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

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.
Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, De Meulder B, Pellet J, Balaur I, Saqi M, Nogueira MM, He F, Parton A, Lemonnier N, Gawron P, Gebel S, Hainaut P, Ollert M, Dogrusoz U, Barillot E, Zinovyev A, Schneider R, Balling R, Auffray C

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

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NPJ systems biology and applications, 4, 2056-7189, 21, 2018

PMID: 29872544

Community-driven roadmap for integrated disease maps.
Ostaszewski M, Gebel S, Kuperstein I, Mazein A, Zinovyev A, Dogrusoz U, Hasenauer J, Fleming RMT, Le Novère N, Gawron P, Ligon T, Niarakis A, Nickerson D, Weindl D, Balling R, Barillot E, Auffray C, Schneider R

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

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Briefings in bioinformatics, , 1477-4054, , 2018

PMID: 29688273

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3).
Bergmann FT, Cooper J, König M, Moraru I, Nickerson D, Le Novère N, Olivier BG, Sahle S, Smith L, Waltemath D

The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.

+ View Abstract

Journal of integrative bioinformatics, , 1613-4516, , 2018

PMID: 29550789

Latest Publications

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

Mazein A, Ostaszewski M, Kuperstein I

NPJ systems biology and applications
4 2056-7189:21 (2018)

PMID: 29872544

Community-driven roadmap for integrated disease maps.

Ostaszewski M, Gebel S, Kuperstein I

Briefings in bioinformatics
1477-4054: (2018)

PMID: 29688273

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3).

Bergmann FT, Cooper J, König M

Journal of integrative bioinformatics
1613-4516: (2018)

PMID: 29550789

Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0.

Cox RS, Madsen C, McLaughlin J

Journal of integrative bioinformatics
1613-4516: (2018)

PMID: 29549707

Quick tips for creating effective and impactful biological pathways using the Systems Biology Graphical Notation.

Touré V, Le Novère N, Waltemath D

PLoS computational biology
14 1553-7358:e1005740 (2018)

PMID: 29447151

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