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

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.

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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, Nguyen T, Roehner N, Bartley B, Bhatia S, Bissell M, Clancy K, Gorochowski T, Grünberg R, Luna A, Le Novère N, Pocock M, Sauro H, Sexton JT, Stan GB, Tabor JJ, Voigt CA, Zundel Z, Myers C, Beal J, Wipat A

People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

+ View Abstract

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, Wolkenhauer O

PLoS computational biology, 14, 1553-7358, e1005740, 2018

PMID: 29447151

Latest Publications

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

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