Modelling in Integrative Biology

Research in our group focuses on biological systems, with an interdisciplinary approach based on the interplay of mathematics computer science and biology. Our research interests are structured around 3 major axes, but the strategy we use to approach them is the same. Data analysis leads to the study and development of computer models and mathematics help describe and predict the dynamics of the phenomena of interest. The mathematical methods we use to study these models are diverse and range from the theory of dynamical systems to partial differential equations or probability theory.

Two examples to illustrate our methods.
 - The first example ("data driven" approach), in collaboration with the team of Jean-Michel Camadro modelling of iron metabolism, oxidative stress and cellular metabolism. The goal is to better understand metabolic diseases and their physiopathological consequences. We have thus created a new simulation algorithm, based on probabilistic Boolean modeling, adapted to the field. In addition to that, we have devised a method of classification of massive data sets which we have made available to the scientific community (AutoClass).

- The second example aims at a better understanding of the transmission of nerve signals (see ANR MANDy).
Phenomena such as the movements of the human body or certain brain diseases such as epilepsy, Parkinson's disease are closely related to the synchronization process of neuronal discharge, large groups of neurons going through mass discharge simultaneously. The purpose of this study is to better understand these synchronization phenomena.

 

Selection of publications

Achcar F., Camadro J-M., Mestivier D. A Boolean probabilistic model metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress. BMC Systems Biology 2011, 5 : 51.
Abstract

Grotta-Ragazzo C., Malta C. P. and Pakdaman K., Metastable Periodic Patterns in Singularly Perturbed Delayed Equations. Journal of Dynamics and Differential Equations, 2010, 22(2): 203-252
Abstract

K. Pakdaman, M. Thieullen, and G. Wainrib. Fluid limit theorems for stochastic hybrid systems with application to neuron models. Advances in Applied Probability,  2010, 42 (3): 761-794.
Abstract
          
Pakdaman K.,Perthame B., Salort D., Dynamics of a structured neuron population. Nonlinearity,  2010,  23:  55-75 
Abstract

Salort D., Transport equations with unbounded  force fields and application to the Vlasov-Poisson equation, Mathematical Models and Methods in Applied Sciences,  2009,  Vol. 19 (02), 199-228.
Abstract

http://dx.doi.org/10.1142/S0218202509003401

Last modified 07/ 5/2011

Top