|M.Sc Student||Chaimovitz Einel|
|Subject||The Regulation of Genes at an Oscillating System|
|Department||Department of Biomedical Engineering||Supervisors||Dr. Ramez Daniel|
|Professor Netanel Korin|
Synthetic biology aims to integrate multi signal and process them in living cells for diagnostic, therapeutic and biotechnology applications. Synthetic biology achieves this by the use of control theory laws derived from mechanical, electrical and computer science fields to devise genetic circuits. The dynamics of genetic circuits can be expressed by using both Michaelis Menten kinetics and Hill equation. While this dynamic set of equations is easy to solve, regulate and simulate, it does not take into account the biological noise. Sources of biological noise are grouped to intrinsic and extrinsic noises. There are many models to predict noise, for specific circuits and parameters, those models are difficult to adapt and don’t offer simple base for comparison. Understanding of the noise is critical when designing genetic circuits, as noise is always present in biology. I will show four different variants of a genetic circuit and compare between them with the method I developed. I will also offer a simple baseline for comparison of noise for Escherichia coli which will be a marker for the complexity of the circuit. This method will help me to derive the optimal circuit for application in a genetic clock. Our aim is to develop a fast, robust genetic clock with no delay or negative feedback. Genetic clock is a building block for biology in general (heart beats, sleep cycles, neuron activity are some example for periodic processes in nature) and for computing machines. I believe that our design for genetic clock will perform better than existing design and integrating it to a microfluidic chip will facilitate a robust continuous oscillation. Microfluidics is an emerging technology; some generic designs are available for simple extended experiments. Those chips offer limited control on the experiment parameters, cells can move randomly and cannot be tracked and the chips contaminate easily. Recently, Ferry et 2011 has shown a custom chip solving those limitations for investigating metabolic processes in nature. In this work, I established protocols for developing and manufacturing microfluidics chips, for assessing the biological noise, using time-lapse microscopy, of MG1655 Escherichia coli strain used in our lab imprinted with several genetic circuits and designed a software tailored for our equipment parameters.