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Systems and Synthetic Biology

Simulation of Myxobacteria fruiting body formation (Antony Holmes)


In this project we are interested in applying computational methods to better understand populations of cells: how they communicate, how they form tissues, and how they generate complex behaviour. In particular, we have been studying how populations of hundreds of thousands of bacteria coordinate their movement in order to have a robust and interesting life cycle. Some bacteria of interest live on surfaces (such as myxobacteria) while others also can move in a liquid medium.

The following papers give some insight into our approach:

Comparing DNA sequences of maize exposed to environmental stress (Christopher Barrington)

Maize Epigenetics

Epigenetics can be defined as the variations on the expression of genes which are temporary but can be inherited over some generations. The "central dogma" of molecular biology is the mapping of nucleotide sequence to cell function via DNA, RNA, and Protein function and shape. A little understood aspect of this sequence of mappings the modulation of this mapping by bindings made to either DNA or RNA.

In this project, we are looking at the effects of environmental stress (such as cold or hot spells) on maize plants, specifically the variations of gene expression over several phases of development.


Synthetic Biology

Synthetic Biology aims to take the knowledge already understood about how cells work and use it to modify cells or even create new ones. Genetic engineering has been used and improved over several decades; with a synthetic biology approach we attempt to develop a more fine-tuned approach. A brief introduction to the subject can be seen in an overview talk.

In a a three-year project in collaboration with Nottingham, Sheffield, and Newcastle Universities we are using principles from the design of programming languages to build tools that can refine a high-level description of biological functionality into precise specifications of what modifications at the nucleotide level need to be made. These modifications are optmized to allow the `new' type of cell to be viable, for the genome changes to not result in undesired properties, and for the specifications to be implementable within current genetic engineering technologies.

biological cell

Digital Cell

Modelling biological systems relies on partial confirmation of results via computational simulation. Mathematical models in common use (whether through differential equations or more sophisticated stochastic approaches) simplify the challenge by considering the cell as a uniform volume where entities are well mixed. Sometimes this can be an over-simplification

We are developing simulation tools which capture in a more detailed way the localization within cells, while still keeping the problem tractable. We are looking at how cells can be compartmentalized and flux between compartments is captured.