Team:Calgary/Modelling

Updates

The modelling aspect for our project is aim to develop programs that enable us to predict and evaluate the Autoinducer-2 (AI-2) signalling system. By creating such models to represent the signalling system, a foundational advancement can be made in the field of Synthetic Biology. We aim to not only create models that accurately represent our system; we plan to use different types of software that make modelling easier. We do this in hope that it will allow individuals with less knowledge in modelling to be able to model their system of choice. As well, mathematical modelling is an important facet to this project because it allows different teams that decide to use our system to know how to optimize the circuit in order to obtain the best results. This year, we decided to model our system in two different ways: Membrane Computing and Matlab-based Modelling.

Membrane computing is a specific branch in computer sciences that focuses on acquiring computational models form the structure and functionality of cells. The knowledge from how cells in tissues are organized, distributed and parallel computing models, as well as compartmentalization and basic data structures can be developed. Compartments are important, especially when representing biological systems because it allows the organization of structures in a hierarchical way. Membranes from a cellular sense, acts as a barrier that divides organelles from their environment and they involved reactions that take place within the compartments of the cell. This allows the compartments to communicate between cells and the environment. Many different types of modelling such as the P system have been developed to model different systems. Ultimately, the goal of membrane computing is to advance our knowledge in bacterial communication in nature by examining individual behaviour of each bacterium. Two goals are developed for the membrane computing team:

1. Using the AHL signalling system that was investigated heavily by the 2008 wetlab iGEM team, basic interactions between agents are explored.
2. Using the AI-2 signalling system developed this year, a computational model will be built.

Through the development of these models, the reactions and their parallel effects can be monitored.

System characterization is essential for understanding the effects specific conditions and inputs by simulation. By using results that are collected from modelling and simulation, optimizations through experimental means are reduced. The combination of mathematics and engineering principles, combined with systems biology can potentially solve many complexities in experimental sciences. If a system can be successfully modelled, there is the potential of reducing money and resource allocations to experimental science. As well, through the use of simulation, certain conditions can be applied to optimize certain results. The goals for the mathematical modelling team are:

1. Develop the stochastic and differential model for the AI-2 signalling system. This will be done in Simbiology, a toolbox in MATLAB that allows individuals to model, design, simulate and analyze different biochemical pathways.
2. Test and model different characteristics of the system: transfer function, response time and the effects in changes of temperature.
3. Robustness of the signalling system.
4. Examine the effects of different synthetic constitutively-active promoters of different strengths.

In nature, biological circuits are robust, which means that their function is insensitive to natural fluctuations in their components. Many engineered circuits can perform a given function, but very few can perform robustly in cells.

Interactions between computing and biology have led to developments in different types of disciplines (mathematical modelling and membrane computing). Membrane computing specifically looks at bacterial communication that happens outside the cell, while matlab-based modelling concentrates on characterizing and optimizing the compatibility of the signalling system. In hope, these two models can provide a greater understanding of the pathway and emphasize the complexities that are linked with systems biology.