Simulate Responses to Biological Variability and Doses
Represent biological variations among different strains, patients, or experimental conditions by creating model variants and perform Monte Carlo simulations to explore the variability of model parameters that influence a model response or therapeutic endpoint. Simulate virtual patients and alternate scenarios without creating multiple copies of your model. Investigate the efficacy and safety of drugs by simulating doses. Evaluate various dosing regimens and determine the optimal dosing schedules.
Create Simulation Scenarios
|Add quantity values, doses, or variants to |
|Generate scenarios from |
|Return number of scenarios from |
|Get entry contents from |
|Update entry contents from |
|Remove entries from |
|Rename entry from |
|Add variant to model|
|Construct variant object|
|Add observable object to SimBiology model|
|Append content to variant object|
|Commit variant contents to model|
|Get variant from model|
|Remove contents from variant object|
|Construct dose object|
|Add dose object to model|
|Create dose objects from groupedData object|
|Return SimBiology dose object|
|Return data from SimBiology dose object as table|
|Set dosing information from table to dose object|
|Simulate SimBiology model|
|Create SimFunction object|
|Find steady state of SimBiology model|
|Prepare model object for accelerated simulations|
|Prepare SimFunction object for accelerated simulations|
|Determine if SimFunction object is accelerated|
|Add observable expressions to SimData|
|Rename observables in SimData|
|Get simulation data from |
|Remove simulation data from |
|Remove simulation data by name from |
|Update observable expressions or units in SimData|
|Select simulation data by name from |
|Select simulation data from |
|Generate parameters by sampling covariate model (requires Statistics and Machine Learning Toolbox software)|
|Sample error based on error model and add noise to input data|
|Multiple stochastic ensemble runs of SimBiology model|
|Show results of ensemble run using 2-D or 3-D plots|
|Get statistics from ensemble run data|
|Function-like interface to execute SimBiology models|
|Define drug dosing protocol|
|Define drug dosing protocol|
|Store alternate component values|
|Object containing expression for post-simulation calculations|
|Solver settings information for model simulation|
|Specify model solver options|
|Options for logged species|
|Dimensional analysis and unit conversion options|
Doses and Variants Basics
- Model Simulation
Simulate dynamic models using various solvers.
- Choosing a Simulation Solver
SimBiology® uses a solver function to compute solutions for a system of differential equations at different time intervals during model simulation.
- Accelerating Model Simulations and Analyses
Accelerate simulation or analysis by converting a model to compiled C code.
- Combine Simulation Scenarios in SimBiology
Combine generated samples using two different methods.
- Explore Biological Variability with Virtual Patients Using SimBiology Model Analyzer
Generate sample values for model parameters to represent virtual patients, simulate to explore tumor growth variability, and investigate the effects of dosing regimens on tumor size.
- Scan Dosing Regimens Using SimBiology Model Analyzer App
Explore multiple dosing amounts that meet efficacy and toxicity thresholds.
- Simulate Groups Using Doses and Variants from Data Set
Perform group simulation using the group-specific doses and variants from a data set.
- Import and Export Variants and Doses from Excel to SimBiology Model Builder
Import and export variant and dosing information saved in an Excel file to the Model Builder app.
- Simulate Biological Variability of the Yeast G Protein Cycle Using Wild-Type and Mutant Strains
Create and apply a variant to the G protein model of a wild-type strain.
- Simulate Model of Glucose-Insulin Response with Different Initial Conditions
Simulate glucose-insulin responses for normal and diabetic subjects.
Troubleshoot SimBiology simulation errors, such as the Integration tolerance not met error, by changing the solver or tolerances.
RelativeTolerance to control the accuracy of integration during simulation.
For model simulation, SimBiology derives ordinary differential equations (ODEs) from model reactions using mass-balance principles.