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Details of Grant 

EPSRC Reference: EP/P017134/1
Title: CONDSYC- CONtextual Design of SYnthetic Circuits
Principal Investigator: Bandiera, Dr L
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: EPSRC Fellowship
Starts: 01 April 2017 Ends: 31 March 2020 Value (£): 280,667
EPSRC Research Topic Classifications:
Synthetic biology
EPSRC Industrial Sector Classifications:
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Panel DatePanel NameOutcome
13 Feb 2017 Eng Fellowship Interviews Feb 2017 Announced
01 Dec 2016 Engineering Prioritisation Panel Meeting 1 and 2 December 2016 Announced
Summary on Grant Application Form
Cells run on programs made of DNA. Although we can read and write in this biological code, the set of rules through which it is executed changes over time, according to the signals the cell receives from the surrounding environment. This strategy ensures cell health, but complicates our efforts to have microbes perform additional functions, for example to produce a drug of interest. To implement a new function in a cell, we need to modify or add new DNA code to the existing master program.

But how should we write it? Can we predict in advance how the cell will execute it under different environmental conditions? Can we identify conditions in which the added code will not impair cellular functions while providing the desired output?

To address these questions I will insert in yeast cells pieces of 'DNA code' written to perform functions of increasing complexity: single units (biological parts) and interconnected units (synthetic circuits). The function they implement will be controlled using chemicals provided to the cells and observed by measuring a fluorescent signal that reports on the executed task. I will then use fluorescence images of the cells, acquired through time-lapse microscopy, to follow over-time the performance of the synthetic circuits under various environmental conditions. These environmental conditions will be selected so as to uncover the execution mechanisms, while minimizing the number of experiments necessary to gain this understanding. Linking the selected environmental conditions to the measured tasks, I will write mathematical formulas, which will allow the output of the code to be predicted under untested experimental conditions.

The set of identified execution rules will enhance our ability to write 'DNA code' that is executed by the cells in the way we desire. We will thus be able to guide the design of code yielding more complex functions with potential applications from medicine to biotechnology.

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