RESEARCH CENTRE

Inria Centre
at Université de Lorraine

IN PARTNERSHIP WITH:

CNRS, Université de Lorraine

2023
ACTIVITY REPORT

Project-Team

BIGS

Biology, genetics and statistics

IN COLLABORATION WITH: Institut Elie Cartan de Lorraine (IECL)

DOMAIN

Digital Health, Biology and Earth

THEME

Modeling and Control for Life Sciences

Contents

Project-Team BIGS

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Stochastic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Estimation and control for stochastic processes . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Algorithms and estimation for graph data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Regression and machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Oncology: tumor growth and heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Gene networks and single-cell data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Epidemiology and e-health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Dynamics of telomeres . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Highlights of the year

5.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 New software, platforms, open data

6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Harissa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.2 MultiRNAflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New results

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6
7.1 Stochastic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
7.1.1 Reconstruction of epigenetic landscapes from single-cell data . . . . . . . . . . . . .
7
7.1.2 Quasi-stationary distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
7.1.3 Fluctuations of balanced urns with infinitely many colours . . . . . . . . . . . . . . .
8
7.1.4 Adaptive dynamics in biological populations . . . . . . . . . . . . . . . . . . . . . . . .
8
7.1.5 Binary Branching Processes with Moran Type Interactions
. . . . . . . . . . . . . . .
8
7.1.6 Multi-type bisexual branching process . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
7.1.7 A branching model for intergenerational telomere length dynamics . . . . . . . . . .
9
7.1.8 Modeling of chronic obstructive pulmonary disease . . . . . . . . . . . . . . . . . . .
9
7.1.9 Numerical simulation of diffusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.2 Regression and machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.2.1 Cramér-von Mises goodness-of-fit tests in regression models . . . . . . . . . . . . . .
7.2.2 Imprecise extension of the kernel density estimator
. . . . . . . . . . . . . . . . . . . 10
7.2.3 Online Big Data Analysis and Online Learning . . . . . . . . . . . . . . . . . . . . . . . 10
7.3 Statistical learning and application in health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.3.1 Invert emulsions alleviate biotic interactions in bacterial mixed culture . . . . . . . . 11
7.3.2 Prediction of silencing experiments on gene networks for chronic lymphocytic leuk-

emia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.3.3 Multidimensional statistical analysis of information for clinical use . . . . . . . . . . 11
7.3.4 Effects of adapted physical activity and education program on endometriosis symp-

toms

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

8 Bilateral contracts and grants with industry

12
8.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

9 Partnerships and cooperations

9.2 International research visitors

12
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9.1.1 Inria associate team not involved in an IIL or an international program . . . . . . . . 12
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
9.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
9.2.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
9.3.1 ERC projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
9.4 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
9.5 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

9.3 European initiatives

10 Dissemination

15
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.5 Contributed talks, posters, workshops,seminars . . . . . . . . . . . . . . . . . . . . . . 17
10.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3.1 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
10.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

11 Scientific production

22
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
11.2 Publications of the year
11.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.4 Cited publications

Project BIGS

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Project-Team BIGS

Creation of the Project-Team: 2011 January 01

Keywords

Computer sciences and digital sciences

A3.1. – Data

A3.2. – Knowledge

A3.2.3. – Inference

A3.3. – Data and knowledge analysis

A3.3.1. – On-line analytical processing

A3.3.2. – Data mining

A3.3.3. – Big data analysis

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.4. – Optimization and learning

A3.4.7. – Kernel methods

A6. – Modeling, simulation and control

A6.1. – Methods in mathematical modeling

A6.1.2. – Stochastic Modeling

A6.2. – Scientific computing, Numerical Analysis & Optimization

A6.2.3. – Probabilistic methods

A6.2.4. – Statistical methods

A6.4. – Automatic control

A6.4.2. – Stochastic control

Other research topics and application domains

B1. – Life sciences

B1.1. – Biology

B1.1.2. – Molecular and cellular biology

B1.1.10. – Systems and synthetic biology

B1.1.11. – Plant Biology

B2.2. – Physiology and diseases

B2.2.1. – Cardiovascular and respiratory diseases

B2.2.3. – Cancer

B2.3. – Epidemiology

B2.4. – Therapies

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Nicolas Champagnat [Team leader, INRIA, Senior Researcher, HDR]

• Coralie Fritsch [INRIA, Researcher]

• Ulysse Herbach [INRIA, Researcher]

• Bruno Scherrer [INRIA, Researcher, HDR]

Faculty Members

• Thierry Bastogne [UL, Associate Professor, HDR]

• Sandie Ferrigno [UL, Associate Professor]

• Anne Gégout-Petit [UL, Professor, HDR]

• Jean-Marie Monnez [UL, Emeritus, HDR]

• Aurélie Muller-Gueudin [UL, Associate Professor]

• Pierre Vallois [UL, Emeritus, HDR]

• Denis Villemonais [UL, Associate Professor, HDR]

• Sophie Wantz-Mézières [UL, Associate Professor]

PhD Students

• Sophie Baland [UL, from Oct 2023]

• Virgile Brodu [UL]

• Mathilde Gaillard [INRIA, from Oct 2023]

• Vincent Hass [UL, until Aug 2023]

• Vincent Kagan [INRIA, from Apr 2023 until Sep 2023]

• Rodolphe Loubaton [UL, ATER, until Aug 2023]

• Anouk Rago [UL]

• Nicolas Zalduendo Vidal [INRIA]

Technical Staff

• Walid Laziri [INRIA, Engineer, until Nov 2023]

• Nathaniel Seyler [INRIA, Engineer, from Oct 2023]

Interns and Apprentices

• Mathilde Gaillard [INRIA, from Apr 2023 until Sep 2023]

Administrative Assistant

• Emmanuelle Deschamps [INRIA]

Project BIGS

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2 Overall objectives

BIGS is a joint team of Inria, CNRS and University of Lorraine, within the Institut Élie Cartan of Lorraine
(IECL), UMR 7502 CNRS-UL laboratory in mathematics, of which Inria is a strong partner. One member
of BIGS, T. Bastogne, comes from the Research Center of Automatic Control of Nancy (CRAN), with which
BIGS has strong relations in the domain “Health-Biology-Signal”. Our research is mainly focused on
stochastic modeling and statistics but also aims at a better understanding of biological systems. BIGS
involves applied mathematicians whose research interests mainly concern probability and statistics. More
precisely, our attention is directed on (1) stochastic modeling, (2) estimation and control for stochastic
processes, (3) regression and machine learning, and (4) statistical learning and application in health. The
main objective of BIGS is to exploit these skills in applied mathematics to provide a better understanding
of issues arising in life sciences, with a special focus on (1) tumor growth and heterogeneity, (2) gene
networks, (3) telomere length dynamics, (4) epidemiology and e-health.

3 Research program

3.1 Introduction

We give here the main lines of our research. For clarity, we made the choice to structure them in four
items. Note that all of these items deal with stochastic modeling and inference, therefore they are all
interconnected.

3.2 Stochastic modeling

Our aim is to propose relevant stochastic frameworks for the modeling and the understanding of biolo-
gical systems. The stochastic processes are particularly suitable for this purpose. Among them, Markov
processes provide a first framework for the modeling of population of cells [82, 64]. Piecewise determin-
istic processes are non-diffusion processes that are also frequently used in the biological context [51, 63,
52]. Among Markov models, we developed strong expertise about processes derived from Brownian mo-
tion and Stochastic Differential Equations [78, 62]. For instance, knowledge about Brownian or random
walk excursions [83, 77] helps to analyse genetic sequences and to develop inference about them. We also
have strong expertise in stochastic modeling of complex biological populations using individual-based
models. These models can be used either from the point of view of asymptotic stochastic analysis [48], e.g.
to study the long term Darwinian evolution of populations, or from the point of view of numerical analysis
of biological phenomena [58, 39]. We also develop mathematical tools for the analysis of the long-time
behavior of stochastic population processes accounting for possible extinction of (sub)populations [49].

3.3 Estimation and control for stochastic processes

We develop inference about the stochastic processes that we use for modeling. Control of stochastic
processes is also a way to optimise administration (dose, frequency) of therapy, such as targeted therapies
in cancer. Our team has a good expertise about inference of the jump rate and the kernel of piecewise-
deterministic Markov processes (PDMP) [43, 42, 2], but there are many directions to go further into. For
instance, previous work made the assumption of a complete observation of jumps and mode, which
is unrealistic in practice. We also tackle the problem of inference of “hidden PDMP”. For example,
in pharmacokinetics modeling inference, we want to account for the presence of timing noise and
identification from longitudinal data. We have expertise on these subjects [44], and we also use mixed
models to estimate tumor growth or heterogeneity [45].

We consider the control of stochastic processes within the framework of Markov Decision Processes
[75] and their generalization known as multi-player stochastic games, with a particular focus on infinite-
horizon problems. In this context, we are interested in the complexity analysis of standard algorithms,
as well as the proposition and analysis of numerical approximate schemes for large problems in the
spirit of [46]. Regarding complexity, a central topic of research is the analysis of the Policy Iteration
algorithm, which has made significant progress in the last years [85, 74, 60, 80], but is still not fully
understood. For large problems, we have an extensive experience of sensitivity analysis of approximate

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Inria Annual Report 2023

dynamic programming algorithms for Markov Decision Processes [79, 67, 81], and we currently investigate
whether/how similar ideas may be adapted to multi-player stochastic games.

3.4 Algorithms and estimation for graph data

Recently, our group has focused its attention on modeling and inference for graph data. A graph data
structure consists of a set of nodes, together with a set of pairs of these nodes called edges. This type of
data is frequently used in biology because they provide a mathematical representation of many concepts
such as biological networks of relationships in a population or between genes in a cell.

Network inference is the process of making inference about the link between two variables, by taking
into account the information about other variables. Reference [84] gives a very good introduction and
many references about network inference and mining. Many methods are available to infer and test
edges in Gaussian graphical models [84, 69, 57, 59]. However, the Gaussian assumption does not hold
when dealing with typical “zero-inflated” abundance data, and we want to develop inference in this case.
Concerning gene networks, most studies have been based on population-averaged data: now that
technologies enable us to observe mRNA levels in individual cells, a revolution in terms of precision,
the network reconstruction problem paradoxically becomes more challenging than ever. Indeed, the
traditional way of seeing a gene regulatory network as a deterministic system with some small external
noise is being challenged by the probabilistic, bursty nature of gene expression revealed at single-cell level.
Our objective is to propose dynamical models and inference methods that fully exploit the particular
time structure of single-cell data. We described a promising strategy in which the network inference
problem is seen as a calibration procedure for a new PDMP model that is able to acceptably reproduce
real single-cell data [61, 76].

Among graphs, trees play a special role because they offer a powerful model for many biological
concepts, from RNA to phylogenetic trees in heterogeneous tumors or through plant structures. Our
research deals with several aspects of tree data. In particular, we work on statistical inference for this type
of data under a given stochastic model. We also work on lossy compression of trees via directed acyclic
graphs. These methods enable us to compute distances between tree data faster than from the original
structures and with a high accuracy.

3.5 Regression and machine learning

Regression models and machine learning aim at inferring statistical links between a variable of interest
and covariates. In biological studies, it is always important to develop adapted learning methods both in
the context of standard data and also for data of high dimension (sometimes with few observations) and
very massive or online data.

Many methods are available to estimate conditional quantiles and test dependencies [73, 65]. Among
them we have developed nonparametric estimation by local analysis via kernel methods [55, 56] and we
want to study properties of this estimator in order to derive a measure of risk based e.g. on confidence
band and test. We study also other regression models like survival analysis, spatio-temporal models
with covariates. Among the multiple regression models, we want to develop omnibus tests that examine
several assumptions together.

Concerning the analysis of high dimensional data, our view on the topic relies on the French data
analysis school, specifically on Factorial Analysis. In this context, stochastic approximation is an essential
tool [66], which allows one to approximate eigenvectors in a stepwise manner [71, 70, 72]. We aim
at performing accurate classification or clustering by taking advantage of the possibility of updating
the information "online" using stochastic approximation algorithms [47]. We focus on several incre-
mental procedures for regression and data analysis like linear and logistic regressions and PCA (Principal
Component Analysis).

We also focus on the biological context of high-throughput bioassays in which several hundreds
or thousands of biological signals are measured for a posterior analysis. We have to account for the
inter-individual variability within the modeling procedure. We aim at developing a new solution based
on an ARX (Auto Regressive model with eXternal inputs) model structure using the EM (Expectation-
Maximisation) algorithm for the estimation of the model parameters.

Project BIGS

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4 Application domains

4.1 Oncology: tumor growth and heterogeneity

We want to propose stochastic processes to model the appearance of mutations and the evolution of
their frequencies in tumor samples, through new collaborations with clinicians who measure a particular
quantity called circulating tumor DNA (ctDNA). The final purpose is to use ctDNA as an early biomarker
of the resistance to a targeted therapy: this is the aim of the project funded by ITMO Cancer that we
coordinate. In the ongoing work on low-grade gliomas, a local database of 400 patients will be soon
available to construct models. We plan to extend it through national and international collaborations
(Montpellier CHU, Montreal CRHUM). Our aim is to build a decision-aid tool for personalised medicine.

4.2 Gene networks and single-cell data

We already mentioned in Section 3.4 our interest in the modeling and inference of transcriptomic bursting
in gene regulatory networks from single-cell data. We are also currently working on the prediction and
identification of therapeutic targets for chronic lymphocytic leukemia from gene expression data. Our
goal is to propose new models allowing to make prediction of gene silencing experiments. Inference
will be performed on gene expression data from patients’ cells suffering from different forms of chronic
lymphocytic leukemia. The goal is to identify therapeutic targets which could be silenced to reduce cell
proliferation.

4.3 Epidemiology and e-health

In the context of personalized medicine, we have many ongoing projects with CHU Nancy. They deal with
biomarkers research, prognostic value of quantitative variables and events, scoring, and adverse events.
We also want to develop our expertise in rupture detection in a project with APHP (Assistance Publique
Hôpitaux de Paris) for the detection of adverse events, earlier than the clinical signs and symptoms. The
clinical relevance of predictive analytics is obvious for high-risk patients such as those with solid organ
transplantation or severe chronic respiratory disease for instance. The main challenge is the rupture
detection in multivariate and heterogeneous signals (for instance daily measures of electrocardiogram,
body temperature, spirometry parameters, sleep duration, etc.). Other collaborations with clinicians
concern foetopathology and we want to use our work on conditional distribution function to explain fetal
and child growth. To that end, we use data from the “Service de fœtopathologie et de placentologie” of
the “Maternité Régionale Universitaire” (CHU Nancy).

4.4 Dynamics of telomeres

Telomeres are disposable buffers at the ends of chromosomes which are truncated during cell division;
so that, over time, due to each cell division, the telomere ends become shorter. By this way, they are
markers of aging. Through a collaboration with Pr A. Benetos, geriatrician at CHU Nancy, we recently
obtained data on the distribution of the length of telomeres from blood cells [9]. We want to work in
three connected directions: (1) refine methodology for the analysis of the available data; (2) propose a
dynamical model for the lengths of telomeres and study its mathematical properties (long term behavior,
quasi-stationarity, etc.); and (3) use these properties to develop new statistical methods.

5 Highlights of the year

5.1 Awards

D. Villemonais has been granted a delegation at Institut Universitaire de France from september 2023 to
august 2028.

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Inria Annual Report 2023

6 New software, platforms, open data

6.1 New software

6.1.1 Harissa

Name: Hartree approximation for inference along with a stochastic simulation algorithm

Keywords: Gene regulatory networks, Reverse engineering, Molecular simulation

Functional Description: Harissa is a Python package for both inference and simulation of gene regulat-
ory networks, based on stochastic gene expression with transcriptional bursting. It was implemen-
ted in the context of a mechanistic approach to gene regulatory network inference from single-cell
data.

News of the Year: This software has a more user-friendly interface and several tutorial notebooks are

now available.

URL: https://github.com/ulysseherbach/harissa

Publications: hal-04208601, hal-04071033, hal-01646910

Contact: Ulysse Herbach

6.1.2 MultiRNAflow

Name: An R package for the analysis of RNAseq raw counts with multiple biological conditions and time

points

Keywords: RNA-seq, Gene regulatory networks, Integrated data analysis, Complex experimental design,

Multiple temporal and biological conditions, Differential expression

Functional Description: The R package MultiRNAflow provides an easy to use unified framework al-
lowing to make both unsupervised and supervised analysis (differential expression analysis) for
RNAseq datasets with an arbitrary number of biological conditions and time points. In particular,
this package makes a deep downstream analysis of differential expression information, e.g. identi-
fying temporal patterns across biological conditions and differentially expresses genes which are
specific to a biological condition for each time.

Release Contributions: First version

URL: https://bioconductor.org/packages/release/bioc/html/MultiRNAflow.html

Contact: Nicolas Champagnat

Participants: Rodolphe Loubaton, Nicolas Champagnat, Pierre Vallois, Laurent Vallat

Partner: CHRU de Strasbourg

7 New results

7.1 Stochastic modeling

Participants:

Sophie Baland, Virgile Brodu, Nicolas Champagnat, Coralie Fritsch,
Mathilde Gaillard, Vincent Hass, Ulysse Herbach, Vincent Kagan, Nath-
aniel Seyler, Pierre Vallois, Denis Villemonais, Nicolás Zalduendo
Vidal.

Project BIGS

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7.1.1 Reconstruction of epigenetic landscapes from single-cell data

Joint work with E. Ventre (ENS Lyon), T. Espinasse (Univ. Lyon 1), G. Benoit (Univ. Rennes 1) and O. Gandril-
lon (ENS Lyon).

The aim of this collaboration is to better understand how living cells make decisions (e.g., differenti-
ation of a stem cell into a particular specialized type), seeing decision-making as an emergent property
of an underlying complex molecular network. Indeed, it is now proven that cells react probabilistically
to their environment: cell types do not correspond to fixed states, but rather to “potential wells” of a
certain energy landscape (representing the energy of the possible states of the cell) that we are trying
to reconstruct. The achievement of last year was to show that the same mathematical model driven by
transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically rel-
evant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene
interactions: the article presenting these results is now published [25]. In addition, the paper proposing
a landscape reconstruction method with application to several datasets has also been published this
year [22].

These results form the starting point of M. Gaillard’s thesis work, which will focus on making links with
interpretable dimension reduction for single-cell RNA-seq data. Finally, we are working with software
engineer N. Seyler on a refactoring of the “Harissa” Python package used in [10] for stochastic simulation
and inference of gene regulatory networks, with the aim of making it modular and scalable. The latest
stable version is available on PyPI and is presented in a dedicated tool paper [30].

7.1.2 Quasi-stationary distributions

We are continuing our research on quasi-stationary distributions (QSD), that is, distributions of Markov
stochastic processes with absorption, which are stationary conditionally on non-absorption. For models
of biological populations, absorption usually corresponds to extinction of a (sub-)population. QSDs
are fundamental tools to describe the population state before extinction and to quantify the large-time
behavior of the probability of extinction.

Thanks to the previous general result of the team in [50], together with B. Cloez (INRAE), we proved
in [16] the exponential convergence of a chemostat model, whose dynamics are highly degenerate due to
a deterministic part, towards a unique quasi-stationary distributions.

We also finalized an important work [15] that provides general criteria for the exponential convergence
of conditional distributions of absorbed Markov processes when the convergence is not uniform with
respect to the initial distribution. Our results allow to characterize a large subset of the domain of
attraction of the minimal QSD and apply to a large range of stochastic processes, including diffusion
processes and perturbed dynamical systems.

In collaboration with E. Strickler (Univ. Lorraine), we also studied in [34] the convergence of general
penalized Markov processes with soft killing in L1 (Monge-Kantorovich) Wasserstein distance. We propose
a simple criterion ensuring uniform convergence of conditional distributions to a unique quasi-stationary
distribution. We give several examples of application where our criterion can be checked, including
Bernoulli convolutions and piecewise deterministic Markov processes, for which convergence in total
variation is not possible.

7.1.3 Fluctuations of balanced urns with infinitely many colours

Joint work with Svante Janson (Uppsala Univ.,Sweden) and Cécile Mailler (Univ. Bath, UK)

In this collaborative study, we delve into the dynamics of measure-valued Pólya processes (MVPPs),
commonly known as Pólya urns with infinitely-many colours. Our study introduces the first second-order
results in the literature on MVPPs, extending classical fluctuation outcomes from finitely-many-colour
Pólya urns to the infinite colour space scenario. The nature of fluctuations in MVPPs is intricately linked
to the “spectral gap”, adding a layer of sophistication to our understanding of these processes.

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Inria Annual Report 2023

By framing MVPPs as stochastic approximations operating within the set of measures on a meas-
urable space E (the colour space), we employ martingale methods and standard operator theory to
rigorously prove convergence and unravel the nuanced fluctuation patterns inherent in these stochastic
approximations [23].

7.1.4 Adaptive dynamics in biological populations

Joint work with Sylvie Méléard (École Polytechnique), Sepideh Mirrahimi (Univ. Montpellier) and Viet Chi
Tran (Univ. Paris Est Marne-la-Vallée).

We continued our study of parameter scalings of individual-based models of biological populations
under mutation and selection, taking into account the influence of negligible but non-extinct populations.
In a work within the ERC SINGER [14], we were able to give an individual-based justification of the
Hamilton-Jacobi equation of adaptive dynamics (see e.g. [68]), with a specific parameter scaling that is
promising for the study of local (in space) extinction of sub-populations. The analysis of models allowing
for such an extinction is the next step of this project. We also wrote an article [26] for the proceedings
of the International Congress of Mathematicians (ICM 2022) where S. Méléard gave an invited talk on
several large population scalings that can be used in evolutionary biology.

We also worked on general evolutionary models of adaptive dynamics under an assumption of large
population and small mutations. We obtained in [13] existence, uniqueness and ergodicity results for
a centered version of the Fleming-Viot process of population genetics, which are key steps to recover
variants of the canonical equation of adaptive dynamics, which describes the long time evolution of the
dominant phenotype in the population, under less stringent biological assumptions than in previous
works such as [48]. We completed this second step in [33].

7.1.5 Binary Branching Processes with Moran Type Interactions

Joint work with Alexander Cox (Univ. Bath, UK) and Emma Horton (Univ. Warwick, UK).

In this collaboration, our focus is on investigating the large population limit of a binary branching
particle system with Moran type interactions. The novel model introduced in this paper features particles
that evolve, reproduce, and die independently. It encompasses branching models and fixed size Moran
type interacting particle systems. The death of a particle may trigger the reproduction of another, while a
branching event may, in turn, lead to the demise of another particle. Our study [17] aims to elucidate the
intricate dynamics of this model. We explore diverse applications of our model, including its relevance
to the neutron transport equation and population size dynamics. We focus on the occupation measure
of the new model, explicitly connecting it to the Feynman-Kac semigroup of the underlying Markov
evolution. Additionally, we quantify the L2 distance between the normalizations of these measures,
providing valuable insights into the convergence behavior of the system.

7.1.6 Multi-type bisexual branching process

The asexual multi-type Galton-Watson branching processes as well as the single-type bisexual processes
have been studied in the literature. In particular, survival condition of the processes are well known in
both cases. However, until now, the multi-type bisexual branching processes have only been studied in
very specific situations and no general mathematical description has been established yet.

In [21], we studied general multi-type bisexual branching processes with superadditive mating
function. We exhibited a necessary and sufficient condition for almost sure extinction, we proved a
law of large numbers for our model and we studied the long-time convergence of the rescaled process.

7.1.7 A branching model for intergenerational telomere length dynamics

Joint Work with Athanasios Benetos (Univ. Lorraine), Lionel Lenôtre (Univ. Haute Alsace) and Simon
Toupance (Univ. Lorraine).

Project BIGS

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In this study, we construct and analyze an individual-based model capturing the evolution of telomere
length in a population across multiple generations [32]. The model, a continuous-time typed branching
process, incorporates individual characteristics such as gamete mean telomere length and age. Our
investigation delves into the Malthusian behavior of the model, and we complement our findings with
numerical simulations to elucidate the impact of biologically relevant parameters on telomere length
dynamics on an evolutionary time scale.

7.1.8 Modeling of chronic obstructive pulmonary disease

Joint work with Isabelle Dupin (Univ. Bordeaux), Élise Maurat (Univ. Bordeaux) and Jean-Marc Sac-Epée
(IECL).

Lung exposure to various types of particules, such as those present in cigarette smoke, can lead to
chronic obstructive pulmonary disease (COPD). COPD bronchi are an area of intense immunological
activity and tissue remodeling, as evidenced by the extensive immune cell infiltration and changes in
tissue structures. This allows the persistent contact between resident cells and stimulated immune cells.
Our hypothesis is that the contact between cells is a major cause of chronic destructive or fibrotic mani-
festations. We aim to analyze the potential cell-cell interactions in situ in human tissues, to characterize
in vitro the dynamics of the interplay, and to define a computational model with intercellular interactions
which fits to experimental measurements and explains the macroscopic properties of cell populations.
The effects of potential therapeutic drugs modulating local intercellular interactions will be tested by
simulations. A paper has been submitted this year [19] (see also [54]).

7.1.9 Numerical simulation of diffusions

In a collaboration with A. Lejay (Inria PASTA team) and their PhD student A. Anagnostakis, D. Villemonais
proposed a method for approximating general, singular diffusions by discrete time and state space
processes [11]. One of the main interests compared to existing methods is to propose a numerical method
whose main computational cost is done upstream and thus represents a fixed cost, independently of the
number of simulations performed afterwards.

7.2 Regression and machine learning

Participants:

Sandie Ferrigno, Jean-Marie Monnez.

7.2.1 Cramér-von Mises goodness-of-fit tests in regression models

Join work with R. Azaïs (Inria, ENS Lyon) and M.-J. Martinez (Univ. Grenoble Alpes).

Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly
heteroscedastic) regression model. Most of them are ‘directional’ in that they detect departures from
a given assumption of the model. Other tests are ‘global’ (or ‘omnibus’) in that they assess whether a
model fits a dataset on all its assumptions. We focus on the task of choosing the structural part of the
regression and the variance functions because they contain easily interpretable informations about the
studied relationship. We consider two nonparametric ‘directional’ tests and one nonparametric ‘global’
test, all based on generalizations of the Cramér-von Mises statistic.

To perform these goodness-of-fit tests, we have developed the R package cvmgof [41], an easy-to-use
tool for practitioners, available from the Comprehensive R Archive Network (CRAN). The package was
updated in 2022 (this is its third version) [40]. This latest version currently allows testing the “regression
function” part of the model. In 2023, we worked to enrich the package by allowing the user to test the

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Inria Annual Report 2023

homoskedasticity/heteroskedasticity of the model. This new version will be submitted to CRAN in 2024
and an associated article is currently being written.

To complete this work, we plan to assess the other assumptions of a regression model such as
the additivity of the random error term. The implementation of these directional tests would enrich
the cvmgof package and offer a complete easy-to-use tool for validating regression models. Another
perspective of this work would be to develop a similar tool for other statistical models widely used in
practice such as generalized linear models.

7.2.2 Imprecise extension of the kernel density estimator

Join work with Bilal Nehme (IECL, Nancy).

The estimation of the probability density function underlying a finite set of observations is a fun-
damental problem that covers a broad range of applications including machine learning. We propose
a new nonparametric method to estimate this function that combines both the Schwartz distribution
theory and the possibility theory. It is an extension of the kernel density estimator that leads to imprecise
estimation, based on a new type of kernel called maxitive kernel. The form of the obtained estimation is
an interval. In collaboration with B. Nehme, S. Ferrigno demonstrated several theoretical properties of the
imprecise estimator. We implement this method using very low complexity algorithms and illustrate some
theoretical properties of the proposed imprecise density estimation as well as a comparative analysis
with other estimation intervals. An associated article is currently being written.

7.2.3 Online Big Data Analysis and Online Learning

A tool for analyzing streaming data is stochastic approximation introduced by Robbins and Monro in
1951, that can be used for example to estimate online parameters of a regression function [53] or centers
of clusters in unsupervised classification [47]. Another type of stochastic approximation processes was
introduced by Benzécri in 1969 for estimating eigenvectors and eigenvalues of the unknown Q-symmetric
expectation of a random matrix A using independent observations of A. In all these processes, it is
assumed that independent observations of the random matrix are oberved and that one or a mini-batch
of observations per step are taken into account. We are interested in the study of cases where we cannot
have independent observations and we define processes where at each step all the observations up to
this step are taken into account without storing them. Experiments we have conducted show that this
second type of process generally converges faster than the first type.

Stochastic approximation of eigenvectors and eigenvalues of the Q-symmetric expectation of a random
matrix In the article [24], we establish an almost sure convergence theorem of an extension of the
stochastic approximation process of Oja for estimating eigenvectors of the unknown Q-symmetric
expectation B of a random matrix, under a correlation model between the incoming random matrices.
This theorem generalizes previous theorems and extends them to the case where the metric Q is unknown
and estimated online in parallel. We suggest constructing processes using past and current observations
at each step without storing them. We prove the almost sure convergence of specific processes to
corresponding eigenvalues. We apply these results to streaming principal component analysis (PCA) of a
random vector Z , when a mini-batch of observations of Z is used at each step or all the observations
up to the current step. We deal with the case of streaming generalized canonical correlation analysis,
interpreted as a PCA with a metric estimated online in parallel.

An extended Oja process for streaming canonical analysis
In the article [36], after recalling an almost
sure convergence theorem of an extended Oja process [24], we present the canonical correlation analysis
(CCA) of two random vectors Z 1 and Z 2 such that there is no affine relation between their components.
Couples of canonical components are interpreted as couples of principal components of the respective
PCA of the linear regression function of Z 1 with respect to Z 2 and Z 2 with respect to Z 1, or as canonical
components of the generalized canonical correlation analysis (gCCA) of Z = (cid:161)Z 1, Z 2(cid:162). In the case of
streaming data, we estimate online in parallel a regression function and canonical components, using at

Project BIGS

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each step a mini-batch of current data or all the data up to the current step to have a faster convergence.
We define two algorithms, the second being extended to gCCA. Using the same methodology for stream-
ing factorial correspondence analysis (FCA) when the components of Z 1 and Z 2 are respectively the
indicators of the exclusive modalities of two categorical variables, we define two algorithms to estimate
online the canonical components, the second being extended to multiple correspondence analysis (MCA).
Finally, we apply this methodology to streaming factorial discriminant analysis (FDA), when there is
no affine relation between the components of Z 1 and the components of Z 2 are the indicators of the
exclusive modalities of a categorical variable.

7.3 Statistical learning and application in health

Participants: Nicolas Champagnat,

Sandie Ferrigno, Anne Gégout-Petit,
Ulysse Herbach, Walid Laziri, Rodolphe Loubaton, Sophie Wantz-
Mézières, Anouk Rago, Pierre Vallois.

7.3.1 Invert emulsions alleviate biotic interactions in bacterial mixed culture

Joint work with A. Dijamentiuk, C. Mangavel and F. Borges from LIBio, Univ. Lorraine.

The large application potential of microbiomes has led to a great need for mixed culture methods.
However, microbial interactions can compromise the maintenance of biodiversity during cultivation
in a reactor. In particular, competition among species can lead to a strong disequilibrium in favor of
the fittest microorganism. The aim of this study was to evaluate the potential of single invert emulsions
to alleviate competition during the culture of antagonistic microorganisms and therefore to maintain
diversity in a more complex mixed culture. Experimental data obtained in this study were analyzed using
a two-way analysis of variance using a fixed effects model, followed by Tukey’s HSD test. In the droplet
size distributions of the invert emulsions, factors involved were the presence or absence of bacteria,
and the incubation of invert emulsions. In bacterial enumerations, factors were the cultivation system
used and the incubation. In community cultivation experiments, differences in Shannon diversity index
between groups of samples were tested using one-way analysis of variance, followed by a Tukey’s HSD
test. An article [18] has been published on this work in 2023.

7.3.2 Prediction of silencing experiments on gene networks for chronic lymphocytic leukemia

Joint work with Laurent Vallat (CHRU Strasbourg).

In this collaboration, we work on the inference of dynamical gene networks from RNAseq and
proteome data. The goal is to infer a model of gene expression allowing to predict gene expression in
cells where the expression of specific genes is silenced (e.g. using siRNA), in order to select the silencing
experiments which are more likely to reduce the cell proliferation. We expect the selected genes to provide
new therapeutic targets for the treatment of chronic lymphocytic leukemia. This year, we have developed
a new method of prediction of the effect of gene silencing, based on the re-exploitation of expression data
of genes not influenced by the silenced gene [27]. We also have developed the package MultiRNAflow
(see Section 6.1.2) for the statistical analysis of temporal gene expression datasets with several biological
conditions (in particular for exploratory analysis and the detection of differentially expressed genes). The
package is described in the application note [35].

7.3.3 Multidimensional statistical analysis of information for clinical use

The start-up EMOSIS develops blood tests relying on flow cytometry in order to improve in vitro diagnosis
of vascular thrombosis. This technology leads to multiparametric measurements on tens of thousands
cells collected from each blood sample. Manual methods of analysis classically used in flow cytometry are
based on data visualization by means of histograms or scatter plots. Computational algorithmic approach

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Inria Annual Report 2023

that would automate and deepen the search of differences or similarities between cell subpopulations
could thus increase the quality of diagnosis.

Recent progresses in the active area of computational methods for dimension reduction suggest
many directions of improvement of the classical approaches for the analysis of flow cytometry data. The
approach that we considered is information geometry, whose principle is to lower the dimensionality
of multiparametric observations by considering the subspace of the parameters of the statistical model
describing the observation, whose points are probability density functions, and which is equipped with
a special geometrical structure. The objective of the reported study is to use an algorithm belonging
to the field of information geometry known as Fisher Information Non-parametric Embedding (FINE)
to analyze flow cytometry data in the context of the specific severe disorder called heparin-induced
thrombocytopenia. This work lead to two communications in conferences [28, 29].

Unfortunately the start-up EMOSIS non longer exists, which put an end to our collaboration.

7.3.4 Effects of adapted physical activity and education program on endometriosis symptoms

Joint work with Géraldine Escriva-Boulley and Lionel Lenôtre (Univ. Haute-Alsace).

Endometriosis is a chronic disease characterized by growth of endometrial tissue outside the uterine
cavity which could affect 200 million women worldwide. One of the most common symptoms of endo-
metriosis is pelvic chronic pain associated with fatigue. This pain can cause psychological distress and
interpersonal difficulties. As for several chronic diseases, adapted physical activity could help to manage
the physical and psychological symptoms.

We are participating in both design and statistical analysis of a randomized-controlled trial, led
by G. Escriva-Boulley, to investigate the potential effects of a videoconference-based adapted physical
activity combined with endometriosis-based education program [20]. This study is one of the first
trials to test the effects of a combined adapted physical activity and education program for improving
endometriosis symptoms and physical activity.

8 Bilateral contracts and grants with industry

8.1 Bilateral contracts with industry

Participants:

Anne Gégout-Petit, Walid Laziri, Sophie Wantz-Mézières.

As part of the French “Plan de relance”, we obtained funds for a 2-year engineering contract with
the start-up EMOSIS based in Strasbourg (from October 1, 2022). Project MOSAiC : MultidimensiOnal
Statistical Analysis of Information for Clinical use. Unfortunetly EMOSIS ordered to file for bankruptcy in
2024 an the project was stopped.

9 Partnerships and cooperations

Participants:

Sophie Baland, Nicolas Champagnat, Coralie Fritsch, Mathilde Gail-
lard, Anne Gégout-Petit, Vincent Hass, Ulysse Herbach, Rodol-
phe Loubaton, Anouk Rago, Pierre Vallois, Denis Villemonais,
Sophie Wantz-Mézières, Nicolas Zalduendo Vidal.

9.1 International initiatives

9.1.1 Inria associate team not involved in an IIL or an international program

MAGO

Project BIGS

13

Title: Modelling and analysis for growth-fragmentation processes

Duration: 2022-2024

Coordinator: Denis Villemonais

Partners:

• Inria Nancy (C. Fritsch, D. Villemonais)

• University College London London (F.X. Briol, O. Key, A. Watson)

Summary: Growth-fragmentation(GF)refers to a collection of mathematical models in which objects –
classically, biological cells – slowly gather mass over time, and fragment suddenly into multiple,
smaller offspring. These models may be used to represent a range of biological processes, in which
an individual reproduces by fission into two or more new individuals, such as the evolution of
plasmids in bacteria populations and protein polymerisation. It is crucial to understand the long-
term behaviour of GF processes so that they can be used to build algorithms to simulate real-world
processes and estimate quantities such as the growth rate of the system, the steady state behaviour,
and the fragmentation rate and kernel, allowing scientists to gain a better understanding of the
behaviour of these complex systems. In this project, we aim to combine probabilistic and statistical
tools to study these processes. In particular, we will employ methods from branching processes,
quasi-stationary distributions and interacting particle systems to study their long-term behaviour
and develop numerical simulations. Further, we will develop likelihood-free methods to estimate
the model parameters, followed by goodness-of-fit tests to analyse the strength of these methods
when working with real data.

9.2 International research visitors

9.2.1 Visits of international scientists

Other international visits to the team

Emma Horton

Status Researcher

Institution of origin: University of Melbourne

Country: Australia

Dates: May 30 - June 2

Context of the visit: collaboration on the growth-coagulation-fragmentation processes.

Mobility program/type of mobility: research stay

Alex Watson

Status Researcher

Institution of origin: University College London

Country: UK

Dates: May 30 - June 2

Context of the visit: collaboration on the growth-coagulation-fragmentation processes in the framework

of the MAGO Inria associate team.

Mobility program/type of mobility: research stay

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Inria Annual Report 2023

9.2.2 Visits to international teams

Research stays abroad

Coralie Fritsch & Denis Villemonais

Visited institution: University College London

Country: UK

Dates: October 30 - November 3

Context of the visit: collaboration with Alex Watson and Emma Horton on the growth-coagulation-

fragmentation processes in the framework of the MAGO Inria associate team.

Mobility program/type of mobility: research stay

Nicolas Champagnat

Visited institution: Pontificia Universidad Católica de Chile, Universidad de Valparaiso

Country: Chile

Dates: March 18 - March 27

Context of the visit: Collaboration with Pablo Marquet and Rolando Rebolledo on niche construction.
After this visit, we applied for the Inria associate team aStoNiche (a Stochastic framework for
modeling Niche construction), that is funded for the period 2024-2027.

Mobility program/type of mobility: research stay

Pierre Vallois

Visited institution: Turku University

Country: Finland

Dates: November 21 - November 25

Context of the visit: Collaboration with Paavo Salminen.

Mobility program/type of mobility: research stay

9.3 European initiatives

9.3.1 ERC projects

N. Champagnat is scientific collaborator of the ERC SINGER (AdG 101054787) on Stochastic dynamics of
sINgle cells, coordinated by S. Méléard (Ecole Polytechnique). He is involved in the research axes “From
stochastic processes to singular Hamilton-Jacobi equations” and “Lineages and time reversed trajectories”
of this project.

9.4 National initiatives

• A. Gégout-Petit was in the committee interviewed by ANR for the IHU Infiny on the subject of
chronic inflammatory bowel diseases. PI: L. Peyrin-Biroulet (Univ. Lorraine and CHRU Nancy).

• ITMO Physics, Mathematics applied to Cancer (from October 2023): “Quantifying and predicting
the evolution of clonal heterogeneity in chronic lymphocytic leukemia”. Funding organisms: ITMO
Cancer, ITMO Technologies pour la santé de l’alliance nationale pour les sciences de la vie et de
la santé (AVIESAN), INCa. Partners: Inria and IECL (Institut Élie Cartan de Lorraine) and CHRU
Strasbourg. Leader: N. Champagnat. Participants: C. Fritsch, U. Herbach, P. Vallois, D. Villemonais.

Project BIGS

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• PEPR Exploratoire Maths-VivES, (starting in spring 2024), target project DyLT (Dynamics of Te-
lomere Length) on “Influence of telomere length dynamics and environmental conditions on
biological and clinical aspects of aging”. Funding organisms: ANR. Partners: Inria Nancy and
Saclay, Institut Élie Cartan de Lorraine (Nancy), CHRU Nancy, Centre de Recherche en Cancérolo-
gie de Marseille and Institut de recherche sur le cancer et le vieillissement (Nice). Coordinators:
N. Champagnat and A. Benetos (CHRU Nancy). Participants: C. Fritsch, A. Gégout-Petit, D. Villem-
onais, S. Baland.

• PEPR Santé Numérique (started in July 2023), project AI4scMed (Multiscale AI for single-cell-based
precision medicine) including WP3: “Regulatory network inference: from dynamical models to
logical models”. Funding organisms: ANR. Partners: Inria, Inserm, CNRS. Coordinator: F. Picard
(CNRS, ENS Lyon). Participants: M. Gaillard, U. Herbach.

• FHU CARTAGE (Fédération Hospitalo Universitaire Cardial and ARTerial AGEing). Leader: Pr.

A. Benetos. Participants: J.-M. Monnez, A. Gégout-Petit.

• ANR JCJC project CRESCENDO (inCRease physical Exercise and Sport to Combat ENDOmetriosis,
AAPG 2022). Coordinator: G. Escriva-Boulley (LISEC, Université de Haute-Alsace). Participant:
U. Herbach.

• GDR 720 IASIS (funded by CNRS). Leader: C. Richard. Participant: S. Wantz-Mézières.

• Réseau Thématique MathSAV (funded by CNRS). Leader: F. Crauste. Participants: N. Champagnat,

C. Fritsch, V. Hass, U. Herbach, R. Loubaton, A. Rago, N. Zalduendo Vidal.

• Chair “Modélisation Mathématique et Biodiversité” between VEOLIA, Ecole Polytechnique, Mu-
seum National d’Histoire Naturelle and Fondation X (funded by VEOLIA). Leader: S. Méléard.
Participants: V. Brodu, N. Champagnat, C. Fritsch, V. Hass, D. Villemonais, N. Zalduendo Vidal.

9.5 Regional initiatives

A. Gégout-Petit is one the two PIs of the interdisciplinary program “Life Travel” of the I-Site “Lorraine
Université d’Excellence” on life trajectories and longevity (under construction).

10 Dissemination

Participants:

Sophie Baland, Virgile Brodu, Nicolas Champagnat, Sandie Ferrigno,
Coralie Fritsch, Mathilde Gaillard, Anne Gégout-Petit, Vincent Hass,
Ulysse Herbach, Vincent Kagan, Rodolphe Loubaton, Jean-Marie Mon-
nez, Anouk Rago, Pierre Vallois, Denis Villemonais, Sophie Wantz-
Mézières, Nicolas Zalduendo Vidal.

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

Member of the organizing committees

• N. Champagnat co-organized the conference A Random Walk in the Land of Stochastic Analysis

and Numerical Probability (September 4-8, CIRM, Luminy) in the honor of Denis Talay.

• C. Fritsch co-organized the 21st INFORMS Applied Probability Society Conference (Centre Prouvé,
Nancy, June 28-30). S. Baland, V. Brodu, M. Gaillard, V. Hass, R. Loubaton, A. Rago and N. Zalduendo
Vidal were members of the logistic crew during the conference.

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Inria Annual Report 2023

• N. Champagnat and D. Villemonais organized the invited session “Quasi-stationary distributions
in numerical stochastic methods and statistics” in the 21st INFORMS Applied Probability Society
Conference (Centre Prouvé, Nancy, June 28-30).

• D. Villemonais co-organized the GdR Branchement first conference in November 2023, Toulouse,

France.

• U. Herbach has been co-organizing the Probability and Statistics weekly seminar at IECL in Nancy

until September.

10.1.2 Scientific events: selection

Chair of conference program committees

• A. Gégout-Petit is chair program committtee of the ENBIS meeting 2024 that will be held in Leuwen,

Belgium in September 2024.

Member of the conference program committees

• A. Gégout-Petit was member of program committee of the ENBIS meeting 2023 that was held in

Valencia, Spain in September.

10.1.3 Journal

Member of the editorial boards

• N. Champagnat is associate editor for ESAIM: Probability & Statistics and Stochastic Models.

• A. Gégout-Petit was guest editor with L. Marco-Almagro (Univ. Politècnica de Catalunya, Barcelona,
Spain) for the Quality and Reliability Engineering International special issue related to the 22nd
Annual Conference of the European Network for Business and Industrial Statistics (ENBIS).

10.1.4 Invited talks

• N. Champagnat gave a plenary talk at the 11ème Biennale Française des Mathématiques Appliquées
et Industrielles (Congrès SMAI 2023) in Le Gosier, Guadeloupe in May. He has been also invited
to give talks at the 43rd Conference on Stochastic Processes and their Applications in Lisbonne,
Portugal in July, the conference Celebrating the mathematics of Michel Benaïm in Bernoulli Center,
Lausanne in August, the conference A random walk in the land of stochastic analysis and numerical
probability at CIRM, Luminy in September and the International Conference on Recent Develop-
ments of Theory and Methods in Mathematical biology, conference of the IRN ReaDiNet network
at NCTS Taipei, Taiwan in October.

• C. Fritsch has been invited to give talks at the 21st INFORMS Applied Probability Society Conference
in Nancy in June and at the Première conférence du GDR Branchement in Toulouse in November.

• U. Herbach has been invited to give talks at Statistics seminar of LMA in Avignon in April, at GT
Bioss workshop in Marseille in July, at Inria MUSCA team seminar in Saclay in September, at
Statistics seminar of IRMA in Strasbourg in October and at LCSB Systems Control group seminar in
Luxembourg in November.

• P. Vallois has been invited to give talks at the Journées de Probabilités 2023 in Angers in June and at

the 21st INFORMS Applied Probability Society Conference in Nancy in June.

• D. Villemonais has been invited to give a talk at the 21st INFORMS Applied Probability Society
Conference in Nancy in June and at the conference Celebrating the mathematics of Michel Benaïm
in Bernoulli Center, Lausanne in August.

• N. Zalduendo Vidal has been invited to give talks at the 21st INFORMS Applied Probability Society
Conference in Nancy in June and at the conference Discrete Randomness in Créteil in December.

Project BIGS

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10.1.5 Contributed talks, posters, workshops,seminars

• V. Brodu has presented a poster at the Conférence internationale Mathematical Population Dy-
namics, Ecology and Evolution, MPDEE 2023 in Marseille in April, at the 21st INFORMS Applied
Probability Society Conference in Nancy in June (where he has been awarded a Best Poster prize),
at the 43rd Conference on Stochastic Processes and their Applications in Lisbon in July, and at the
GdR Branchement first conference in Toulouse in November.

• N. Champagnat has been invited to give a talk at the Ecodep-Biostochastic Workshop: Model-
ling Time Series and Stochastic Processes at Las Cruces Marine Station, Chile in March. He has
been also invited to give a (remote) seminar talk at the Seminar of differential equations, Instytut
Matematyczny Wroclaw, Poland in November.

• C. Fritsch gave talks at the Journées INRAE - Inria 2023 in Nancy in July and at the Journée de la

donnée en Meurthe et Moselle in Nancy in October.

• U. Herbach has given talks at Statistical Methods for Post Genomic Data workshop (SMPGD 2023)
in Ghent (Belgium) in February, at 21st International Conference on Computational Methods
in Systems Biology (CMSB 2023) in Luxembourg in September and at CENTURI Conference on
Information networks in biological systems in Cargèse in October.

• A. Rago has presented a poster at Journée scientifique autour de l’IA in Nancy in February and
at Statlearn23 in Montpellier in April, and has given a talk [27] at Journées de Statistique 2023 in
Bruxelles in July.

• P. Vallois has given a seminar talk at Univ. Sorbonne Paris Nord in November.

• D. Villemonais gave a talk at the Journée Santé Numérique in Nancy in November.

• N. Zaluendo Vidal gave talks at the Seminario de Probabilidades de Chile (online) in August, at
the Workshop L2 in Probability and Statistics in Metz in September and at the Séminaire Image
Optimisation et Probabilités in Bordeaux in October.

10.1.6 Scientific expertise

• N. Champagnat evaluated a research project submitted to Shape-Med@Lyon.

• C. Fritsch has been a member of the Committee for junior permanent research positions of Inria

Nancy - Grand Est.

• A. Gégout-Petit was expert for the Messidore call 2023 (Méthodologie des ESSais cliniques Innovants,

Dispositifs, Outils et Recherches Exploitant les données de santé et biobanques) of INSERM.

• A. Gégout-Petit was member of two different selection committees for Professor (Univ. de Pau et de
l’Adour (UPPA) and Univ. Rennes 1) and one selection committee for a Maître de conférence for
Aix-Marseille Université.

• U. Herbach evaluated a research project submitted to ANR AAPG 2023 as a scientific expert for the

“Interfaces: mathematics, digital sciences - biology, health” panel.

10.1.7 Research administration

• V. Brodu is an elected representative of doctoral students at the doctoral school committee (local

scale), and also at the doctoral college committee (regional scale).

• N. Champagnat is elected member of the Commission d’Evaluation of Inria since September,
member of the COMIPERS (hiring committee for non-permanent positions) of Inria Nancy –
Grand Est, substitute member of the Comité de Centre of Inria Nancy – Grand Est, local researcher
(correspondant local) representing the COERLE (Inria’s Ethic Committee) at Inria Nancy – Grand
Est, and he was responsable scientifique for the library of Mathematics of IECL until October.

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Inria Annual Report 2023

• C. Fritsch was a member, until August, of the Commission du Développement Technologique of the
Inria Research Center of Nancy - Grand Est and of the Commission du personnel of IECL. She is an
elected member of the Commission d’Évaluation of Inria since September.

• A. Gégout-Petit is director of the research unit IECL (Institut Elie Cartan de Lorraine), Mathematics

laboratory of Univ. Lorraine (200 members).

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

BIGS faculty members have teaching obligations at Univ. Lorraine and are teaching at least 192 hours
each year. They teach probability and statistics at different levels (Licence, Master, Engineering school).
Many of them have pedagogical responsibilities.

• T. Bastogne is in charge of the research master program “Santé Numérique et Imagerie Médicale”

with the Faculty of Medicine, Univ. Lorraine.

• S. Ferrigno is in charge (since september 2023) of the “DU Big Data and Data Science” in ENSMN,

Univ. Lorraine.

• D. Villemonais is the head of the Mathematical Engineering Major of ENSMN, Univ. Lorraine.

• Licence: V. Brodu, Probability theory tutorial, 40h, L3, first year of ENSMN, Univ. Lorraine.

• Licence: V. Brodu, Numerical Analysis tutorial, 20h, L3, first year of ENSMN, Univ. Lorraine.

• Master: N. Champagnat, Introduction to Quantitative Finance, 12h, M1, second year of ENSMN,

Univ. Lorraine.

• Master: N. Champagnat, Introduction to Quantitative Finance, 9h, M2, third year of ENSMN, Univ.

Lorraine.

• Master: S. Ferrigno, Experimental designs, 6h, M1, fourth year of EEIGM, Univ. Lorraine.

• Master: S. Ferrigno, Data analyzing and mining, 36h, M1, second year of ENSMN, Univ. Lorraine.

• Master: S. Ferrigno, Modeling and forecasting, 32h, M1, second year of ENSMN, Univ. Lorraine.

• Master: S. Ferrigno, Training projects, 18h, M1/M2, second and third year of ENSMN, Univ. Lorraine.

• Licence: S. Ferrigno, Descriptive and inferential statistics, 60h, L2, second year of EEIGM, Univ.

Lorraine.

• Licence: S. Ferrigno, Statistical modeling, 60h, L2, second year of EEIGM, Univ. Lorraine.

• Licence: S. Ferrigno, Mathematical and computational tools, 20h, L3, third year of EEIGM, Univ.

Lorraine.

• Licence: S. Ferrigno, Training projects, 40h, L1/L3, first, second and third year of EEIGM, Univ.

Lorraine.

• Master: C. Fritsch, Inverse problem, 18h, M1, second year of ENSMN, Univ. Lorraine.

• Licence: C. Fritsch, Probability Theory tutorial, 27h, L3, first year of ENSMN, Univ. Lorraine.

• License: M. Gaillard, Numerical analysis and Optimization tutorial, 23h, L3, first year of ENSMN,

Univ. Lorraine.

• Master: A. Gégout-Petit, Statistics, modeling, data analysis, 80h, master in applied mathematics,

Univ. Lorraine.

• Licence: V. Hass, Mathématiques FIGIM 1A, 38h, L1/L2, first year of ENSMN, Univ. Lorraine.

Project BIGS

19

• Licence: V. Hass, Mathématiques FIGIM 2A, 19h, L2, second year of ENSMN, Univ. Lorraine.

• Licence: V. Hass, Probabilités, 40h, L3, first year of ENSMN, Univ. Lorraine.

• Licence: V. Hass, Analyse numérique et optimisation, 45h, L3, first year of ENSMN, Univ. Lorraine.

• Licence: V. Hass, Recherche opérationnelle, 18h, L3, first year of ENSMN, Univ. Lorraine.

• Master: V. Hass, Méthodes stochastiques pour le calcul, 14h, M1, second year of ENSMN, Univ.

Lorraine.

• Licence: R. Loubaton, Modélisation Statistique, 21.5h, L2, prépa intégrée de l’EEIGM, Univ. Lor-

raine.

• Licence: R. Loubaton, EDP, 20h, L2, prépa intégrée de l’EEIGM, Univ. Lorraine.

• Licence: R. Loubaton, Algèbre des matrices, 21.5h, L1, prépa intégrée de l’EEIGM, Univ. Lorraine.

• Licence: R. Loubaton, alcul différentiel, 21.5h, L1, prépa intégrée de l’EEIGM, Univ. Lorraine.

• Master : A. Rago, Analyse de données, 18h, M1, second year of ENSMN, Univ. Lorraine.

• Master : A. Rago, Statistiques pour la grande dimension, 18h, M2 IMSD/third year of ENSMN, Univ.

Lorraine.

• Master: D. Villemonais, Probability Theory II, 63h, M1, second year of ENSMN, Univ. Lorraine.

• Master: D. Villemonais, Stochastic processes, 32h, Master 2 MFA, Univ. Lorraine.

• Master: D. Villemonais, Modeling and forecasting, 14h, M1, second year of ENSMN, Univ. Lorraine.

• License: D. Villemonais Probability Theory I, 57h, L3, first year of ENSMN, Univ. Lorraine.

• Master: S. Wantz-Mézières, Learning and analysis of medical data, 36h, with J.M. Moureaux, M2

SNIM, Univ. Lorraine.

• Licence: S. Wantz-Mézières, Applied mathematics for management, financial mathematics, Prob-

ability and Statistics, 160h, IUT Nancy-Charlemagne (L1/L2/L3), Univ. Lorraine.

• Licence: S. Wantz-Mézières, Probability, 100h, first year in TELECOM Nancy (initial and apprentice-

ship cursus), Univ. Lorraine.

• Licence: N. Zalduendo Vidal, Probability Theory tutorial, 20h, L3, first year of ENSMN, Univ.

Lorraine.

10.2.2 Supervision

PhD

• PhD in progress: Sophie Baland, “Telomere length dynamics : modelisation, estimation and applic-
ation to diagnostic support systems” since September 2023, funding LUE. Advisors: S. Toupance
(Univ. Lorraine) and D. Villemonais.

• PhD in progress: Virgile Brodu,“Émergence des allométries dans les systèmes écologiques : com-
portement stationnaire de modèles déterministes et stochastiques de flux d’énergie et de biomasse”,
grant ENS Lyon. Advisors: S. Billiard (Univ. Lille), N. Champagnat, C. Fritsch.

• PhD in progress: Mathilde Gaillard, “Processus de Markov déterministes par morceaux et inférence
bayésienne de réseaux de gènes”, grant PEPR Santé Numérique, since October 2023. Advisors:
A. Gégout-Petit, U. Herbach.

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Inria Annual Report 2023

• PhD: Vincent Hass, “Individual-based models in adaptive dynamics and long time evolution under
assumptions of rare advantageous mutations” [31], ATER in ENSMN. Defense on the 21st September.
Advisor: N. Champagnat.

• PhD in progress: Anouar Jeddi, “Convergence of individual-based population models to Hamilton-
Jacobi equations” since September 2023, grant ERC SINGER (Ecole Polytechnique). Advisors:
S. Méléard (Ecole Polytechnique) and N. Champagnat.

• PhD in progress: Vincent Kagan, “Asymptotic behavior of epidemiological epidemiological mod-
els with individual viral load” since September 2023, funding Université de Lorraine. Advisors:
E. Strickler (Univ. Lorraine) and D. Villemonais.

• PhD: Rodolphe Loubaton, “Caractérisation des cibles thérapeutiques dans un programme génique
tumoral”, ATER in EEIGM. Defense on the 21st December. Advisors: N. Champagnat and L. Vallat
(CHRU Strasbourg).

• PhD in progress: Anouk Rago,“Inférence de réseaux de gènes dynamiques et prédiction d’expériences
d’interventions biologiques dans des cellules cancéreuses”, grant Région Grand-Est and Inria. Ad-
visors: N. Champagnat, A. Gégout-Petit.

• PhD: Nicolás Zalduendo Vidal, “Processus de branchement bi-sexués multi-types”, grant Inria-

Cordi. Defense on the 18th December. Advisors: C. Fritsch, D. Villemonais.

Other

• Engineer: Walid Laziri, “Flow cytometry data analysis” (Plan de relance, contract with the start-up

EMOSIS), until November. Advisors: A. Gégout-Petit, S. Mézières.

• M2 internship: Mathilde Gaillard, “Couplage et vitesse de convergence de processus de Markov

déterministes par morceaux” (Univ. Lyon 1). Advisor: U. Herbach.

• M2 internship: Anouar Jeddi, “Convergence of individual-based population models to Hamilton-
Jacobi equations” (M2 MSV, Univ. Paris-Saclay) Advisors: S. Méléard (Ecole Polytechnique), S. Mir-
rahimi (Univ. Montpellier), V.C. Tran (Univ. G. Eiffel) and N. Champagnat.

• M2 internship: Vincent Kagan, “Asymptotic behavior of epidemiological epidemiological models

with individual viral load”, funding Inria. Advisors: E. Strickler (IECL) and D. Villemonais.

• M2 SNIM Research Project: Marie Camonin, “Etude prospective de l’impact de certaines muta-
tions génétiques dans le traitement des gliomes de bas grade”, Advisors: S. Wantz-Mézières et
J.-M. Moureaux (CRAN).

• M2 IMSD Research Project: Nicolas Dinant, “Apport des statistiques spatiales dans l’étude de la
localisation des tumeurs cérébrales”, Advisors: S. Wantz-Mézières and J.-M. Moureaux (CRAN).

• PIDR 2A Telecom-Nancy: A. Chevallier, A. Crivelli and T. Kieffer, “Machine Learning pour la mesure
automatique du volume des tumeurs cérébrales”, Advisors: S. Wantz-Mézières et J.-M. Moureaux
(CRAN).

• M1 ENSMN Research project: Antonin Clerc, “Émergence des allométries dans les écosystèmes”

(full-year research project). Advisors: V. Brodu and C. Fritsch.

• M1 ENSMN Research project: two 2nd year students, “Continuous-time Markov chains” (from

September). Advisor: M. Gaillard.

Project BIGS

10.2.3 Juries

21

• N. Champagnat was referee for the PhD theses of Léo Meyer (Univ. Orléans, 09/10/2023), Van
Hai Thai (Univ. Nantes, 28/09/2023), Imane Akjouj (Univ. Lille, 29/06/2023) and Anaïs Rat (Aix-
Marseille Univ., 31/05/2023). He was also president of the PhD committee of Aleksian Ashot
(Univ. Saint-Etienne, 20/11/2023). He was also examiner for the PhD thesis of Vincent Hass (Univ.
Lorraine, 26/09/2023).

• N. Champagnat and P. Vallois were examiner for the PhD thesis of Rodolphe Loubaton (Univ.

Lorraine, 21/12/2023).

• C. Fritsch and D. Villemonais were examiner for the PhD thesis of Nicolás Zalduendo-Vidal (Univ.

Lorraine, 18/12/2023).

• A. Gégout-Petit was examiner for the HdR jury of Nathalie Krell (Univ. Rennes 1, 01/06/2023).

• A. Gégout-Petit was referee for the PhD theses of Maéva Kyheng (Université de Lille, 01/03/2023)
; Fatima Ezzahra MANA (Univ. Troyes, 03/09/2023). She was president of the PhD committee
of Jérémie Frigério (Univ. Dijon, 18/12/2023) ; Jeremy Borderieu (AgroParistech, 14/12/2023) ;
Rodolphe Loubaton (Univ. Lorraine, 21/12/2023) and examiner for the PhD of Nicolas Zalduendo
(Univ. Lorraine, 18/12/2023).

• V. Brodu has been part of the jury for the thesis prize awarded by Métropole du Grand Nancy

(rewarding PhD theses that are creative, innovative, and/or implanted in the local territory).

10.3 Popularization

10.3.1 Education

• V. Brodu supervised a maths club for highschool students in Lycée Jeanne d’Arc, Nancy. This club

hosted a dozen students for two hours sessions, on a weekly basis.

• J.-M. Monnez wrote an unpdated version of the lecture notes [37] presented in the 2022 Activity

Report.

• S. Wantz-Mézières organised a Research Training Week on Neuro-oncology and Numerics, for

medical and engineering students in Nancy in March.

10.3.2 Interventions

• S. Ferrigno: Advisor of a group of EEIGM students, “Ateliers expérimentaux : Mécanique et Stat-

istique” Project, various high schools, Nancy.

• S. Ferrigno: Advisor of a group of EEIGM students, “La main à la Pâte” Project, Institut médico-

éducatif (IME), Commercy.

• S. Ferrigno: Advisor of a group of EEIGM students, “La main à la Pâte”, “CGénial” Projects, Collèges

Paul Verlaine in Malzéville and La Craffe in Nancy.

• S. Ferrigno: Advisor of a group of EEIGM students, “La main à la Pâte” Project, elementary schools,

Nancy.

• C. Fritsch gave three talks as part of the “Chiche!” program at Lycée De La Salle, Metz, in December.

• M. Gaillard gave a talk as part of the “Chiche!” program at Lycée René Cassin, Mâcon, in October.
She presented her educational background and possible directions for a doctoral thesis in mathem-
atics; explained what mathematical modeling is, and illustrated it with a stochastic gene expression
model.

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Inria Annual Report 2023

• U. Herbach gave a talk “Les maths peuvent-elles servir à vaincre le cancer ?” as part of a training
course organized by Maison pour la science en Lorraine for secondary and high school teachers, at
Faculté de Pharmacie de Nancy (campus Brabois) in October.

• A. Rago participed to the regional finals (Université de Lorraine) of “MT180”.

11 Scientific production

11.1 Major publications

[1] R. Azaïs, S. Ferrigno and M.-J. Martinez. ‘cvmgof: an R package for Cramér-von Mises goodness-of-
fit tests in regression models’. In: Journal of Statistical Computation and Simulation 92.6 (2022),
pp. 1246–1266. DOI: 10.1080/00949655.2021.1991346. URL: https://hal.science/hal-031
01612.

[2] R. Azaïs and A. Muller-Gueudin. ‘Optimal choice among a class of nonparametric estimators of the
jump rate for piecewise-deterministic Markov processes’. In: Electronic Journal of Statistics 10.2
(2016), pp. 3648–3692. DOI: 10.1214/16-EJS1207. URL: https://hal.science/hal-01168651.

[3] N. Champagnat, S. Méléard and V. C. Tran. ‘Multiscale eco-evolutionary models: from individuals
to populations’. In: International Congress of Mathematicians, ICM 2022. Vol. 7. 1. fully virtually,
Russia: EMS Press, 15th Dec. 2023, pp. 5656–5678. DOI: 10.4171/ICM2022/24. URL: https://inr
ia.hal.science/hal-04407958.

[4] N. Champagnat and D. Villemonais. ‘General criteria for the study of quasi-stationarity’. In: Elec-
tronic Journal of Probability (2023). DOI: 10.1214/22-EJP880. URL: https://inria.hal.scien
ce/hal-01672979.

[5] Y. Efroni, G. Dalal, B. Scherrer and S. Mannor. ‘How to Combine Tree-Search Methods in Reinforce-
ment Learning’. In: AAAI 19 - Thirty-Third AAAI Conference on Artificial Intelligence. Honolulu,
Hawai, United States, 27th Jan. 2019. URL: https://inria.hal.science/hal-02273713.

[6] E. Eyraud, E. Maurat, J.-M. Sac-Epee, P. Henrot, M. Zysman, P. Esteves, T. Trian, H. Bégueret, P.-O.
Girodet, M. Thumerel, R. Hustache-Castaing, R. Marthan, F. Levet, P. Vallois, C. Contin-Bordes,
P. Berger and I. Dupin. Short-range interactions between fibrocytes and CD8+ T cells in COPD
bronchial inflammatory response. 21st Oct. 2022. DOI: 10 . 1101 / 2022 . 10 . 21 . 513138. URL:
https://hal.science/hal-03958836.

[7] C. Fritsch, D. Villemonais and N. Zalduendo. ‘The Multi-type Bisexual Galton-Watson Branching
Process’. In: Annales de l’I.H.P. Probabilités et statistiques (2024). URL: https://hal.science/ha
l-03696115.

[8]

J.-M. Monnez. ‘Stochastic approximation of eigenvectors and eigenvalues of the Q-symmetric
expectation of a random matrix’. In: Communications in Statistics - Theory and Methods 53.5 (2024),
pp. 1669–1683. DOI: 10.1080/03610926.2022.2107225. URL: https://hal.science/hal-039
56687.

[9] S. Toupance, D. Villemonais, D. Germain, A. Gégout-Petit, E. Albuisson and A. Benetos. ‘The
individual’s signature of telomere length distribution’. In: Scientific Reports 9.1 (24th Jan. 2019),
pp. 1–8. DOI: 10.1038/s41598-018-36756-8. URL: https://inria.hal.science/hal-01925
000.

[10] E. Ventre, U. Herbach, T. Espinasse, G. Benoit and O. Gandrillon. ‘One model fits all: Combining
inference and simulation of gene regulatory networks’. In: PLoS Computational Biology 19.3
(27th Mar. 2023), e1010962. DOI: 10.1371/journal.pcbi.1010962. URL: https://inria.hal
.science/hal-04071033.

Project BIGS

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11.2 Publications of the year

International journals

[11] A. Anagnostakis, A. Lejay and D. Villemonais. ‘General diffusion processes as the limit of time-space
Markov chains’. In: The Annals of Applied Probability 33.5 (2023), pp. 3620–3651. DOI: 10.48550/a
rXiv.2206.03713. URL: https://inria.hal.science/hal-02897819.

[12] T. Bastogne, L. Hassler, J. Bruniaux, M. Thomassin, X. Gidrol, E. Sulpice and F. Navarro. ‘A Bayesian
implementation of quality-by-design for the development of cationic nano-lipid for siRNA transfec-
tion’. In: IEEE Transactions on NanoBioscience 22.3 (July 2023), pp. 455–466. DOI: 10.1109/TNB.20
22.3213412. URL: https://hal.science/hal-04212321.

[13] N. Champagnat and V. Hass. ‘Existence, uniqueness and ergodicity for the centered Fleming-Viot
process’. In: Stochastic Processes and their Applications 166 (Dec. 2023), p. 104219. DOI: 10.1016/j
.spa.2023.09.006. URL: https://hal.science/hal-03596777.

[14] N. Champagnat, S. Méléard, S. Mirrahimi and V. Chi Tran. ‘Filling the gap between individual-
based evolutionary models and Hamilton-Jacobi equations’. In: Journal de l’École polytechnique —
Mathématiques 10 (2023), pp. 1247–1275. DOI: 10.5802/jep.244. URL: https://hal.science
/hal-03656608.

[15] N. Champagnat and D. Villemonais. ‘General criteria for the study of quasi-stationarity’. In: Elec-
tronic Journal of Probability (2023). DOI: 10.1214/22-EJP880. URL: https://inria.hal.scien
ce/hal-01672979.

[16] B. Cloez and C. Fritsch. ‘Quasi-stationary behavior for a piecewise deterministic Markov model of
chemostat: the Crump-Young model’. In: Annales Henri Lebesgue (2024). URL: https://inria.ha
l.science/hal-03660678.

[17] A. M. Cox, E. Horton and D. Villemonais. ‘Binary branching processes with Moran type interactions’.
In: Annales de l’I.H.P. Probabilités et statistiques (2024). URL: https://inria.hal.science/hal-
03715678.

[18] A. Dijamentiuk, C. Mangavel, A. Elfassy, F. Michaux, J. Burgain, E. Rondags, S. Delaunay, S. Ferrigno,
A.-M. Revol-Junelles and F. Borges. ‘Invert emulsions alleviate biotic interactions in bacterial mixed
culture’. In: Microbial Cell Factories 22.1 (Dec. 2023), p. 16. DOI: 10.1186/s12934-022-02014-w.
URL: https://hal.univ-lorraine.fr/hal-03953726.

[19]

I. Dupin, E. Eyraud, É. Maurat, J.-M. Sac-Épée and P. Vallois. ‘Probabilistic Cellular Automata mod-
eling of intercellular interactions in airways : complex pattern formation in patients with Chronic
Obstructive Pulmonary Disease’. In: Journal of Theoretical Biology (5th Mar. 2023), p. 111448. DOI:
10.1016/j.jtbi.2023.111448. URL: https://hal.science/hal-03572045.

[20] G. Escriva-Boulley, C.-A. Philip, S. Warembourg, L. Lenôtre, P. Flore, P. Faure, T. Michy, V. Letouzey, C.
Arnold, C. Piluso, L. Chalmel, R. Kacem, G. F. Blum, R. Detayrac, C. Trocmé, I. Brigaud, U. Herbach,
P. Branche, E. Faller and A. Chalabaev. ‘Effects of a physical activity and endometriosis-based
education program delivered by videoconference on endometriosis symptoms: the CRESCENDO
program (inCRease physical Exercise and Sport to Combat ENDOmetriosis) protocol study’. In:
Trials 24.1 (27th Nov. 2023), p. 759. DOI: 10.1186/s13063-023-07792-1. URL: https://hal.sc
ience/hal-04365506.

[21] C. Fritsch, D. Villemonais and N. Zalduendo. ‘The Multi-type Bisexual Galton-Watson Branching
Process’. In: Annales de l’I.H.P. Probabilités et statistiques (2024). URL: https://hal.science/ha
l-03696115.

[22] N. P. Gao, O. Gandrillon, A. Páldi, U. Herbach and R. Gunawan. ‘Single-cell transcriptional un-
certainty landscape of cell differentiation’. In: F1000Research 12 (20th Apr. 2023), p. 426. DOI:
10.12688/f1000research.131861.2. URL: https://hal.science/hal-04082461.

[23] S. Janson, C. Mailler and D. Villemonais. ‘Fluctuations of balanced urns with infinitely many
colours’. In: Electronic Journal of Probability 28 (2023), pp. 1–72. DOI: 10.1214/23-EJP951. URL:
https://hal.science/hal-03508437.

24

[24]

Inria Annual Report 2023

J.-M. Monnez. ‘Stochastic approximation of eigenvectors and eigenvalues of the Q-symmetric
expectation of a random matrix’. In: Communications in Statistics - Theory and Methods 53.5 (2024),
pp. 1669–1683. DOI: 10.1080/03610926.2022.2107225. URL: https://hal.science/hal-039
56687.

[25] E. Ventre, U. Herbach, T. Espinasse, G. Benoit and O. Gandrillon. ‘One model fits all: Combining
inference and simulation of gene regulatory networks’. In: PLoS Computational Biology 19.3
(27th Mar. 2023), e1010962. DOI: 10.1371/journal.pcbi.1010962. URL: https://inria.hal
.science/hal-04071033.

International peer-reviewed conferences

[26] N. Champagnat, S. Méléard and V. C. Tran. ‘Multiscale eco-evolutionary models: from individuals
to populations’. In: International Congress of Mathematicians, ICM 2022. Vol. 7. 1. fully virtually,
Russia: EMS Press, 15th Dec. 2023, pp. 5656–5678. DOI: 10.4171/ICM2022/24. URL: https://inr
ia.hal.science/hal-04407958.

[27] A. Rago, N. Champagnat, A. Gégout-Petit and L. Vallat. ‘Simulation d’expériences d’intervention
biologique dans des cellules cancéreuses à partir de données temporelles d’expression de gènes’.
In: 54es Journées de Statistique de la SFdS (JdS 2023). Bruxelles, Belgium, 2023. URL: https://inr
ia.hal.science/hal-04408929.

Conferences without proceedings

[28] W. Laziri, F. Allemand, A. Gégout-Petit and S. Wantz-Mézières. ‘Méthodes de réduction de dimen-
sion basées sur l’algorithme FINE pour le clustering de patients à partir de données de cytométrie
en flux’. In: 54es Journées de Statistiques de la SFDS. Bruxelles (BEL), Belgium, 3rd July 2023. URL:
https://hal.science/hal-04406372.

[29] W. Laziri, A. Gégout-Petit, F. Allemand and S. Wantz-Mézières. ‘Dimension reduction methods based
on FINE algorithm for clustering patients from flow cytometry data’. In: ENBIS 2023 Conference.
Valencia, Spain, 10th Sept. 2023. URL: https://hal.science/hal-04406413.

Scientific book chapters

[30] U. Herbach. ‘Harissa: Stochastic Simulation and Inference of Gene Regulatory Networks Based
on Transcriptional Bursting’. In: Computational Methods in Systems Biology. Vol. 14137. Lecture
Notes in Computer Science. Springer, 9th Sept. 2023, pp. 97–105. DOI: 10.1007/978-3-031-4269
7-1_7. URL: https://hal.science/hal-04208601.

Doctoral dissertations and habilitation theses

[31] V. Hass. ‘Individual-based models in adaptive dynamics, asymptotic behaviour and canonical
equation : the case of small and frequent mutations’. Université de Lorraine, 26th Sept. 2023. URL:
https://hal.univ-lorraine.fr/tel-04295339.

Reports & preprints

[32] A. Benetos, O. Coudray, A. Gégout-Petit, L. Lenôtre, S. Toupance and D. Villemonais. A branching
model for intergenerational telomere length dynamics. 15th Oct. 2023. URL: https://hal.scienc
e/hal-04242595.

[33] N. Champagnat and V. Hass. Convergence of individual-based models with small and frequent
mutations to the canonical equation of adaptive dynamics. 17th Mar. 2023. URL: https://hal.sc
ience/hal-04034027.

[34] N. Champagnat, E. Strickler and D. Villemonais. Uniform Wasserstein convergence of penalized

Markov processes. 28th June 2023. URL: https://hal.science/hal-04145204.

Project BIGS

25

[35] R. Loubaton, N. Champagnat, P. Vallois and L. Vallat. MultiRNAflow: integrated analysis of temporal
RNA-seq data with multiple biological conditions. 2023. URL: https://inria.hal.science/hal
-04407596.

[36]

J.-M. Monnez. An extended Oja process for streaming canonical analysis. 2023. URL: https://hal
.science/hal-04351700.

11.3 Other

Educational activities

[37]

J.-M. Monnez. ‘Analyse des données en flux. Analyse en composantes principales et méthodes
dérivées’. Doctoral. France, 19th June 2023. URL: https://hal.science/hal-03690459.

Softwares

[38]

[SW ] U. Herbach, Harissa: tools for mechanistic gene network inference from single-cell data ver-
sion 3.0.7, 2023. LIC: BSD 3-Clause. HAL: 〈hal-03370296〉, URL: https://hal.science/hal-03
370296, VCS: https://github.com/ulysseherbach/harissa, SWHID: 〈swh:1:dir:315fa8cd
b8abd2801f545dca59dd136584572a3d〉.

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10.1016/j.matpur.2013.10.003. URL: https://hal.inria.fr/hal-00784166.

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[49] N. Champagnat and D. Villemonais. ‘Exponential convergence to quasi-stationary distribution
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<scratchpad>
- Main topic: Research Centre BIGS (Biology, Genetics and Statistics) at Inria Centre at Université de Lorraine
- Key points: 
  - BIGS is a joint team of Inria, CNRS, and University of Lorraine, focusing on stochastic modeling and statistics
  - Research areas include tumor growth and heterogeneity, gene networks, telomere length dynamics, and epidemiology
  - The team has developed new software, such as Harissa and MultiRNAflow, for inference and analysis of gene regulatory networks
  - BIGS has collaborated with various institutions, including the European Research Council (ERC), and has received funding from the French "Plan de relance"
- Structure: 
  - The document is divided into sections, including Activity Report, New Software, New Results, Bilateral contracts and grants, and Partnerships and cooperations
  - Each section provides an overview of the team's activities, research, and collaborations
- Potential artifacts to ignore: 
  - Web-related elements, such as links, HTML tags, and unnecessary formatting
  - Unrelated or redundant information
</scratchpad>

<final_summary>
The BIGS research centre at Inria Centre at Université de Lorraine focuses on stochastic modeling and statistics, with a strong emphasis on biology and life sciences. The team has developed new software, such as Harissa and MultiRNAflow, to analyze gene regulatory networks and has collaborated with various institutions, including the European Research Council (ERC). BIGS has explored research areas including tumor growth and heterogeneity, gene networks, telomere length dynamics, and epidemiology. The team has also received funding from the French "Plan de relance" and has established partnerships with various institutions to advance their research and collaborations.</final_summary>
The BIGS research centre at Inria Centre at Université de Lorraine focuses on stochastic modeling and statistics, with a strong emphasis on biology and life sciences. The team has developed new software, such as Harissa and MultiRNAflow, to analyze gene regulatory networks and has collaborated with various institutions, including the European Research Council (ERC). BIGS has explored research areas including tumor growth and heterogeneity, gene networks, telomere length dynamics, and epidemiology. The team has also received funding from the French "Plan de relance" and has established partnerships with various institutions to advance their research and collaborations.