Transcript Deterministic Global Parameter Estimation for a Budding

Modeling the Cell Cycle with JigCell and
DARPA’s BioSPICE Software
Faculty:
Kathy Chen+
Cliff Shaffer*
John Tyson+
Layne Watson*
Students:
Nick Allen*
Emery Conrad+
Ranjit Randhawa*
Marc Vass*
Jason Zwolak*
Departments of Computer Science* and Biology+,
Virginia Tech
Blacksburg, VA 24061
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.
cell division
mitosis
(M phase)
G1
G2
DNA replication
(S phase)
Modeling Techniques
We use ODEs that describe the rate at which each
protein concentration changes

Protein A degrades protein B:
d [ B]
 c[ A]
dt
… with initial condition [A](0) = A0.
Parameter c determines the rate of degradation.
Modeling Lifecycle
Data Notebook
Wiring Diagram
Differential Equations
Analysis
Experimental
Databases
Parameter Values
Simulation
Comparator
Data Notebook
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)
http://jigcell.biol.vt.edu
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)