Team:Calgary/Modelling/Basic

Overview

• Updates
  
• Notebook & Parts

Membrane Computing Modelling

• Introduction
  
• Tutorials
• Results
• Paper

Differential Equation Modelling

• Method
  
• Results
  
• Basic Definitions

We've reached modelling, the fifth stop on our tour! We've looked in to two different methods of modelling our system: Differential Equation Based Modelling and Membrane Computing. Here, you can explore the similarities and differences, as well as the functions of each method. As well, you can find the results of our characterization of the signalling pathway. Once you're done, we'll move on to the Second Life component of the project HERE.

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OVERVIEW

System characterization allows us to examine the effects of certain conditions and inputs by simulation. It is practical to use simulation to evaluate certain conditions and thus optimize results. This may potentially save money and resources as well as experimental time dedicated to a project. The foundations of mathematics and engineering principles when combined with systems biology can potentially solve many complexities inherent in experimental sciences. The differential equations based modelling team have used computational software (Matlab and Simbiology) built the signalling cascade in the interface and have successfully ran simulations that can answer some of the complex problems faced in the lab.

There are two types of models that we have considered:

1. Differential: This model uses equations that involved derivatives to describe the systems' species. By solving the equation, it is possible to find how concentrations of species changes over time relative to the initial conditions. This is effective for modelling homogeneous systems with high concentrations of chemicals and it is best used for smaller networks. 2. Stochastic: This model is based on probabilistic equation to describe the likeliness of certain chemical reactions to occur between certain types of molecules in networks. These equations can be used to calculate quantities of all species of molecules at the end of a small time step based on initial conditions and random variable input. It is possible to determine how quantities change over time after multiple time steps. Each simulation results in slightly different plots and it requires several trials to determine the average behaviour. This type of model is best used for small numbers of molecules because this type of model takes into account of random nature of molecular interactions and the probability of rare events occuring.