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The ERATO Systems Biology Workbench Michael Hucka, Hamid Bolouri, Andrew Finney, Herbert Sauro ERATO Kitano Systems Biology Project California Institute.

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Presentation on theme: "The ERATO Systems Biology Workbench Michael Hucka, Hamid Bolouri, Andrew Finney, Herbert Sauro ERATO Kitano Systems Biology Project California Institute."— Presentation transcript:

1 The ERATO Systems Biology Workbench Michael Hucka, Hamid Bolouri, Andrew Finney, Herbert Sauro ERATO Kitano Systems Biology Project California Institute of Technology, Pasadena, CA, USA Principal Investigators: John Doyle, Hiroaki Kitano Collaborators: Adam Arkin (BioSpice), Dennis Bray (StochSim), Igor Goryanin (DBsolve), Les Loew (Virtual Cell), Pedro Mendes (Gepasi/Copasi), Masaru Tomita (E-Cell)

2 2 Background Modeling, simulation & analysis are critical –Huge volumes of data –Many disparate findings Rapid rate of software tool development –Roles: data filtering, model creation, model simulation –Many groups are creating many tools Different packages have different niche strengths reflecting expertise & preferences of the group Strengths are often complementary to those of other packages

3 3 Complementary Strengths of Tools Tool 7 Tool 3 Tool 4 Tool 2 Tool 6 Tool 5 Tool 1 Multistate reactions/stochastic Reaction/Diffusion Visualization of networks Optimization Bifurcation analysis Handle large systems

4 4 Problems No single package answers all needs of modelers No single tool is likely to do so in the near future –Range of capabilities is large –New techniques (  new tools) evolving too rapidly Researchers are likely to continue using multiple packages for the foreseeable future Problems in using multiple tools: –Simulations & results often cannot be shared or re-used –Duplication of software development effort

5 5 Goal & Approach Systems Biology Workbench goal: to provide software infrastructure that –Enables sharing of simulation/analysis software & models –Enables collaboration between software developers Two-pronged approach: –Develop a common model exchange language SBML: Systems Biology Markup Language –Develop an environment that enables tools to interact SBW: Systems Biology Workbench

6 6 Systems Biology Markup Language Problem: –Many software tools, few common exchange formats Difficult to take advantage of multiple tools Difficult to establish repositories of models A Solution (In Principle): –Define a common exchange language Use a simple, well-supported, textual substrate (XML) Add components that reflect the natural conceptual constructs used by modelers in the domain

7 7 Domain: biochemical network models A model is described using a list of components: –Beginning of model definition »List of unit definitions (optional) »List of compartments »List of species »List of parameters (optional) »List of rules (optional) »List of reactions –End of model definition Each component has a specific structure Structure of Model Definitions in SBML

8 8 Example S1S1 X2X2 X1X1 k 1 *X 0 k 2 *S 1 k 3 *S 1 X0X0

9 9 Example (cont.)

10 10 Example (cont.)

11 11 Example (cont.)

12 12 Example (cont.)...

13 13 Some Points About SBML Users do not write in XML — software tools do! SBML is being defined incrementally –SBML Level 1 covers non-spatial biochemical models Intentionally kept simple for maximal compatibility –SBML Level 2 will extend Level 1 with more facilities Defined in abstract form (UML) + textual descriptions –Used to define XML encoding + XML Schema Level 2 Level 3? Level 1 E.g.: Composition Geometry Arrays … others

14 14 Systems Biology Workbench Open-source, integrated software environment that enables sharing of computational resources –Allows software developers to build interprocess communications facilities into their applications From the user’s perspective: –One SBW-enabled application can interact with another –Each application or module offers services to others E.g.: ODE solution, time-based simulation, visualization, etc.

15 15 From the User’s Perspective

16 16 From the User’s Perspective

17 17 From the User’s Perspective

18 18 Behind the Scenes

19 19 From the Programmer’s Perspective Desirable features –Small application programming interface (API) –Libraries that implement inter-program communications –A registry of services for applications to query –XML-based model representation (SBML) Uses well-known, proven technologies –Communications via message-passing over plain sockets –Modular, distributed, broker-based architecture API provides two styles: –“Low-level”: call/send operations + directory services –“High-level”: object-oriented interface layered on top

20 20 Driving Principles Keep it language-neutral –We’ll provide C++, Java, Delphi libraries for Win, Linux But can be implemented in any language Keep it simple –Simple message-passing scheme Avoid complexity & size of CORBA Easy to make cross-platform compatible Easy to make distributed –Simple low-level API, convenient higher-level API Make sure contributors benefit –Open source development –Symmetric infrastructure: no application dominates

21 21 Modules Planned Data filtering & preparation Database support –E.g.: web searching, storage management Model definition & manipulation –E.g.: scripting languages, visual editors Equation solvers –E.g.: ODEs, DAEs, stochastic Analysis & visualization tools –E.g.: bifurcation, 2-D/3-D/4-D plotting Optimization & parameter searching

22 22 Summary & Availability SBML –Level 1 specification is publicly available –Support being added by other groups to their apps SBW –Preliminary test implementation completed –Production version is now in development Draft API definition & other info available –http://www.cds.caltech.edu/erato/sbw/docshttp://www.cds.caltech.edu/erato/sbw/docs –Expect first public release in November at ICSB 2001 (http://www.icsb2001.org)http://www.icsb2001.org

23 23

24 24 Outline 1) Background and motivations I.e., why are we doing this? 2) Systems Biology Markup Language (SBML) Model description language & exchange format 3) Systems Biology Workbench Infrastructure for connecting software tools

25 25 Scope of SBML Domain: biochemical network models S1S1 S2S2 S3S3 X0X0 X1X1 K 1 *X 0 - S 1 *K 2 S 3 /K i V max *S 1 K m +S 1 k 3 *S 2 k 4 *S 2 - k 5 *S 3 k 6 *S 3 k 7 *S 3 *S 1 2 X2X2


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