Team:Imperial College London/Drylab/Protein Production

Protein Production Based on the module 1 genetic circuit, a LacI-IPTG inducible promoter is responsible for kickstarting the production of the drug.
 * In the absence of IPTG, LacI represses the production of the drug (Cellulase or PAH)
 * When IPTG is introduced, the LacI repressing pathway is “de-repressed”, and some output protein is produced.

Our goals
The modelling aims to provide an overview and better understanding of the M1 system’s function by:
 * Characterizing the system.
 * Modeling to account for several factors that may reduce/hinder the production of the protein drug such as:
 * Lac promoter leakiness
 * IPTG toxicity
 * Stability of output protein

This module is an integral part of the design, as large-scale commercialization of the drug of interest depends on finding the optimal conditions for protein production. We implemented a system of differential equations, having made some assumptions and predictions about how the system will behave.

 About the model assumptions and predictions! 

The System
Genetic circuits can be simplified using ODEs. A good introduction to modelling of genetic circuits is provided in [3]. By clicking on the link below we can see how genetic circuits were implemented in this system.

 about the equations and what they mean! 

Summary of simulation results

 * When we introduce IPTG into the system, it temporarily removes LacI from the system. Hence, during this period of time, we produce the drug of interest.
 * When the effects of IPTG wear off, the system returns to equilibrium.
 * The more IPTG we add in, the higher the amount of output protein.

<img style="vertical-align:bottom;" width=50px align="left" src="http://i691.photobucket.com/albums/vv271/dk806/II09_Learnmore.png"></a> about the simulations! </i></b>


 * The effects of IPTG toxicity were investigated and we found that for these concentration ranges, IPTG is not toxic to cells. Click on the link to see the analyzed results:See IPTG growth curves
 * The constants in this model are arbitrary. We justify our usage of these values with a more detailed dynamical analysis of the system, which shows that it can only have fixed points[1]. [[Media:II09_Prot_stability analysis.pdf | System stability analysis (read about the equations before opening this file, it's intimidating!)]]

Conclusions
NOTE: These will be better understood once the reader has gone through the details ("Learn More").
 * The greater the strength of the Lac promoter, the greater the repressive action of LacI prior IPTG induction.
 * The greater the Lac promoter leakiness (kleak) the greater the basal amount of expression of protein of interest, prior IPTG induction.
 * The greater the amount of IPTG introduced, the greater the production of protein of interest.
 * Here we assumed that the range of IPTG we have introduced is non-toxic for our cells. Growth curves will tell us whether IPTG does limit cell growth at the ranges we are interested in.

Goal of the modelling:
The modelling aims to provide an overview and better understanding of the M1 system’s function by:


 * Characterizing the LacI-IPTG system that is responsible for the production of the drug of interest.
 * Modeling and accounting for several factors that may reduce/hinder the production of our output protein of interest such as:
 * Lac promoter leakiness
 * IPTG toxicity
 * Stability of output protein

The conclusions from the models should help people from the WETLAB to plan their experiments and take into account these considerations as possible limitations/factors to look out for. Note that this module is an integral part of our design, as large-scale commercialization of our product of interest depends on finding the optimal conditions for protein production, and we would preferably like to produce a tuneable output. Hence why we think that our simulations will be useful.

Note that the pros of this type of modeling are that, although it provides us a general understanding of the system, there are several factors that we are still not accounting for. Biological processes are stochastic and there is no limit to how much “realism” we can include in our simulations. These are preliminary models, and the compromise level of detail is sufficient to provide us with the understanding we need, but the conclusion is that there is always room for improvement.

Enzymatic reactions
After IPTG is added, the Lac promoter is induced. Our protein of interest, either PAH or cellulase, is synthesized and builds up within the bacteria. In the small intestines, the capsule will be degraded and the protein of interest will released. The protein of interest will now show enzyme activity and act upon its substrate within the small intestines.

The protein of interest is an enzyme. It will bind to specific substrates and increase the rate of their conversion into products. Therefore, by monitering either the substrate concentration or the product concentration, we can indirectly see the activity of the enzyme. This is quantitated by enzyme activity, which is the rate of substrate utilisation / product formation per unit time.

The raw data to calculate enzyme activity comes from the enzyme assays conducted in the wet lab. To track the quantity of protein of interest being produced, we rely on the Michaelis-Menten plot to relate enzymatic activity to concentration of protein present.

Goal of the modelling
This modelling aims to better understand the enzymatic action of our protein of interest:


 * Characterise the enzymatic activity of our protein of interest
 * Subsequently, model the relationship between the quantity of protein being produced and the enzyme activity using a simple Michaelis-Menten enzyme kinetics model, taking into account factors that could impair enzymatic activity

This model is useful in more accurately predicting the effect of our protein of interest as these are not just simple proteins, but also have enzymatic activity in themselves.

This model will allow an understanding of the activity of the enzymes and allow the WETLAB to have an idea of the magnitude of the activity of the enzymes. More importantly, after the results from enzyme assays are obtained, the model should provide a means of relating the activity output data to the concentration of the enzyme.

Details of the model are found HERE

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