Research

Overall Theoretical Objectives

The overall objective of SISTM is to develop statistical methods for the integrative analysis of health data, especially those related to clinical immunology to answer specific questions risen in the application field. To reach this objective we are developing statistical methods belonging to two main research areas:

  • Statistical and mechanistic modeling, especially based on ordinary differential equation systems, fitted to population and sparse data

  • Statistical learning methods in the context of high-dimensional data

These two approaches are used for addressing different types of questions. Statistical learning methods are developed and applied to deal with the high dimensional characteristics of the data. The outcome of this research leads to hypotheses linked to a restricted number of markers. Mechanistic models are then developed and used for modeling the dynamics of a few markers. For example, regularized methods can be used to select relevant genes among 20000 measured with microarray technology, whereas differential equations can be used to capture the dynamics and relationship between several genes followed over time by a q-PCR assay or RNA-seq.

Overall Applied Objectives

Data are generated in clinical trials or biological experimentations. Our main application of interest is the immune response to vaccine or other immune interventions (such as exogenous cytokines), mainly in the context of HIV infection. The methods developed in this context can be applied in other circumstances but the focus of the team on immunology is important for the relevance of the results and their translation into practice, thanks to a longstanding collaboration with several immunologists and the implication of the team in the Labex Vaccine Research Institute (http://vaccine-research-institute.fr/fr/). Exemples of objectives related to this application field are:

  • To understand how immune response is generated with immune interventions (vaccines or interleukines)

  • To predict what would be the immune response to a given immune intervention for designing next studies and adapting interventions to individual patients

Last activity report : 2016