Report Open Access

How to organise and run an ISBE modelling service

Vik, Jon Olav; Omholt, Stig; van Nieuwpoort, Frans; Snoep, Jacky; Mone, Martijn; Westerhoff, Hans; Nickerson, David; Juty, Nick; Goble, Carole; Stanford, Natalie; Hoefer, Thomas; Martins dos Santos. Vitor; Hermjakob, Henning; La Novere, Nicolas

ISBE will empower the experimental research community to make modelling part of their lab routine. Here we discuss how to define and structure ISBE modelling services, building on D8.2's review of the purpose and overall design principles for modelling services. We identify the components of ISBE modelling services and propose an organizational structure to achieve uniform coverage of services from a heterogeneous network of expertises. A roadmap concludes the report, proposing initial low-budget services for selected modelling frameworks, then outlining a strategy for growth and scalability of ISBE modelling services.

How to define and structure ISBE modelling services is a many-faceted question. First and foremost, one must identify the purpose of each service (what will it achieve?), which also entails specifying its inputs and outputs. It can be useful to group kinds of services into broader categories, either based on their function, or based on their organizational structure including staffing and funding.

Modelling services will include automated analyses of published or user-provided models, fitting of parameters to user data, and refinement and adaptation of existing models. However, it also makes sense to include related services such as stewardship and training in the broad term "modelling services".

A coordinating Systems Biology Centre (cSBC) will interconnect national Systems Biology Centres (nSBCs) to make the collective expertise of participating nations easily accessible for all European researchers. Chapter 2.3 concretizes this with illustrations of the variety of modelling services that ISBE is likely to offer, and how to route requests from clients to ISBE tools and expertise. Levels of service may range from fully automatic tools to long-term per­sonal involvement, with access to more demanding services prioritized in cooperation with funders. Likewise, there will be several kinds and levels of documentation and user support.

We advocate a focus on user needs in prioritizing what ISBE is to offer, in specifying services, and in routing expertise to clients. Clear descriptions of the data requirements for each modelling approach helps clients figure out their options and see what additional data they might require to open more options for modelling. Standardized virtual experiments can form an articulated link between modellers and experimentalists, and can be used to stream­line the confrontation of models with data, e.g. in parameter estimation and model selection.

As candidates for pilot services to be offered by the interim "ISBE light", we have identified modelling frameworks that span a wide range of biologically interesting questions within a well-defined set of user inputs. These include ordinary differential equations (where the rates-of-change of state variables is a function of the current value of those state variables) and constraint-based models (based on stoichiometry in biochemical reaction networks).

The demand for modelling services is anticipated to increase sharply once ISBE succeeds in transforming the practice of biological research and application. To meet this demand, we identify five key issues of scalability: Recruitment of providers, routing of clients to providers, strategic focusing of effort, training and education of users, and standardization and curation to streamline knowledge management, each of which is detailed in Chapter 5.2.

Files (333.6 kB)
Name Size
333.6 kB Download
All versions This version
Views 3030
Downloads 1313
Data volume 4.3 MB4.3 MB
Unique views 3030
Unique downloads 1313


Cite as