RESEARCH CENTRE

Inria Paris Centre

IN PARTNERSHIP WITH:

Institut Pasteur

2023
ACTIVITY REPORT

Project-Team

INBIO

Experimental and Computational Methods
for Modeling Cellular Processes

IN COLLABORATION WITH: Centre de Bioinformatique, Biostatistique et
Biologie Intégrative

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team INBIO

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Population dynamics emerging from randomness in single cells . . . . . . . . . . . . . . . .
3.2 Optimal experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Cybergenetics – real time control of biological processes . . . . . . . . . . . . . . . . . . . . .
3.4 Platforms for automated reactive experiments . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Preamble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Understanding resistance and tolerance to antibiotic treatments . . . . . . . . . . . . . . . .
4.3 Optimization of protein production in yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Optimizing protein production using cybergenetics approaches - Publication in Nature
Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 New results

7.1 Maximizing protein production by keeping cells at optimal secretory stress levels using
real-time control approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Bayesian filtering for model predictive control of stochastic gene expression in single cells
7.3 Optimal control of bioproduction in the presence of population heterogeneity . . . . . . .

8 Partnerships and cooperations

8.1 International research visitors

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 Dissemination

9.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.1 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.2 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.3 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.1 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.2.2 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 Scientific production

10.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2 Publications of the year

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Project INBIO

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Project-Team INBIO

Creation of the Project-Team: 2019 November 01

Keywords

Computer sciences and digital sciences

A3.1.1. – Modeling, representation

A3.4.4. – Optimization and learning

A3.4.5. – Bayesian methods

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

A6.1.2. – Stochastic Modeling

A6.1.4. – Multiscale modeling

A6.3.1. – Inverse problems

A6.3.3. – Data processing

A6.4.1. – Deterministic control

A6.4.3. – Observability and Controlability

Other research topics and application domains

B1.1.2. – Molecular and cellular biology

B1.1.7. – Bioinformatics

B1.1.8. – Mathematical biology

B1.1.10. – Systems and synthetic biology

B2.4.2. – Drug resistance

B5.10. – Biotechnology

B9.8. – Reproducibility

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientist

• Gregory Batt [Team leader, Inria, Senior Researcher, HDR]

Post-Doctoral Fellows

• Angelica Frusteri Chiacchiera [Institut Pasteur, from Jul 2023]

• Allyson Holmes [Institut Pasteur, until Jun 2023]

• Esteban Lebrun [Institut Pasteur, from Oct 2023]

PhD Students

• Alicia Da Silva [Inria, from Oct 2023]

• Henri Galez [Institut Pasteur]

• Viktoriia Gross [Institut Pasteur]

Technical Staff

• Sara Napolitano [Institut Pasteur, Engineer]

Interns and Apprentices

• Konstantin Achkasov [Institut Pasteur, Intern, from Mar 2023 until Aug 2023]

• Hong Duong Ngo [Institut Pasteur, Intern, from Feb 2023 until Aug 2023]

Administrative Assistant

• Nelly Maloisel [Inria]

Visiting Scientists

• Angelica Frusteri Chiacchiera [University di Pavia, until Jun 2023]

• Lorenzo Pazotti [University di Pavia]

External Collaborator

• François Bertaux [Lesaffre]

2 Overall objectives

The main objective of our research is to understand, control, and optimize cellular processes in single
cells and at the population level. We combine experimental and theoretical work within a single team.

Our focus is on developing methods and models that take stochasticity of intracellular processes and
heterogeneity of cell populations into account. To this end, we use both mixed-effects models as well as
continuous-time Markov chains and their diffusion approximations. We develop methods for efficiently
calculating with such models and use them to design optimally informative experiments and to reverse
engineer unknown cellular processes from experimental data. Furthermore, we deploy models in order
to optimally construct and optimally control synthetic gene circuits.

We have recently started to set up our own biology laboratory at Institut Pasteur. We develop novel
experimental platforms that are designed to be fully automated, controllable by our own software,

Project INBIO

3

and capabale of updating the experimental plan in response to incoming measurements. Optogenetic
actuation of intracellular processes, coupled to real time fluorescence measurements by microscopy or
flow cytometry, then allows us to connect cellular processes with models and algorithms in real time.

The spritit of our work is that experimental platforms and circuits should be constructed with our the-
oretical work in mind, while our mathematical methods should be usable to adress concrete experimental
questions in the lab.

3 Research program

3.1 Population dynamics emerging from randomness in single cells

Dynamics of cell populations growing in isolation or as part of some ecological system are often shaped
by biochemical processes inside cells, for instance when these processes convey resistance to stressors
or trigger cell fate decisions in reponse to environmental conditions. Understanding how stochastic
reaction events inside single cells affect emerging population dynamics, and how selection effects at
the population level feed back to shape single cell characteristics of cells in the population, is one of the
key questions in biology. We develop multi-scale modeling approaches that allow us to derive emerging
population dynamics from mechanistic descriptions of stochastic reaction networks inside single cells.
In the past, we have used such approaches to study how stochasticity in restriction-modification systems,
acting as simple bacterial innate immune systems, propagates to the ecology of bacteria and bacterial
viruses and shapes the dynamics of bacterial populations. More generally, we develop and use these
approaches in connection with experimental work in our lab for understanding and controlling the
dynamics of populations in cases where controllable system inputs inherently operate at the level of
single cells (e.g. optogenetics) but the output of interest is at the level of populations (e.g. bioproduction).

3.2 Optimal experimental design

One of the major problems in reverse engineering biochemical processes inside cells is that cellular
processes are high-dimensional and complex with many unknown parameters while the available data
is low dimensional and corrupted by measurement errors. Such problems can be alleviated by ensuring
that the experimental plan is designed to yield data that provides as much information as possible about
the unknown model parameters. We develop mathematical approaches and computational tools that
can be used to calculate the expected amount of information that can be gained from a given experiment
given a specification of either a stochastic model of the system (described above) or a deterministic model
based on ordinary differential equations. These information calculation approaches are then coupled to
optimization tools and used to plan maximally informative experiments in our applications.

3.3 Cybergenetics – real time control of biological processes

Cells have evolved uncountable numbers of feedback circuits to regulate their functionalities in the
presence of changing environmental conditions. But can such feedback control also be externalized and
placed under control of scientists? Early work on this topic suggested that optogenetic systems, allowing
for external regulation of gene expression, have the potential to serve as an interface between cells and
experimental platform that gives a computer the power to stir the functioning of cells via the application
of light. We develop all the tools required to realize automated computer control of intracellular processes.
On the experimental side, we develop yeast strains that are equipped with optogenetic promoters to
drive various functionalities. On the mathematical side, we develop models and software to equip our
experimental platforms with the appropriate programs to realize sucessful feedback control, both at the
level of single cells (microscopy) and at the level of populations (bioreactors and plate reader).

3.4 Platforms for automated reactive experiments

The core scientific activity of the team is to connect mathematical methods with biological applications
in our lab. The interface between the two sides, that is the experimental platforms, is therefore of crucial
importance for the success of our activities. However, platforms that can be purchased by vendors are

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Inria Annual Report 2023

typically delivered without the capacity to adapt the experimental plan in response to incoming measure-
ments, a functionality that is crucially needed for deploying our computational methods (e.g. feedback
control). Therefore, we develop novel experimental platforms and/or extend existing platforms with addi-
tional software and hardware that allows us to perform automated reactive experiments. Concretely, we
develop a microscopy platform and control software for yeast that uses a digital micromirror device to
expose single cells to targeted light signals that can be adjusted in real time in response to measurements
taken from the cell. Furthermore, we develop a platform of 16 parallel small scale automated bioreactors,
each equipped with controllable LEDs to allow for optogenetic gene expression and long-term reactive
experiments in tightly controlled conditions. Automation of the platform is achieved via a low-cost
open-source pipetting robot that samples all reactors to a benchtop cytometer in which single cell
gene expression is measured in all sampled cells of all reactors. Finally, we develop software to take full
control of a commercial plate reader with liquid injection capabilities (Tecan Spark). This platform
allows us to use a Raspberry Pi to pilot 96 parallel reactive experiments where optical density is used as a
readout of bacterial growth.

4 Application domains

4.1 Preamble

Since most of our research is at the interface of mathematics and biology, there often is no clear split
between mathematical reaseach objectives and applications. For instance, feedback control of gene
expression is simultaneously a mathematical and an applied problem.

4.2 Understanding resistance and tolerance to antibiotic treatments

The non-susceptibility of pathogenic bacteria to antibiotic treatments is a major health problem. Bacteria
might escape treatments in two ways: being resistant or being tolerant. Whereas resistant bacteria can
multiply in presence of antibiotics, tolerant bacteria can merely survive. Yet, tolerance is increasingly
recognized as a major player in treatment failure. In particular, an increasing fraction of commensal and
pathogenic E coli bacteria express extended-spectrum β-lactamases and/or carbapenemases. When indi-
vidual bacteria die as a consequence of antibiotic treatments, these enzymes are released and hydrolyze
the antibiotic molecules in the environment, conveying a transient protection to the remaining bacteria
that lasts until the enzymes are degraded themselves. Understanding how this collective antibiotic
tolerance (CAT) shapes population dynamics is difficult yet important for optimally killing bacterial
populations: when a second antibiotic dose is applied directly after a first dose it will not be effective since
the antibiotics will be degraded by the enzymes released from bacteria killed after the first dose; when the
second dose is applied too late the surviving bacterial population will have regrown to a large size. Our
plate reader platform allows us to apply complex temporal patterns of antibiotic treatments to bacteria
over nearly two days. Parallezing such treatments in the 96 well plates allows us to generate rich data sets
and to calibrate population dynamics models that can be used to optimize temporal treatment plans.
One of the applied objectives of our team is to use these capacities to study a collection of fully-sequenced
clinical isolates treated with a broad range of clinically important antibiotics and grown in various media.
Ideally, this will lead to an approach that can be used to assay tolerance to antibiotics in hospitals instead
of, or in addition to, standard antibiotic susceptibility tests, detecting resistance.

4.3 Optimization of protein production in yeast

Many proteins are of technological or therapeutical importance. The yeast S. cerevisiae is an interesting
organism for protein bioproduction since it combines a relatively fast growth rate with good capacities to
perform post-translational modifications needed for protein maturation and full functionality. However,
imposing a strong demand on protein production to the cell places a significant burden on its physiology,
either at the protein production level or at the maturation and secretion levels. Using systems and
synthetic biology approaches, we aim at better understanding the origins of the production bottlenecks
and then using modeling and control approaches, we aim at finding optimal control solutions for
bioproduction. Three different strategies are envisioned. In the first approach, bioproduction stress

Project INBIO

5

sensors are used to observe in real time the physiological state of the cell, and the demand is externally
tuned based on the stress level of the cell population. In the second approach, the stress sensor is used
to tune the response capacities of the cell to the external demand, thus creating an internal feedback
loop. In the third approach, we control the fraction of the producing cells by engineering an artificial
differentiation system that implements the partial differentiation of grower cells into producer cells. The
optimization problem is then to find the optimum ratio based on the external environment of the cells.

5 Social and environmental responsibility

5.1 Footprint of research activities

A significant part of our daily research activities involves molecular biology work and consumes plasticware
and various chemicals. We also work on lab automation and develop experimental platform to paralellize
experiments. However, we work with small biorectors (15 to 50mL), so volumes of cell cultures remain
very modest.

We also occasionally use a computer cluster, notably for optimization, but the jobs remain relatively

modest on a yearly basis.

Allyson Holmes and Henri Galez are part of a committee working on the quantification of the carbon
footprint of the computational works done in the Computational Biology Department of Institut Pasteur.

5.2 Impact of research results

Regarding biological developments, we have two main research directions.

The first one deals with the optimization of bioproduction. Bioproduction is a domain of strategic
importance. The field is higly technological and rapidly growing at the global scale. The market for
biopharmaceuticals alone, that notably include vaccines and monoclonal antibodies, is estimated to
$400B to $500B. France imports > 70% of its vaccines and > 95% of its monoclonal antibodies and lacks
sovereignty. Therefore this field has a strong social, medical and economical importance.

The second research direction deals with antibiotic stewardship. The spread of antimicrobial resist-
ance is a both a health and an ecological problem of global impact. Antibiotic stewardship aims at using
these drugs in more appropriate ways. To do so, one has to better understand and quantify bacterial
response to antibiotic treatments.

Therefore our two main research directions are both tightly connected with important health and

social issues.

6 Highlights of the year

6.1 Optimizing protein production using cybergenetics approaches - Publication in

Nature Communications

Using our automated experimental platform and an optogenetic induction system for gene expression,
we caracterized in depth the impact on cell physiology of the demand for protein secretion in yeast. We
found that for hard-to-secrete proteins strong demands led to excessive stresses and to a "burn-out" state
for the cell.

This knowledge was then used to propose an external control strategy that should maximize protein
secretion by keeping the stress at a maximal acceptable level. This strategy was experimentally validated
and led to an improved production in comparison to the full induction strategy.

Our proposed regulation strategy is based on the simple measurement of mean UPR stress levels
in cells and is in principle compatible with and complementary to the extensive chassis-engineering
optimization strategies found in industrial applications. Moreover, this work also demonstrates that
cybergenetic approaches can effectively be used to optimize cellular processes of interests.

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Inria Annual Report 2023

7 New results

7.1 Maximizing protein production by keeping cells at optimal secretory stress levels

using real-time control approaches

Participants:

Sebastian Sosa-Carrillo, Henri Galez, Sara Napolitano, François Ber-
taux, Gregory Batt.

The production of recombinant proteins is a problem of major industrial and pharmaceutical im-
portance. Secretion of the protein by the host cell considerably simplifies downstream purification
processes. However, it is also the limiting production step for many hard-to-secrete proteins. Current
solutions involve extensive chassis engineering to favor trafficking and limit protein degradation triggered
by excessive secretion-associated stress. In this contribution, we proposed instead a regulation-based
strategy in which induction is dynamically adjusted based on the current stress level of the cells. Using
a small collection of hard-to-secrete proteins and a bioreactor-based platform with automated cyto-
metry measurements, we demonstrated that the regulation sweet spot is indicated by the appearance
of a bimodal distribution of internal protein and of secretory stress levels, when a fraction of the cell
population accumulates high amounts of proteins, decreases growth, and faces significant stress, that is,
experiences a secretion burn-out. In these cells, adaptations capabilities are overwhelmed by a too strong
production. With these notions, we defined an optimal stress level based on physiological readouts. Then,
using real-time control, we demonstrated that a strategy that keeps the stress at optimal levels increases
production of a single-chain antibody by 70

7.2 Bayesian filtering for model predictive control of stochastic gene expression in

single cells

Participants:

Zachary Fox, Gregory Batt, Jakob Ruess.

In this contribution, we described a method for controlling the production of protein in individual
cells using stochastic models of gene expression. By combining modern microscopy platforms with
optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which
can induce protein production. Here we used a finite state projection based stochastic model of gene
expression, along with Bayesian state estimation to control protein copy numbers within individual
cells. We compared this method to previous methods that use population based approaches. We also
demonstrated the ability of this control strategy to ameliorate discrepancies between the predictions of a
deterministic model and stochastic switching system.

7.3 Optimal control of bioproduction in the presence of population heterogeneity

Participants: Davin Lunz, Jakob Ruess, J Frederic Bonnans.

Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations.
In the study of single-cell dynamics this is typically accounted for. However, on the population level
this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The
homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous
counterparts and may thus lead to severely suboptimal control of bioproduction. In this contribution, we
introduced a comprehensive mathematical framework for solving bioproduction optimal control prob-
lems in the presence of heterogeneity. We studied population-level models in which such heterogeneity
is retained, and proposed order-reduction approximation techniques. The reduced-order models take
forms typical of homogeneous bioproduction models, making them a useful benchmark by which to

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study the impotance of heterogeneity. Moreover, the derivation from the heterogeneous setting shed
light on parameter selection in ways a direct homogeneous outlook cannot, and revealed the source of
approximation error. With view to optimally controlling bioproduction in microbial communities, we
asked the question: when does optimising the reduced-order models produce strategies that work well in
the presence of population heterogeneity? We showed that, in some cases, homogeneous approximations
provide remarkably accurate surrogate models. Nevertheless, we also demonstrated that this is not
uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In
these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

8 Partnerships and cooperations

8.1 International research visitors

8.1.1 Visits of international scientists

Participants:

Lorenzo Pasotti, Angelica Frusteri.

Lorenzo Pasotti, assistant professor at the Department of Electrical, Computer and Biomedical
Engineering and at the Centre for Health Technologies of the University of Pavia (Italy), and Angelica
Frusteri, a postdoctoral scientist in his group, have been invited for three months in the InBio team.

8.2 National initiatives

• Institut de Convergence Inception (2016-2025) on the “Emergence of Diseases in Populations and
in Individuals”, coordinated by T. Bourgeron (Institut Pasteur). Partner institutes include Institut
Pasteur, Paris Sciences et Lettres, Université de Paris, AP-HP, and research teams from CEA, CNRS,
INSERM and INRA.

The Inception’s goal is to develop a core structure to mobilize data resources, numerical sciences,
and fundamental experimental biology in a range of health issues. It uses integrative biology, social
science and data science to understand the emergence of diseases in populations and in individuals.
Inception provides funding for the PhD work of Viktoriia Gross.

• PPR Antibiorésistance Anoruti (2021-2025) on the “Analysis of non-response to antibiotics in vivo:
application to Escherichia coli urinary tract infections”, coordinated by I. El Meouche (Inserm).

The objective of Anoruti is to identify the different factors involved in the fact that some bacteria
sensitive to an antibiotic in vitro do not respond to treatment in vivo.

• PPR Antibiorésistance Seq2Diag (2021-2026) on “Whole genome sequencing and artificial in-
telligence to characterize and diagnose antibiotic resistance and capacity to escape treatment”,
coordinated by P. Glaser (Institut Pasteur).

Genomic sequencing has revolutionized microbiological surveillance and molecular epidemiology.
The objective of the Seq2Diag project is to provide a proof of concept for its use in hospital and
veterinary laboratories as a diagnostic tool for in silico antibiotic sensitivity testing.

• ANR JCJC SmartSec (2022-2024) on “Matching maximal host capacities: stress-informed, self-
tuning bioproduction circuits”, coordinated by F. Bertaux (Lesaffre) with G. Batt (Inria and Institut
Pasteur).

Bioproduction requires diverting resources normally used by host cells for growth and self-replication
towards the production of desired molecules. Achieving maximal resource diversion without com-
promising the essential functions of the host is of critical importance, but is particularly challenging.
To tackle this challenge, SmartSec aims at designing host-aware circuits, with application to the
production of secreted proteins.

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Inria Annual Report 2023

9 Dissemination

9.1 Promoting scientific activities

9.1.1 Scientific events: selection

Gregory Batt is a member of the International Programme Committee of the 10th International Confer-
ence on the Foundations of Engineering in Biology (FOSBE 2024).

9.1.2 Journal

Gregory Batt has been a reviewer for Nature Communications.
Esteban Lebrun has been a reviewer for Lab on a Chip.
Lorenzo Pasotti has been a reviewer for Nature Communications, Nature Chemical Biology, and

Microbial Cell Factories.

9.1.3 Invited talks

Gregory Batt gave invited presentations at the International Symposium on Synthetic Biology, at Technishe
University Darmstadt (Germany), and at the Symposium on Synthetic and Systems Biology (BioSynSys),
at INSA Toulouse (France). He also gave invited seminars at the Microbial Processes and Interactions
(MiPI) lab, at Université de Liège - Gembloux Agro-Bio Tech (Belgium), at the Laboratoire Jean Perrin,
Sorbonne Université (Paris), and at the Advanced Light Microscopy seminar series, Institut Pasteur (Paris).

Henri Galez gave an invited seminar at Imperial College London (UK).
Sara Napolitano gave an invited talk at the Learning Planet Institute (Paris).
Lorenzo Pasotti gave invited seminar at the Department of Bioengineering, University of Maryland

(USA), and at the Department of Biology and Biotechnology, University of Pavia (Italy).

9.1.4 Scientific expertise

Gregory Batt has been an expert for grant evaluation at BPI France.

9.1.5 Research administration

Gregory Batt has been the deputy-diretor and then director of the Computational Biology department at
Institut Pasteur. He is also a member of the Bureau du Comité des Equipes Projets at Inria Paris.

9.2 Teaching - Supervision - Juries

Esteban Lebrun gave a course on "Introduction to Synthetic Biology" at the Microbiology Master of
Université de Caen Normandie.

Lorenzo Pasotti gave courses on "Bioinformatics and Synthetic Biology" at the Bioengineering Master

of University of Pavia and on "Bioinformatics" at the Biotechnology Master of University of Pavia.

9.2.1 Supervision

Gregory Batt is (co-)supervising PhD students:

• PhD in progress: Viktoriia Gross, “An integrative approach to characterize the two sides of enzyme
mediated antibiotic escape: resistance and tolerance". Started in Oct. 2020. Supervision by Imane
El Meouche, Erick Denamur and Gregory Batt.

• PhD in progress: Alicia da Silva, “A screening and learning approach for protein secretion in yeast".

Started in Oct. 2023. Supervision by Senta Blanquet, Etienne Jourdier and Gregory Batt.

Sara Napolitano and Gregory Batt are co-supervising a PhD student:

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9

• PhD in progress: Henri Galez, “Engineering an autocrine-like system for screening libraries of
protein secreting strains in yeast". Started in Sept. 2022. Supervision by Sara Napolitano and
Gregory Batt.

Henri Galez, Sara Napolitano, and Gregory Batt supervised the internship of Hong Duong Ngo (M2
student, Paris Cité University). Viktoriia Gross and Gregory Batt supervised the internship of Konstantin
Achkasov (M1 student, Sorbonne University).

9.2.2 Juries

Gregory Batt has been member of the HDR committee of Manish Kushwaha (Paris-Saclay University) and
of the PhD committees of Eve Tasiudi (ETH Zurich), Romain Aubry (Poitiers University), and Esteban
Lebrun (Paris-Saclay University).

He has also been member of the thesis advisory committees of Lena Le Quellec (Paris-Saclay Univer-

sity/Institut Pasteur) and Madison Lenormand (Sorbonne University/Institut Pasteur).

10 Scientific production

10.1 Major publications

[1] S. Sosa-Carrillo, H. Galez, S. Napolitano, F. Bertaux and G. Batt. ‘Maximizing protein production
by keeping cells at optimal secretory stress levels using real-time control approaches’. In: Nature
Communications 14.1 (17th May 2023), p. 3028. DOI: 10 . 1038 / s41467 - 023 - 38807 - 9. URL:
https://inria.hal.science/hal-03940186.

10.2 Publications of the year

International journals

[2] Z. Fox, G. Batt and J. Ruess. ‘Bayesian filtering for model predictive control of stochastic gene
expression in single cells’. In: Physical Biology (21st June 2023). DOI: 10.1088/1478-3975/ace094.
URL: https://inria.hal.science/hal-04148503.

[3] D. Lunz, J. F. Bonnans and J. Ruess. ‘Optimal control of bioproduction in the presence of population
heterogeneity’. In: Journal of Mathematical Biology 86.3 (6th Feb. 2023), p. 43. DOI: 10.1007/s0028
5-023-01876-x. URL: https://inria.hal.science/hal-03445175.

[4] D. Lunz and J. F. Bonnans. ‘Modelling and optimal control of a two-species bioproducing microbial
consortium’. In: SIAM Journal on Applied Mathematics 83.1 (13th Feb. 2023), pp. 144–171. DOI:
10.1137/22M1476113. URL: https://inria.hal.science/hal-03479385.

[5] S. Sosa-Carrillo, H. Galez, S. Napolitano, F. Bertaux and G. Batt. ‘Maximizing protein production
by keeping cells at optimal secretory stress levels using real-time control approaches’. In: Nature
Communications 14.1 (17th May 2023), p. 3028. DOI: 10 . 1038 / s41467 - 023 - 38807 - 9. URL:
https://inria.hal.science/hal-03940186.
<scratchpad>
- Main topic: Research Centre INBIO (Institut National de Biologie Intégrative) at Inria Paris Centre
- Key points:
  - Focus on understanding, controlling, and optimizing cellular processes in single cells and populations
  - Combination of experimental and theoretical work in fields like biology, computer science, and mathematics
  - Development of methods and models to account for stochasticity and heterogeneity in cellular processes
  - Application domains include antibiotic resistance, protein production in yeast, and bioproduction optimization
- Structure: The document is organized into sections on project-team, activity report, activity domains, social and environmental responsibility, and dissemination
- Potential artifacts to ignore: HTML tags, links, and web-related elements
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The INBIO research centre at Inria Paris Centre focuses on understanding, controlling, and optimizing cellular processes in single cells and populations. The centre combines experimental and theoretical work in biology, computer science, and mathematics to develop methods and models that account for stochasticity and heterogeneity in cellular processes. Applications of this research include optimizing protein production in yeast, understanding antibiotic resistance, and improving bioproduction processes. The centre's work aims to bridge the gap between mathematical modeling and biological experimentation, with a focus on real-time control and optimization of cellular processes.
The INBIO research centre at Inria Paris Centre focuses on understanding, controlling, and optimizing cellular processes in single cells and populations. The centre combines experimental and theoretical work in biology, computer science, and mathematics to develop methods and models that account for stochasticity and heterogeneity in cellular processes. Applications of this research include optimizing protein production in yeast, understanding antibiotic resistance, and improving bioproduction processes. The centre's work aims to bridge the gap between mathematical modeling and biological experimentation, with a focus on real-time control and optimization of cellular processes.