Modeling%20the%20Cell%20Cycle%20with%20JigCell%20and%20DARPA

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Modeling the Cell Cycle with JigCell and DARPAs
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

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The Fundamental Goal of Molecular Cell Biology
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ApplicationCell 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.

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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) A0.
• Parameter c determines the rate of
  degradation.

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Modeling Lifecycle
Data Notebook
Wiring Diagram
Differential Equations
Parameter Values
Analysis
Simulation
Comparator
Data Notebook
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Tysons Budding Yeast Model

• Tysons 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

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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

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JigCell Model Builder
(Frogegg model)
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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

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JigCell Run Manager
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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

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Comparator
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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

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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)