Volterra models of competition between cancerous and healthy
cells. The aim is to control cancer progression while reducing the harmful effects of chemotherapy.
This strategy aims to strike a balance between eliminating cancer cells while preserving healthy tissues,
providing a more refined method to manage the impact of cancer treatments. This is the subject of the
PhD thesis of Pauline Mazel.

8.8 Optimal bacterial resource allocation for metabolite production.

Participants:

Jean-Luc Gouzé, Walid Djema, Javier Innerarity Imizcoz.

We investigate the optimization of resource allocation in micro-organisms, specifically aiming to
maximize their volumetric growth rate. This study is based on a simplified model of E. coli metabolism,
structured around a system of two Ordinary Differential Equations with an integrated control variable. The
mathematical analysis employs the Infinite Horizon Maximum Principle, an adaptation of Pontryagin’s
Maximum Principle. This approach is further augmented by numerical resolution and simulation of
the resulting trajectories. The initial phase of our research aligns with previous studies by considering a
stable environment, an assumption that simplifies the problem’s complexity. However, recognizing that
micro-organisms often evolve in fluctuating environments, the second phase extends this analysis to
settings with periodic substrate variations. Here, we aim to determine an optimal periodic substrate that
enhances growth beyond constant conditions. The third phase of our research continues the exploration
of an optimal periodic environment. This aspect is particularly relevant in artificially controlled settings
aimed at increasing biomass yield. As in the preceding sections, rigorous theoretical analysis is conducted,
followed by numerical resolution to pinpoint the most effective environment and resource allocation
strategy. This is the subject of the PhD thesis of Javier Innerarity Imizcoz.

8.9 Optimizing waste-to-energy systems for profitable energy production.

Participants: Walid Djema, Jean-Luc Gouzé.

We address the critical challenge of identifying reliable energy sources as the global energy sector
transitions from fossil fuels to renewables. Our focus is on waste-to-energy (WTE) systems, which stand
out as a promising alternative to solar and wind power due to their operational independence from
weather conditions for instance. This aspect underscores the necessity of investing in waste-to-energy

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Inria Annual Report 2023

initiatives to create sustainable and economically viable activities. To this end, we have developed a
comprehensive bio-economic model that takes into account the investment in a specific waste-to-energy
technology, with the goal of optimizing the energy production process and establishing a sustainable
business model. We use direct and indirect optimization techniques to validate and demonstrate the
effectiveness of the provided optimal strategies [35].

8.10 Design of ecologically friendly plant production systems

Participants:

Suzanne Touzeau,
Frédéric Grognard,
Valentina Baldazzi,
Ludovic Mailleret, Clotilde Djuikem, Aurelien Kambeu Youmbi,
Frank Kemayou Mangwa, Joseph Junior Penlap Tamagoua.

8.10.1 Ecophysiological modelling of plant-microbiota interactions

Root-knot nematodes (RKN) are microscopic root parasites that cause considerable yield losses in nu-
merous crops worldwide. We are particularly interested in understanding the mechanisms that underlie
plant tolerance, that is the ability of certains plants to sustain RKN infestation with limited damages.
To address this, we built an ecophysiological model of plant growth, including both the vegetative and
the reproductive phases, coupled with a model of nematode population dynamics. Briefly, the plant is
divided into shoots, which provide carbon, and roots, which provide water for plant growth. During the
reproductive phase, fruit onset marks the addition of a new carbon sink for the plant. Nematodes are
explicitly considered as feeding on plant resources, so that any change in the plant physiological status or
plant composition will in turn affect pest growth and multiplication, and vice versa. The apparition of
fruits, in particular, can substantially modify the ressource allocation pattern of the plant, with important
consequences on plant susceptibility to pest attack. The model was calibrated for two plant species
using experimental data collected in the framework of INRAE ArchiNem project (2020-2021). A dedicated
calibration pipeline was developed in order to combine heterogeneous data at different time-scales. At
term, the model will be used to explore the dynamical behaviour of the system and to gain insight into
the relative role of plant development and phenotypic traits (including physiological and architectural
features), pest development and environmental factors in the progression of the infection.

This work is part of the PhD thesis of Joseph Penlap Tamagoua (Inria-INRAE funding 2022-2025) and
was presented at several international conferences [65, 47]. Two publications are currently in preparation,
focusing respectively on the experimental results and on model development and calibration.

8.10.2 Epidemiological modelling of plant-enemy interactions

Epidemiological models in tropical agriculture We developed and analyzed dynamical systems de-
scribing plant-parasite interactions, in order to better understand, predict and control the evolution of
damages in crops, with applications in tropical agriculture, in the framework of the EPITAG associate
team with Cameroon. We considered several pathosystems.

• Coffee berry borers are insects that mostly develop and feed inside coffee berries, and hence cause
major crop damages worlwide. We developed a model describing the coffee berry borer dynamics
based on the insect life cycle and the berry availability during a single cropping season. This PDE
model includes a berry age structure to account for CBB preference for mature berries [16, 56]. This
research pertains to Yves Fotso Fotso’s PhD thesis [68].

• Coffee leaf rust (CLR) is a leaf disease caused by a fungus, Hemileia vastatrix, that has a major impact
on coffee production around the world. We studied a multi-seasonal model of CLR development
in a coffee plantation, with continuous dynamics during the rainy season and a discrete event to
represent the simpler dynamics during the dry season [14, 42]. We also performed a bifurcation
analysis of a more complex model taking the development stage of the leaves into account (paper
under revision). This work is part of Clotilde Djuikem’s PhD thesis that was defended in 2023 [48].

Project BIOCORE

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• Cabbage is a very important food crop for small farmers in Cameroon. We developed a self-
financing model of the crop, which includes the interaction between cabbage and the diamondback
moth, one of its major pest. The main point of this model is the inclusion of the financial balance
of the farm, used for buying young plants and biopesticide spraying. We did a bifurcation analysis
of this bioeconomic model and identified situations with forward or backward bifurcations [45].
This work is part of Aurelien Kambeu Youmbi’s ongoing PhD thesis.

• Bananas, including plantains, are major staple foods in many tropical countries, including Cameroon.
These plants are affected by burrowing nematodes (Radopholus similis) that create root lesions
and induce great damages. We developed a model of the plant-pest interactions with the original
feature that infestation intensity may vary within the root. We did a bifurcation analysis of this
model and developed an optimal control against the pest. This work is part of Frank Kemayou
Mangwa’s ongoing PhD thesis.

Spatial population dynamics of biological control agents We have been involved for several years in a
mixed modeling-experimental approach to explore the spatio-temporal dynamics of populations, with
special interest to micro-wasp parasitoids [86, 80]. With such an approach, we explored how positive
density-dependence in growth or dispersal interact with spatial heterogeneity to impact population
spread [20]. In this context, we showed that the expansion rate is not only determined by the current
environmental conditions at the edge of the population, but is also strongly influenced by the conditions
encountered at previous times and locations by the moving front of the population. This research has
been performed in collaboration with Elodie Vercken (ISA).

Concurrently, we are exploring the correlation between biological control agents movement char-
acteristics at different scales, from laboratory experimental characterization to semi-field dispersal on
parasitoids belonging to the genus Trichogramma. A specific mid-scale laboratory device to study insect
dispersal over several meters has been designed [13] and used to understand how insect movements
shape group dispersal in such parasitoïds [10]. This research was part of the PhD theses of Victor Burte
(defended 2018) and Melina Cointe (defended 2023, [76]) and was performed in collaboration with
Vincent Calcagno (ISA).

8.11 Design and control of managed ecosystems

Participants:

Jean-Luc Gouzé,
Ludovic Mailleret, Olivier Bernard,
Francesca Casagli, Bruno Assis Pessi.

Frédéric Grognard,

Suzanne

Touzeau,
Juan Carlos Arceo,

8.11.1 Design and control of synthetic microbial ecosystems

In the framework of ANR project Ctrl-AB, we considered a synthetic algal-bacterial consortium. The
co-culture of E. coli with Chlorella could lead to higher biomass and lipid productivity. We developed a
model, studied its dynamical behaviour and built observers to optimize some output [30]. Moreover, we
studied the effects of control on the system (PhD thesis of Rand Aswad, Grenoble, in collaboration with E.
Cinquemani (Microcosme )).

In the framework of IPL Cosy (led by E. Cinquemani), we studied the coexistence of two strains of
bacteria E. Coli in a bioreactor. The strains had been modified synthetically. The aim was to obtain
a better productivity in the consortium than in a single strain, by control methods [84]. We obtained
optimization results for the optimal production or yield [19].

8.11.2 Design of biological control strategies

Sterile insect technique The sterile insect technique (SIT) consists in releasing irradiated sterile indi-
viduals, usually males, that can mate but produce no offspring. SIT is used to reduce pest populations
in an agricultural context. However, a small fraction of irradiated insects may escape sterilization and
remain fertile. We showed that when residual fertility is below a threshold value, wild populations can

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Inria Annual Report 2023

be driven to extinction by flooding the landscape with sterile males. Nevertheless, even if the residual
fertility exceeds the aforementioned threshold value, substantial decreases in outbreak levels can be
achieved [62, 41, 61]. In the framework of Taha Belkayate’s internship, we extended these results to take
remating into account. This work pertains to Marine Courtois’s ongoing PhD thesis.

Predator releases for coffee leaf rust Biological control was added to the multi-seasonal impulsive
model describing coffee leaf rust spread in a plantation (section 8.10.2), using predators through one or
more discrete introduction events over the year. Analytical and semi-numerical studies were performed
to identify how much and how frequently predators needed to be introduced. We showed that the best
strategy to efficiently control the disease depends on the predator mortality: low mortality parasites must
be released only once a year, while high mortality parasites should be released more frequently to ensure
their persistence in the plantation [14]. This work pertains to Clotilde Djuikem’s PhD thesis that was
defended in 2023 [48].

Optimal control for coffee berry borers Controling coffee berry borers is particularly challenging,
as the insects spend most of their life cycle inside berries. Pest control was introduced in the model
describing the interactions between borers and coffee berries (section 8.10.2), based on the combination
of a biopesticide (entomopathogenic fungus such as Beauveria bassiana), that is sprayed and persist on
the berries, and traps. Using optimal control theory, we showed a synergy between the two controls for
profit optimization [16]. We also investigated how to optimize biopesticide spraying considering it as an
impulsive control [56]. This research pertains to Yves Fotso Fotso’s PhD thesis [68].

8.11.3 Sustainable management of plant resistance

We studied other plant protection methods dedicated to fight plant pathogens. Among these methods,
there is the introduction of plant cultivars that are resistant to one pathogen. This often leads to the
appearance of virulent pathogen strains that are capable of infecting the resistant plants.

We built a generic spatio-temporal epidemiological model representing (fungal) disease spread on
annual field crops in a multi-pathogen context. This work benefits from data collected in INRAE projects
COCODIV and DYNAMO on wheat diseases. It will pe pursued in the ENDURANCE and PAPEETE projects
(section 10.4.1).

An epidemiological model of gene-for-gene interaction has been designed, considering increased
defense to pathogen infections following previous exposure to a pathogen or an elicitor, namely priming.
Priming provides a sort of immunity to virulent pathogens for resistant plants having undergone an infec-
tion attempt by an avirulent pathogen. We developed an epidemiological model to explore how mixing
two distinct resistant varieties can reduce disease prevalence. We considered a pathogen population
composed of three genotypes infecting either one or both varieties. We found that host mixtures should
not contain an equal proportion of resistant plants, but a biased ratio to minimize disease prevalence,
and that it should contain a lower proportion of the costliest resistance for the pathogen to break [12].
This was done in collaboration with Frédéric Hamelin (Institut Agro) in the framework of Pauline Clin’s
thesis.

We also participated in an opinion paper advocating that the theoretical framework of population
genetics could bridge the gap existing between evolution/epidemiology approaches and molecular
approaches to the durability of resistance problem [26]. This paper is the basis of the ENDURANCE ANR
project (section 10.4.1).

9 Bilateral contracts and grants with industry

Participants: Olivier Bernard, , Diego Penaranda, , Madalena Chaves, , Jean-

Luc Gouzé.
.

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9.1 Bilateral contracts with industry

- Inalve: with the Inalve start-up we develop a breakthrough process that we patented, in which microalgae
grow within a moving biofilm. The objective of the collaboration is to optimize the process by enhancing
productivity, while assessing and reducing the environmental footprint.

9.2 Bilateral grants with industry

- Exactcure: in the collaboration with the start-up Exactcure (Nice), the goal of the project is to study
personalized pharmacokinetic models. We have regular contacts with Exactcure, which hired our PhD
student Lucie Chambon.
- Inalve: Inalve is funding half of the PhD thesis of Diego Penaranda-Sandoval on the life cycle analysis of
processes with low technological maturity. The other half is coming from a PACA region grant.

10 Partnerships and cooperations

10.1 International initiatives

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

Inria International Program

Blue Edge

Participants: Olivier Bernard, Francesca Casagli, Jineth Oviedo , Ignacio Fierro,

Romain Ranini.

Title: Artificial Intelligence and optimization for cleaner biotechnological processes

Duration: 2021 ->

Coordinator: David Jeison (david.jeison@pucv.cl)

Partners:

• Pontifical Catholic University of Valparaíso Valparaíso (Chili)

Inria contact: Olivier Bernard

Summary: Recycling organic wastes and at the same time removing pollution, producing fertilizers
and energy has become a central issue for reaching sustainable development. Fundamentally,
the question is how to recycle nitrogen, carbon and phosphorus within an integrated process
involving microorganisms and reduced flux of pollutants towards the natural environment. We
will more specifically target innovative wastewater treatment processes involving microalgae, and
recirculating aquaculture systems. Developing optimal control strategies for these two dynamical
processes is the subject of intense researches at the international scale. The teams involved in
Blue Edge have experimental pilot systems (PUCV, Cetaqua), developed models for the considered
processes (BIOCORE, PUCV), the skills to design advanced algorithms for data driven supervision
(Inria CL) and control (BIOCORE) that can further be implemented thanks to the ODIN+ software
(BIOCORE).

EPITAG

Participants:

Suzanne Touzeau, Frédéric Grognard, Clotilde Djuikem , Joseph Pen-
lap.

Title: Epidemiological Modelling and Control for Tropical Agriculture

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Inria Annual Report 2023

Duration: 2022-2024

Coordinator: Samuel Bowong (sbowong@gmail.com)

Partners:

• Université de Douala (Cameroun)

Inria contact: Suzanne Touzeau

Summary: EPITAG gathers French and Cameroonian researchers, with a background in dynamical
systems and control and with an interest in crop diseases. Crop pests and pathogens are responsible
for considerable yield losses and represent a threat to food security. Their control is hence a major
issue, especially in Cameroon, where agriculture is an important sector in terms of revenues and
employment. To help design efficient strategies for integrated pest management, mathematical
models are particularly relevant. Our main objective is to study the epidemiology and management
of tropical crop diseases, with a focus on Cameroon and Sub-Saharan Africa. Our approach consists
in developing and analyzing dynamical models describing plant-parasite interactions, in order to
better understand, predict and control the evolution of damages in crops. To ensure the relevance
of our models, field experts and stakeholders need to be closely associated. We will focus on pest
and pathogens that affect major staple food and cash crops, such as cocoa plant mirids, plantain
and banana plant-parasitic nematodes, coffee berry borers, coffee leaf rust, maize stalk borers,
cabbage diamondback moths, papaya mealybugs, etc. To tackle these issues, we jointly supervise
master and PhD students.

10.2 International research visitors

10.2.1 Visits of international scientists

• Jineth Arango Oviedo (PUC - VALPARAISO), spent one year (until Apr 2023) in the framework of the

Blue Edge Associate Team.

• Elisabeth Gualda Alonso UNIV ALMERIA from Univ. Almeria spent two months (Sep 2023-Dec

2023).

• In the framework of the EPITAG Associate Team, we had the long term visit of Aurelien Kambeu
Youmbi (UNIV DSCHANG CAMEROON), from Feb 2023 until Apr 2023 and Frank Kemayou Mangwa
(UNIV DOUALA), from Feb 2023 until Apr 2023.

• In the framework of the ITN DigitAlgaesation, we had the visit of David Morgado Pereira (CENT-
RALESUPELEC, Nov-Dec 2023), Rebecca Nordio (University of Almeria, from Mar 2023 until May
2023), Tehreem Syed (TU Dresden, School of Engineering, until Mar 2023).

• Gustavo Ribeiro da Silva São Paulo State University/Universidade Estadual Paulista UNESP, from

Apr 2023 until May 2023.

10.3 European initiatives

10.3.1 H2020 projects

DigitAlgaesation DigitAlgaesation project on cordis.europa.eu

Title: A knowledge-based training network for digitalisation of photosynthetic bioprocesses

Duration: From March 1, 2021 to February 28, 2025

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

Project BIOCORE

23

• IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE, United Kingdom

• MINT ENGINEERING GMBH, Germany

• UNIVERSITA DEGLI STUDI DI PADOVA (UNIPD), Italy

• SIEMENS PROCESS SYSTEMS ENGINEERINGLIMITED (SPSE Ltd), United Kingdom

• PROVIRON HOLDING NV (PROVIRON), Belgium

• TMCI PADOVAN SPA, Italy

• DANMARKS TEKNISKE UNIVERSITET (TECHNICAL UNIVERSITY OF DENMARK DTU),

Denmark

• UNIVERSIDAD DE ALMERIA (UNIVERSIDAD DE ALMERIA), Spain

• WAGENINGEN UNIVERSITY (WU), Netherlands

• CENTRALESUPELEC, France

• TECHNISCHE UNIVERSITAET DRESDEN (TUD), Germany

• GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER (LUH), Germany

Inria contact: Olivier Bernard

Coordinator: Fabrizio Bezzo (Univ. Padova)

Summary: Microalgae and other photosynthetic microorganisms represent a highly promising source for
food, feed, chemicals, and fuels. Europe has been leading world research and industrial deployment
of microalgae based technologies. However, despite the enormous potential and the impressive
R&D effort, industrial use of microalgae is still at its first developmental stage. A major step forward
can derive by the development and implementation of digital technologies, capable of automatizing
and optimizing culture conditions at industrial scale. Europe has a tradition of leading researches
in the field of automatic control for biotechnological processes. As envisaged by DigitAlgaesation,
the widespread definition and adoption of effective tools for better design and operation urgently
requires skilled multidisciplinary scientists and engineers, who can develop and implement the next
generation of sustainable production process with enhanced productivity, reduced environmental
impact and costs, despite climate fluctuations that may strongly affect microalgae productivity.
All this demands a European commitment to concerted, inter- and transdisciplinary research and
innovation.

DigitAlgaesation will train 15 early-stage researchers (ESRs) in all aspects of microalgae technolo-
gical innovation to pave the way towards a knowledge-based breakthrough in monitoring methods
and instrumentation, biological modeling and simulation, and automatic control. By training in
scientific, technical and soft skills, they will become highly sought-after scientists and engineers for
the rapidly emerging microalgae-based industry and broader bioprocessing industries of Europe.

10.4 National initiatives

10.4.1 National programmes

• ANR PhotoBioFilm Explorer: The first objective of the PhotoBioFilm project (2021-2024) is to
explore the activity of the molecules produced within a microalgae biofilm, and explaining its
resistance to contamination. The second objective is to identify, characterize and produce novel
biocompounds with benefits for human or animal health. The target is antibiotics, but other
activities will be tested, especially antiviral activities. Biocore will be in charge of the biofilm
modeling and the optimization of the production of the molecules of interests. Project coordinated
by O. Bernard.

• ANR Ctrl-AB: The objectives of the Ctrl-AB project (2021-2024) are (i) to develop new control
methods for the optimization of the productivity of a microbial community, and (ii) to demonstrate
the effectiveness of these methods on a synthetic algal-bacterial consortium. Interestingly, co-
culturing of E. coli with Chlorella leads to higher biomass and lipid productivity. Improved growth

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Inria Annual Report 2023

of Chlorella occurs despite competition of E. coli for the same substrates. On top of its ability to
produce molecules like vitamins, which are necessary for algal growth, the bacteria also produce
carbon dioxide (CO2), which is the substrate of the photosynthesis of the algae. The algae can
produce oxygen (O2) fueling bacterial growth, thus giving rise to a mutualistic pattern of interactions
giving rise to several challenges for modeling and controlling this artificial ecosystem. Project
coordinated by J.-L. Gouzé.

• ANR Maximic: The goal of the project (2017-2022) is to design and implement control strategies in
a bacterium for producing at maximal rate a high value product. It is coordinated by H. de Jong
(IBIS Grenoble), and involves members of Biocore and McTao.

• ANR InSync: This project (2022-2027) aims to decipher intercellular synchronization mechanisms
responsible for robust rhythms in peripheral clocks. Focusing on hepatocytes, and using both 2D
cultures and 3D spheroids, we will study cell communication patterns and cell clock synchroniz-
ation. Project coordinated by M. Chaves, in collaboration with F. Delaunay (Institut Biologie de
Valrose, UniCA) and L.Tournier (MaIAGE, INRAE Jouy-en-Josas).

• ANR Barrier: This proof of concept project (2023-20274) with multidisciplinary expertise is willing
to demonstrate, from the laboratory to a pilot process, that selected bacteria can protect microal-
gae when growing in contaminated wastewaters, providing higher algal resilience, productivity
and bioremediation efficiency in wastewater treatments. It is coordinated by O. Pringault (IRD,
Mediterranean Institute of Oceanography).

• ANR SuzuKIISS:ME "Gérer Drosophila SuzuKII grâce aux Insectes Super Stériles : Maturation et
Efficacité" (2022-2025). This project covers the ground from the development of the operational
capacity and release strategies to deploy Sterile Insect Techniques (SIT), to the socio-economic
impact of SIT on the control of the fruit fly Drosophila Suzukii.

• Plan Cancer, Imodrez: The objective of this project (2018-2022) is to understand cancer drug
response heterogeneity using tumor single-cell dynamics and developing mathematical models
and computational approaches. A project coordinated by J. Roux (IRCAN).

• ITMO Cancer Aviesan, Cellema: The objective of this project (2022-2025) is to determine the
molecular factors that regulate tumor cell response dynamics to immune cell cytotoxicity and
contribute to the development of diagnosis tools for the rational design of cancer combination
therapies. We will use single-cell response data to develop mathematical models and combine
them with machine learning algorithms to enhance prediction of same-cell responses. Project
coordinated by M. Chaves, in collaboration with J. Roux (IPMC, CNRS) and D. Oyarzun (Univ.
Edinburgh).

• ADEME- Biomsa: “Development of an environmental environmental biorefinery Methane Struvite-
Algae” (2021-2023)coordinated by F. Beline, INRAE. The objective of this project is to assess, through
a combination of experimental and modeling approach the performance of microalgae for wastewa-
ter treatment.

• SIGNALIFE: Biocore is part of this Labex (scientific cluster of excellence, 2nd period 2020-2024)
whose objective is to build a network for innovation on Signal Transduction Pathways in Life
Sciences, and is hosted by the University Côte d’Azur.

• UMT FIORIMED: FioriMed is a Mixed Technology Unit created in January 2015 to strengthen the
production and dissemination of innovation to the benefit of ornamental horticulture. Horticultural
greenhouses are seen as a "laboratory" for the actual implementation of agroecology concepts
with the possibility of generic outcomes being transfered to other production systems. The main
partners of UMT FioriMed are ASTREDHOR (National Institute of Horticulture) and the ISA Joint
Research Unit of INRAE-CNRS-UniCA.

• EcoPhyto - CeraTIS Corse: “Territorial management of the Mediterranean fruit fly in Corsica by
the Sterile Insect Technique” (2020-2022). This project is based on a pilot field experiment of sterile
male releases and it integrates population dynamics and socio-economic approaches.

Project BIOCORE

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• EcoPhyto - INTERLUDE: “Territorial innovations to reduce phytoparmaceutical products for the
sustainable production of vegetable crops” (2020-2022). BIOCORE members participate in a case
study that focuses on the agroecological management of soil pests and pathogens in Provence.

• PEPR Agroécologie et Numérique - MISTIC “Microbiomes de plantes cultivées et TIC” (2023-2028).
Our objective within this flagship project of the PEPR is to model and analyze plant microbial com-
munities and their functioning, as well as to design minimal microbial communities guaranteeing
maintained functions.

10.4.2 Inria funding

• Inria Startup Studio, DareWin: (2022-2023). The DareWin project is consolidating the bases of
a startup which will develop Darwinian selection approaches in highly controlled bioreactors to
naturally select and improve microalgal strains of industrial interest.

10.4.3 INRAE funding

• COCODIV: “Crop disease co-occurrence in cereal systems: determinants, role and management
of cultivated and wild diversity from plot scale to landscape level integration”, INRAE SuMCrop
Metaprogramme (2022–2023), in which Biocore is a partner with INRAE Sophia Antipolis.

• DYNAMO: “Drivers of the epidemic dynamics of wheat rusts at the landscape scale of the Zone
Atelier Plaine & Val de Sèvre”, INRAE SPE Division (2022-2024), in which Biocore is a partner with
INRAE Sophia Antipolis.

11 Dissemination

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

General chair, scientific chair

• O. Bernard organized the ALGORESEAU workshop: "Microalgae and numerical twins", organized

online by Pole Mer in December, the 6th of December.

• Francesca Casagli was elected co-chair in the new Task Group of IWA (International Water Associ-

ation) on algae and phototrophic microbes modelling.

• Walid Djema co-organized, with Jean Clairambault (Inria Paris) and Catherine Bonnet (Inria Saclay),
a tutorial session on the modeling and analysis of cancer as part of the European Control Conference
2023 in Romania.

Member of the organizing committees

• F. Casagli and O. Bernard were in the organizing committee of the Workshop on modelling photo-
trophic systems, in Quebec city, September the 24th. " how can we improve mathematical models
to promote and optimize phototrophic systems for water remediation and resource recovery?"

• We organise a monthly scientific seminar together with the MACBES project-team in which external
guests and collaborators are regularly invited. We also organise a yearly four day-retreat where we
share our work of the year; this year, it took place mid-october in Porquerolles.

• Marielle Péré was one of the organizers of the Inria PhD Student Seminars, taking place every two

weeks.

• Suzanne Touzeau was a member of the scientific and organizing committees of the MPDEE 2023

conference, CIRM Marseille, France, 24-28 April 2023.

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Inria Annual Report 2023

11.1.2 Scientific events: selection

Chair of conference program committees

• O. Bernard was the head of the academic scientific committee of the AlgaEurope conference which

took place in December 1st to 4th 2023 in Prague.

Reviewer

• All BIOCORE members have been reviewers for the major 2023 conferences in our field: Conference
on Decision and Control, European Control Conference, International Federation of Automatic
Control.

11.1.3 Journal

Member of the editorial boards

• Madalena Chaves is an Associated Editor of SIAM Journal on Applied Dynamical Systems (SIADS),
since January 2015. She is an Associated Editor of the Conference Editorial Board (CEB) of the IEEE
Control Systems Society, since August 2020. She is also an Associated Editor for the new IEEE Open
Access Journal on Control Systems.

• Jean-Luc Gouzé is an Associated Editor of the journal Frontiers in Applied Mathematics and

Statistics (Mathematical Biology).

• Suzanne Touzeau is an Academic Editor of PLOS ONE, since August 2018.

Reviewer - reviewing activities

• All BIOCORE members have been reviewers for the major journals in their field: Automatica, IEEE
Transactions on Automatic Control, Journal of Mathematical Biology, Mathematical Biosciences,
New Phytologist...

11.1.4 Invited talks

• Madalena Chaves gave talks at the Canceropole PACA Webinar (30 May 2023) and at the Journées

Nationales de l’Informatique Mathématique, Paris (April 2023).

• Ludovic Mailleret gave a talk at the conference "Le MOnde des Mathématiques Industrielles",

MOMI2023, Sophia Antipolis, April 2023.

• F. Casagli was invited to give a talk within the INRAE workshop "Sensors and numerical twins" on

"Hybrid modelling of algae-bacteria systems" the 4th-5th Decembre in Lyon.

• O. Bernard was invited to give a talk at University of Quebec at Rimouski "Modelling an algae-
bacteria artificial ecosystem for recovering nitrogen and phosphorus from wastewater " the 28th of
September 2023.

• O. Bernard was invited to give a talk at the 3rd Inria-DFKI European Summer School on AI (IDESSAI
2023) " Hybrid modelling of artificial microbial ecosystems for bioenergy production and waste
water treatment " the 5th of September 2023.

11.1.5 Scientific expertise

• Walid Djema is appointed as the Inria representative on the Scientific Council of the “Académie

d’Excellence 4: Complexité et Diversité du Vivant" de l’IDEX JEDI, UniCA.

• O. Bernard is in the Scientific Advisory Board of the "Ferment du futur" Grand challenge of France

2030.

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• O. Bernard is a member of the scientific committe of IRD (Institute of Research for the Development)
in the CSS5 section (models and data). He participated to the recruitement juries for researchers
and research directors at IRD.

• O. Bernard is in the Scientific and Pedagogic committee of the UniCA- EUR LIFE and in the steering

committee of Federal Recherche Institut (IFR) Marine Ressources (MARRES).

• O. Bernard is a member of the scientific committee of the Inalve company.

• Suzanne Touzeau participated in selection panels for INRAE junior research scientists.

• Madalena Chaves, Frédéric Grognard, and Ludovic Mailleret are members of the INRAE Commis-
sion Scientifique Spécialisée (CSS) for Mathématique, Informatique, Sciences et Technologies du
numérique, Intelligence artificielle et Robotique (MISTI).

• Walid Djema was invited as guest participant in the CT-Control programming sprint organized
by J.-B. Caillau and O. Cots. This event focused on collaborative efforts in the field of numerical
optimization, specifically centered around the development of analysis tools for two distinct
optimal control problems. Our team contributed to include projects from A. Gharib’s research on
Microalgae cultures and O. Dekkaki’s work on waste management. Our involvement entailed the
utilization of both direct and indirect optimization techniques tailored to address optimal control
challenges within the life sciences domain.

11.1.6 Research administration

• Walid Djema was elected to the Center Committee (Comité de Centre) of Inria Sophia Antipolis.

• Olivier Bernard and Walid Djema are members of the local sustainable development committee

(Commission Locale de Développement Durable (CLDD)) at Inria Sophia Antipolis.

• Madalena Chaves is member of the local Inria committee for doctoral studies (CSD) and head of
the committee NICE for welcoming external researchers (post-docs, “delegations”). She is also a
representative of Inria at the Canceropole PCA. She is the coordinator of the UE Biologie Systémique
1 of the new Master option on Bioinformatics and Computational Biology of the EUR Sciences du
Vivant et de la Santé. Madalena Chaves is a member of the scientific committee of Labex Signalife
(since 2020).

• Frédéric Grognard is a member of the steering committee of Academy 3, Space, Environment, Risk
and Resilience of UCA-JEDI. He is co-head of the MSc Risk of UCA-JEDI and is a member of the
Scientific Committee of the Agroecosysems department of INRAE.

• Ludovic Mailleret is the head of the M2P2 team (Models and Methods for Plant Protection) of ISA.
He is in the Unit and scientific council of Institut Sophia Agrobiotech, and in the council of the
INRAE PACA centre.

• Ludovic Mailleret is an elected member (since 2020) of the Scientific and Pedagogic Council (CoSP)

of the EUR LIFE (Graduate school in Life and Health Sciences ) of Université Côte d’Azur.

• Suzanne Touzeau is a member of the steering committee of the INRAE Metaprogramme SuM-

Crop“Sustainable Management of Crop Health” (since 2016).

• Valentina Baldazzi and Suzanne Touzeau are elected members of the Institut Sophia Agrobiotech

council.

• Jean-Luc Gouzé is in the Inria committee supervising the doctoral theses (until June), a member
of the steering committee of Labex SIGNALIFE of Université Côte d’Azur, and of the scientific
committee of Académie 4 (Life Sciences) of UniCA. He is a member of the board of the SFBT
(French Speaking Society for Theoretical Biology).

• O. Bernard is a member of the ADT (Technological Development Actions) commission at Inria and

of the local commission for sustainable development.

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11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• Master: F. Grognard (30h ETD) and W. Djema (15h ETD), “Elements of mathematical modelling",

M1, MSc in Environmental Hazards and Risks Management, Université Côte d’Azur, France.

• Master: O. Bernard (24h ETD) and W. Djema (24h ETD), “Enseignement d’Intégration EI – ST5

Méthanisation”, CentraleSupélec, Paris-Saclay, France.

• Licence: W. Djema (40h ETD); “Mathématiques pour Biologistes: Analyse et Modélisation", L1

Université Côte d’Azur UniCA, France.

• Licence: W. Djema (32h ETD); “Math0: Enjeux", L1 Université Côte d’Azur UniCA, France.

• Licence: W. Djema (20h ETD); “Statistiques pour les Biologistes", L1 Université Côte d’Azur UniCA,

France.

• Master: O. Bernard (4.5h ETD), “Bioenergy from microalgae”, M2, Master International Energy
Management : alternatives pour l’énergie du futur, Ecole Nationale Supérieure des Mines de Paris,
France.

• Master: O. Bernard (18h ETD), “Modeling biotechnological processes”, M2, Ecole CentraleSupelec,

Saclay, France.

• Master: O. Bernard (18h ETD), “Automatic Control applied to biotechnological processes”, M2,

Ecole CentraleSupelec, Saclay, France.

• Master: O. Bernard (6h ETD), “Cultivation and use of Microalgae”, Master Mares, Université Côte

d’Azur, France.

• Licence: O. Bernard (35h ETD), “Use and optimization of photobioreactors”, Université de Pau et

des pays de l’Adour, France.

• BUT: Odile Burckard (12h ETD) "Fondamentaux de la programmation" 1st year, IUT de Nice Côte

d’Azur (Département Réseaux et Télécommunications).

• Licence: Clotilde Djuikem (40h ETD) "Analyse et modélisation", PO1, Sciences de la vie, Université

Côte d’Azur, France

• Licence: Clotilde Djuikem (16h ETD) "Fondements 2", PO1 Sciences, ingénierie, technologie,

environnement, Université Côte d’Azur, France

• Licence : Clotilde Djuikem (24h ETD) "Intro. à la modélisation mathématique et numérique", L3,
1st year Engineering in "Genie de l’eau", Polytech Nice Sophia, Université Côte d’Azur, France

• Licence: Clotilde Djuikem (28h ETD)"Statistiques et R", L3, 1st year Engineering in Modeling and

Applied Mathematics, Polytech Nice Sophia, Université Côte d’Azur, France

• Licence: Pauline Mazel (16h TD) and Joseph Junior Penlap Tamagoua (32h TD), “Fondements
mathématiques 1 - partie algèbre linéaire”, L1, Portail Sciences et Technologies, Université Côte
d’Azur, France

• Licence: Frédéric Grognard (42h ETD) and Ludovic Mailleret (24h ETD), “Equations différentielles
ordinaires et systèmes dynamiques”, L3, 1st year Engineering in Modeling and Applied Mathematics,
Polytech Nice Sophia, Université Côte d’Azur, France

• Licence: Joseph Junior Penlap Tamagoua (28h TD), "Approximation de fonctions, intégrales et

EDO", L3 Mathématiques, Université Côte d’Azur, France

• Master : Jean-Luc Gouzé (20h ETD), Madalena Chaves (13.5h ETD), “Modeling biological networks
by ordinary differential equations”, M1, 4t h year Engineering in Génie biologique, Polytech Nice
Sophia, Université Côte d’Azur, France.

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• Master: Frédéric Grognard (21h ETD) and Ludovic Mailleret (21h ETD), “Bio-Mathématiques”, M1,
2nd year Engineering in Modeling and Applied Mathematics, Polytech Nice Sophia, Université Côte
d’Azur, France.

• Research School: Suzanne Touzeau gave a lecture at the CIMPA school “Mathematical and statistical

modeling of complex systems", N’Djamena, Chad, 2–13 January 2023.

• Licence: C. Djuikem – ATER contract (24h ETD), "Processus Stochastiques", L3, 1st year Engineering
in Modeling and Applied Mathematics, Polytech Nice Sophia, Université Côte d’Azur, France.

• Licence: C. Djuikem – ATER contract (60h ETD), "Statistiques", L1 Sciences de la vie, Université

Côte d’Azur, France.

11.2.2 Supervision

• HDR: Frédéric Grognard. "Modelling and control of managed ecosystems, from bioreactors to

agroecosystems", Université Côte d’Azur, defended February 20th.

• PhD defended the 9th of February 2023: B. Assis Pessi. “Optimization of microalgae efficiency using
reduced metabolic models”, Université Côte d’Azur. 2019-2023. Supervisors: O. Bernard and L.
Giraldi (team Calisto).

• PhD defended in 2023 : Yan GAO. "Optimisation of microalgae biofilms as a future source of

biomolecules", Université de Saclay, 2018-2023. Supervisors: F. Lopes and O. Bernard.

• PhD in progress: R. Ranini. "Deep leanring approaches for enhancing models in oceanography",

UniCA, since 2022. Supervisors: L. Guidi and O. Bernard.

• PhD in progress: R. Asswad. “Développement de stratégies de contrôle pour les consortiums
microbiens synthétiques”, since October 2022, Université Grenoble-Alpes Supervisors J.-L. Gouzé
and E. Cinquemani (Microcosme, Inria Grenoble).

• PhD in progress: Pauline Mazel. “Modeling and control of cancer cell population dynamics", since

oct. 2023. Supervisors: F. Grognard (director, Macbes) and W. Djema (co-director, Biocore).

• PhD in progress: Javier Innerarity Imizcoz. “Optimal bacterial resource allocation for metabolite
production", since nov. 2023. Supervisors: Jean-Luc Gouzé (director, Macbes), Francis Mairet
(co-director, Ifremer Nantes) and W. Djema (co-supervisor, Biocore).

• PhD: Marielle Péré. “Modeling cancer drug response dynamics in single-cells to predict the
emergence of drug-tolerant cells", Université Côte d’Azur, defended March 27th. Supervisors:
Madalena Chaves and Jérémie Roux.

• PhD: Clotilde Djuikem. “Modelling and control of perennial plant phytopathogens”, Université
Côte d’Azur, defended January 5th. Supervisors: Frédéric Grognard, Suzanne Touzeau and Samuel
Bowong (Univ. Douala, Cameroon).

• PhD: Melina Cointe. “Mieux prédire la propagation spatiale de groupes de trichogrammes pour
améliorer le biocontrôle : de l’écologie du mouvement à la dispersion dans les cultures”, Université
Côte d’Azur, defended Octobre 13th. Supervisors Vincent Calcagno, Ludovic Mailleret.

• PhD in progress: Aurelien Kambeu Youmbi. "Self-Financing Model for Cabbage Crops with Pest
Management", University of Dschang,Cameroon, since 2020. Supervisors: Berge Tsanou, Suz-
anne Touzeau and Frédéric Grognard.

• PhD in progress: Odile Burckard. "Coupling, synchronization, and control of cellular oscillators
through mathematical modeling and analysis", Université Côte d’Azur, since 2021. Supervisor:
Madalena Chaves.

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Inria Annual Report 2023

• PhD in progress: Marine Courtois. "Modélisation de la technique de l’insecte stérile dans un
contexte agricole : comment intégrer les réalités biologiques et techniques pour optimiser son
déploiement ?", Université Côte d’Azur, since 2022. Supervisors Ludovic Mailleret, Suzanne Touzeau
and Louise Van Oudenhove De Saint Gery.

• PhD in progress: I. Fierro Ulloa. “Development and analysis of a digital twin for monitoring, control
and optimization applications in microalgae: the Microalgae Model”, since September 2021, UniCA.
Supervisor: O. Bernard.

• PhD in progress: A. Gharib. “Robust control of microalgae processes accounting for future meteor-
ology”, since September 2021. (UniCA, ITN Digitalgaesation). Supervisors: O. Bernard (director)
and W. Djema (co-director).

• PhD in progress: D. Penaranda Sandoval. “Environmental impact assesment of technologically

immature processes”, Université Côte d’Azur. Since 2020. Supervisor: O. Bernard.

• PhD in progress: Joseph Junior Penlap Tamagoua. "Ecophysiological modeling of plant-nematode
interactions: Understanding the origins and consequences of differential plant susceptibility",
Université Côte d’Azur, since 2022. Supervisors: Valentina Baldazzi, Frédéric Grognard and Suz-
anne Touzeau.

• PhD in progress: Frank Kemayou Mangwa. "Mathematical modeling and analysis of the impact of
Radopholus similis on the banana-plantain production", University of Douala, Cameroon, since
2022. Supervisors: Samuel Bowong, Suzanne Touzeau and Frédéric Grognard.

• PhD in progress: Benjamin Böbel. "Mathematical models for robustness and control of intercellular
coupling and synchronization between peripheral circadian clocks", Université Côte d’Azur, since
April 2023. Supervisors: Madalena Chaves and Jean-Luc Gouzé.

11.2.3 Master theses and internships

• L1: M. Scalabrino ‘Modelling of Algae growth with Neural Network A Computer-Model Machine-

Learning Approach.” UniCA, F. Casagli and O. Bernard.

• L2: Jules Baldous. "Inflation écologique, interactions proies-prédateurs et modélisation des aug-

mentoriums." Supervisor Ludovic Mailleret.

• M2: Taha Belkhayate. "Modélisation de la technique de l’insecte stérile avec sexage imparfait",
Université de Pau et des Pays de l’Adour. Supervisors: Marine Courtois, Louise Van Oudenhove De
Saint Gery and Ludovic Mailleret.

• M2: Javier Innerarity Imizcoz (Sorbonne Univ.). “Optimal bacterial resource allocation", UniCA/Inria.

Supervisors: Jean-Luc Gouzé (Macbes) and W. Djema (Biocore).

• M2: Eliane Younes (Montpellier Univ.). “Analysis of a healthy-cancer cell competition model",

UniCA/Inria. Supervisor: W. Djema (Biocore).

• M2: Annalisa Reali (School of Civil and Environmental Engineering, Politecnico di Milano, Italy).
"Modelling of algae-bacteria systems for processing marine wastewaters". Supervisor: F. Casagli
and O. Bernard.

• M1: Dylan Cormet. "Développement d’un package Julia pour l’épidémiologie saisonnière des

maladies des plantes", Polytech Nice. Supervisors: Ludovic Mailleret and Frédéric Grognard

11.2.4 Juries

• O. Bernard was referee of the PhD of J. Sebile-Meilleroux, December 21th (Univ. Nantes).

• O. Bernard was referee of the PhD of J. Farinacci, University of Strasbourg (June, the 14th, 2023),

Project BIOCORE

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• O. Bernard was referee of the PhD of A. Saccardo, University of Padova (It) (2023).

• Jean-Luc Gouzé was a member of the jury for the PhD thesis of Clotilde Djuikem (UniCA), for
the HDR of Frédéric Grognard (UniCA), and was a rapporteur for the HDR of D. Bichara (Univ.
Bordeaux).

• Jean-Luc Gouzé was in the PhD committee of P. Jacquet (Univ. Grenoble) and A. Pavlou (Univ.

Grenoble).

• Madalena Chaves was a rapporteur for the PhD thesis of Ana Bulovic (Institut für Biologie, Humboldt-
Universität zu Berlin, April 17th 2023). She was a member of the jury for the PhD thesis of Maaike
Sansgter (Université Grenoble Alpes, May 11th 2023).
Madalena Chaves is in the Comité de Suivi Doctoral of: Romain Michelucci (UniCA), Adel Anabi
(UniCA), Joseph Penlap (UniCA), and Clémence Métayer (Institut Curie).

11.3 Popularization

11.3.1 Education

• Madalena Chaves was selected to be part of a group of 101 Portuguese Women in Science, high-
lighted to showcase several branches of science and research, and motivate young generations to
follow a scientific education. Photos of each scientist with a short phrase are on exhibition at the
Pavilhão do Conhecimento - Ciência Viva museum in Lisbon and in a booklet.

11.3.2 Interventions

• Charlotte Gaviard was interviewed for the Tribune Côte d’Azur to present the DareWin Evolution

project for which she received the i-PhD price.

• Walid Djema gave a presentation in PHD SEMINARS at INRIA (23/01/2023).

• Francesca Casagli was invited for a talk at TEDX Cannes 2023 to highlight her research work.

(24/11/2023).

• O. Bernard gave all-public presentations in MANDELIEU and Venanson.

12 Scientific production

12.1 Major publications

[1] S. Almeida, M. Chaves and F. Delaunay. ‘Control of synchronization ratios in clock/cell cycle
coupling by growth factors and glucocorticoids’. In: Royal Society Open Science 7.2 (2020), p. 192054.
DOI: 10.1098/rsos.192054. URL: https://hal.sorbonne-universite.fr/hal-02505080.

[2] 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.

[3] C. Baroukh, R. Muñoz-Tamayo, J.-P. Steyer and O. Bernard. ‘DRUM: A New Framework for Metabolic
Modeling under Non-Balanced Growth. Application to the Carbon Metabolism of Unicellular
Microalgae’. In: PLoS ONE 9.8 (Aug. 2014). e104499. DOI: 10.1371/journal.pone.0104499. URL:
https://hal.inria.fr/hal-01097327.

[4] O. Bernard. ‘Hurdles and challenges for modelling and control of microalgae for CO2 mitigation
and biofuel production’. In: Journal of Process Control 21.10 (2011), pp. 1378–1389. DOI: 10.1016
/j.jprocont.2011.07.012. URL: http://hal.inria.fr/hal-00848385.

[5] F. Casagli, F. Béline, E. Ficara and O. Bernard. ‘Optimizing resource recovery from wastewater with
algae-bacteria membrane reactors’. In: Chemical Engineering Journal 451 (Jan. 2023), p. 138488.
DOI: 10.1016/j.cej.2022.138488. URL: https://inria.hal.science/hal-03932262.

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12.2 Publications of the year

International journals

[6] F. Aubree, B. Lac, L. Mailleret and V. Calcagno. ‘Migration pulsedness alters patterns of allele
fixation and local adaptation in a mainland-island model’. In: Evolution - International Journal
of Organic Evolution 77.3 (1st Mar. 2023), pp. 718–730. DOI: 10.1093/evolut/qpac067. URL:
https://hal.inrae.fr/hal-04047090.

[7] N. Augier, M. Chaves and J.-L. Gouzé. ‘Weak synchronization and convergence in coupled genetic
regulatory networks: Applications to damped oscillators and multistable circuits’. In: International
Journal of Robust and Nonlinear Control 33.9 (June 2023), pp. 4867–4892. DOI: 10.1002/rnc.6061.
URL: https://inria.hal.science/hal-03270268.

[8] 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.

[9] P. Bernhard, M. Deschamps and G. Zaccour. ‘Large satellite constellations and space debris: explor-
atory analysis of strategic management of the space commons’. In: European Journal of Operational
Research 304.3 (1st Feb. 2023), pp. 1140–1157. DOI: 10.1016/j.ejor.2022.04.030. URL: https:
//inria.hal.science/hal-03909662.

[10] V. Burte, M. Cointe, G. Perez, L. Mailleret and V. Calcagno. ‘When complex movement yields simple
dispersal: behavioural heterogeneity, spatial spread and parasitism in groups of micro-wasps’. In:
Movement Ecology 11.1 (Dec. 2023), p. 13. DOI: 10.1186/s40462-023-00371-8. URL: https://h
al.inrae.fr/hal-04047110.

[11] F. Casagli, F. Béline, E. Ficara and O. Bernard. ‘Optimizing resource recovery from wastewater with
algae-bacteria membrane reactors’. In: Chemical Engineering Journal 451 (Jan. 2023), p. 138488.
DOI: 10.1016/j.cej.2022.138488. URL: https://inria.hal.science/hal-03932262.

[12] P. Clin, F. Grognard, D. Andrivon, L. Mailleret and F. M. Hamelin. ‘The proportion of resistant
hosts in mixtures should be biased towards the resistance with the lowest breaking cost’. In: PLoS
Computational Biology 19.5 (25th May 2023), e1011146. DOI: 10.1371/journal.pcbi.1011146.
URL: https://hal.inrae.fr/hal-04143995.

[13] M. Cointe, V. Burte, G. Perez, L. Mailleret and V. Calcagno. ‘A double-spiral maze and hi-resolution
tracking pipeline to study dispersal by groups of minute insects’. In: Scientific Reports 13.1 (Dec.
2023), p. 5200. DOI: 10.1038/s41598-023-31630-8. URL: https://hal.inrae.fr/hal-04144
010.

[14] C. Djuikem, F. Grognard and S. Touzeau. ‘Impulsive modelling of rust dynamics and predator
releases for biocontrol’. In: Mathematical Biosciences 356 (23rd Jan. 2023), p. 108968. DOI: 10.1016
/j.mbs.2023.108968. URL: https://inria.hal.science/hal-03952381.

[15]

I. Fierro, L.-D. Lu and O. Bernard. ‘Theoretical growth rate of microalgae under high/low-flashing
light’. In: Journal of Mathematical Biology (2023). URL: https://hal.science/hal-03936421.

[16] Y. Fotso Fotso, S. Touzeau, F. Grognard, B. Tsanou and S. Bowong. ‘Optimal Control of Coffee
Berry Borers: Synergy Between Bio-insecticide and Traps’. In: Journal of Optimization Theory and
Applications 196.3 (2023), pp. 882–899. DOI: 10.1007/s10957-022-02151-7. URL: https://hal
.inrae.fr/hal-03954863.

[17] Y. Gao, O. Bernard, A. Fanesi, P. Perré and F. Lopes. ‘The impact of light/dark regimes on structure
and physiology of Chlorella vulgaris biofilms’. In: Frontiers in Microbiology 14 (24th Oct. 2023). DOI:
10.3389/fmicb.2023.1250866. URL: https://hal.science/hal-04388644.

[18] Y. Gao, J. I. Fierro U, P. Perré, F. Lopes and O. Bernard. ‘Microalgal biofilm behavior under light/dark
cycles’. In: IFAC-PapersOnLine 56.2 (2023), pp. 9739–9744. DOI: 10.1016/j.ifacol.2023.10.288.
URL: https://inria.hal.science/hal-04371850.

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[19] 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.

[20] T. Morel-Journel, M. Haond, L. Dunan, L. Mailleret and E. Vercken. ‘Colonisation debt: when
invasion history impacts current range expansion’. In: Peer Community Journal 3 (28th June 2023),
e59. DOI: 10.24072/pcjournal.293. URL: https://hal.science/hal-04236191.

[21] D. Morgado, A. Fanesi, T. Martin, S. Tebbani, O. Bernard and F. Lopes. ‘Exploring the dynamics
of astaxanthin production in Haematococcus pluvialis biofilms using a rotating biofilm-based
system’. In: Biotechnology and Bioengineering (15th Dec. 2023). DOI: 10.1002/bit.28624. URL:
https://hal.science/hal-04388661.

[22] R. Nordio, E. Rodríguez-Miranda, F. Casagli, A. Sánchez-Zurano, J. L. Guzmán and G. Acién. ‘ABACO-
2: a comprehensive model for microalgae-bacteria consortia validated outdoor at pilot-scale’. In:
Water Research 248 (Jan. 2024), p. 120837. DOI: 10.1016/j.watres.2023.120837. URL: https:
//inria.hal.science/hal-04397215.

[23] D. Penaranda, H. Bonnefond, F. Guihéneuf, M. Morales and O. Bernard. ‘Life cycle assessment of
an innovative rotating biofilm technology for microalgae production: An eco-design approach’. In:
Journal of Cleaner Production 384 (Jan. 2023), p. 135600. DOI: 10.1016/j.jclepro.2022.135600.
URL: https://inria.hal.science/hal-03920632.

[24] B. A. Pessi, C. Baroukh, A. Bacquet and O. Bernard. ‘A universal dynamical metabolic model rep-
resenting mixotrophic growth of Chlorella sp. on wastes’. In: Water Research 229 (2023), p. 119388.
DOI: 10.1016/j.watres.2022.119388. URL: https://hal.science/hal-03920752.

[25]

S. Rossi, M. Mantovani, F. Marazzi, M. Bellucci, F. Casagli, V. Mezzanotte and E. Ficara. ‘Microalgal
cultivation on digestate: Process efficiency and economics’. In: Chemical Engineering Journal
Volume 460.141753 (15th Mar. 2023). DOI: 10.1016/j.cej.2023.141753. URL: https://inria
.hal.science/hal-04398162.

[26] M. Saubin, C. Louet, L. Bousset, F. Fabre, P. Frey, I. Fudal, F. Grognard, F. M. Hamelin, L. Mailleret,
S. Stoeckel, S. Touzeau, B. Petre and F. Halkett. ‘Improving sustainable crop protection using
population genetics concepts’. In: Molecular Ecology 32.10 (May 2023), pp. 2461–2471. DOI: 10.11
11/mec.16634. URL: https://hal.science/hal-03394837.

[27] A. G. Yabo, M. Safey El Din, J.-B. Caillau and J.-L. Gouzé. ‘Stability analysis of a bacterial growth
model through computer algebra’. In: MathematicS In Action 12.1 (26th Sept. 2023), pp. 175–189.
DOI: 10.5802/msia.37. URL: https://hal.science/hal-03671432.

Invited conferences

[28] L. Mailleret. ‘Hands on Pluto : dynamical systems with reactive Julia notebooks’. In: Julia day.

Sophia Antipolis, France, 12th Jan. 2023. URL: https://hal.inrae.fr/hal-04144081.

[29] L. Mailleret. ‘Pulsed perturbations in population dynamics’. In: MOMI2023 - Le MOnde des Math-
ématiques Industrielles. Sophia Antipolis (06), France, 3rd Apr. 2023. URL: https://hal.inrae.f
r/hal-04144067.

International peer-reviewed conferences

[30]

J. C. Arceo, O. Bernard and J.-L. Gouzé. ‘Observer Design via Non-Monotone Lyapunov Functions
for Monitoring Heterotrophic Microalgae’. In: IfacOnLine. 22nd IFAC World Congress. Yokohama,
Japan, 2023. URL: https://inria.hal.science/hal-04229860.

[31] H. Bonnefond, C. Gaviard, E. Pruvost, A. Talec, A. Sciandra and O. Bernard. ‘Strain optimization by
continuous selection pressure: a focus on phycocyanin production’. In: AlgaEurope 2023. Prague,
Czech Republic, 2023. URL: https://hal.science/hal-04275349.

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[32] O. Burckard, M. Chaves, F. Delaunay and M. Teboul. ‘Couplage et synchronisation d’horloges
circadiennes périphériques’. In: Médecine du Sommeil. 48ème congrès de la Société Francophone
de Chronobiologie. Vol. 20. 4. Paris, France, Dec. 2023, p. 249. DOI: 10.1016/j.msom.2023.10.02
1. URL: https://hal.science/hal-04391603.

[33] F. Casagli and O. Bernard. ‘Hybrid modelling of algae-bacteria processes for resource recovery’. In:
AlgaEurope 2023. Prague, Czech Republic, 12th Dec. 2023. URL: https://inria.hal.science/h
al-04397640.

[34] F. Casagli, M. Scalabrino, I. Fierro and O. Bernard. ‘Hybrid Machine LearningMechanistic Modeling
Of AlgaeBacteria Processes Under Various Climatologies’. In: 11TH IWA SYMPOSIUM ON MODEL-
LING AND INTEGRATED ASSESSMENT. Quebec city, Canada, 23rd Sept. 2023. URL: https://inr
ia.hal.science/hal-04397307.

[35] O. Cherkaoui Dekkaki and W. Djema. ‘Optimal control of a bioeconomic model applied to the
recovery of household waste’. In: 2023 American Control Conference (ACC). San Diego, United
States: IEEE, 31st May 2023, pp. 2135–2140. DOI: 10.23919/ACC55779.2023.10156431. URL:
https://inria.hal.science/hal-04393997.

[36] W. Djema, C. Bonnet, H. Ozbay and F. Mazenc. ‘A practical cell density stabilization technique
through drug infusions: a simple pathfinding approach’. In: IEEE Xplore. 2023 European Control
Conference (ECC 2023). 2023 European Control Conference (ECC). Bucharest, Romania, 13th June
2023. DOI: 10.23919/ECC57647.2023.10178254. URL: https://inria.hal.science/hal-04
379210.

[37]

I. Fierro, W. Djema and O. Bernard. ‘Optimal control of microalgae culture accounting for photoin-
hibition and light attenuation’. In: IFAC World Congress 2023. Yokohama, Japan, 9th July 2023. URL:
https://inria.hal.science/hal-04251736.

[38] A. Gharib, F. Casagli, W. Djema, P. Moñino Fernandez, R. Chin-On and O. Bernard. ‘Validation
of a simple and adaptive prediction model for temperature in outdoor microalgae cultivation
systems’. In: 14th European Congress of Chemical Engineering and 7th European Congress of
Applied Biotechnology (ECCE 14 & ECAB 7). Berlin, Germany, 2023. URL: https://inria.hal.sc
ience/hal-04390889.

[39] A. Gharib, W. Djema, F. Casagli and O. Bernard. ‘An auto-adaptive heat transfer model predicting
the temperature evolution in photobioreactors’. In: 22nd IFAC World Congress: Yokohama, Japan.
Vol. 56. 2. Yokohama, Japan, 2023, pp. 9715–9720. DOI: 10.1016/j.ifacol.2023.10.284. URL:
https://inria.hal.science/hal-04390482.

[40] D. Penaranda, M. Morales, F. Guihéneuf and O. Bernard. ‘LCA for technologies at low technological
readiness level: A case study on microalgae cultivation system’. In: CILCA 2023 - X Conferencia
Latinoamericana de Análisis de Ciclo de Vida. Viña del Mar, Chile, 24th July 2023. URL: https://h
al.science/hal-04284517.

Conferences without proceedings

[41] M. Courtois, K. Rastello, F. Grognard, L. Mailleret, S. Touzeau and L. van Oudenhove. ‘How does
residual fertility impact the effectiveness of the sterile insect technique in controlling Ceratitis
capitata?’ In: MPDEE 2023 - Mathematical Population Dynamics, Ecology and Evolution. Marseille,
France, Apr. 2023. URL: https://hal.inrae.fr/hal-04144121.

[42] C. Djuikem, F. Grognard and S. Touzeau. ‘Impulsive modelling of rust dynamics and predator
releases’. In: CMPD6 - Computational and Mathematical Population Dynamics. Winnipeg, Canada,
23rd May 2023. URL: https://hal.inrae.fr/hal-04140599.

[43]

[44]

J. I. Fierro U and O. Bernard. ‘Neural ODEs for phytoplankton modeling’. In: Journées scientifiques
INRIA Chile. Valparaiso (Chile), Chile, 2023. URL: https://inria.hal.science/hal-04390836.

J. I. Fierro U and O. Bernard. ‘Physic-informed neural networks for microalgae modeling’. In: 14th
European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology.
Berlin, Germany, 2023. URL: https://inria.hal.science/hal-04390804.

Project BIOCORE

35

[45] A. Kambeu Youmbi, S. Touzeau, F. Grognard and B. Tsanou. ‘Self-financing model for cabbage crops’.
In: MPDEE 2023 - Conference on Mathematical Population Dynamics, Ecology and Evolution.
Marseille, France, 24th Apr. 2023. URL: https://hal.inrae.fr/hal-04140598.

[46] C. Kounoudji, S. Touzeau, F. Grognard, L. van Oudenhove and L. Mailleret. ‘A sex- and stage-
structured population dynamics model for pest control using the sterile insect technique’. In:
MPDEE 2023 - Mathematical Population Dynamics, Ecology and Evolution. Marseille, France,
24th Apr. 2023. URL: https://hal.inrae.fr/hal-04144139.

[47]

J. J. Penlap Tamagoua, S. Touzeau, F. Grognard and V. Baldazzi. ‘Modelling plant - nematode inter-
actions to understand plant tolerance’. In: MPDEE 2023 - Conference on Mathematical Population
Dynamics, Ecology and Evolution. Marseille, France, 24th Apr. 2023. URL: https://hal.inrae.f
r/hal-04223383.

Doctoral dissertations and habilitation theses

[48] C. Djuikem. ‘Mathematical modelling and control of perennial plant phytopathogens’. Université

Côte d’Azur, 5th Jan. 2023. URL: https://theses.hal.science/tel-04026720.

[49] Y. Gao. ‘Optimisation of microalgae biofilms as a future source of biomolecules’. Université Paris-

Saclay, 9th Feb. 2023.

[50] F. Grognard. ‘Modelling and control of managed ecosystems, from bioreactors to agroecosystems’.

Université Côte D’Azur, 20th Feb. 2023. URL: https://inria.hal.science/tel-04176866.

[51] M. Péré. ‘Modeling cancer drug response dynamics in single-cells to predict the emergence of
drug-tolerant cells’. Université Côte d’Azur, 27th Mar. 2023. URL: https://inria.hal.science
/tel-04350085.

[52] B. A. Pessi. ‘Optimization of microalgae efficiency using reduced metabolic models’. Université

Côte d’Azur, 9th Feb. 2023. URL: https://theses.hal.science/tel-04075649.

Reports & preprints

[53] O. Burckard and M. Chaves. Analytic solutions for the circadian oscillator characterize cycle dynamics
and its robustness. 21st Dec. 2023. DOI: 10.21203/rs.3.rs-3772537/v1. URL: https://hal.sc
ience/hal-04391710.

[54] M. Cointe, V. Burte, G. Perez, L. Mailleret and V. Calcagno. A double-spiral maze and hi-resolution
tracking pipeline to study dispersal by groups of minute insects. 2023. DOI: 10.1101/2022.09.06
.506709. URL: https://hal.science/hal-04050823.

[55]

J. Cresson, M. Péré and A. Szafranska. Numerical approach to the identification of fractional differ-
ential equations and applications in epidemics. 11th Apr. 2023. URL: https://hal.science/hal-
04065555.

[56] Y. Fotso Fotso, S. Touzeau, B. Tsanou, F. Grognard and S. Bowong. Optimal impulsive control of
coffee berry borers in a berry age-structured epidemiological model. 2023. URL: https://hal.scie
nce/hal-04100767.

[57] Y. Gao, O. Bernard, A. Fanesi, P. Perré and F. Lopes. The effect of light intensity on microalgae biofilm
structures and physiology under continuous illumination. 22nd June 2023. DOI: 10.21203/rs.3.r
s-3058230/v1. URL: https://hal.science/hal-04388626.

[58] H. Kanso, M.-M. Memah, V. Baldazzi, B. Quilot-Turion and C. Baey. Assessing Inter-Individual
Genetic Variability in Peach Sugar Metabolism through Reliable Parameter Estimation of a Kinetic
Model. 13th Aug. 2023. DOI: 10.1101/2023.08.10.552764. URL: https://hal.science/hal-0
4215456.

[59] T. Morel-Journel, M. Haond, L. Dunan, L. Mailleret and E. Vercken. Colonisation debt: when invasion
history impacts current range expansion. 15th Nov. 2022. DOI: 10.1101/2022.11.13.516255. URL:
https://hal.inrae.fr/hal-04144037.

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Inria Annual Report 2023

Other scientific publications

[60] O. Burckard and M. Chaves. ‘Benchmark for quantitative characterization of circadian clock models’.
In: 21st International Conference on Computational Methods in Systems Biology. Vol. LNCS.LNBI-
14137. Computational Methods in Systems Biology 21st International Conference, CMSB 2023,
Luxembourg City, Luxembourg, September 13–15, 2023, Proceedings. Luxembourg City, Luxem-
bourg, 13th Sept. 2023. URL: https://inria.hal.science/hal-04379973.

[61] M. Courtois, K. Rastello, F. Grognard, L. Mailleret, S. Touzeau and L. van Oudenhove. ‘Sterile Insect
Technique for crop protection: accounting for residual fertility’. In: Le MOnde des Mathématiques
Industrielles, MOMI2023. Sophia Antipolis, France, 3rd Apr. 2023. URL: https://hal.inrae.fr
/hal-04144059.

[62] M. Courtois, K. Rastello, F. Grognard, L. Mailleret, S. Touzeau and L. van Oudenhove. ‘Sterile insect
technique for crop protection: accounting for residual fertility’. In: DSABNS 2023 - 14th Conference
on Dynamical Systems Applied to Biology and Natural Sciences. Bilbao, Spain, Spain, 5th Feb. 2023.
URL: https://hal.inrae.fr/hal-04049975.

[63] D. Penaranda, F. Casagli, F. Beline and O. Bernard. ‘Life cycle analysis of water resource recovery
facilities based on algae-bacteria processes’. In: Watermatex 2023 - 11th IWA Symposium on
Modelling and Integrated Assessment. Quebec City, QC, Canada, 23rd Sept. 2023. URL: https://h
al.science/hal-04284605.

[64] D. Penaranda, M. Morales, F. Guihéneuf and O. Bernard. ‘LCA of a next-generation microalgae
cultivation system: Eco-design approach for emerging technologies’. In: LCM 2023 - The 11th
International Conference on life cycle Management. Lille, France, 6th Sept. 2023. URL: https://h
al.science/hal-04284550.

[65]

J. J. Penlap Tamagoua, S. Touzeau, F. Grognard and V. Baldazzi. ‘Ecophysiological modelling of
plant-nematode interactions to understand plant tolerance’. In: DSABNS 2023 Conference - 14th
International Conference on Dynamical Systems Applied to Biology and Natural Sciences. Bilbao,
Spain, 2023. URL: https://hal.inrae.fr/hal-04146030.

12.3 Other

Scientific popularization

[66] F. Aubree, B. Lac, L. Mailleret and V. Calcagno. ‘Life is not a long quiet river: consequences of
migration pulsedness for adaptation and divergence’. In: Complex Days. Complex Days 2023.
Complex Days 3. Nice, France, 7th Feb. 2023. URL: https://hal.science/hal-04043134.

[67] M. Cointe, V. Burte, G. Perez, L. Mailleret and V. Calcagno. ‘Bridging behavior and spatial propaga-
tion: a new double spiral maze and image analysis pipeline to study dispersal in groups of minute
insects’. In: Complex Days. UCA Complex Days 2023. Complex Days 3. Nice, France, 7th Feb. 2023.
URL: https://hal.science/hal-04030917.

12.4 Cited publications

[68] Y. Fotso Fotso. ‘Modelling, analysis and control of coffee berry borer’. Université de Dschang
(Cameroun), 17th Jan. 2022. URL: https://hal.archives-ouvertes.fr/tel-03608463.

[69] O. Bernard, L.-D. Lu and J. Salomon. ‘Mixing strategies combined with shape design to enhance
productivity of a raceway pond’. In: ADCHEM 2021 - 16th IFAC Symposium on Advanced Control of
Chemical Processes. Vol. 54. 3. Venice, Italy, June 2021, pp. 281–286. DOI: 10.1016/j.ifacol.202
1.08.255. URL: https://hal.archives-ouvertes.fr/hal-03017414.

[70] O. Bernard, L.-D. Lu and J. Salomon. ‘Optimal periodic resource allocation in reactive dynamical
systems: Application to microalgal production’. In: International Journal of Robust and Nonlinear
Control 33.9 (2023), pp. 4989–5010.

[71] O. Burckard, M. Teboul, F. Delaunay and M. Chaves. ‘Cycle dynamics and synchronization in a
coupled network of peripheral circadian clocks’. In: Interface Focus 12.3 (June 2022), p. 20210087.
DOI: 10.1098/rsfs.2021.0087. URL: https://inria.hal.science/hal-03685352.

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37

[72] F. Casagli and O. Bernard. ‘Simulating biotechnological processes affected by meteorology: Applica-
tion to algae–bacteria systems’. In: Journal of Cleaner Production 377 (Dec. 2022), p. 134190. DOI:
10.1016/j.jclepro.2022.134190. URL: https://inria.hal.science/hal-03935662.

[73] F. Casagli, J. A. Oviedo, D. Jeison, F. Beline and O. Bernard. ‘Outdoor N2O emissions measurements
in algae-bacteria systems guided by modelling’. In: AlgaEurope 2022. EABA (European Algae Bio-
mass Association). Rome, Italy, Dec. 2022. URL: https://inria.hal.science/hal-04398017.

[74] F. Casagli, S. Rossi, J.-P. Steyer, O. Bernard and E. Ficara. ‘Balancing Microalgae and Nitrifiers for
Wastewater Treatment: Can Inorganic Carbon Limitation Cause an Environmental Threat?’ In:
Environmental Science and Technology 55.6 (Mar. 2021), pp. 3940–3955. DOI: 10.1021/acs.est.0
c05264. URL: https://hal.inrae.fr/hal-03219531.

[75] F. Casagli, G. Zuccaro, O. Bernard, J.-P. Steyer and E. Ficara. ‘ALBA: a comprehensive growth model
to optimize algae-bacteria wastewater treatment in raceway ponds’. In: Water Research 190 (Feb.
2021). Il y avait 2 UT pour le même DOI - correction faite dans le WOS, p. 116734. DOI: 10.1016/j
.watres.2020.116734. URL: https://hal.inria.fr/hal-03142211.

[76] M. Cointe. ‘Du laboratoire au terrain, du mouvement à la dispersion : étude de la propagation
des parasitoïdes du genre Trichogramma à différentes échelles spatiales et temporelles’. Theses.
Université Côte d’Azur, Oct. 2023. URL: https://theses.hal.science/tel-04390275.

[77] C. Djuikem, A. G. Yabo, F. Grognard and S. Touzeau. ‘Mathematical modelling and optimal control
of the seasonal coffee leaf rust propagation’. In: ADHS 2021 - 7th IFAC Conference on Analysis and
Design of Hybrid Systems. Vol. 54. IFAC-PapersOnLine 5. IFAC. Brussels, Belgium, July 2021, pp. 193–
198. DOI: 10.1016/j.ifacol.2021.08.497. URL: https://hal.inria.fr/hal-03274865.

[78]

J.-L. Gouzé, A. Rapaport and Z. Hadj-Sadok. ‘Interval observers for uncertain biological systems’.
In: Ecological modelling 133 (2000), pp. 45–56. URL: http://dx.doi.org/10.1016/S0304-3800
(00)00279-9.

[79] G. M. Grimaud, F. Mairet, A. Sciandra and O. Bernard. ‘Modeling the temperature effect on the
specific growth rate of phytoplankton: a review’. In: Reviews in Environmental Science and Biotech-
nology (Aug. 2017). URL: https://hal.inria.fr/hal-01576871.

[80] M. Haond. ‘Impact de la capacité de charge de l’environnementsur les dynamiques d’expansions
de métapopulations. Théories et applications à un système expérimental hôte-parasitoïde’. PhD
thesis. Université Côte d’Azur, 2019.

[81] L. Mailleret and F. Grognard. ‘Global stability and optimisation of a general impulsive biological
control model’. In: Mathematical Biosciences 221.2 (2009), pp. 91–100. URL: http://dx.doi.org
/10.1016/j.mbs.2009.07.002.

[82] L. Mailleret and V. Lemesle. ‘A note on semi-discrete modelling in the life sciences’. In: Philosophical
Transactions of the Royal Society A: Mathematical,Physical and Engineering Sciences 367.1908 (2009),
pp. 4779–4799. DOI: 10.1098/rsta.2009.0153. eprint: http://rsta.royalsocietypublishi
ng.org/content/367/1908/4779.full.pdf+html. URL: http://rsta.royalsocietypubli
shing.org/content/367/1908/4779.abstract.

[83] P. Masci, O. Bernard and F. Grognard. ‘Continuous Selection of the Fastest Growing Species in the

Chemostat’. In: Proceedings of the IFAC conference. Seoul, Korea. 2008.

[84] 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.

[85] M. Meyer, A. Paquet, M.-J. Arguel, L. Peyre, L. Gomes-Pereira, K. Lebrigand, B. Mograbi, P. Brest,
R. Waldmann, P. Barbry, P. Hofman and J. Roux. ‘Profiling the Non-genetic Origins of Cancer Drug
Resistance with a Single-Cell Functional Genomics Approach Using Predictive Cell Dynamics’. In:
Cell Systems 11.4 (Oct. 2020), pp. 367–374. DOI: 10.1016/j.cels.2020.08.019. URL: https://h
al.science/hal-03001650.

[86] T. Morel Journel. ‘Stratégies d’introduction d’organismes dans une environment spatialement

structuré’. PhD thesis. SVS, Université Nice Sophia Antipolis, 2015.

38

[87]

Inria Annual Report 2023

J. A. Oviedo, R. Muñoz, A. Donoso-Bravo, O. Bernard, F. Casagli and D. Jeison. ‘A half-century of
research on microalgae-bacteria for wastewater treatment’. In: Algal Research - Biomass, Biofuels
and Bioproducts 67 (Sept. 2022), p. 102828. DOI: 10.1016/j.algal.2022.102828. URL: https:
//inria.hal.science/hal-03935846.

[88] M. Péré, M. Chaves and J. Roux. ‘TRAIL-induced apoptosis signaling modeling identifies dynamic
signs of efficient cell death initiation, enabling an early prediction of the cell response’. In: CMSB
2021 - 19th International Conference on Computational Methods in Systems Biology. Bordeaux,
France, Sept. 2021. URL: https://inria.hal.science/hal-03522480.

[89] M. Péré, D. Oyarzun, M. Chaves and J. Roux. ‘Analyzing single-cell trajectories with augmented
machine learning methods predicts cancer drugs response in live-cell microscopy assays’. In:
ICSB 2022 - 21th international conference on systems biology. Berlin, Germany, Oct. 2022. URL:
https://hal.science/hal-03868584.

[90] M. Péré, D. A. Oyarzun, J. Roux and M. Chaves. ‘Modeling isogenic cancer cell response upon vary-
ing TRAIL stimulations to decipher the kinetic determinants of cell fate decision’. In: Foundations
of Systems Biology in Engineering (FOSBE). Cambridge, Massachusetts, United States, Aug. 2022.
URL: https://hal.science/hal-03868311.

[91] B. A. Pessi, E. Pruvost, A. Talec, A. Sciandra and O. Bernard. ‘Does temperature shift justify microal-
gae production under greenhouse?’ In: Algal Research - Biomass, Biofuels and Bioproducts 61 (Jan.
2022), p. 102579. DOI: 10.1016/j.algal.2021.102579. URL: https://hal.science/hal-035
22868.

[92] B. Polizzi, A. Fanesi, F. Lopes, M. Ribot and O. Bernard. ‘Understanding photosynthetic biofilm
productivity and structure through 2D simulation’. In: PLoS Computational Biology 18.4 (2022),
e1009904.
<scratchpad>
- Main topic: Research Centre at Université Côte d’Azur, focusing on modeling and control of artificial ecosystems
- Key points:
  - Development of new technologies for sustainable growth of living organisms
  - Use of mathematical modeling and computer tools to design devices, systems, and processes containing living cells or individuals
  - Application domains: bioenergy, CO2 fixation, biological control for plants and micro-plants production systems, biological depollution
  - Collaboration with industry partners, national and international initiatives
- Structure: The document is organized into sections, including project description, team members, research program, application domains, social and environmental responsibility, highlights of the year, new software and platforms, new results, and dissemination.
- Potential artifacts to ignore: web-related elements, such as links, HTML tags, and unnecessary text
</scratchpad>

<final_summary>
The Research Centre at Université Côte d’Azur focuses on developing new technologies for sustainable growth of living organisms through mathematical modeling and computer tools. The centre's research program includes application domains such as bioenergy, CO2 fixation, biological control for plants and micro-plants production systems, and biological depollution. Collaboration with industry partners and national and international initiatives is also an important aspect of the centre's work. The centre's research aims to design devices, systems, and processes containing living cells or individuals to decrease pollution, use of chemicals, or to produce bioenergy in a sustainable way.
The Research Centre at Université Côte d’Azur focuses on developing new technologies for sustainable growth of living organisms through mathematical modeling and computer tools. The centre's research program includes application domains such as bioenergy, CO2 fixation, biological control for plants and micro-plants production systems, and biological depollution. Collaboration with industry partners and national and international initiatives is also an important aspect of the centre's work. The centre's research aims to design devices, systems, and processes containing living cells or individuals to decrease pollution, use of chemicals, or to produce bioenergy in a sustainable way.