SISTM Research team
Statistics In System biology and Translational Medicine
The challenge is to analyze these BIG DATA to answer clinical and biological questions by using appropriate statistical methods. With data on the machinery of a cell to the clinical status of individuals in any circumstances including in clinical trials, new tools are needed to translate information obtained from complex systems into knowledge. This has led to the field of « systems biology » and « systems medicine » by extension, which naturally takes place in the context of translational medicine that links clinical and biological research.
The statistical analysis of these data is facing several issues:
- There are more parameters (p) to estimate than individuals (n)
- The types/nature of data are various
- The relationship between variables is often complex (e.g. non linear) and can change over time to tackle these issues we are developing specific approaches for these questions, often related to immunology.
The methods are mainly based on either mecanistic modeling using differential equation systems or on statistical learning methods. The general paradigm of our approach is to include as much information as available to answer a given question. This information comes from the available data but also from prior biological information available defining the structure of the model or restricting the space of the parameter values. We develop and apply our methods mainly for applications belonging to clinical research especially HIV immunology. For instance, several projects are devoted to the modelling of the response to antiretroviral treatments, immune interventions or vaccine in HIV infected patients.
Applications are provided by the Vaccine Research Institute (VRI), other teams in the research centre and the Bordeaux Hospital Clinical Trial Unit (CTU).
Axis 1: Mecanistic modelling
When studying the dynamics of a given marker, say the HIV concentration in the blood (HIV viral load), one can for instance use descriptive models summarizing the dynamics over time in term of slopes of the trajectories. These slopes can be compared between treatment groups or according to patients’ characteristics. Another way for analyzing these data is to define a mathematical model based on the biological knowledge of what drives infection dynamics. Having a good mechanistic model in a biomedical opens doors to various applications beyond a good understanding of the data.
Axis 2: High dimensional data
Working on omics data such as genomics (DNA), transcriptomics (RNA) or proteomics (proteins), but also other types of data, such as those arising from the combination of large observational databases (e.g. in pharmacoepidemiology or environmental epidemiology), the challenge is that data sets usually contain many variables, much more than the observations.
Furthermore, conventional methods, such as linear models, are inefficient and most of the time even inapplicable. Therefore, more than the storage and calculation capacities, the challenge is the comprehensive analysis of these datasets from molecular pathways to clinical response of a population of patients needs specific approaches and a very close collaboration with the providers of data (the immunologists, the virologists, the clinicians…). The objective is either to select the relevant information or to summarize it for understanding or prediction purposes.
International and industrial relations
- Vaccine Research Institute (Hôpital Henri Mondor, Creteil) As “Labex”, the labovatory of Excellence is an extension of ANRS (French research HIV Hepatitis) program to accelerate the vaccines development of against HIV and the hepatitis C.
- Immunobiology department, Institute of Child Health, University College London
- Statistical Center for HIV/AIDS Research & Prevention (SHARP),
- Vaccine and Infectious Diseases Division of the Fred Hutchinson Cancer Research Center
- Department of Systems and Computational Biology at Albert Einstein College of Medicine, New York
- School of Mathematics and Physics at the University of Queensland
Regularly international researchers and students visit the SISTM team they are working with such as :
- Robin CALLARD (UCL, London, UK),
- Raphael GOTTARDO (Vaccine and Infectious Disease Division of the Fred Hutchinson Cancer Research Center, Seattle ,USA),
- Jessica GRONSBELL (Harvard T H Chan, School of Public Health, Boston, USA),
- Samson KOELLE (University of Washington, Seattle, USA)
- Cristian Meza, Associate Professor of the Universidad de Valparaiso (Chili), member of the research center CIMFAV
- David Conesa, Associate Professor of the Universidad de Valencia (Espagne), member of the research group GEEITEMA
- Sam Doerken, PhD student of the University of Freiburg (Allemagne), member of the Institute for Medical Biometry and Statistics
- The EBOVAC2 was funded under IMI2 Ebola+ programme that was launched in response to the Ebola virus disease outbreak. The project aims to assess the safety and efficacy of a novel prime boost preventive vaccine regimen against Ebola Virus Disease (EVD). The project is coordinating by Inserm U897 under the scientific responsibility of Professor Rodolphe Thiébaut.
- European HIV Vaccine Alliance (EHVA), EU Horizon 2020 project, is a major milestone as an EU platform for the discovery and evaluation of novel prophylactic and therapeutic vaccine candidates.
Research teams of the same theme :
- BIOCORE - Biological control of artificial ecosystems
- CARMEN - Modélisation et calculs pour l'électrophysiologie cardiaque
- COMMEDIA - Computational mathematics for bio-medical applications
- DRACULA - Multi-scale modelling of cell dynamics : application to hematopoiesis
- INBIO - Experimental and Computational Methods for Modeling Cellular Processes
- M3DISIM - Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine
- MAMBA - Modelling and Analysis for Medical and Biological Applications
- MONC - Mathematical modeling for Oncology
- NUMED - Numerical Medicine
- XPOP - Statistical modelling for life sciences