Computational Systems Biology (O. Radulescu / A. Parmeggiani)

Computational Systems Biology

 

Specifics sites:

https://systems-biology-lphi.cnrs.fr/

 

HEADS OF THE TEAM

RADULESCU Ovidiu (Professor, University of Montpellier)

 

TEAM MEMBERS

BHARDWAJ Inayat  Doctorante inayat.bhardwaj[arobase]umontpellier.fr
BUFFARD Marion Doctorante marion.buffard[arobase]umontpellier.fr
DESOEUVRES Aurélien Doctorant This email address is being protected from spambots. You need JavaScript enabled to view it.
DOUAIHY Maria Doctorant maria.douaihy[arobase]gmail.com
RADULESCU Ovidiu Professeur UM This email address is being protected from spambots. You need JavaScript enabled to view it.
TOPNO Rachel Doctorante rachel.kt1208[arobase]gmail.com

 

RESEARCH PROJECTS

We are  developing mathematical approaches for learning and analysing  mechanistic models of  biological systems at various levels of  organisation, with a focus on the host-pathogen interactions in several  infectious diseases (malaria, HIV) and on cancer.
 
Mathematical  modelling of biological systems, in their full details, is a daunting  challenge. In order to cope realistically with the dynamics of molecular  pathways and gene networks in the cell, bottom-up models use thousands  of variables. Furthermore, in models of tissues, populations of cells  with complex single cell dynamics must be described collectively within a  spatially heterogeneous framework. In order to cope with this  complexity, we develop rigorous and automated methods for generating  hierarchies of simplified models that keep, at each scale, only  essential processes and components. Our modelling approaches provide  solutions to many problems in fundamental biology and medicine.
 
We  also develop novel AI methods for extracting information from  biological data. Our vision in this field is to combine data driven  “black box” models with knowledge driven “white box” models within  hybrid AI approaches.
 

 

Theme 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.

 

Theme 2 :

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).

 

We are  developing mathematical approaches for learning and analysing  mechanistic models of  biological systems at various levels of  organisation, with a focus on the host-pathogen interactions in several  infectious diseases (malaria, HIV) and on cancer.
 
Mathematical  modelling of biological systems, in their full details, is a daunting  challenge. In order to cope realistically with the dynamics of molecular  pathways and gene networks in the cell, bottom-up models use thousands  of variables. Furthermore, in models of tissues, populations of cells  with complex single cell dynamics must be described collectively within a  spatially heterogeneous framework. In order to cope with this  complexity, we develop rigorous and automated methods for generating  hierarchies of simplified models that keep, at each scale, only  essential processes and components. Our modelling approaches provide  solutions to many problems in fundamental biology and medicine.
 
We  also develop novel AI methods for extracting information from  biological data. Our vision in this field is to combine data driven  “black box” models with knowledge driven “white box” models within  hybrid AI approaches.
 

Some recent developments and projects :

  • Mechanical instabilities of tubular lipid membranes, protein/protein and protein/membrane interactions,
  • collective dynamical properties of low-dimensional lattices gas mimicking motor protein intracellular processes;
  • tubuline and microtubule response under a hydrostatic pressure;
  • nucleation growth and maturation of Mycobacterium smegmatis biofilms,
  • in silico description of fluorescent microscopy and spectroscopy experiments.

Past projects :

  • non-equilibrium and stochastic properties of a single molecular motor;
  • mechanisms of force production and transport of acto-myosin systems under ATP depletion. 
 
A few projects:
 
Formal methods in systems biology  (project SYMBIONT) https://systems-biology-lphi.cnrs.fr/formal/
 
Stochastic dynamics of gene expression  https://systems-biology-lphi.cnrs.fr/stochastic/
 
Machine learning and AI methods for systems biology  https://systems-biology-lphi.cnrs.fr/AI/
 
Cancer heterogeneity and resistance to treatment https://systems-biology-lphi.cnrs.fr/cancer/
 
Molecular aspects of infection https://systems-biology-lphi.cnrs.fr/infection/
 
 
2020 Publications
 
    • K. Tantale, E. Garcia-Oliver, A. L’Hostis, Y. Yang, MC. Robert, T. Gostan, M. Basu, A. Kozulic-Pirhern JC. Andrau, F. Muller, E. Basyuk*, O. Radulescu*, E. Bertrand*. Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting. 2020, in revision Nature Communications. *corresponding authors. Bioarxiv doi: https://doi.org/10.1101/2020.08.25.265413.
    • M.Dejean, VL. Pimmett, C. Fernandz, A. Trullo, E. Bertrand, O. Radulescu, M. Lagha. Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics. 2020, in revision Nature Communications.
    • N.Kruff, C.Lueders, O.Radulescu, T.Sturm, S.Walcher. Algorithmic Reduction of Biological Networks with Multiple Time Scales, 2020, in review Mathematics in Computer Science. https://arxiv.org/abs/2010.10129
    • M. Buffard, A. Naldi, M. Deckert, RM. Larive, O. Radulescu, PJ Coopman. The comparison of Syk signaling networks reveals the potential molecular determinants of its tumor promoter or suppressor functions. 2020, in review Biomolecules.
    • GCP. Innocentini, A. Hodgkinson, F. Antoneli, A. Debussche, O.Radulescu. Pushforward method for piecewise deterministic biochemical simulations. 2020, in review Theoretical Computer Science, Elsevier. https://arxiv.org/pdf/2009.06577.pdf
    • O.Radulescu. Tropical Geometry of Biological Systems. Invited talk CASC 2020, LNCS 12291, Springer Nature. https://hal.archives-ouvertes.fr/hal-02949563/file/CASC%283%29.pdf
    • H.Rahkooy, O.Radulescu, T.Sturm. A Linear Algebra Approach for Detecting Binomiality of Steady State Ideals of Reversible Chemical Reaction Networks. CASC 2020, LNCS 12291, Springer Nature. https://arxiv.org/pdf/2002.12693.pdf
    • A. Desoeuvres, G. Trombettoni, O. Radulescu, Interval Constraint Satisfaction and Optimization for Biological Homeostasis and Multistationarity. CMSB 2020, LNBI 12314, Springer Nature. https://www.biorxiv.org/content/biorxiv/early/2020/05/15/2020.05.14.095315.full.pdf
    • N.Theret, J.Feret, A.Hodgkinson, P.Boutillier, P.Vignet, O.Radulescu. Integrative models for TGF-b signalling and extracellular matrix. In Biology of Extracellular Matrix 7, 2020, Springer Nature, ISBN-13: 978-3030583293. https://hal.inria.fr/hal-02458073/document
 
2019 Publications
 
 
2018 Publications
 
  • M  Bellec, O Radulescu, M Lagha, Remembering the past: mitotic bookmarking  in a developing embryo. Current Opinion in Systems Biology (2018) 11,  41-49. https://www.sciencedirect.com/science/article/pii/S245231001830057X
  • AW  F. Boulier, F. Fages, O. Radulescu, S. Samal, A. Schuppert, W. Seiler,  T, The SYMBIONT Project: Symbolic Methods for Biological Networks, F1000  Research 7, 1341. ACM Communications in Computer Algebra 2019, 52:67-70
  • J  Dufourt, A Trullo, J Hunter, C Fernandez, J Lazaro, M Dejean, L  Morales, K N Schulz, C.Favard, M.M. Harrison, O. Radulescu, M. Lagha.  Temporal Control of Transcription by Zelda in living Drosophila embryos,  Nature Communications, 2018, 9 (1): 5194. https://www.nature.com/articles/s41467-018-07613-z
  • A  Hodgkinson, G Uzé, O Radulescu, D Trucu. Signal propagation in sensing  and reciprocating cellular systems with spatial and structural  heterogeneity. Bulletin of mathematical biology, (2018) 1-37. https://arxiv.org/abs/1802.10176
  • A  Hodgkinson, O Radulescu. An in silico spatio-structural mathematical  model for plastic drug resistance in heterogeneous melanoma  subpopulations. Cancer Research (2018) 78 (10), 69-70
  • G  Innocentini, A Hodgkinson, O Radulescu. Time Dependent Stochastic mRNA  and Protein Synthesis in Piecewise-deterministic Models of Gene  Networks. Frontiers in Physics. (2018) 6, 46. https://www.frontiersin.org/articles/10.3389/fphy.2018.00046/full
  • Vigneron  S, Sundermann L, Labbé JC, Pintard L, Radulescu O, Castro A, Lorca T.  Cyclin A-cdk1 Dependent Phosphorylation of Bora Is the Triggering Factor  Promoting Mitotic Entry. Developmental Cell. (2018) Jun  4;45(5):637-650.e7. https://www.sciencedirect.com/science/article/pii/S1534580718303629
- 2015 Publications -

Sanchez A., Cattoni D., Walter J.-C., Rech J., Parmeggiani A., Nollmann M., Bouet J.-Y. (2015) Stochastic Self-Assembly of ParB Proteins Builds the Bacterial DNA Segregation Apparatus, Cell Systems, vol. 1 p.163-173

Golushko I.Y., Rochal S.B., Parmeggiani A., Lorman V.L. (2015) Instabilities and shape variation phase transitions in tubular lipid membranes, arXiv preprint arXiv :1501.00258

Baiesi M., Carlon E., Parmeggiani A. (2015) Fundamental Problems in Statistical Physics XIII Special Issue, Physica A Statistical Mechanics and its Applications 418, 1-5
Ciandrini L. (2015) Molecular motors with a stepping cycle: from theory to experiments.  Proceedings of Traffic and Granular Flow '13, pp 619-627, Springer

Samal S.S., Grigoriev D., Fröhlich H., Radulescu O. (2015): Analysis of reaction network systems using tropical geometry. In: V.P. Gerdt, W. Koepf, W.M. Seiler, E.V. Vorozhtsov (eds.) Computer Algebra in Scientific Computing, 17th International Workshop (CASC 2015), Lecture Notes in Computer Science, vol. 9301, pp. 422--437. Springer, Aachen, Germany

Radulescu O., Vakulenko S., Grigoriev D. (2015) : Model reduction of biochemical reactions networks by tropical analysis methods. Mathematical Models of Natural Phenomena 10(3), 124-138

Fardin M-A., Radulescu O., Morozov A., Cardoso O., and Lerouge S..  (2015) Stress diffusion in shear banding wormlike micelles. Journal of Rheology, in press

Radulescu O., Samal S.S., Naldi A., Grigoriev D. and Weber A. (2015) Symbolic dynamics of biochemical pathways as finite state machines. 13th International Conference (CMSB 2015), Lecture Notes in Computer Science, in press.

 

- 2014 Publications -

Ciandrini L., Neri I., Walter J.C., Dauloudet O., Parmeggiani A., Motor protein traffic regulation by supply-demand balance of resources. Physical Biology 11 (2014), 056006-1/17, selected as the Physical Biology Highlights of 2014, http ://iopscience.iop.org/1478-3975/page/Highlights-of-2014

Rohani N., Parmeggiani A., Winklbauer R., Fagotto F. Variable Combinations of Specific Ephrin Ligand/Eph Receptor Pairs Control Embryonic Tissue Separation PLoS Biology 12 (2014), e1001955-1/21, Synopsis by R. Robinson,“Bind and Separate : How Ephrins and Their Receptors Create Tissue Boundaries”, PLoS, Biol 12 (2014), e1001956.

Ciandrini L., Romano M.C., Parmeggiani A., Stepping and crowding of molecular motors : statistical kinetics from an exclusion process perspective - Biophysical Journal 107 (2014), 1176-1184

Parmeggiani A., Neri I., Kern N. (2014) Modelling Collective Cytoskeletal Transport and Intracellular Traffic - The Impact of Applications on Mathematics - 1-25

Noel V., Grigoriev D., Vakulenko S., Radulescu O. (2014) Tropical and Idempotent Mathematics and Applications, Contemporary Mathematics vol. 616, chap. Tropicalization and tropical equilibration of chemical reactions. American Mathematical Society.

Soliman S., Fages F., Radulescu O. (2014) : A constraint solving approach to model reduction by tropical equilibration. Algorithms for Molecular Biology 9(1), 24.

 

- 2013 Publications -

Raguin A.,  Parmeggiani A., Kern N. (2013) Role of network junctions for the totally asymmetric simple exclusion process Physical Review E 88 - 042104-1/15

Neri I., Kern N., Parmeggiani A. (2013) Exclusion processes on networks as models for cytoskeletal transport New Journal of Physics 15 -  085005-1/54 ; Focus on Soft Mesoscopics : Physics for Biology at a Mesoscopic Scale

Fargier G., Favard C., Parmeggiani A., Sahuquet Q., Mérezègue F., Morel A., Denis M., Molinari N., Mangeat P.H., Coopman P.J., Montcourrier P.I. (2013) Centrosomal targeting of Syk kinase is controlled by its catalytic activity and depends on microtubules and the dynein motor, FASEB J. 27 109-122;

Neri I., Kern N., Parmeggiani A. (2013) Modelling intracellular traffic : an interplay between passive diffusion and active transport,Phys. Rev. Lett. Phys. Rev. Lett. 110. 098102;

Turci F., Parmeggiani  A., Pitard E., Romano  M. C., Ciandrini  L.,  (2013) Transport on a Lattice with Dynamical Defects, Phys. Rev. E 87. 012705-1/8

Innocentini G.C.P., Forger M., Ramos A.F., Radulescu O., Hornos J.E.M. (2013) Multi-modality and flexibility of stochastic gene expression. Bulletin of Mathematical Biology, 75:2600-30.

Noel V., Vakulenko S., Radulescu O.  (2013) A hybrid mammalian cell cycle model. Electronic Proceedings in Theoretical Computer Science - 125: 68-83.

Sen P., Vial H.J., Radulescu O. (2013) Kinetic modelling of phospholipid synthesis in Plasmodium knowlesi unravels crucial steps and relative importance of multiple pathways. BMC Systems Biology, 7:123.

 
 

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