Team:IIT Bombay India/Project

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	Multiple feed-backs in biological systems

Experimental Results:

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. The model was also extended to represent the growth on lactose to study the effect of noise on the growth.

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 feedbacks. 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.

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-== '''Stochastic modeling of the laci system with multiple feedback using langevin approach''' ==
+== '''Multiple feed-backs in biological systems''' ==
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-| Biological systems are known to have a great degree of regulation in their activity, and this higher level of control is attributed to the multiple levels of feedback that exist within any biochemical pathway. As a means of gaining deeper insight within the utility of multiple feedback loops, we have constructed 4 strains containing plasmids with modified lac operon. The lacI produced as a result of the expression can inhibit its own expression, causing one level of feedback, while it can also suppress the replication of plasmid, providing another level of control on the number of processes itself. By combination of these 2 controls, four different strains are possible. We wish to demonstrate the better control in the strain with multiple feedbacks as compared to the strain with no control by characterizing the inherent or stochastic error present in the system through simulations. Further, IPTG can bind to the lacI present in the system which would act as a repressor. Thus IPTG can act as an inducer for the system. The effects of varying concentrations of IPTG are also studied, the understanding being that a system with high IPTG concentration resembles that of an open loop system.
+| Experimental Results:
+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.
+. 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.
+. A phenomenological model was developed to represent the four constructs and langevian approach was used to estimate the variability due to the stochastic process. The model was also extended to represent the growth on lactose to study the effect of noise on the growth.
+. 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 feedbacks. 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.
-Further, any genetic regulation is manifested in the phenotype observed. Since lac operon is concerned with the successful utilization of lactose, we also characterize the growth of the system on lactose. The unrestrained expression of lacI represents a burden on the system, since lactose would be taken away by the existing lacI. Thus in the strain with multiple feedbacks, since it exhibits a greater control and reduction in noise for lacI expression, we also expect it to show greater growth with lesser error.  Thus, ultimately, the genetic regulation achieved on the expression of lac operon is shown to ultimately control an observed phenotype, which is the culture growth.
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