Difference between revisions of "CellularMemory:Hysteresis in Mammalian Cells"
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==Mathematical Modeling== | ==Mathematical Modeling== | ||
− | Again, the mathematical modeling behind this biological design will not be discussed in detail, as the [[CellularMemory:Mathematical Models |mathematical modeling]] section of this wiki paper has covered most of the principles behind the modeling of memory circuits already. In short, a differential equation was derived that described the rate of SEAP production in terms of activator concentration and EM concentration. The degradation rate of the activator, the promoter strength, and the dissociation constant for EM binding to the repressor were all determined/estimated based on experimental data. Cooperativity of both the activator and the repressor was set equal to 2. By inputting these constants into the differential equation for the rate of SEAP production and setting it equal to 0, the concentration of EM required to move between states could be calculated for a given initial concentration of activator in the system. This revealed hysteresis. The concentration of EM required to move from the on to the off state was calculated to be approximately 500 ng/mL. Depending on the basal expression of the activator in the off state, the concentration of EM required to move from the off to the on state was shown to vary, however, for normal levels of basal expression, this EM concentration was estimated to be >800 ng/mL. Once again, cooperativity of binding was required to achieve bistability in the system. This mathematical model also utilized experimentally determined constants to predict the input and output values of the system. These mathematical predictions could then be compared to experimental results (see Figure 3 below). | + | Again, the mathematical modeling behind this biological design will not be discussed in detail, as the [[CellularMemory:Mathematical Models |mathematical modeling]] section of this wiki paper has covered most of the principles behind the modeling of memory circuits already. In short, a differential equation was derived that described the rate of SEAP production/dilution in terms of activator concentration and EM concentration. The degradation rate of the activator, the promoter strength, and the dissociation constant for EM binding to the repressor were all determined/estimated based on experimental data. Cooperativity of both the activator and the repressor was set equal to 2. By inputting these constants into the differential equation for the rate of SEAP production/dilution and setting it equal to 0, the concentration of EM required to move between states could be calculated for a given initial concentration of activator in the system. This revealed hysteresis. The concentration of EM required to move from the on to the off state was calculated to be approximately 500 ng/mL. Depending on the basal expression of the activator in the off state, the concentration of EM required to move from the off to the on state was shown to vary, however, for normal levels of basal expression, this EM concentration was estimated to be >800 ng/mL. Once again, cooperativity of binding was required to achieve bistability in the system. This mathematical model also utilized experimentally determined constants to predict the input and output values of the system. These mathematical predictions could then be compared to experimental results (see Figure 3 below). |
==Results== | ==Results== | ||
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==Conclusions== | ==Conclusions== | ||
− | The hysteretic system described in this paper was one of the earliest attempts to construct a memory network in a mammalian cell. As the authors state, this paper showed that "network design principles evolved in ''E. coli'' also function in a mammalian cell. Such genericness among gene control circuitries increases hope for successful therapeutic interventions in future gene therapy and tissue engineering" (Kramer, 2005). In other words, if gene circuits such as this one can be tested in simple systems and moved into more complex ones, then the prospect of one day having the ability to control such things as human cell differentiation is not as far off as might be expected. This technology has vast implications for all | + | The hysteretic system described in this paper was one of the earliest attempts to construct a memory network in a mammalian cell. As the authors state, this paper showed that "network design principles evolved in ''E. coli'' also function in a mammalian cell. Such genericness among gene control circuitries increases hope for successful therapeutic interventions in future gene therapy and tissue engineering" (Kramer, 2005). In other words, if gene circuits such as this one can be tested in simple systems and moved into more complex ones, then the prospect of one day having the ability to control such things as human cell differentiation is not as far off as might be expected. This technology has vast implications for all sorts of [[Medical_Applications_of_Synthetic_Biology_-_Samantha_Simpson |medical treatments]]. |
In addition to successfully engineering and modeling a gene network in mammalian cells, this work also demonstrates the design of a hysteretic memory system which could have applications in many types of more complex synthetic devices. In the [[CellularMemory:Permanent Memory in Eukaryotes |next paper]], a similar network will be discussed that confers permanent memory. | In addition to successfully engineering and modeling a gene network in mammalian cells, this work also demonstrates the design of a hysteretic memory system which could have applications in many types of more complex synthetic devices. In the [[CellularMemory:Permanent Memory in Eukaryotes |next paper]], a similar network will be discussed that confers permanent memory. |
Latest revision as of 19:46, 6 December 2007
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