Modeling the Cell Cycle with JigCell and DARPA’s BioSPICE Software Departments of Computer Science* and Biology +, Virginia Tech Blacksburg, VA Faculty: Kathy Chen + Cliff Shaffer * John Tyson + Layne Watson * Students: Nick Allen * Emery Conrad + Ranjit Randhawa * Marc Vass * Jason Zwolak *

The Fundamental Goal of Molecular Cell Biology

Application: Cell Cycle Modeling How do cells convert genes into behavior? Create proteins from genes Protein interactions Protein effects on the cell Our study organism is the cell cycle of the budding yeast Saccharomyces cerevisiae.

mitosis (M phase) DNA replication (S phase) cell division G1 G2

Modeling Techniques We use ODEs that describe the rate at which each protein concentration changes Protein A degrades protein B: … with initial condition [A](0) = A 0. Parameter c determines the rate of degradation.

Modeling Lifecycle Data Notebook Wiring Diagram Differential Equations Parameter Values AnalysisSimulation Comparator Data Notebook ExperimentalDatabases

Tyson’s Budding Yeast Model Tyson’s model contains over 30 ODEs, some nonlinear. Events can cause concentrations to be reset. About 140 rate constant parameters Most are unavailable from experiment and must set by the modeler “Parameter twiddling” Far better is automated parameter estimation

JigCell Current Primary Software Components: JigCell Model Builder JigCell Run Manager JigCell Comparator Automated Parameter Estimation (PET) Bifurcation Analysis (Oscill8)

JigCell Model Builder (Frogegg model)

Mutations Wild type cell Mutations Typically caused by gene knockout Consider a mutant with no B to degrade A. Set c = 0 We have about 130 mutations each requires a separate simulation run

JigCell Run Manager

Phenotypes Each mutant has some observed outcome (“experimental” data). Generally qualitative. Cell lived Cell died in G1 phase Model should match the experimental data. Model should not be overly sensitive to the rate constants. Overly sensitive biological systems tend not to survive

Comparator

BioSPICE DARPA project Approximately 15 groups Many (not all) active systems biology modelers and software developers represented 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

Tools Specifications for defining models (SBML) Standards for data representation, APIs Simulators (equation solvers; stochastic) Automated parameter estimation Analysis tools (plotters, bifurcation analysis, flux balance, etc.) Database support for simulations (data mining)