SimBiology

Modeling

SimBiology lets you represent a model of a biological or a pharmacological mechanism just as you would draw it on a piece of paper. Using a reaction network modeling approach, SimBiology lets you model drug pharmacokinetics and pharmacodynamics (PK/PD), biological systems, and chemical reaction kinetics.

SimBiology automatically constructs the ODEs based on the model structure and the math underlying individual interactions, providing an alternative to an ODE-based representation of the model.

You can create models using a block diagram editor or programmatically. You can also import models from a built-in library of PK models or a Systems Biology Markup Language (SBML) file.

Diagram view of a gene regulatory network in the SimBiology block diagram editor (right). The model wizard (left) lets you automatically create PK models based on user-defined parameters.

Building Models

SimBiology models consist of three basic building blocks:

Species represent dynamic states of the model, typically the concentration or amount of an entity, such as a drug, protein, gene, or metabolite. Species are connected to each other via reactions.

Reactions represent interactions between one or more species, such as transformation, flow, transport, and binding processes.

Compartments represent physically isolated regions in which you can associate sets of species.

Specifying Model Dynamics

SimBiology provides two additional modeling constructs for specifying model dynamics:

Rules let you define relationships between model components that cannot be represented as a reaction. For example, you can set the value of a parameter as a function of the value of another parameter or the concentration of another species.

Simulation events let you define a sudden, discrete change in model behavior based on a condition you specify. For example, you can use an event to reset a parameter value at a certain time point or when a certain concentration threshold is crossed.

Exploring a Model

SimBiology lets you explore what-if scenarios without creating multiple copies of the same model. You can create model variants to store a set of parameter values or initial conditions that is different from the base model configuration. For example, you can use model variants to store parameter values for multiple cell lines, drug compounds, or mutant strains. Similarly, you can create multiple dosing strategies and apply them to evaluate model responses.