LPHI Computational Systems Biology

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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

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STOCHASTIC DYNAMICS OF GENE EXPRESSION

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MACHINE LEARNING AND AI METHODS FOR SYSTEMS BIOLOGY

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CANCER HETEROGENEITY AND RESISTANCE TO TREATMENT

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MOLECULAR ASPECTS OF INFECTION

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Publications

    • Tantale, K., Garcia-Oliver, E., Robert, MC. et al. Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting. Nat Commun 12, 4503 (2021).  https://doi.org/10.1038/s41467-021-24462-5
    • Pimmett, V.L. et al. .Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motitfs on promoter state dynamics.  Nat Commun 12, 4504 (2021).
    • Kruff, N. et al. Algorithmic reduction of biological networks with multiple time scales. Mathematics in Computer Science 15, 3, 499-534 (2021). https://link.springer.com/article/10.1007/s11786-021-00515-2
    • Buffard M, Naldi A, Freiss G, Deckert M, Radulescu O, Coopman PJ, Larive RM. Comparison of SYK Signaling Networks Reveals the Potential Molecular Determinants of Its Tumor-Promoting and Suppressing Functions. Biomolecules. 2021; 11(2):308. https://doi.org/10.3390/biom11020308
    • Topno, R., Singh, I., Kumar, M. et al. 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
    • 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
  • 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

.

    • 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
 
  • 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

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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.

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Marion Buffard

PhD student

Marion Buffard is an engineer in Biology from Université de Technologie de Compiègne (UTC). She has then completed a master degree in bioinformatic (BCD at Université de Montpellier). She is now a 4th year PhD student working at the interface between Biology, Systems Biology and Bioinformatics. She develops Pho2Net, a software for signaling pathway reconstruction in cancer from phosphoproteomic data. She also works on kinase inhibitor resistance modelisation in melanoma.

Aurélien Desoeuvres

PhD student

Aurélien Desoeuvres is a phD student in System Biology under the supervision of Ovidiu Radulescu. He is currently working on model reduction, multistationarity, and homeostasis.

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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.

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.

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. 

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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.

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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.

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Deniz Fettahoglu

intern

Deniz Fettahoglu is a second year engineering student at Ecole Centrale de Nantes and an intern in Systems Biology under the supervision of Ovidiu Radulescu . He is focusing on the stochasticity and the spatial coupling mechanisms of the meiotic entry in Xenopus Laevis ovocytes.

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Former members
Arran Hodgkinson
Partho Sarathi Sen
Aurélien Naldi
Vincent Noel
Satya Swarup Samal
Joachim Rambeau
Charbel Choufani
Yohann Trivino

Laboratory of Pathogen Host Interactions
UMR 5235 – Université Montpellier
Place Eugène Bataillon, Bât. 24, CC107, 2ème étage
34095 MONTPELLIER Cedex 5