Team:BCCS-Bristol/Modeling

Approach

Due to the nature of this project, a wide range of modelling techniques are required to help understand ideas and guide experimentation in the wet lab. The reason for this is that in addition to the creation of synthetic genetic circuits to regulate the internal state of a cell, we require the cell to physically interact with the environment in such a way that by bringing a large number of them together, all following to same rules, emergent behaviours are exhibited - the ultimate outcome of the project. To do this it is necessary to take into consideration the movement (chemotaxis) and physical force exerted between large numbers of cells in an environment as well as other aspects such as chemical diffusion, fluid dynamics and individual cell states.

Both types of model will make heavy use of computing resources. We are looking to utilise MATLAB for tasks such as numerical integration and statistical analysis and Java for the development of a stochastic agent based framework that will allow us to simulate bacteria chemotaxis, physical interactions between bacteria/particles and chemical fields, including diffusion. The simulation environment will form the majority of the modelling work and once we can incorporate the GRN dynamics within each bacterium (agent) we hope to be able to perform virtual experiments that mimic those in the wet lab.

The major limitiation of this approach is that the simulation of huge numbers of bacteria requires huge amounts of computing power. This means that for any reasonably sized simulations we will have to seriously consider optimising our code to make most efficient use of local resources (e.g. multi-threading) and for larger simulations allow for execution on distributed computing architectures. With this in mind we are aiming to gain access to the [http://www.acrc.bris.ac.uk/acrc/hpc.htm Blue Crystal] high performance computing cluster at the university to attempt some large scale simulations.

Model Details

Gene Regulatory Networks (GRNs)	Stochastic Agent Based Simulation	Hybrid Model
To understand the dynamics of genetic networks that we design, differental equations are used to model the changes in concentration of mRNA and proteins.	With the project requiring global behaviour to emerge via the physical en masse movement of bacteria, stochastic agent based simulations are used to investigate the viability and limits of different approaches.	With the aim of improving simulation accuracy this model integrates the intercellular GRN dynamics with the extra-cellular interactions of the stochastic agent based simulation.

Modelling Notebook

• Progress Report - To track our progress see our weekly reports. These let you know what we have been up to, including comments on the reasons behind any important decisions that were made.
• Modelling Parameters - Full listings of all parameters investigated for the modelling part of the project, with values that have been chosen, reasons for their selection and references to the original sources.

Results

Coming soon!