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Team:IIT Bombay India
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| + | == '''Stochastic modeling of the laci system with multiple feedback using langevin approach''' == | ||
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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. | ||
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| + | 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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Stochastic modeling of the laci system with multiple feedback using langevin approach |
| 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.
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