<div class="accordionContent",><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Overview"><b>Here is a short overview of the two models.</b></a><br /><br /></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten"><b>Model based on Michaelis Menten Kinetics</b></a><br />Comparison between different amplification models (HIV1 and TEV) based on Michaelis Menten kinetics. However, Michaelis Menten kinetics does not apply to our system. Therefore, it had to be modelled from first principle (see below using law of mass action).<br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten#HIV1"><img src="http://www.openwetware.org/images/0/03/Slide2.JPG" height="150" width="200" alt="Model using HIV1"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten#TEV"><img src="http://www.openwetware.org/images/4/48/TEV.jpg" height="150" width="200" alt="Model using TEV"/></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action"><b>Model based on Law of Mass Action</b></a><br />Comparison between these 3 different models: Simple production, 1-step and 2-step amplification.<br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_preA:_Simple_Production_of_Dioxygenase"><img src="http://www.openwetware.org/images/7/7f/Simple_production.JPG" height="100" width="300" alt="Simple Production"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_A:_Activation_of_Dioxygenase_by_TEV_enzyme"><img src="http://www.openwetware.org/images/1/1c/1-step_amplification.JPG" height="100" width="300" alt="1-step amplification"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_B:_Activation_of_Dioxygenase_by_TEV_or_activated_split_TEV_enzyme"><img src="http://www.openwetware.org/images/0/02/2-step_amplification.JPG" height="100" width="300" alt="2-step amplification"/></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Variables1"><b>Variables and Constants</b></a><br />Here are the variables and constants that are used in the Output Amplification Model.<br /><br /></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Protein_Display"><b>Protein Display Model</b></a><br />This is a model showing when the ComD receptor will be activated (after proteins have been cleaved).<br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Variables2"><b>Variables and Constants</b></a><br />Here are the variables and constants that are used in the Protein Display Model.<br /><br /></div>
<div class="accordionButton"><b>Feedback from Wetlab</b></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Experiments1"><b>Experiments for the Output Amplification Model</b></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Experiments2"><b>Experiments for the Protein Display Model</b></a><br /><br /></div>
</div>
Example on how Valencia 2006 team used SimulLink to simulate their project: [http://www.igem.upv.es/igem06/images/7/7d/ECOLITASTER.ppt Valencia 2006 PowerPoint presentation]
*It was one of the longest steps as we were struggling to compromise the specifications with the viable designs.
|-
2. Influence of Design on Modelling and vice versa:
| Date || 09 || 10 || 11 || 12 || 13 || 14-15
*All modelling done was meant to give answers to questions that arose in the design phase.
|-
*Once designs were chosen, they were modelled. It was found that 2 step amplification is not likely to be effiecient, so it was decided that only 1step amplification will be taken forward to assembly. This was a significant conclusion as it would take weeks in the labroatory to find that out.
| Objective || || || || Find constants
*The doubt about big enough gradient of AIPs to be established in the extracellular space to set off receptor was rationalised by modelling. The model allowed to determine conditions for the system to work
| Find protein production constants and TEV reaction rate constants ||
3. Influence of Modelling on Assembly and vice versa:
|-
*The results from modelling allowed to progress with assembly
| Completion || || || ||We didn't manage to complete the task || The orders of magnitude established - ready to run simulations ||
4. Influence of Assembly on Testing and vice versa:
|}
*testing has been planned ahead, so assembly contructs have been modified to allow some testing methods like: purification or negative control.
5. Influence of Testing on Specifications and vice versa:
===Week 7===
*We did not get there yet. However, if the specifications would not be met by the results, we would need to try redesigning the system or, in case of no alternative, changing the specifications.
*Many experiments were diesgned specifically on the request of modellers in order to find parameters for the models. Obtaining those paramters would increase the reliability of the models.
|-
| Date || 16 || 17 || 18 || 19 || 20 || 21-22
|-
| Objective || Implementing the constant ranges in the output model. Comparing the results between the models. ||
*Start modelling the protein display signalling to find the concentrations.
*Explain the oscillations that are occuring in the output amplification model
||
*Research stiff differential equations
*Research on receptor (especially MAP kinase)
*Finalise results for amplification model
*Prepare presentation
||
*During meeting it was decided that we should test amplificiation model for its sensitivity to change of parameter values. This is to determine the key parameters to be measured in the lab.
||
*Discuss the experiments for amp. module further
*Start on modelling the protein display on cell surface
||
|-
| Completion || The task is accomplished. However, unexplained oscillations are observed for some specific values ||
We got rid of the oscillations in our model (by using ode15s instead of ode45).
||
*Presentation ready
*Reading on stiff equations done
||
*Key sensitive parameters determined
*Experiments initially discussed with Chris
||
*Further consult on experiments for determining parameters needed for modelling with James and Wolf
*Initial thought for a protein display model implemented in MatLab
|| *Test the protein display model and find to what kind of values it's sensitive to
|| || || || ||
|-
| Completion || || || || || ||
|}
===Week 9===
===Week 10===
=Notebook=
==Week 4 and 5==
==Output Amplification Model==
Motivation: We have come up with a simple concept of amplification of output done by enzymes. Before the final constructs are assembled within the bacterial ogranism, it is beneficial for us to model the behaviour of our design.
The questions that need to be answered:
# How beneficial is the use of amplification? (compare speeds of response of transcription based output to amplified outputs)
# How many amplification steps are beneficial to have? (if too many amplification steps are involved, the associated time delay with expressing even amplfiied output may prove not to be beneficial.)
# Does mixing of amplfication levels have a negative influence on the output? Is it better to use TEV all the way or HIV1? Modelling should allows us to make a decision on which design is more efficient.
===First attempt===
{|
| [[Image:Slide2.JPG|450px|thumb|center|alt=A|At each stage of amplification a distinct protease is being used ]]
| [[Image:Model_output_011.jpg|450px|thumb|center|alt=A|At each stage of amplification a distinct protease is being used ]]
|}
---------
{|
| [[Image:Slide1.JPG|450px|thumb|center|alt=A|TEV is used at both stages of amplification]]
| [[Image:Model_output_010.jpg|450px|thumb|center|alt=A|TEV is used at both stages of amplification]]
|}
===Second attempt===
{|
| [[Image:Model_output_020.jpg.jpg|450px|thumb|center|alt=A|Model improved to account for the enzymes (protease action) ]]
|
|}
====Implementation in Matlab====
The Matlab code for the different stages of amplification and diagrams can be found [http://www.openwetware.org/wiki/Image:Modelling.docx here].
====Kinetic constants====
{| border="1"
! Quality
! GFP
! TEV
! split TEV
! split GFP
|-
|Km and Kcat
| Doesn't apply
|[http://peds.oxfordjournals.org/cgi/reprint/14/12/993 TEV constants (Km and kcat)]
| 40% of whole TEV
| Doesn't apply
|-
| half-life or degradation rate
| Half-life of GFP in Bacillus = 1.5 hours - ref. Chris
| ?
| ?
| Half-life shorter than GFP
|-
| production rate in B.sub
| ?
| ?
| ?
| ?
|}
====Conclusions====
We couldn't obtain all the necessary constants. Hence, we decided to make educated guesses about possible relative values between the constants as well as varying them and observing the change in output.
As the result, we concluded that the amplification happens at each amplification level proposed. It's magnitude varies depending on the constants. There doesn’t seem to be much difference in substitution of TEV with HIV1.
==Week 6==
==Output Amplification Model - Modified version==
===Michaelis Menten kinetics does not apply===
We cannot use Michaelis-Menten kinetics because of its preliminary assumptions, which our system does not fulfil.
These assumptions are:
*''Vmax is proportional to the overall concentration of the enzyme.''
But we are producing enzyme, so Vmax will change! Therefore, the conservation E0 = E + ES does not hold for our system.
*''Substrate >> Enzyme.''
Since we are producing both substrate and enzyme, we have roughly the same amount of substrate and enzyme.
*''Enzyme affinity to substrate has to be high.''
Therefore, the model above is not representative of the enzymatic reaction. As we cannot use the Michaelis-Menten model we will have to solve from first principle (which just means writing down all of the biochemical equations and solving for these in Matlab).
===Changes in the system===
GFP is not used any more as an output. It is dioxygenase acting on the catechol (activating it into colourful form). Catechol will be added to bacteria, it won't be produced by them. Hence, basically in our models dioxynase is going to be treated as an output as this enzyme is recognised as the only activator of catechol in our system. This means that change of catechol into colourful form is dependent on dioxygenase concentration.
=== Models: ===
====Model preA: Production of Dioxygenase====
This model includes transcription and translation of the dioxygenase.
It does not involve any amplification steps. It is our control model against which we will be comparing the results of other models.
====Model A: Activation of Dioxygenase by TEV enzyme====
The reaction can be rewritten as:
TEV + split Dioxygenase <-> TEV-split Dioxygenase -> TEV + Dioxygenase.
This is a simple enzymatic reaction, where TEV is the enzyme, Dioxygenase the product and split Dioxygenase the substrate.
Choosing k1, k2, k3 as reaction constants, the reaction can be rewritten in these four sub-equations:
#[T'] = -k1[T][sD] + (k2+k3)[TsD] + sT - dT[T]
#[sD']= -k1[T][sD] + k2[TsD] + ssD - dsD[sD]
#[TsD'] = k1[T][sD] - (k2+k3)[TsD] - dTsD[TsD]
#[D'] = k3[TsD] - dD[D]
These four equations were implemented in Matlab, using a built-in function (ode45) which solves ordinary differential equations. The Matlab code for this module can be found [http://www.openwetware.org/wiki/Image:Matlab_Code_1.docx here].
[[Image:Fsd.jpg|450px|thumb|center|alt=A|Results of the Matlab simulation, setting all constants to 1]]
'''Implementation in TinkerCell'''
*Another approach to model the amplification module would be to implement it in a program such as TinkerCell (or CellDesigner). It would also be useful to check whether the Matlab model works.
[[Image:Tinkercell.JPG|450px|thumb|center|alt=A|LHS: Network implemented in TinkerCell, RHS: constants and results ]]
====Model B: Activation of Dioxygenase by TEV or activated split TEV enzyme====
#T + sTa + sTb + sD <-> T-sD --> T + D + sTa + sTb
#T + D + sTa + sTb <-> T-sTa --> T + Tsa + D + sTb
#T + D + Tsa + sTb <-> T-sTb --> T + Tsb + Tsa + D
#T + Tsa + Tsb + D <-> Tsa-Tsb --> Ts + T + D
#T + sTa + sTb + sD -- T-sTa --> T + Tsa + sTb + sD
#T + Tsa + sTb + sD -- T-sD --> T + Tsa + sTb + D
#T + Tsa + sTb + D -- T-sTb --> T + Tsa + Tsb + D
#T + Tsa + Tsb + D -- Tsa-Tsb --> T + Ts + D
#T + sTa + sTb + sD -- T-sTb --> T + Tsb + sTa + sD
#T + Tsb + sTa + sD -- T-sD --> T + Tsb + sTa + D
#T + Tsb + sTa + D -- T-sTa --> T + Tsb + Tsa + D
#T + Tsb + Tsa + D -- Tsa-Tsb --> T + Ts + D
#T + sTa + sTb + sD -- T-sTa --> T + Tsa + sTb + sD
#Ts + T + sD -- Ts-sD --> Ts + T + D
This version includes the following features:
*2 amplification steps (TEV and split TEV)
*Split TEV is specified to have a and b parts
*TEVa is forbidden to interact TEVa (though in reality there could be some affinity between the two). Same for interaction between Tevb and Tevb
*Both TEV and TEVs are allowed to activate dioxugenase molecule
*Dioxugenase is assumed to be active as a monomer
*Activate split TEV (TEVs) is not allowed to activate sTEVa or sTEVb (this kind of interaction is accounted for in the next model version)
*There is no specific terms for time delays included
The MatLab code can be found [http://openwetware.org/wiki/Image:Model_-_TEV-sTEV-sD_Code-diagram-results.docx here].
Note that no final conclusions should be drawn before realistic estimates for kinetic constants are included. It wasn’t done so far.
{|
| [[Image:ModelA.jpg|400px|thumb|center|alt=A|All chemical species appearing in the model]]
| [[Image:ModelA_(1).jpg|450px|thumb|center|alt=A|Network of the improved model]]
|-
| [[Image:ModelA_result1.jpg|450px|thumb|center|alt=A|Resulting graphs part 1. Compare the production graphs of TEV (transcribed and translated from scratch and the Dioxugenase which is the final species in the whole cascade]]
| [[Image:ModelA_result2.jpg|450px|thumb|center|alt=A|Resulting graphs part 2.]]
|}
====Model C: Further improvement====
This model is not implemented yet.
This version adds the following features:
* activated split TEV (TEVs) is allowed to activate not only sD but sTEVa and sTEVb
{|
| [[Image:ModelB_%281%29.jpg|450px|thumb|center|alt=A|Network of the further improved model]]
| [[Image:ModelB_%282%29.jpg|450px|thumb|center|alt=A|Network of the further improved model (continued)]]
|}
==Week 7 and 8==
==Output Amplification Model - Results==
The major concern of the results that we got (in particular for small concentrations < 10^(-4) mol*dm^(-3)) was that the solver was oscillating about positive or zero values but marking concentration values below zero too. It was recognised as faulty and probably leading the solver to false solutions.
Trying to prevent the ode solver going crazy, the following precuations were implemented:
* function preventing solver from going to negative values (does it really work) - still some marginally negative values show
* Scaling - all the values were scaled up by a factor of 10^6 as working on small numbers could be problematic for matlab (especially at the beggining of the cascade). Once the result is generated by the solver the resulting matrix is scaled back down by 10^6
===Model pre-A===
This is the result for the simulation of simple production of Dioxygenase. It can be seen that the concentration will tend towards a final value of around 8.5*10^-6 mol dm^(-3). This final value is dependent on the production rate (which we have just estimated!).
[[Image:Model_pre_A_result.jpg|450px|thumb|center|alt=A|Results of the Matlab simulation of Model preA]]
===Model A===
When we entered production and degradation rates into our model, it did not seem to work properly (e.g. we got negative concentrations as our output). We found out that this is due to our set of differential equations being stiff. Since ode45 cannot solve stiff differential equations, we had to switch to using ode15s.
*'''Initial Concentration'''
The initial concentration of split Dioxygenase, c0, determines whether the system is amplifying. The minimum concentration for any amplification to happen is 10^-2 mol dm^(-3). If the initial concentration of split Dioxygenase is higher, then the final concentration of Dioxygenase will be higher as well (see graphs below). '''Note that the obtained threshold value is above the maximum value that can be generated in the cell according to Model pre-A!!! '''
[[Image:Comparison_a_-_prea.jpg|450px|thumb|center|alt=A|Comparison between Model pre-A and Model A. Initial concentration of split Dioxygenase: 0.01 mol dm^(-3)]]
*'''Changing Km:'''
Km is indirectly proportional to the "final concentration" (which is the concentration at the end of the simulation), i.e. the bigger the evalue of Km, the smaller the "final concentration" will be.
Different Km values determine how quickly the amplification will take place.
(Also, it was found that the absolute value of k1 and k2 entered into Matlab does not change the outcome as long as the ratio between them (Km~k2/k1) is kept constant. This is important when simulating (in case entering very high values for k1 and k2 takes too long to simulate).
*'''Changing kcat'''
Model predicts the concentration values for dioxygenase to raise quicker and to higher values for increasing values of kcat=k3. That indicates that k3 is actually the slowest step in enzymatic reaction and allows us to appreciate how our system is dependent on kinetic properties of enzyme.
*'''Changing production rate'''
At the moment, our biggest source of error could be the production rate, which we weren't able to obtain from literature. So, we had to estimate (see below) the value of the production rate. We hope to be able to take rough measurements of that value in the lab as it has big effect on models' behaviour.
====Sensitivity of Model A (20/08/2010)====
We want to determine how our system reacts if different parameters are changed. This is to find out which parameters our system is very sensitive to.
| Initial concentration of split Dioxygenase || Change of one order of magnitude in the initial concentration, c0, gives change of oen order of magnitude in the output concentration (range: 1>c0>10^-5). Loses sensitivity for extremely high or low values.
|-
| Km ||Change of one order of magnitude results in change of output concentration by one order of magnitude (0.01<Km<100). At values smaller than 0.01, the sensitivity is lost. For higher values than 100 the sensitivity is at least the same as the change of order of magnitude.
|-
| kcat || kcat proportional to dioxygenase production (1-to-1 sensitivity for all values) for an initial concentratio of 0.01 mol/dm^3. For very high initial concentrations, the system is very sensitive to changes in kcat.
|-
| Production rate of TEV || 1-1 sensitivity for most values. At some point the system’s response is limited by the initial concentration of sD, so for very high TEV production rates not much change is observed.
|-
| Production rate of split Dioxygenase || Not much influence on 1-step amplification. However, the value seems to be crucial for simple production of Dioxygebase (1-1 order of magnitude sensitivity).
|-
| Degradation rates || Sensitive within the relevant range. Not very sensitive for values smaller than 10^-6;. For high degradation rates (1>degradation rate>0.01): unexplainable behaviours.
|}
Hence, the system is sensitive to most of the constants (given a particular range of values). The most crucial one, however, seems to be the initial concentration of split Dioxygenase.
===Model B===
*'''Initial Concentration'''
The initial concentration of split Dioxygenase, c0, determines whether the system is amplifying. The minimum concentration for any amplification to happen is noted to be '''higher''' than in case of ModelA. It is between 10^(-3) and 10^(-2) mol dm^(-3).
The behaviour in varying the initial concentration obeys similar relationship as the one of Model A: If the initial concentration of split Dioxygenase is higher, then the final concentration of Dioxygenase will be higher as well (see graphs below).
*'''Model A vs. B'''
Having run both models with the same initial conditions (c0=0.01 mol dm^(-3)).
It has been noted that Model B does not generate very siginificant amplfication over the Model A. Hence, it would be more sensible to integrate a one step amplification module.
[[Image:Comparison_prea%2C_a%2C_b.jpg|450px|thumb|center|alt=A|Comparison between Models pre-A, A and B]]
=> 1 division every 1/1.5=0.6667 of an hour (40mins)
To deduce degradation rate use the following formula:
τ_(1⁄2)=ln2/k
Where τ_(1⁄2)=0.667 hour, k – is the degradation rate
k=ln2/τ_(1⁄2) = 0.000289 s^(-1)
|-
| Production rate
(TEV and dioxygenase)
|
We had real trouble finding the production rate values in the literature and we hope to be able to perform experiments to obtain those values for (TEV protease and catechol 2,3-dioxygenase). The experiments will not be possible to be carried out soon, so for the time being we suggest very simplistic approach for estimating production rates.
We have found production rates for two arbitrary proteins in E.Coli. We want to get estimates of production rates by comparing the lengths of the proteins (number of amino-acids).
As this approach is very vague, it is important to realise its limitations and inconsistencies:
*Found values are taken from E.Coli not B.sub.
*The two found rates are of the same value for quite different amino-acid number which indicates that protein folding is limiting the production rates (we use the chosen approach as the only way of getting the estimate of order of reaction)
LacY production = [http://bionumbers.hms.harvard.edu/bionumber.aspx?s=y&id=100738&ver=0&hlid=29205 100 molecules/min] ([http://www.uniprot.org/uniprot/P02920 417 Amino Acids])
LacZ production = [http://bionumbers.hms.harvard.edu/bionumber.aspx?s=y&id=100737&ver=0&hlid=29206 100 molecules/min] ([http://www.uniprot.org/uniprot/P00722 1024 AA])
As production rate needs to be expressed in concentration units per unit volume, the above number is converted to mols/s and divided by the cell volume → 2.3808*10^(-10) mol*dm^(-3)*s^(-1)
We will treat these numbers as guiding us in terms of range of orders of magnitudes. We will try to run our models for variety of values and determine system’s limitations.
|-
|Kinetic parameters
of dioxygenase
|
Initial velocity of the enzymatic reaction was investigated at pH 7.5 and 30 °C.
*Wild type (we use that one)
Km = 10μM
kcat = 52 s^(−1)
*Mutated type
Km = 40μM
kcat = 192 s^(−1)
Consequently, the kcat/Km 4.8 of the mutant was slightly lower than kcat/Km 5.2 of the wild type, indicating that the mutation has little effect on catalytic efficiency.
Dimensions of Basillus subtilis (cylinder/rod shape) in reach media (ref. bionumbers):
# diameter: d= 0.87um
# length: l=4.7 um in rich media
This gives: Volume= π∙(d/2)^2∙l=2.793999 μm^3≈ 2.79∙10^(-15) dm^3
|-
| Split TEV
production rates
|*Assume the both parts of split TEV are half of size of the whole TEV (3054/2=1527 AA)
*The length of the coil is 90 AA
The whole construct is then: 1617 AA
→ split TEV production rate ≈ 1.2606*10^(-10) mol*dm^(-3)*s^(-1)
|}
==Receptor and Surface protein model==
===Protein Display (20/08/2010)===
The peptide concentration required to activate ComD is 10 ng/ml [http://ukpmc.ac.uk/backend/ptpmcrender.cgi?accid=PMC40587&blobtype=pdf (see this paper)]. This is the threshold value for ComD.
We want to know how long it takes until the threshold will be reached.
This is an enzymatic reaction, which can be written as:
*S + E <--> E-S --> P + E
*Substrate (S) = Protein
*Enzyme (E) = TEV (Protease)
*Product (P) = Peptide
This can be modelled in a very similar way to the 1-step amplification model, however, all the constants and initial concentrations will be different.
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<div class="accordionButton"><b>Overview</b></div>
<div class="accordionContent",><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Overview"><b>Here is a short overview of the two models.</b></a><br /><br /></div>
<div class="accordionButton"><b>Output Amplification Model</b></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten"><b>Model based on Michaelis Menten Kinetics</b></a><br />Comparison between different amplification models (HIV1 and TEV) based on Michaelis Menten kinetics. However, Michaelis Menten kinetics does not apply to our system. Therefore, it had to be modelled from first principle (see below using law of mass action).<br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten#HIV1"><img src="http://www.openwetware.org/images/0/03/Slide2.JPG" height="150" width="200" alt="Model using HIV1"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Michaelis_Menten#TEV"><img src="http://www.openwetware.org/images/4/48/TEV.jpg" height="150" width="200" alt="Model using TEV"/></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action"><b>Model based on Law of Mass Action</b></a><br />Comparison between these 3 different models: Simple production, 1-step and 2-step amplification.<br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_preA:_Simple_Production_of_Dioxygenase"><img src="http://www.openwetware.org/images/7/7f/Simple_production.JPG" height="100" width="300" alt="Simple Production"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_A:_Activation_of_Dioxygenase_by_TEV_enzyme"><img src="http://www.openwetware.org/images/1/1c/1-step_amplification.JPG" height="100" width="300" alt="1-step amplification"/></a> <a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Mass_Action#Model_B:_Activation_of_Dioxygenase_by_TEV_or_activated_split_TEV_enzyme"><img src="http://www.openwetware.org/images/0/02/2-step_amplification.JPG" height="100" width="300" alt="2-step amplification"/></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Variables1"><b>Variables and Constants</b></a><br />Here are the variables and constants that are used in the Output Amplification Model.<br /><br /></div>
<div class="accordionButton"><b>Protein Display Model</b></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Protein_Display"><b>Protein Display Model</b></a><br />This is a model showing when the ComD receptor will be activated (after proteins have been cleaved).<br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Variables2"><b>Variables and Constants</b></a><br />Here are the variables and constants that are used in the Protein Display Model.<br /><br /></div>
<div class="accordionButton"><b>Feedback from Wetlab</b></div>
<div class="accordionContent"><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Experiments1"><b>Experiments for the Output Amplification Model</b></a><br /><br /><a href="http://www.openwetware.org/wiki/IGEM:Imperial/2010/Experiments2"><b>Experiments for the Protein Display Model</b></a><br /><br /></div>
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Engineering approach to project
1. Influence of Specification on Design and vice versa:
It was one of the longest steps as we were struggling to compromise the specifications with the viable designs.
2. Influence of Design on Modelling and vice versa:
All modelling done was meant to give answers to questions that arose in the design phase.
Once designs were chosen, they were modelled. It was found that 2 step amplification is not likely to be effiecient, so it was decided that only 1step amplification will be taken forward to assembly. This was a significant conclusion as it would take weeks in the labroatory to find that out.
The doubt about big enough gradient of AIPs to be established in the extracellular space to set off receptor was rationalised by modelling. The model allowed to determine conditions for the system to work
3. Influence of Modelling on Assembly and vice versa:
The results from modelling allowed to progress with assembly
4. Influence of Assembly on Testing and vice versa:
testing has been planned ahead, so assembly contructs have been modified to allow some testing methods like: purification or negative control.
5. Influence of Testing on Specifications and vice versa:
We did not get there yet. However, if the specifications would not be met by the results, we would need to try redesigning the system or, in case of no alternative, changing the specifications.
6. Influence of components not adjacent to each other in the cycle:
Testing may influence modelling as the results of the two do not match.
Many experiments were diesgned specifically on the request of modellers in order to find parameters for the models. Obtaining those paramters would increase the reliability of the models.
