1. BioSPICE and Problem-Solving Environments for Systems Biology Clifford A. Shaffer Department of Computer Science Virginia Tech

2. Systems Biology Focus on regulatory mechanisms for biochemical networks –Start with a wiring diagram Some example problems: –Cell Cycle –Circadian Rhythms

3. Cln3 Mass Budding Cln2 SBF Bck2 and Clb5 MBF DNA synthesis Clb? SCF P Sic1 Cln2 Sic1 Clb5 Swi5 Sister chromatid separation Unaligned Xsomes Cdc20 Clb5 Clb2 Cdh1 Clb2 Cdc20 Sic1 Clb2 Mcm1 Mitosis

4. synthesisdegradationsynthesis degradation binding activationinactivation

5. Time (min) Sic1 mass Clb2 Cln2 Cdh1 Simulation of the budding yeast cell cycle G1S/M Cdc20

6. Usage Scenario Data Notebook Wiring Diagram Differential Equations Parameter Values Analysis Simulation Comparator Data Notebook ExperimentalDatabases

7. The Cell (Modeler) Cycle Outer Loop: –Define Reaction Equations Inner Loop: –Adjust parameters, initial conditions

8. Fundamental Activities Collect information –Search literature (databases), Lab notebooks Define/modify models –A user interface problem Run simulations –Equation solvers (ODEs, PDEs, deterministic, stochastic) Compare simulation results to experimental data –Analysis

9. Our Mission: Build Software to Help the Modelers Now: Typical cycle time for changing the model is one month –Collect data on paper lab notebooks –Convert to differential equations by hand –Calibrate the model by trial and error –Inadequate analysis tools Goal: Change the model once per day. –Bottleneck should shift to the experimentalists

10. Tools Specifications for defining models (markup languages) “Electronic Lab Notebooks” and access to literature, experiments, etc. User interface for specifying models, parameters, initial conditions Automated parameter estimation (calibration) Analysis tools for comparing simulation results and experimental results Database support for simulations (data mining)

11. BioSPICE DARPA project Approximately 15 groups Many (not all) of the systems biology modelers and software developers An explicit integration team Goal: Define mechanisms for interoperability of software tools, build an expandable problem solving environment for systems biology Result: software tools contributed by the community to the community