This project is led by G. Nuel since 2013. The primary objective of this project is to study the genetic factors in important age-dependent diseases like cancer, diabetes or rare genetic diseases. The challenge is to combine state-of-art survival analysis methods in the context of genetic dependence in (possibly large) pedigrees through Bayesian networks.

Our project initially focused on breast and ovarian cancers and BRCA mutations in partnership with Institut Curie. Beside this ongoing collaboration, we work now with Saint-Antoine Hospital on MSI Cancer (Lynch syndrome). This theme of research is one of the two priority axis of the recently obtained SIRIC CURASMUS (Sorbonne Université). SIRIC (sites de recherche intégrée sur le cancer ; 7 SIRIC in France) is an excellence from the National Cancer Institute (INCA) to acknowledge a cancer site highly involved in translational research.

In this context, we are are interested in:

• belief propagation in pedigrees
• detection and estimation of cohort-effects
• development of cure models
• clinical cancer genetics

Main partners involved:

• LPSM (CNRS 8001), Sorbonne Université: G. Nuel, A. Lefebvre
• Institut Curie: D. Stoppa-Lyonnet, A. de Pauw
• MAP5 (CNRS 8145), Paris Descartes: F. Alarcon, O. Bouaziz, V. Goepp
• MSI Cancers, Saint-Antoine Hospital: A. Duval, V. Jonchère

Grants: INSERM/IRESP DECURION (2013-2016, €120K), LNCC PhD grant (2013-2016, €100K), LNCC PhD grant (2018-2021, €100K), SATT IDF, SIRIC CURASMUS.

Stochastic modeling in Neuroscience has experienced an important development since the last ten years. Our objective is to address this topic with the recent achievements of Probability Theory and Statistics in connexion with our colleagues in computational and/or experimental Neuroscience. We hope that the mathematical approach will help to build simplified but biologically relevant models, to identify small sets of determinant parameters or to single out generic mechanisms that may help practitioners. We use both theory and numerics.

In the team we are interested at the same time in a single neuron and in populations of neurons. At the cell level we have focused on the role of the ion channels on the excitability of the neuron. Ion channels may undergo mutations and a dysfunction in the ion channels may cause pathologies (called channelopathies). Rythmic behaviours are also fundamental, at the cell level as well as for populations. Indeed alteration of natural rythm or emergence of collective periodic behaviour (synchrony) can be associated to neurologic diseases (epilepsy, Parkinson's disease, Alzheimer's disease in particular).

A few mathematical key words relevant for us: Piecewise Deterministic Markov Processes in finite and infinite dimension, stochastic Kuramoto model, synchronization, coupled random oscillators, hypoelliptic diffusions, long time behavior of time dependent multidimensional diffusions, thinning method, parameter estimation, fast-slow systems.

Michèle Thieullen has supervised the PhD theses of Gilles Wainrib (co-direction with K. Pakdaman IMJ Univ. Paris-Diderot), Alexandre Genadot, Vincent Renault (co-directed with Emmanuel Trélat LJLL-UPMC), Alexis Vigot and Nicolas Thomas (co-directed with Vincent Lemaire LPSM-UPMC).

We have benefited from the support of the french National Research Agency (ANR): projet ANR blanc MANDy (2009-2012), coordinator Michèle Thieullen (SBG, LPSM), 23 members, 4 poles: LPSM-UPMC, Univ. of Orléans (around Nils Berglund), INRIA Sophia-Antipolis (around Etienne Tanré), IMJ (Institut Jacques Monod) Univ. Paris-Diderot (around Khashayar Pakdaman). This project MANDy gathered colleagues from Probability Theory, PDE, Dynamical Systems and Computational Neuroscience.

The project is devoted to the development of statistical methods specifically designed for analysing different types of ecological networks: trophic, mutualistic, competition or antagonistic and host-parasite systems. We propose to create a unique consortium of researchers combining applied statisticians with long-standing experience on life-science modeling and ecologists at the forefront of their domain to tackle the challenges posed by the advanced statistical modeling of ecological networks. Our proposal includes 1) integrating space and time dimensions in ecological networks modeling and developing tools for the comparison of networks along environmental gradients; 2) integrating multiple types of interactions, taking advantage of covariate information (such as species traits, distributions, phylogenies and environmental variables) when available; 3) incorporating sampling effects in our analyses and 4) providing predictions on ecosystem response to environmental changes.

This project is funded by the french National Research Agency (ANR): projet ANR EcoNet (2019-2024), coordinator Catherine Matias (SBG, LPSM). See more on the project's webpage.

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