BME494s2013 Project Team1: Difference between revisions
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[[Image:Bgalv1.jpg|thumb|right|210px|Figure 1: Original Bgal Concentration vs. Time with I = 0.32]] | |||
[[Image:Bgal0.25.jpg|thumb|right|210px|Figure 2: Bgal Concentration vs. Time with I = 0.25]] | |||
[[Image:Bgal0.064.jpg|thumb|right|210px|Figure 3: Bgal Concentration vs. Time with I = 0.064]] | |||
===AN INTERACTIVE MODEL=== | ===AN INTERACTIVE MODEL=== | ||
We used a model of the natural Lac operon to understand how changing the parameter values changes the behavior of the system. By changing the initial concentration of input (IPTG in this case), we were able to estimate the threshold that produces an "on" state in the system. | We used a model of the natural Lac operon to understand how changing the parameter values changes the behavior of the system. By changing the initial concentration of input (IPTG in this case), we were able to estimate the threshold that produces an "on" state in the system. | ||
Initially, the code had the concentration at 0.32 which is seen in the β-galactoside (Bgal concentration) vs. time plot (Figure 1). This value was changed again to 0.25 in determining the threshold that produces this "on" state (Figure 2). After proceeding to go up and down with these a values, a threshold was indeed found where the concentration of IPTG is about 0.064 (Figure 3). | |||
<!-- Continue this paragraph by explaining how you interacted with the MatLab model. Include two or more images showing different output curves that were generated when you altered the IPTG concentration --> | <!-- Continue this paragraph by explaining how you interacted with the MatLab model. Include two or more images showing different output curves that were generated when you altered the IPTG concentration --> | ||
Revision as of 14:36, 28 April 2013
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Overview & PurposeSarah
Background
"Only LacZ and LacY appear to be necessary for lactose catabolism" [3].
Design: Our genetic circuitJulia OUR GENE SWITCH: THE PARTS <tab>pSB1A3-1 is a high copy number plasmid. The replication origin is a pUC19-derived pMB1 (copy number of 100-300 per cell). The terminators bracketing pSB1A3 MCS are designed to prevent transcription from inside the MCS from reading out into the vector.
Building: Assembly SchemeEmily
Testing: Modeling and GFP Imaging
A LAC SWITCH MODELWe used a previously published synthetic switch, developed by Ceroni et al.[5], to understand how our system could potentially be modeled and simulated. The graphic to the left depicts the relationships between the parameters of the Lac Operon switch described by Ceroni using a network diagram illustration. The parameters shown in the illustration relate to cell processes and could be used in forming a cohesive mathematical model of the cell's operation. In order to approximate the behavior of this set-up, a mathematical model can be developed based upon the relationships between the processes found in the cell. These relationships can be expressed in mathematical terms using numbers that relate to the system, including creation or decay rates, concentrations, or various constants. The actual values for these parameters can be sourced from experimentation, literature, or a predefined steady-state. If a model is well-defined and the necessary parameters known, a person may use the model to ascertain the state of a cell at a given point in time. For example, if an experimenter wanted to know the decay of the GFP protein molecules at a given point in time in a single cell, the following equation could be written using the notation found in the table below. Decay = G × λG/L The formula takes the concentration of the GFP protein in molecules per cell ("G") and multiplies it by the protein degradation rate in minutes-1 ("λG/L"). This results in a decay value for GPF in molecules per minute per cell. The Ceroni et al. model and the network diagram illustration use the table of variables and parameters seen below in their representation of the Lac switch. The variables related to a particular cell process are located near to that process in the network diagram illustration.
AN INTERACTIVE MODELWe used a model of the natural Lac operon to understand how changing the parameter values changes the behavior of the system. By changing the initial concentration of input (IPTG in this case), we were able to estimate the threshold that produces an "on" state in the system. Initially, the code had the concentration at 0.32 which is seen in the β-galactoside (Bgal concentration) vs. time plot (Figure 1). This value was changed again to 0.25 in determining the threshold that produces this "on" state (Figure 2). After proceeding to go up and down with these a values, a threshold was indeed found where the concentration of IPTG is about 0.064 (Figure 3).
COLLECTING IMPERICAL VALUES TO IMPROVE THE MODELSarah
Stakeholder Assessment
SUPPORTS & UNDERSTANDS
Our Team
Works Cited |