1. Vertical Integration Across Biological Scales A New Framework for the Systematic Integration of Models in Systems Biology University College London CoMPLEX DTI Beacon Project

2. Talk Outline 1. The Need for a new Computational Framework in Systems Biology 2. Main Features of our suggested Framework 3. An example: Neta’s sleeping and feeding patterns 4. Framework proposal in detail 5. Next steps (Items 3 and 4 only if time permits)

3. Status of biological modelling Cornucopia of models Different paradigms Different assumptions Based on different experimental evidence Created in ‘foreign’ languages Documented without standardisation “Manual integration”, often at the code level No repository of models

4. Two Main Challenges The Processing Challenge Model Integration Tool Support Visualization Automatic model generation The Information Management Challenge Cataloguing existing models Linking to experimental results and previous modeling results

5. A unified modeling language or framework - The need Currently existing modeling languages: Were developed in other fields, then applied to Systems Biology. Address only a certain aspect of biological systems, completely ignoring other aspects. Do not address the need for model integration. No language was developed, as a result of an analysis of the requirements for modeling in Systems biology.

6. Requirement Survey Diversity of biological processes to be modeled Biological Organization Different modeling Schemes ODE’s, Stochastic, Process algebra Many different tools for available for each scheme Iterative Modelling Process Model verification: One should be able to use the model to make predictions, which would later be verified by further experiments The model would then be modified, in light of the new experimental results.

7. Requirements Survey - Conclusion The main features of the new modeling approach should be: Modularity – probably through componentization Modularity also serves to support gradual, piece wise development Heterogeneity - Integration of models created in different schemes. Support for Meaningful Integration Care for Semantics (Probably through use of Ontologies) Representation and observance of assumptions and constraints

8. Suggested Component Based, MiddleWare Framework Consists of a component middleware and a set of supporting services Context/Interpretation Repository, Model Repository Models are executed in their original language, using their native environment, in a distributed manner Instantiated models are exposed as components, having well defined interfaces. A new language enables one to create composite models. An orchestrator executes a composite model, by calling the different engines to execute the submodels it is composed of, and integrating the computational results

9. Proposed Middleware Architecture Orchestrator Interpretation Service Context Service Compounder Engine Wrapper Mathematica (Running model C) Engine Wrapper Xppaut (running model B) 1 3 5 65 Engine Wrapper C++ (Running model A) 2 4 7 7 8 9 9

10. Neta’s Sleeping and feeding patterns

11. Neta’s Sleeping and Feeding Patterns – A model Assume: x(t) - Neta’s sleepiness at any given time (1- very alert, 10 very sleepy), y(t) - Neta’s hunger level at any given time (1 – bottle rejected in disgust, 10 – had been screaming for a bottle for the last 10 minutes) z(t) - Neta’s interest level in the current activity provided her, at any given time (1 – not interested, up to 10, very interested) a and b are parameters for the model: a is Neta’s daily nutritional requirement, and b is Neta’s daily sleep requirement. Then: dx/dt = b  x(t) - y(t) – z(t) dy/dt = -x(t)+a  y(t) + z(t)

12. One Model, Different Interfaces: A single model can support a diverse range of functionality, or interfaces: Each possible interface has its own required context “If you provide me with …., I will be able to tell you …. Thus, each model’s meta-data, should specify a context/provided interface table

13. The context/provided interface table for Neta’s model:

14. Proprietry and Standard Engines Models are interpreted using engines. The engine actually implements the interfaces (functionality), according to the model specs. An engine can be seen as a Model Instance Factory

15. Engine – A Model Instance Factory Model Instance Factory O. Margo Neta’s Model Context 1 (a,b,polytrack z(t)) Model Instance ‘yellow’ (supports yellow methods) Creates

16. Engine – A Model Instance Factory Model Instance Factory O. Margo Neta’s Model Context 2 (a,b) Model Instance ‘green’ (supports green methods) Creates

17. Proprietary & Standard Engines How does one exploit a model’s functionality - How does one communicate with the model? Usually this is done in a proprietary, specific way, provided and specified by the engine on which the model is interpreted. But we would like this communication, or model usage, to be performed in a standard way, in order to enable model integration The solution: Use wrappers. Wrappers also expose the Engine’s Model Instance Factory functionality in a standard way We have already written such wrappers for Xppaut and Mathematica.

18. Standard Engines and Engine Wrappers Proprietry Engine Very_weird_call1(input in proprietry format, output in proprietry format) Wrapper Standard_call1(in put, output in standard format) Outside World Standard Engine

19. The Orchestrator The Orchestrator is an Engine, that can interpret composite models It is mainly a coordinator, or a workflow execution service. May run models either sequentially or concurrently May use a compounder to solve concurrently two or more models, which form a feedback loop Composite Models are specified in the Composite Models Language – BeCMolla (Beacon Composite Models Language)

20. A Simple Example(1) Assume we also have : Liat’s Neta model, depicting Neta’s interest level in the current activity: dz/dt = C(i) – k*(a*x(t-t 0 )+y(t-t 0 )) The NHS standard feeding calculator When provided when Neta’s age and weight, gives you Neta’s daily feeding requirements (a) and hours of sleep required (b)

21. A Simple Example (2) We can now create a composite model, to predict Neta’s feeding, sleeping, and interest level patterns: This composite model would provide the following functionality:

22. A Simple Example(3) This model is composed, and will be solved, as follows: The NHS model would be run first, and then Ofer and Liat’s model would be solved together, using a compounder NHS Ofer Liat

23. A Simple Example (4) Orchestrator Interpretation Service Context Service Compounder Engine Wrapper Mathematica (Running model C) Engine Wrapper Xppaut (running model B) 1 3 5 65 Engine Wrapper C++ (Running model A) 2 4 7 7 8 9 9

24. Where now? We need to define the ‘model components’ interface language Select an underlying middleware architecture we would like to use: Web Services? Web Services + (OGSA|BEPL|XLANG|W3C Choreography)? Java objects? CCA? SBW? Devise the numerical algorithms which can be used to integrate models together Actually build a framework (an implementation)