Team:IIT Bombay India/Project

Multiple feed-backs in biological systems

The effect of multiple feedback loops on biological system is unclear. In our iGEM -2009 project we have experimentally demonstrated the effect of multiple feedback loops on the gene expression using synthetic genetic circuits with none, one and two negative feedback loops. The plasmids were designed using standard biobricks available using pLac and pTet promoters with inducible and fixed copy numbers. Experiments and modeling studies demonstrated that the system with multiple feedback loops was less noisy compared to others. The copy number was quantified by measuring YFP expression using FACS to obtain the mean expression and variability. Control feedback analysis demonstrated that gain margin increases and phase sensitivity indicated reduction in the noise due to multiple feedbacks. Growth experiments on lactose were also conducted to characterize the effect of synthetic network on the phenotypic response. The experiments and modeling studies demonstrated that the variability observed in the protein expression was propagated to the phenotypic level. Both agar plate experiments and growth rate measurements demonstrated lower variability for the strain with multiple feedback loops as compared to the open system. The growth experiment also demonstrated that the strain with multiple feedback loop design could optimally synthesize proteins to the availability of lactose thus minimizing burden of protein synthesis and maximizing growth rate.

Modeling study

We have developed models for the expression of copy number (as YFP) and lacI (as CFP) for the four constructs (zero feedback (open loop), single feedback (on copy number), single feedback (on LAcI) and double feedback (on both copy number and LacI). Three modeling strategies have been attempted.

1. Detailed mechanistic model accounting for LacI binding to the promoter site and balance on the copy number and LacI concentration. Effect of IPTG on protein expression as measured by YFP was characterized and compared with model. Further the model was extended to represent synthesis of beta-gal expression and was related to growth on lactose. The model was able to capture the experimental observations. The simulations also indicated the burden versus growth for the various strains developed.

2. A phenomenological model was developed to represent the four constructs and langevian approach was used to estimate the variability due to the stochastic process.

3. The model was linearized around LacI expression and the system was represented in a block diagram to carry out the feedback analysis. Frequency response analysis using magnitude and phase Bode plots was used to characterize the effect of multiple feed-backs. Magnitude bode plot for the sensitivity function demonstrated that the noise was reduced for the multiple feedback system. External white noise was introduced into the block diagram to study its effect. All simulations were developed using simulink platform of MATLAB.