Our Work
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.
FORMAL METHODS IN SYSTEMS BIOLOGY
STOCHASTIC DYNAMICS OF GENE EXPRESSION
MACHINE LEARNING AND AI METHODS FOR SYSTEMS BIOLOGY
CANCER HETEROGENEITY AND RESISTANCE TO TREATMENT
MOLECULAR ASPECTS OF INFECTION
Publications
- NA Viswan et al, (2024) Hierarchical Optimization of Biochemical Networks, https://hal.science/hal-04593669/file/hierarchical_optimization%20%284%29.pdf
- Kumar, P. et al. Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach. 2024.03.04.583326 Preprint at https://doi.org/10.1101/2024.03.04.583326 (2024).
- Thibault Greugny, E., Fages, F., Radulescu, O., Szmolyan, P. & Stamatas, G. N. A skin microbiome model with AMP interactions and analysis of quasi-stability vs stability in population dynamics. Theoretical Computer Science 983, 114294 (2024). https://arxiv.org/pdf/2310.15201
- Pimmett, V. et al. Dissecting the dynamics of coordinated active transcriptional repression in a multicellular organism. 2024.02.05.577724 Preprint at https://doi.org/10.1101/2024.02.05.577724 (2024).
- Desoeuvres, A. et al. Reduction of Chemical Reaction Networks with Approximate Conservation Laws. SIAM J. Appl. Dyn. Syst. 256–296 (2024) doi:10.1137/22M1543963. https://arxiv.org/abs/2212.13474
- Desoeuvres, A. et al. A Computational Approach to Polynomial Conservation Laws. SIAM J. Appl. Dyn. Syst. 813–854 (2024) doi:10.1137/22M1544014. https://arxiv.org/pdf/2212.14881
- Bhardwaj, I. et al. Diverse and weakly immunogenic var gene expression facilitates malaria infection. medRxiv 2023–12 (2024). Preprint at https://www.medrxiv.org/content/medrxiv/early/2024/01/02/2023.12.27.23300577.full.pdf
- Radulescu, O. et al. Identifying Markov chain models from time-to-event data: an algebraic approach. Preprint at https://doi.org/10.48550/arXiv.2311.03593 (2023).
- Esnault, C. et al. G-quadruplexes are promoter elements controlling nucleosome exclusion and RNA polymerase II pausing. 2023.02.24.529838 Preprint at https://doi.org/10.1101/2023.02.24.529838 (2023).
- Douaihy, M., Topno, R., Lagha, M., Bertrand, E. & Radulescu, O. BurstDECONV: a signal deconvolution method to uncover mechanisms of transcriptional bursting in live cells. Nucleic Acids Research 51, e88 (2023). https://academic.oup.com/nar/article/51/16/e88/7233929
- Dandou, S. et al. Improved prediction of the response duration to MAPK inhibitors in patients with advanced melanoma using baseline genomic data and machine learning algorithms. 2023.12.07.23299389 Preprint at https://doi.org/10.1101/2023.12.07.23299389 (2023).
- Damour, A., Slaninova, V., Radulescu, O., Bertrand, E. & Basyuk, E. Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency. Viruses 15, 1969 (2023). https://www.mdpi.com/1999-4915/15/9/1969
- Arslan, J. et al. Efficient 3D reconstruction of whole slide images in melanoma. in Medical Imaging 2023: Digital and Computational Pathology 12471 463–475 (SPIE, 2023). https://cnrs.hal.science/hal-03834014/
- Lüders, C., Sturm, T. & Radulescu, O. ODEbase: a repository of ODE systems for systems biology. Bioinformatics Advances 2, vbac027 (2022).
- Lüders, C., Sturm, T. & Radulescu, O. ODEbase: A Repository of ODE Systems for Systems Biology. arXiv preprint arXiv:2201.08980 (2022).
- Lüders, C., Bellot, E., Fages, F., Radulescu, O. & Soliman, S. Symbolic Methods for Biological Networks D2.1 Report on Scalable Methods for Tropical Solutions (T1.2). (Inria Saclay, 2022).
- Hodgkinson, A., Trucu, D., Lacroix, M., Le Cam, L. & Radulescu, O. Computational model of heterogeneity in melanoma: designing therapies and predicting outcomes. Frontiers in Oncology 1245 (2022).
- Hodgkinson, A. et al. Mitotic Memory as Spontaneous Symmetry Breaking in the Cell. In ICSB proceedings, Berlin (2022).
- Fettahoglu, D., Kumar, P., Castro, A., Lorca, T. & Radulescu, O. Stochasticity of Meiotic Entry in Xenopus Oocytes. in ICSB proceedings, Berlin (2022).
- Arslan, J. et al. Introducing [MALMO]: Mathematical approaches to modelling metabolic plasticity and heterogeneity in Melanoma. in RITS 2022 – Recherche en Imagerie et Technologie pour la Santé (Brest, France, 2022).
- Desoeuvres, A., Szmolyan, P. & Radulescu, O. Qualitative Dynamics of Chemical Reaction Networks: An Investigation Using Partial Tropical Equilibrations. in Computational Methods in Systems Biology (eds. Petre, I. & Păun, A.) 61–85 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-031-15034-0_4.
- Dahmani, C. et al. Resistance to BRAF inhibitors: A lesson from clinical observations. Medecine Sciences: M/S 38, 570–578 (2022).
- Bellec, M. et al. The control of transcriptional memory by stable mitotic bookmarking. Nat Commun 13, 1176 (2022).
- Arslan, J. et al. Introducing [MALMO]: Mathematical approaches to modelling metabolic plasticity and heterogeneity in Melanoma. in RITS 2022 – Recherche en Imagerie et Technologie pour la Santé (Brest, France, 2022)
- Topno, R., Singh, I., Kumar, M. & Agarwal, P. Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer. BMC Cancer 21, 220 (2021). https://doi.org/10.1186/s12885-021-07928-z
- Topno, R., Nazam, N., Kumari, P., Kumar, M. & Agarwal, P. Integrative genome wide analysis of protein tyrosine phosphatases identifies CDC25C as prognostic and predictive marker for chemoresistance in breast cancer. Cancer Biomarkers 32, 491–504 (2021).
- Tantale, K. et al. Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting. Nat Commun 12, 4503 (2021). https://doi.org/10.1186/s12885-021-07928-z
- Pimmett, V. L. et al. Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics. Nat Commun 12, 4504 (2021). https://www.nature.com/articles/s41467-021-24461-6
- Kumar, P., Li, J. & Surulescu, C. Multiscale modeling of glioma pseudopalisades: contributions from the tumor microenvironment. Math. Biol. 82, 49 (2021).
- Kruff, N., Lüders, C., Radulescu, O., Sturm, T. & Walcher, S. Algorithmic Reduction of Biological Networks with Multiple Time Scales. Comput.Sci. 15, 499–534 (2021). https://link.springer.com/article/10.1007/s11786-021-00515-2
- Innocentini, G. C. P., Hodgkinson, A., Antoneli, F., Debussche, A. & Radulescu, O. Push-forward method for piecewise deterministic biochemical simulations. Theoretical Computer Science 893, 17–40 (2021). https://arxiv.org/pdf/2009.06577.pdf
- Buffard, M. et al. Comparison of SYK Signaling Networks Reveals the Potential Molecular Determinants of Its Tumor-Promoting and Suppressing Functions. Biomolecules 11, 308 (2021). https://doi.org/10.3390/biom11020308
- Buffard, M. et al. LNetReduce: Tool for Reducing Linear Dynamic Networks with Separated Timescales. in Computational Methods in Systems Biology (eds. Cinquemani, E. & Paulevé, L.) 238–244 (Springer International Publishing, Cham, 2021). doi:10.1007/978-3-030-85633-5_15.
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- 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
- Kenneth A Barr, John Reinitz, Ovidiu Radulescu. An in silico analysis of robust but fragile gene regulation links enhancer length to robustness. Plos Comp. Biol. 2019, 15 (11): e1007497. https://journals.plos.org/ploscompbiol/article?rev=2&id=10.1371/journal.pcbi.1007497
- J. Dufourt, M. Baellec, O. Messina, A. Trullo, C.Favard, O. Radulescu, M. Lagha. Zelda, le maestro du réveil du génome zygotique. Médecine/sciences 2019, 35 (11) : 821-841.
- Marion Buffard, Aurélien Naldi, Ovidiu Radulescu*, Peter J. Coopman*, Romain M. Larive*, Gilles Freiss*. Network Reconstruction and Significant Pathway Extraction Using Phosphoproteomic Data from Cancer Cells. Proteomics, 2019, 19 (21-22) : 1800450. *equal contribution. https://www.researchgate.net/profile/Ovidiu_Radulescu2/publication/335561936_Network_reconstruction_and_Significant_Pathway_Extraction_Using_Phosphoproteomic_Data_From_Cancer_Cells/links/5dea041c92851c83646575b3/Network-reconstruction-and-Significant-Pathway-Extraction-Using-Phosphoproteomic-Data-From-Cancer-Cells.pdf
- GCP Innocentini, F Antoneli, A Hodgkinson, O Radulescu. Effective computational methods for hybrid stochastic gene networks. LNCS, 2019, 11773:60-77. https://arxiv.org/pdf/1905.00235.pdf
- O.Radulescu and A.Devenyi. Is biological randomness a statistical physics concept? LINKs series, 2019, 120-123. https://hal.archives-ouvertes.fr/hal-02532029/document
- Russel Bradford, James H Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, M Kosta, Ovidiu Radulescu, Thomas Sturm, Andreas Weber. Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks. Journal of Symbolic Computation, 2019, 98:84-119. https://arxiv.org/pdf/1902.04882.pdf
- Arran Hodgkinson, Laurent Le Cam, Dumitru Trucu, and Ovidiu Radulescu, Spatio-Genetic and Phenotypic Modelling Elucidates Resistance and Re-Sensitisation to Treatment in Heterogeneous Melanoma, Journal of Theoretical Biology, 2019, 466:84-105.https://www.biorxiv.org/content/10.1101/463877v1.abstract
- Satya Swarup Samal, Jeyashree Krishnan, Ali Hadizadeh Esfahani, Christoph Lüders,Andreas Weber and Ovidiu Radulescu, Metastable regimes and tipping points of biochemical networks with potential applications in precision medicine, in Automated Reasoning for Systems Biology, Pietro Lio and Paolo Zuliani etds., 2019, Springer, pp 269-295.
- 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
- S Vakulenko, O Radulescu, I Morozov, A Weber. Centralized Networks to Generate Human Body Motions. Sensors 2017, 17 (12): 2907. https://www.mdpi.com/1424-8220/17/12/2907/htm
- E Kim, LM Tenkès, R Hollerbach, O Radulescu. Far-from-equilibrium time evolution between two gamma distributions. Entropy 2017, 19 (10): 511. https://www.mdpi.com/1099-4300/19/10/511/pdf
- Matthew England, Hassan Errami, Dima Grigoriev, Ovidiu Radulescu, Thomas Sturm, Andreas Weber. Symbolic Versus Numerical Computation and Visualization of Parameter Regions for Multistationarity of Biological Networks. Proceedings CASC 2017. https://link.springer.com/chapter/10.1007/978-3-319-66320-3_8
- Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Kosta, Ovidiu Radulescu, Thomas Sturm, Andreas Weber. A Case Study on the Parametric Occurrence of Multiple Steady States. Proceedings ISAAC 2017. https://arxiv.org/pdf/1704.08997.pdf
- A.Kozulic-Pirher, K Tanatale, F Muller, M Robert, C Zimmer, J Andrau, E Margeat, A L’Hostis, O Radulescu, E Bertrand, E Basyuk. A real time, single molecule view of transcription in living human cells. FEBS Journal 2017, 284: 169.
- Mounia Lagha, Teresa Ferraro, Jeremy Dufourt, Ovidiu Radulescu, Matilde Mantovani. Transcriptional Memory in the Drosophila Embryo. Mechanisms of Development 145 (2017) S137.
- Satya Swarup Samal, Ovidiu Radulescu, Andreas Weber, Holger Fröhlich. Linking metabolic network features to phenotypes using sparse group lasso. Bioinformatics 2017, 33 (21): 3445:3453. https://academic.oup.com/bioinformatics/article/33/21/3445/3923798
- A.Naldi, R.M.Larive, U.Czerwinska, S.Urbach, P.Montcourrier, C.Roy, J.Solassol, G.Freiss, P.J.Coopman and O.Radulescu. Reconstruction and Signal Propagation Analysis of the Syk Signaling Network in Breast Cancer Cells. PLOS Computational Biology (2017) 13: e1005432. https://journals.plos.org/ploscompbiol/article?rev=2&id=10.1371/journal.pcbi.1005432
- Vakulenko S, Morozov I, Radulescu O. Maximal switchability of centralized networks. Nonlinearity (2016) 29: 2327. https://arxiv.org/pdf/1606.02859.pdf
- Sommer-Simpson J, Reinitz J, Fridlyand L, Philipson L, Radulescu O. Hybrid Reductions of Computational Models of Ion Channels Coupled to Cellular Biochemistry. Lecture Notes in Computer Science (2016) 9859: 273-288. https://www.researchgate.net/profile/Ovidiu_Radulescu2/publication/307584555_Hybrid_Reductions_of_Computational_Models_of_Ion_Channels_Coupled_to_Cellular_Biochemistry/links/5bef2dd84585150b2bbc6566/Hybrid-Reductions-of-Computational-Models-of-Ion-Channels-Coupled-to-Cellular-Biochemistry.pdf
- Samal SS, Naldi A, Grigoriev D, Weber A, Théret Nathalie, Radulescu O. Geometric analysis of pathways dynamics: application to versatility of TGF-b receptors. Biosystems (2016) 149: 3-14. https://www.sciencedirect.com/science/article/pii/S0303264716301174
- Innocentini GCP, Forger M, Radulescu O, Antoneli F. Protein synthesis driven by dynamical stochastic transcription. Bulletin of mathematical biology (2016) 78: 110-131. https://arxiv.org/pdf/1406.3089.pdf
- Innocentini G, Guiziou S, Bonnet J, Radulescu O. Analytic framework for a stochastic binary biological switch. Physical Review E (2016) 94: 062413. https://www.biorxiv.org/content/biorxiv/early/2016/08/09/068759.full.pdf
- Sen P, Vial HJ, Radulescu O. Mathematical Modeling and Omic Data Integration to Understand Dynamic Adaptation of Apicomplexan Parasites and Identify Pharmaceutical Targets. In: Sylke M, Cerdan R, Radulescu O, Selzer PM, editors. Comprehensive Analysis of Parasite Biology: From Metabolism to Drug Discovery (2016) John Wiley & Sons. pp.457.
- Sylke M, Cerdan R, Radulescu O, Selzer PM. Comprehensive Analysis of Parasite Biology: From Metabolism to Drug Discovery (2016) John Wiley & Sons.
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- Samal SS, Grigoriev D, Froehlich H, Weber A, Radulescu O. A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions. Bulletin of Mathematical Biology (2015) 77: 2180-2211. https://arxiv.org/pdf/1510.04716.pdf
- Samal SS, Grigoriev D, Froehlich H, Radulescu O. Analysis of Reaction Network Systems Using Tropical Geometry. In: Gerdt VP, Koepf W, Seiler WM, Vorozhtsov EV, editors. Lecture Notes in Computer Science (2015) 9301: 424-439. https://arxiv.org/pdf/1508.05717.pdf
- Radulescu O, Vakulenko S, Grigoriev D. Model Reduction of Biochemical Reactions Networks by Tropical Analysis Methods. Mathematical Modelling of Natural Phenomena (2015) 10: 124-138. https://arxiv.org/pdf/1503.01414.pdf
- Radulescu O, Samal SS, Naldi A, Grigoriev D, Weber A. Symbolic Dynamics of Biochemical Pathways as Finite States Machines. In: Roux O, Bourdon J, editors. Lecture Notes in Computer Science (2015) 9308 104-120. https://arxiv.org/pdf/1504.07833.pdf
- Fardin M-A, Radulescu O, Morozov A, Cardoso O, Browaeys J, Lerouge S. Stress diffusion in shear banding wormlike micelles. Journal of Rheology (2015) 59: 1335-1362.https://www.researchgate.net/profile/Julien_Browaeys/publication/281935522_Stress_diffusion_in_shear_banding_wormlike_micelles/links/560552dc08aeb5718ff134bc.pdf
Our Team
Ovidiu Radulescu
Professor
Ovidiu Radulescu is a Professor of Systems Biology at the University of Montpellier (since 2009). He has obtained his phD in Theoretical Solid State Physics in Orsay (1994). He also holds a MS degree in Probability Theory from the University of Marne-la-Valée (1996) and a higher doctorate (habilitation) in Applied Mathematics from the University of Rennes 1 (2006). He was previously post-doc in the Institute of Theoretical Physics in Nijmegen (1996-1998), then post-doc in the IRC in Polymer Science and Technology and the Physics Department of the University of Leeds (1998-1999), assistant professor in mathematics at the University of Rennes 1 (1999-2009) and associate member of the French National Institute for Research in Computer Science and Automation (INRIA, 2005-2007). His current scientific interests are concerned with multiscale dynamic modelling, machine learning and emerging properties of biological systems with applications in systems biology and medicine.
Sarah Dandou
PHD STUDENT
Sarah is a PHD student under the supervision of Ovidiu Radulescu (LPHI) and Romain Larive (IRCM). She has an engineering degree in bioinformatics from the National Institute of Applied Sciences of Lyon (INSA Lyon, France), a training at the interface between computer science, biology, mathematics and statistics applied to living systems. Sarah is currently working on AI methods applied to clinical data and mechanistic modeling applied to Systems Biology. Her main subject of study is the modelisation of kinase inhibitor treatment resistance in melanoma. She is interested precisely in how to predict in a personalized way the appearance of resistance to treatment in a patient.
Rachel Topno
PhD student
Rachel Topno is a PhD student in Systems Biology under the supervision of Ovidiu Radulescu (LPHI) and Edouard Bertrand (IGH). She has a Bachelor’s and a Master’s degree in Physics from University of Delhi, India and a Post Graduate Diploma in Applied Statistics from Indira Gandhi National Open University (IGNOU). She was previously a research intern in the field of bioinformatics at Amity University. Rachel is currently working on Machine learning and AI methods for systems biology. Her main focus of study is the role of extrinsic and intrinsic noise in stochastic gene expression of HIV-1.
Inayat Bhardwaj
PhD student
Inayat Bhardwaj is a first year PhD student at LPHI under the supervision of Ovidiu Radulescu and Antoine Claessens. She completed her BS-MS dual degree in basic sciences from Indian Institute of Science Education and Research, Mohali. The main goal of her thesis is to model antigenic-variation in malaria with recombination to explain long term parasitemia.
Maria Douaihy
PhD student
Maria Knaiir Al Douaihy is a PhD student under the supervision of Ovidiu Radulescu (LPHI) and Mounia Lagha (IGMM). She has a Bachelor’s degree in pure Mathematics from the Lebanese University and a Master’s in Applied Mathematics from Aix-Marseille University. Maria is currently involved in studying Machine Learning and AI methods for Systems Biology. She is focusing on transcriptional bursting in space and time in the developmental embryo of Drosophila melanogaster. She is interested precisely in the effect of the extrinsic noise and the transcriptional memory throughout the different cell cycles.
Pawan Kumar
post doc
Pawan Kumar is a postdoc fellow in the computational system biology group at LPHI. He has obtained his PhD in biomathematics from TU Kaiserslautern, Germany. He completed his M.Tech in Industrial Mathematics and Scientific Computing from IIT Madras, India and RWTH Aachen, Germany. He also holds a M.Sc degree in Mathematics from IIT Madras, India. His research interests include mathematical modeling and simulation of complex biological systems. Being a part of the MALMO project, he is currently working on mechanistic modeling of different aspects of Melanoma.
Manvel Gasparyan
Post Doc
Manvel Gasparyan is a postdoc fellow in the computational systems biology group at LPHI. He obtained his Master’s degree in Mathematics and Applications from the University of Rennes in Rennes, France, and his PhD in Bioscience Engineering with a focus on Mathematical Modelling of Biochemical Systems from the University of Ghent in Belgium. Parallel to his PhD studies, he served as a lecturer at Ghent University Global Campus in Incheon, Korea. He joined LPHI from the University of Seoul in Seoul, Korea, where he completed his first PhD in Mathematical Modelling in Systems Toxicology. His research interests include mathematical modelling and simulation of complex biological systems.
Former members
Charbel Choufani
Yohann Trivino
Marion Buffard
Aurélien Desoeuvres
Deniz Fettahoglu
Laboratory of Pathogen Host Interactions
UMR 5235 – Université Montpellier
Place Eugène Bataillon, Bât. 24, CC107, 2ème étage
34095 MONTPELLIER Cedex 5