Life Sciences Research for Lifelong Health

Nicolas Le Novère

Research Summary

Our group uses bioinformatic methods and mathematical modelling to study the basic processes of life. Biological research now relies on the generation and analyses of large amounts of quantitative data, coming for example from nucleic acid sequencing and mass spectrometry.

Such data need to be processed, quantified and put in context. This is done using software tools and statistics. Based on the information acquired from experiments and existing literature one can build mathematical models that can then be simulated under various conditions.

The success or failure of reproducing observed behaviours tell us if we adequately understand the mechanisms of life. This activity is an important part of what is now called "systems biology". The systems biology paradigm recognises that the behaviour of any living system emerges from the interactions between many of its components and cannot be fully understood by studying those components in isolation.

The main biological focus of the group is to understand how cellular and molecular systems interpret signals from their environment and adapt their behaviour as a consequence. This entails understanding how the various cells receive and transduce the signal, the interplay of different signalling pathways, and the final outcome for cell physiology, including gene expression and cell fate.

​Our main biological models are the synaptic signalling between neurons of the central nervous system, and the maintenance and differentiation of stem cells.

Latest Publications

BioModels: expanding horizons to include more modelling approaches and formats.
Glont M, Nguyen TVN, Graesslin M, Hälke R, Ali R, Schramm J, Wimalaratne SM, Kothamachu VB, Rodriguez N, Swat MJ, Eils J, Eils R, Laibe C, Malik-Sheriff RS, Chelliah V, Le Novère N, Hermjakob H

BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing.

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Nucleic acids research, , 1362-4962, , 2017

PMID: 29106614

PTEN Regulates PI(3,4)P2 Signaling Downstream of Class I PI3K.
Malek M, Kielkowska A, Chessa T, Anderson KE, Barneda D, Pir P, Nakanishi H, Eguchi S, Koizumi A, Sasaki J, Juvin V, Kiselev VY, Niewczas I, Gray A, Valayer A, Spensberger D, Imbert M, Felisbino S, Habuchi T, Beinke S, Cosulich S, Le Novère N, Sasaki T, Clark J, Hawkins PT, Stephens LR

The PI3K signaling pathway regulates cell growth and movement and is heavily mutated in cancer. Class I PI3Ks synthesize the lipid messenger PI(3,4,5)P3. PI(3,4,5)P3 can be dephosphorylated by 3- or 5-phosphatases, the latter producing PI(3,4)P2. The PTEN tumor suppressor is thought to function primarily as a PI(3,4,5)P3 3-phosphatase, limiting activation of this pathway. Here we show that PTEN also functions as a PI(3,4)P2 3-phosphatase, both in vitro and in vivo. PTEN is a major PI(3,4)P2 phosphatase in Mcf10a cytosol, and loss of PTEN and INPP4B, a known PI(3,4)P2 4-phosphatase, leads to synergistic accumulation of PI(3,4)P2, which correlated with increased invadopodia in epidermal growth factor (EGF)-stimulated cells. PTEN deletion increased PI(3,4)P2 levels in a mouse model of prostate cancer, and it inversely correlated with PI(3,4)P2 levels across several EGF-stimulated prostate and breast cancer lines. These results point to a role for PI(3,4)P2 in the phenotype caused by loss-of-function mutations or deletions in PTEN.

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Molecular cell, , 1097-4164, , 2017

PMID: 29056325

Significance of stroma in biology of oral squamous cell carcinoma.
Vucicevic Boras V, Fucic A, Virag M, Gabric D, Blivajs I, Tomasovic-Loncaric C, Rakusic Z, Bisof V, Le Novere N, Velimir Vrdoljak D

The worldwide annual incidence of oral squamous cell carcinoma (OSCC) is over 300,000 cases with a mortality rate of 48%. This cancer type accounts for 90% of all oral cancers, with the highest incidence in men over 50 years of age. A significantly increased risk of developing OSCC exists among smokers and people who consume alcohol daily. OSCC is an aggressive cancer that metastasizes rapidly. Despite the development of new therapies in the treatment of OSCC, no significant increase in 5-year survival has been recorded in the past decades. The latest research suggests focus should be put on examining tumor stroma activation within OSCC, as the stroma may contain cells that can produce signal molecules and a microenvironment crucial for the development of metastases. The aim of this review is to provide an insight into the factors that activate OSCC stroma and hence faciliate neoplastic progression. It is based on the currently available data on the role and interaction between metalloproteinases, cytokines, growth factors, hypoxia factor and extracellular adhesion proteins in the stroma of OSCC and neoplastic cells. Their interplay is additionally presented using the Systems Biology Graphical Notation in order to sublimate the collected knowledge and enable the more efficient recognition of possible new biomarkers in the diagnostics and follow-up of OSCC or in finding new therapeutic targets.

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Tumori, , 2038-2529, 0, 2017

PMID: 28885677

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Keywords

bioinformatics
computational biology
mathematical modelling
systems biology

Latest Publications

BioModels: expanding horizons to include more modelling approaches and formats.

Glont M, Nguyen TVN, Graesslin M

Nucleic acids research
1362-4962: (2017)

PMID: 29106614

PTEN Regulates PI(3,4)P2 Signaling Downstream of Class I PI3K.

Malek M, Kielkowska A, Chessa T

Molecular cell
1097-4164: (2017)

PMID: 29056325

Significance of stroma in biology of oral squamous cell carcinoma.

Vucicevic Boras V, Fucic A, Virag M

Tumori
2038-2529:0 (2017)

PMID: 28885677

SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks.

Dalle Pezze P, Le Novère N

BMC systems biology
11 1752-0509:46 (2017)

PMID: 28395655

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016.

Schreiber F, Bader GD, Gleeson P

Journal of integrative bioinformatics
13 1613-4516:289 (2016)

PMID: 28187405

The health care and life sciences community profile for dataset descriptions.

Dumontier M, Gray AJ, Marshall MS

PeerJ
4 2167-8359:e2331 (2016)

PMID: 27602295

The systems biology format converter.

Rodriguez N, Pettit JB, Dalle Pezze P

BMC bioinformatics
17 1471-2105:154 (2016)

PMID: 27044654

Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine.

Pir P, Le Novère N

Methods in molecular biology (Clifton, N.J.)
1386 1940-6029:331-50 (2016)

PMID: 26677190

Enabling surface dependent diffusion in spatial simulations using Smoldyn.

Seeliger C, Le Novère N

BMC research notes
8 1756-0500:752 (2015)

PMID: 26647064

SBOL Visual: A Graphical Language for Genetic Designs.

Quinn JY, Cox RS, Adler A

PLoS biology
13 1545-7885:e1002310 (2015)

PMID: 26633141