Parent Category: Laboratoires Published: Friday, 17 February 2012

Biological Physics and Systems Biology





 LPHI - UMR 5235

 Palce Eugène Bataillon, 34095 Montpellier


Phone: +33 4 67 14 32 76







Our team develops physical, mathematical and computer science approaches for the understanding of the functioning of biological systems. At the centre of our approaches is the multi-scale modelling of the biological processes by using statistical physics, dynamical systems and stochastic processes techniques. Our priority is to identify system's critical targets and essential mechanisms. This knowledge can be used to elaborate new therapies against complex diseases.

Main aims of research:

Aim 1: Large regulatory networks: from molecular interactions to biological function

We develop mathematical methods for reconstruction and analysis of large biochemical networks involved in cellular signalling and metabolism. These networks are described as complex systems of interacting molecules, together with their dynamics in space and in time. Although our approaches can be applied to the understanding of regulatory processes in all organisms, we study with particular emphasis networks of higher eukaryotes, involved in systems biology of human health and disease. To improve the effectiveness of mathematical modelling we take into account and benefit from notable properties of biological regulation networks such as modularity, multiscaleness, and robustness. Some recent developments and projects: i) cross-talk of signaling pathways in cancer (Ewing sarcoma, cervical cancer); ii) cell cycle hybrid modeling; iii) lipid metabolism in various species (fatty acid balance in mice liver during fasting, phospholipid synthesis in Plasmodium falciparum); iv) canalization of early development stages in dipteran insects; v) robustness of complex regulatory networks by dimension compression; vi) stochastic networks.

Aim 2: Biological physics of molecular assemby: from individual molecule to supramolecular organization and dynamics

Using statistical mechanics principles and stochastic processes, we develop physical models of cellular processes at molecular and supramolecular scales, such as protein transport over a filament or filament networks, or protein-protein interactions on nanotubular structures. More generally, we are interested in non-equilibrium collective phenomena of nucleation, growth and transport through the cytoplasm, over membranes, filaments or complex filament networks. This knowledge is relevant also to model experiments in silico and interact with experimentalists in biology and biophysics.

Aim 3: Multiscale approaches: from individual molecule and molecular interactions to virtual cell

This goal combines 1) and 2). By using across scales descriptions, we aim to integrate in our models both physical processes and regulatory networks. Our methodology can be described as hierarchical modelling. It allows descriptions of biological systems at different scales and it is based on model reduction and model conversion techniques. By setting solid theoretical basis for hierarchical modelling, we contribute to the larger international effort endeavoring the future creation of an integrated model of the whole cell (virtual cell).


Keywords: Statistical mechanics, physical-mathematical principles, stochastic processes, networks, systems biology, biological robustness, model reduction, developmental biology models, signaling networks models, metabolic networks models.



Last publications

  • M.L. Ferguson, D. Le Coq, M. Jules, S. Aymerich, O.Radulescu, N. Declerck, C.A. Royer. Reconciling molecular regulatory mechanisms with noise patterns of bacterial metabolic promoters in induced and repressed states, Proceedings of the National Academy of Sciences USA (2012) 109: 155.
  • O.Radulescu, A.N.Gorban, A.Zinovyev, V.Noel. Reduction of dynamical biochemical reaction networks in computational biology. Frontiers in Bioinformatics and Computational Biology (2012) 3: 131.
  • V. Noel, D. Grigoriev, S. Vakulenko, O. Radulescu. Tropical geometries and dynamics of biochemical networks. Application to hybrid cell cycle models. Electronic Notes in Theoretical Computer Science (2012) 284 : 75–91.
  • V.Noel, D.Grigoriev, S.Vakulenko, O.Radulescu. Hybrid models of the cell cycle molecular machinery. Electronic Proceedings in Theoretical Computer Science (2012) 92 : 88-105.
  • S.Vakulenko, O.Radulescu. Flexible and robust patterning by centralized gene networks, Fundamenta Informaticae 119 (2012) 1-25.
  • S.Vakulenko, O.Radulescu. Flexible and robust networks. Journal of Bioinformatics and Computational Biology (2012) 2:1241011.
  • A.Crudu, A.Debussche, A.Muller, O.Radulescu, Convergence of stochastic gene networks to hybrid piecewise deterministic processes, Annals of Applied Probability (2012) 22: 1822-1859.
  • O.Radulescu, GCP Innocentini, JEM Hornos. Relating network rigidity, time scale hierarchies, and expression noise in gene networks, Physical Reviews E 85 (2012) 041919.
  • M.L. Ferguson, D. Le Coq, M. Jules, S. Aymerich, O.Radulescu, N. Declerck, C.A. Royer. Reconciling molecular regulatory mechanisms with noise patterns of bacterial metabolic promoters in induced and repressed states. PNAS (2012), 109, 155.
  • I. Neri, N. Kern & A. Parmeggiani, Totally Asymmetric Simple Exclusion Process on networks, Phys. Rev. Lett. 107 (2011), 068702. Featured by Physical Review Focus.
  • M.L. Ferguson, D. Le Coq, M. Jules, B.Chun, S. Aymerich, O.Radulescu, N. Declerck, C.A. Royer. Two Types of Transcriptional Repression in Living Cells of Bacillus Subtilis Characterized by Number and Brightness Analysis. Biophysical Journal (2011) 100: 175-176.
  • Ovidiu Radulescu, Anne Siegel, Elisabeth Pécou, Clément Chatelain, Sandrine Lagarrigue. Genetically regulated metabolic networks: Gale-Nikaido modules and differential inequalities, in Transactions in Computational Systems Biology XIII, LNBI 6575, Springer 2011.
  • V. Noel, S. Vakulenko, O. Radulescu. Algorithm for identification of piecewise smooth hybrid systems: application to eukaryotic cell cycle regulation. Algorithms in Bioinformatics. Lecture Notes in Computer Science (2011) 6833: 225–236.



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