Nicolas Le Novère
Speaker of Workshop 1
Will talk about: Describing the whole life-cycle of modelling in neuroscience
The research interests of Le Novère revolve around the modelling of signal transduction in neurons, ranging from the molecular structure of proteins involved in neurotransmission, to signalling pathways in relation with synaptic plasticity. Using different modelling approaches at different levels, his group provided new insights on mechanisms leading to cooperativity, integration, and decoding of intracellular signals. Along with the modelling activity, the group develops services for the community such as data resources or modelling software. Le Novère is one of the creators of the Systems Biology Markup Language (SBML), and has been involved in it development since 2000. Along with the tools and resources developed for computational systems biology (e.g., BioModels Database, the reference for exchanging models), Le Novère initiated a coherent set of standards and ontologies in systems biology modeling, such as the Minimum Information Required In the Annotation of Models (MIRIAM), the Minimum Information About a Simulation Experiment (MIASE), the Simulation Experiment Description Markup Language (SED-ML) and the Systems Biology Ontology (SBO), that together aim to facilitate the exchange and reuse of models, as well as their integration with other types of biological data. Over the last few years, he coordinated the development of the Systems Biology Graphical Notation (SBGN), the equivalent for biochemistry of the circuit diagrams for engineering.
A decade ago, the creation of the Systems Biology Markup Language (SBML) changed the way people exchanged, verified and re-used models in systems biology. The robustness and versatility of this format, coupled to a wide software support, fostered the emergence of an entire area of research centered on model processing such as encoding, annotation, merging, comparison and integration with other datasets. Recently, new languages appeared that complement the model description, such as SED-ML to describe the simulation experiments or SBRML to encode the numerical results. In neurosciences, other fledgling efforts cover for instance multi-compartment neurons with NeuroML, and neuronal networks with NineML. More are needed to cover the while spectrum of computational models used in neurosciences. The developers of those initiatives are in contact, and try to improve the interoperability of the languages, for instance by sharing metadata. Similar development guidelines, governance principles and quality checks are needed, in order to provide the community with a serious infrastructure. One can hope to see, in a not too elusive future, the creation of a coherent set of non-overlapping standards that will support not only the various modeling approaches and scales needed to simulate human functions and dysfunctions, but also cover model structure, parametrisation, simulation and numerical output. Such a toolkit will allow bridging genomics, computational neuroscience and drug discovery.
