2023Activity reportProject-TeamBIGS

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 regulatory networks, based on stochastic gene expression with transcriptional bursting. It was implemented 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 allowing 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. identifying 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

7.1.1 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. Gandrillon (ENS Lyon).

The aim of this collaboration is to better understand how living cells make decisions (e.g., differentiation 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 relevant 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 $L^1$ (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.

By framing MVPPs as stochastic approximations operating within the set of measures on a measurable 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 $L^2$ 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).

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 manifestations. 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.

7.2.1 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  40, 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)  41. 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 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 fundamental 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.

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 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 endometriosis 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.

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

9.2.1 Visits of international scientists

9.2.2 Visits to international teams

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.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

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 Developments 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.

10.1.5 Contributed talks, posters, workshops,seminars

• V. Brodu has presented a poster at the Conférence internationale Mathematical Population Dynamics, 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: Modelling 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 $L^2$ 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.
• 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).

10.2.1 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.
• 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. Lorraine.
• 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, Probability and Statistics, 160h, IUT Nancy-Charlemagne (L1/L2/L3), Univ. Lorraine.
• Licence: S. Wantz-Mézières, Probability, 100h, first year in TELECOM Nancy (initial and apprenticeship 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 application 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 : comportement 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.
• 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 models 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. Advisors: 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. Mirrahimi (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 mutations 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.

10.2.3 Juries

• 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).

10.3.1 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 Statistique” 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 mathematics; explained what mathematical modeling is, and illustrated it with a stochastic gene expression model.
• 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”.

International journals

• 11 articleA.Alexis Anagnostakis, A.Antoine Lejay and D.Denis Villemonais. General diffusion processes as the limit of time-space Markov chains.The Annals of Applied Probability3352023, 3620-3651HALDOIback to text
• 12 articleT.Thierry Bastogne, L.Lucie Hassler, J.Jonathan Bruniaux, M.Magalie Thomassin, X.Xavier Gidrol, E.Eric Sulpice and F.Fabrice Navarro. A Bayesian implementation of quality-by-design for the development of cationic nano-lipid for siRNA transfection.IEEE Transactions on NanoBioscience223July 2023, 455-466HALDOI
• 13 articleN.Nicolas Champagnat and V.Vincent Hass. Existence, uniqueness and ergodicity for the centered Fleming-Viot process.Stochastic Processes and their Applications166December 2023, 104219HALDOIback to text
• 14 articleN.Nicolas Champagnat, S.Sylvie Méléard, S.Sepideh Mirrahimi and V.Viet Chi Tran. Filling the gap between individual-based evolutionary models and Hamilton-Jacobi equations.Journal de l'École polytechnique — Mathématiques102023, 1247-1275HALDOIback to text
• 15 articleN.Nicolas Champagnat and D.Denis Villemonais. General criteria for the study of quasi-stationarity.Electronic Journal of Probability2023HALDOIback to text
• 16 articleB.Bertrand Cloez and C.Coralie Fritsch. Quasi-stationary behavior for a piecewise deterministic Markov model of chemostat: the Crump-Young model.Annales Henri Lebesgue2024HALback to text
• 17 articleA. M.Alexander M.G Cox, E.E Horton and D.D Villemonais. Binary branching processes with Moran type interactions.Annales de l'I.H.P. Probabilités et statistiques2024HALback to text
• 18 articleA.Alexis Dijamentiuk, C.Cecile Mangavel, A.Annelore Elfassy, F.Florentin Michaux, J.Jennifer Burgain, E.Emmanuel Rondags, S.Stéphane Delaunay, S.Sandie Ferrigno, A.-M.Anne-Marie Revol-Junelles and F.Frédéric Borges. Invert emulsions alleviate biotic interactions in bacterial mixed culture.Microbial Cell Factories221December 2023, 16HALDOIback to text
• 19 articleI.Isabelle Dupin, E.Edmée Eyraud, É.Élise Maurat, J.-M.Jean-Marc Sac-Épée and P.Pierre Vallois. Probabilistic Cellular Automata modeling of intercellular interactions in airways : complex pattern formation in patients with Chronic Obstructive Pulmonary Disease.Journal of Theoretical BiologyMarch 2023, 111448HALDOIback to text
• 20 articleG.Géraldine Escriva-Boulley, C.-A.Charles-André Philip, S.Sophie Warembourg, L.Lionel Lenôtre, P.Patrice Flore, P.Patrice Faure, T.Thierry Michy, V.Vincent Letouzey, C.Carole Arnold, C.Claire Piluso, L.Loic Chalmel, R.Ramzi Kacem, G. F.Georges Fabrice Blum, R.Renaud Detayrac, C.Candice Trocmé, I.Isabelle Brigaud, U.Ulysse Herbach, P.Patricia Branche, E.Emilie Faller and A.Aïna 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.Trials241November 2023, 759HALDOIback to text
• 21 articleC.Coralie Fritsch, D.Denis Villemonais and N.Nicolás Zalduendo. The Multi-type Bisexual Galton-Watson Branching Process.Annales de l'I.H.P. Probabilités et statistiques2024HALback to text
• 22 articleN. P.Nan Papili Gao, O.Olivier Gandrillon, A.András Páldi, U.Ulysse Herbach and R.Rudiyanto Gunawan. Single-cell transcriptional uncertainty landscape of cell differentiation.F1000Research12April 2023, 426HALDOIback to text
• 23 articleS.Svante Janson, C.Cécile Mailler and D.Denis Villemonais. Fluctuations of balanced urns with infinitely many colours.Electronic Journal of Probability282023, 1-72HALDOIback to text
• 24 articleJ.-M.Jean-Marie Monnez. Stochastic approximation of eigenvectors and eigenvalues of the Q-symmetric expectation of a random matrix.Communications in Statistics - Theory and Methods5352024, 1669-1683HALDOIback to textback to text
• 25 articleE.Elias Ventre, U.Ulysse Herbach, T.Thibault Espinasse, G.Gérard Benoit and O.Olivier Gandrillon. One model fits all: Combining inference and simulation of gene regulatory networks.PLoS Computational Biology193March 2023, e1010962HALDOIback to text

International peer-reviewed conferences

• 26 inproceedingsN.Nicolas Champagnat, S.Sylvie Méléard and V. C.Viet Chi Tran. Multiscale eco-evolutionary models: from individuals to populations.International Congress of Mathematicians, ICM 202271fully virtually, RussiaEMS PressDecember 2023, 5656-5678HALDOIback to text
• 27 inproceedingsA.Anouk Rago, N.Nicolas Champagnat, A.Anne Gégout-Petit and L.Laurent Vallat. Simulation d'expériences d'intervention biologique dans des cellules cancéreuses à partir de données temporelles d'expression de gènes.54es Journées de Statistique de la SFdS (JdS 2023)Bruxelles, Belgium2023HALback to textback to text

Conferences without proceedings

• 28 inproceedingsW.Walid Laziri, F.Frédéric Allemand, A.Anne Gégout-Petit and S.Sophie Wantz-Mézières. Méthodes de réduction de dimension basées sur l'algorithme FINE pour le clustering de patients à partir de données de cytométrie en flux.54es Journées de Statistiques de la SFDSBruxelles (BEL), BelgiumJuly 2023HALback to text
• 29 inproceedingsW.Walid Laziri, A.Anne Gégout-Petit, F.Frédéric Allemand and S.Sophie Wantz-Mézières. Dimension reduction methods based on FINE algorithm for clustering patients from flow cytometry data.ENBIS 2023 ConferenceValencia, SpainSeptember 2023HALback to text

Scientific book chapters

• 30 inbookU.Ulysse Herbach. Harissa: Stochastic Simulation and Inference of Gene Regulatory Networks Based on Transcriptional Bursting.14137Computational Methods in Systems BiologyLecture Notes in Computer ScienceSpringerSeptember 2023, 97-105HALDOIback to text

Doctoral dissertations and habilitation theses

• 31 thesisV.Vincent Hass. Individual-based models in adaptive dynamics, asymptotic behaviour and canonical equation : the case of small and frequent mutations.Université de LorraineSeptember 2023HALback to text

Reports & preprints

• 32 miscA.Athanasios Benetos, O.Olivier Coudray, A.Anne Gégout-Petit, L.Lionel Lenôtre, S.Simon Toupance and D.Denis Villemonais. A branching model for intergenerational telomere length dynamics.October 2023HALback to text
• 33 miscN.Nicolas Champagnat and V.Vincent Hass. Convergence of individual-based models with small and frequent mutations to the canonical equation of adaptive dynamics.March 2023HALback to text
• 34 miscN.Nicolas Champagnat, E.Edouard Strickler and D.Denis Villemonais. Uniform Wasserstein convergence of penalized Markov processes.June 2023HALback to text
• 35 miscR.Rodolphe Loubaton, N.Nicolas Champagnat, P.Pierre Vallois and L.Laurent Vallat. MultiRNAflow: integrated analysis of temporal RNA-seq data with multiple biological conditions.2023HALback to text
• 36 miscJ.-M.Jean-Marie Monnez. An extended Oja process for streaming canonical analysis.2023HALback to text

Educational activities

• 37 unpublishedJ.-M.Jean-Marie Monnez. Analyse des données en flux. Analyse en composantes principales et méthodes dérivées.June 2023, DoctoralFranceHALback to text

Softwares

• 38 softwareU.Ulysse Herbach. Harissa: tools for mechanistic gene network inference from single-cell data.3.0.72023 lic: BSD 3-Clause.HALSoftware HeritageVCS