IGEM:IMPERIAL/2007/Cell By Date/Design

Cell by Date

• Introduction
• Specifications
• Design
• Modelling
• Fabrication
• Testing
• Validation
• Notes
• References

This is the main page for Cell By Date 2 this page will be split up into various sections in the future.

1. Our Target : Beef

We all know that beef goes off. The dominant organisms leading to the spoilage of beef depend of the beef's composition and the environmental conditions under which the beef is stored. For refrigerated packaged beef Pseudomonas spp. were dominate areobically while Lactobacillus was dominant anaerobically. (12.Labuza,1993)

There also seems to be a general rule for beef that when the bacterial count reaches 10^7cm^-2 , off odours and slime production occour and the beef is considered off. (13.Food Hygiene,Microbiology and HACCP)&(10.Leak,1999)

The Gompertz's model is widely used when considering beef spoilage as it has been shown to fit growth data very well(12.Labuza,1993). Using the Gompertz model we can get the specific growth rate, Lag phase duration (LPD) and maximum population density (MPD) for bacterial growth at a particular constant temperature. And then using these we can determine the Activation Energy Ea, for the beef spoilage rxn. For U2 grade Argentinian beef stored in polyethylene and SARAN PVC the Ea ranged from 80kJmol^-1 to 220kJmol^-1 for a range of bacteria. (11.Giannuzzi,1997)

One contary value for the Ea of the beef spoilage rxns is given by Leak (10.Leak,1999) who calculated Ea=30kJmol^1. The difference between Leak's value and that of Giannuzzi probably lies in their packaging methods, i do not know Leak's packaging methods so this is just a guess.

2. Our System : pTET mimicing a TTI

Several Technologies have already been developed to addressed the problem of monitoring the heat exposure of products in the cold chain. One particular family of these products are called Temperature Time Integrators (TTI).(2.Labuza,2006)

The key aspect of a TTI is that they are based on a phenomenon which can act as a signal to a consumer for example, eg. a colour change. The rate at which this change occours needs to be temperature dependant so it can mimic the effect temperature has on the spoiling of meat eg. change happens quicker at higher temperatures. In order for a TTI to accurate report the spoilage rxn of beef the activation energy of the two rxns needs to be similar. For example a difference between the two Ea's less than 20kJmol^-1 would result in the TTI estimating the thermal history of the beef to be within 1 degree C of the actual history.).(1.Taoukis,2006)

I have tried to calculate the Ea of our system, pTET linked to Mut3BGFP, and have got a value of 1.5kJmol^-1. If we take the Leak's Ea of the spoilage rxn to be 30kJmol^-1 then our TTI will be accurate to 2 Degrees. However if we take the Giannuzzi's highest value of 220kJmol^-1 and assuming Taoukis estimation rule to be linear, our TTI will be accurate to 10 degrees C when considering the thermal history of the beef.

The only thing we can change with our system is the DNA concentration and we are currently researching the effect this will have on our system. My guess is that it will not change the activation energy of our system but merely the extent to which our system's output is visible.

3. End Result and what has to be done yet

The deliverables of this project pertain to how effective our system is as a TTI for hamburger meat.

One deliverable of this project would be a specification sheet showing how well our system works in a variety of scenarios. By looking at the various plots I would like on this specification sheet I can determine what needs to be done in the next few weeks. Firstly I would like to generate a isothermal 'shelf life curve' of our system which seems to be the method used in industry to represnet how a TTI works (Labuza,2006). Secondly I would like to present how system behaves under some dynamic temperature conditions, representing the potential breakdowns in the cold chain as this is the main area in which TTI are useful (Tauokis,2006).

Now working backwards from this end point I can see what needs to be done:

1. Define & verify shelf lifes for meat (for a given packaging & type) under as many temperature scenarios as possible
2. Research which of the scenarios in 1 we can re-create in our lab
   1. Temps above room temp : can leave for days on end
   2. Temps below room temp :
      1. 4 degrees can leave for days on end
      2. Any other temps will can only do for 9-5 (6 hour period)
      3. May be able to achieve above by shuttling stuff between Kirsten's lab and our own (not a very good method)
3. Precict what will happen in each scenario we can re-create eg. at 4 degrees C we may not get a visible signal after 8 days
4. Research increasing lifespan of sytem by adding ATP and t-RNA at the start of system life
5. Determine optimum level of DNA concentration to use
   1. This will happen tomorrow for pTET at 37 degrees C ( as this is a temp we are sure to get expression at)
6. Determine packaging for system eg. PCR tube so that evaporation won't be a problem
7. Calibration curve for DsRed Express so that we can give meaning to our fluoresence levels

Rough Timetable:

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Thrusday 27th Setpember : Determine time taken to go off at 37 degrees C to see if we can use conc expt for something else

                         Check we have enough maxiprep DNA for tomorrow
                         Check we have ordered another batch of cell extract for next week

Friday 28th September : Determine Temperature Scenarios for next week

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Monday 1st October - Friday 5th October : pTET-Mut3B temperature scenario tests

Sunday 7th October : Preliminary Presentation (20 slides in total) and Wiki submission deadline

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Monday 8th October - Friday 14th October : pTET-DsRED temperature scenario tests

                                          DsRed Calibration curve 

Wednesday 10th October : Preliminary Write Up (presentation & wiki) Review : decide what left to be done

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