CONCLUSIONES Y RECOMENDACIONES

The practice of systems biology relies on interfaces, and in particular interfaces between the entities we study, whether molecules, pathways or cells, or interfaces between tools. If these interfaces are to be generic enough to allow all users to leverage on existing toolkits, the existence of community-developed, well-supported standards is a funda- mental requirement, in addition to open resources for tools and parts. Over the last decade or so, several efforts have been launched in this direction, addressing encoding formats, ontologies and databases. Some of these are now well-established in the field and play a significant role in increasing the size and quality of quantitative models. More importantly, they have served as a catalyst to improve the collaborative nature of the computational systems biology community.

Standards of reporting (MIRIAM and MIASE)

Camille Laibe, Nick Juty, Dagmar Köhn

Most published quantitative models in biology are lost to the community because they are insufficiently characterised, which prevents them from being reused. With today’s increased interest in detailed biochemical models, it was neces- sary to define a minimum quality standard for the encoding of these models. The Minimal Information Requested in the Annotation of Models (MIRIAM) is a set of rules for curating quantitative models of biological systems. Their application enables users to search collections of curated models with precision, quickly identify the biological phe- nomena that a given curated model or model constituent represents, and facilitates model reuse, model composition into large subcellular models, and format conversion. An important part of the standard concerns the controlled annotation of model components, based on Uniform Resource Identifiers (URIs). MIRIAM Resources is an online infrastructure created to enable interoperability of this annotation (www.ebi.ac.uk/miriam/). The core of this resource is a catalogue of data types, whether controlled vocabularies or primary data resources, which provides the means to generate and resolve MIRIAM URIs. The use of MIRIAM annotations by the community is still growing, and software tools have been developed that use URIs as a glue to merge models and to integrate other datasets. MIRIAM’s guide- lines deal mostly with the structure of the models but in order to use the models to run simulations and obtain numeri- cal results, one needs additional information. The Minimum Information About a Simulation Experiment (MIASE) is a fledgling effort to agree upon a set of mandatory information to include with relevant publications. Both MIRIAM and MIASE are part of MIBBI, a more general effort to coordinate the development of reporting guidelines.

Ontologies in systems biology

Nick Juty, Dagmar Köhn, Camille Laibe

Whilst many controlled vocabularies exist that can be directly used to relate quantitative models to biological knowl- edge, there was previously no classification of the concepts themselves used in quantitative modelling. One of the goals of the Systems Biology Ontology (SBO; www.ebi.ac.uk/sbo/) is to facilitate the immediate identification of the relationship between a model component and the model structure (Chelliah et al., 2009). SBO is currently made up of six different vocabularies: 1) an ontology of entities which may participate in an interaction, a process or relationship of biological significance (for example: ‘enzyme’ and ‘ribonucleic acid’); 2) a taxonomy of the roles of reaction par- ticipants (e.g. ‘catalyst’, ‘competitive inhibitor’); 3) a controlled vocabulary for parameter roles in quantitative models (for instance: ‘forward unimolecular rate constant’ and ‘Michaelis constant’); 4) a list of modelling frameworks that specify how to interpret a mathematical expression (such as: ‘continuous framework’ or ‘discrete framework’); 5) a classification of mathematical expressions used in biochemical modelling (e.g. ‘mass action rate law’, ‘Henri-Michaelis- Menten rate law’); and 6) a catalogue of interactions (for example: ‘non-covalent binding’ and ‘transport reaction’). The annotation of quantitative model components with SBO terms adds a layer of semantics necessary to convert models between different formalisms, to link mathematical representations of biochemical models with graphical notations such as the Systems Biology Graphical Notation (see overleaf), or semantically enriched computing formats to repre- sent biochemical knowledge such as BioPAX. To complete SBO, which is designed to enrich model descriptions, we are developing an ontology of simulation methods (KiSAO; www.ebi.ac.uk/compneur-srv/kisao/) aimed to be used with SED-ML (see below), and an ontology to characterise numerical descriptions of dynamic behaviours (TEDDY; www.ebi.ac.uk/compneur-srv/teddy/). Resear ch in 2009 – The Le Novèr e Gr oup

126

Formal languages to encode models and simulations

Sarah Keating, Dagmar Koehn, Nicolas Le Novère, Nicolas Rodriguez

The Systems Biology Markup Language (SBML) is an XML language designed to facilitate the exchange of biologi- cal models between different simulators. SBML is now an established standard in the field of systems biology, and is supported by several EMBL-EBI resources such as Reactome, IntAct and BioModels Database. While bringing minor corrections and clarifications to the current specification of the language (Level 2, Version 4; Hucka et al., 2008), we are now working to develop the new generation of SBML. The field of computational systems biology is now so wide and diverse that a single language, supported by all tools, cannot cover every approach. SBML Level 3 will therefore be modular, with a mandatory core package and optional modules. The group is particularly working on packages to represent multi-component, multi-state species, qualitative models, space and geometry, and hierarchical modelling. We use our generic SBML editor (www.ebi.ac.uk/compneur-srv/SBMLeditor.html) as a benchmark to test possible packages and for various related projects of the group. We also provide software to convert to and from SBML. While SBML encodes the mathematical structure of the models, it does not specify how to obtain numerical results from this description. Together with simulator developers, we are creating a complementary format, the Simulation Experiment Description Markup Language (SED-ML; http://biomodels.net/sed-ml/; Köhn & Le Novère, 2008). A SED-ML file defines which models to simulate, how to modify them, which simulation approach to apply, how to post-process the numerical results and how to report them.

Systems Biology Graphical Notation

Nicolas Le Novère, Lu Li

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical diagrams, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynman notation or the process flow diagram were instrumental for the adoption of concepts in their fields. With the advent of systems biology, and more recently of synthetic biology, the need for precise and unambiguous graphical descriptions of biochemical processes has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. We developed the Systems Biology Graphical Notation (SBGN; www.sbgn.org/, Le Novère et al., 2009), a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created: the Process Descriptions, the Entity Relationships and the Activity Flows. These three idioms enable scientists to represent any network of biochemical interactions in a standardised way, which can then be interpreted unambiguously. The set of symbols used is limited and the gram- mar kept as simple as possible, to also allow its use in textbooks and education. Shared SBGN languages will foster efficient and accurate representation, storage, exchange and reuse of information on biological knowledge, e.g. sig- nalling pathways, metabolic and gene regulatory networks, between the communities of biologists, theoreticians and computational biologists.

BioModels Database

Chen Li, Lukas Endler, Nicolas Rodiguez, Vijayalakshmi Chelliah

For computational modelling to become more widely used in biological research, modellers must be able to exchange and share their results. BioModels Database (www.ebi.ac.uk/biomodels/) is a data resource that allows modellers to store, search and retrieve published mathematical models of biological interest. Models are annotated and linked to other relevant data resources. BioModels Database accelerates computational modelling efforts by allowing research- ers to leverage each others’ work more directly (Endler et al., 2009). It also supports improved and more accurate communication of research results by allowing journal publishers to encourage the submission of models in the same electronic format, stored in a common, publicly accessible location. Finally, the database provides examples of work- ing models for educational purposes, allowing inexperienced modellers to find ready-to-use models for exploration. BioModels Database has been developed in collaboration with the California Institute of Technology and is now the largest database of curated models worldwide (containing more than 429 models and 39,000 reactions). This status is recognised by BioMedCentral, Nature Publishing Group and the Public Library of Science, all of which request deposi- tion of models upon submission of manuscripts to several hundreds of journals. We regularly release new versions of the database, with new features for both users and curators.

FUTURE PROJECTS AND GOALS

In forthcoming years, the activity of the group will continue along two orthogonal directions. Our research work on modelling neuronal signalling at the level of the dendritic spine will expand to include other signalling pathways (MAPK, TrkB, PI3K) and tackle problems such as the role of scaffolding proteins or the synchronisation of calcium waves and phosphorylation gradients. Building on the growth of the BioModels Database, we will also carry out research on model composition, with the aim of improving component identification and reaction matching to build large-scale models of cellular compartments such as dendritic spines. Our involvement in developing standards and resources for systems biology will continue, with the goal of completing the puzzle of representations and ontologies

Resear ch in 2009 – The Le Novèr e Gr oup

127

so as to efficiently integrate the different levels of description of biochemical and cellular processes, qualitative, quan- titative and experimental (figure 2).

Figure 2. Matrix of the available standards, formats and ontologies for the description of models, simulations and results.

Group Members Visitor Scientist Stuart Edelstein Postdoctoral Fellows Noriko Hiroi* Melanie Stefan* Software Engineers Sarah Keating* Camille Laibe Chen Li

Nicolas Rodriguez (shared among several research groups)

Scientific Database Curators

Vijayalakshmi Chelliah Lukas Endler Nick Juty PhD Students Lu Li Michele Mattioni Dominic Tölle Trainees Ranjita Dutta-Roy* Marine Dumousseau*

*Indicates part of the year only

Publications 2008

Guerlet, G., et al. (2008). Comparative models of P2X2 receptor support inter-subunit ATP- binding sites. Biochem. Biophys. Res. Commun., 375, 405-409

Hucka M., et al. (2008) Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions. Nat. Prec., doi:10.1038/npre.2008.2715.1 Herrgård M.J., et al. (2008) A con- sensus yeast metabolic network reconstruction obtained from a com- munity approach to systems biology Nat. Biotechnol., 26, 1155-1160 Köhn, D. & Le Novère, N. (2008) SED-ML – An XML Format for the Implementation of the MIASE Guidelines CMSB 2008. In 'Lecture Notes in Computer Science (includ- ing subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)', Heiner, M. & Uhrmacher, A.M. (eds), 5307, 176- 190, Springer-Verlag

Le Novère, N. (2008). Multiscale modelling of neuronal signalling. In 'Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)', Heiner, M. & Uhrmacher, A.M. (eds), 5307, 176- 190, Springer-Verlag

2009

Chelliah, V., et al. (2009) Data Integration and Semantic Enrichment of Systems Biology Models and Simulations. In 'Lecture Notes in Computer Science (including sub- series Lecture Notes in Artificial Intelligence and Lecture Notes

in Bioinformatics)', Paton, N.W., Missier, P. & Hedeler, C. (eds), 5647, 5-15, Springer-Verlag

Dräger, A., et al. (2009)

SBML2LATEX: Conversion of SBML files into human-readable reports. Bioinformatics, 25, 1455-1456 Endler, L., et al. (2009) Designing and encoding models for Synthetic Biology. J. Roy. Soc. Int., 6, S405-S417

Le Novère, N., et al. (2009) The Systems Biology Graphical Notation. Nat. Biotechnol., 27, 735-741 Stefan, M.I., et al. (2009) Computing phenomenologic Adair-Klotz con- stants from microscopic MWC parameters. BMC Sys. Biol., 3, 68 Wolkenhauer, O., et al. (2009) SysBioMed report: Advancing sys- tems biology for medical applica- tions. IET Sys. Biol., 3, 131-136

Other EMBL publications

Fernandez, E., et al. (2006). DARPP- 32 is a robust integrator of dopamine and glutamate signals. PLoS Comput. Biol., 2, e176

Stefan M.I., et al. (2008). An allos- teric model of calmodulin explains differential activation of PP2B and CaMKII. Proc. Natl Acad. Sci. USA, 105, 10768-10773 Resear ch in 2009 – The Le Novèr e Gr oup

129