Revision as of 19:39, 19 October 2016

SENSOR MODULE

OVERVIEW

Our idea was to identify bowel infection and its possible causes based on the intestine level of nitric oxyde (NO), which is infection specific, and of N-acyl homoserine-lactone (AHL), which is microbiota specific. Thus, the main goal was to detect the simultaneous presence of those two chemicals in an abnormal amount.

Additionally, we found out in our interview with Prof. Christophe Lacroix that lactate is also a molecule of interest in inflammatory bowel disease (IBD) research: lactate plays an important role in microbiome metabolism and recent studies suggest its presence in high amounts in certain cases of severe IBD.

For this reasons, we decided that two type of sensors are interesting to develop in order to investigate the causes of IBD: One sensor to associate the presence of both AHL and NO, and another sensor to associate lactate and NO.

SENSOR MODULE

Figure 1: Two alternative designs for the sensor module. Left: A sensor module that associates the simultaneous presence of the inflammatory marker nitric oxide (NO) and the microbiotic marker AHL. Right: Association of NO with the microbiotic marker lactate.

GOALS

To get an overall overview of the behavior and to compute a dose response curve.
To identify how the biological design may influence the the behavior of our system.
To identify sensitive parameters that can be tuned.
To compare alternative designs.

Figure 1: NorR overview.

Nitric Oxyde Sensor

In the absence of NO, NorR is produced constitutively and binds to the PnorV promoter, which leads to a repression of the gene transcription. When NO is present in the medium, it binds cooperatively to the hexameric form of NorR and activates the promoter.

ASSUMPTIONS

We assume here that the binding of NorR to the promoter PnorV does not affect the binding between NO and NorR. Thus, the reactions \begin{align*} NorR+NO&\rightleftharpoons NorR_{NO}\\ \end{align*} and \begin{align*} PnorV_{NorR}+NO&\rightleftharpoons PnorV1\\ \end{align*} have the same reaction rate (PnorV1 is the complex consisting of PnorV, NO and NorR). Under this assumption, the system of equation can be simplified as follows:

REACTIONS

NOrR SYSTEM:

\begin{align*} &\rightarrow NorR\\ NO+NorR&\rightleftharpoons NorR_{NO}\\ 2NorR_{NO}&\rightleftharpoons DNorR_{NO2}\\ 2NorR &\rightleftharpoons DNorR\\ DNorR+NO&\rightleftharpoons DNorR_{NO1}\\ DNorR_{NO1}+NO&\rightleftharpoons DNorR_{NO2}\\ DNorR_{NO2}+PnorV0&\rightleftharpoons PnorV1\\ DNorR_{NO2}+PnorV1&\rightleftharpoons PnorV2\\ DNorR_{NO2}+PnorV2&\rightleftharpoons PnorV3\\ PnorV3&\rightarrow mRNA_{Bxb1}\\ NorR&\rightarrow\\ DNorR&\rightarrow \\ DNorR_{NO1}&\rightarrow\\ DNorR_{NO2}&\rightarrow\\ NorR_{NO}&\rightarrow\\ mRNA_{Bxb1}&\rightarrow\\ \end{align*}

Species	Description
NO	Nitric Oxyde produced from DETA/NO reaction
NorR	NorR constitutively produced inside E. coli cells
NorR _NO	NorR with one NO molecule bound
DNorR	NorR dimer, regulatory protein PnorV operon
DNorR _NO1	NorR dimer with one NO molecule bound
DNorR _NO2	NorR dimer with two NO molecules bound
PnorV _i	PnorV promoter with i sites occupied by DNoR _NO2
PnorV ₃	Active promoter, PnorV promoter with 3 sites occupied by DNoR _NO2

For parameter values, please check PARAMETERS

RESULTS

The sensor module must be able detect the different species with high specificity and a sensitivity that lies in the physiological concentration range. In this section we will explain how the we applied our model to provide useful insights for the biological implementation of the system.

REQUIREMENTS

NO sensor sensitivity range = [2 uM - 200 uM]
The system must be as fast as possible.

KEY IDEA

Ideally, we need a dose response curve alignment for the sensor activation and the activation of the hybrid promoter. This ensures that the information on the inflammatory and candidate markers propagates to the switch and finally to the reporter. Based on flow cytometry data, the level of inflammation can then be inferred.

PARAMETERS OF INTEREST

Production rate NorR
Degradatioin rate of NorR

These parameters are tunable and will allow us to tune the kinetics and the steady-state concentration of NorR in the system.

PARAMETER TUNING

We aim for a medium promoter activation (20-60% activation) for NO concentrations around 2 uM and high promoter activation (>70% activation) for NO concentrations around 200 uM. Since the NorR production and degradation rates are the tunable parameters, we investigated their impact on the promoter activation at [NO] = 2 uM and [NO] = 200 uM. The model shows that the ratio between the degradation and production rate needs to lie between 0.65 - 1.20 (see Fig. 3 ) and that the degradation rate needs to be faster than 2.5 nM/min (see Fig. 4 ).

Figure 3: This simulation was run with an input concentration of NO = 2 uM, the ideal lower limit of the sensor's dynamic. As explained before, we would like to achieve dose response alignment between the sensor and the hybrid promoter, in order to ensure optimal information transmission through the genetic circuit. In order to avoid false negative results during the conditioning phase, we aim for an activation between 20% and 60% at [NO] = 2 uM. To achieve this, the model suggests that we need to keep the ratio of the degradation and production rate between 0.65 - 1.20.

Figure 4: This simulation was run with an input concentration of NO = 200 uM, the ideal upper limit of the sensor's dynamic range. At this point we want a full activation of the sensor, which means at least 70% activation of the promoter. To achieve this, the model suggests that the NorR production rate needs to be faster than 2.5 nM/min.

Figure 5: Dose response of the NO sensor for different NorR production rates. For each production rate, the degradation rate has been adjusted such that it meets the criteria identified in Fig. 3 and 4.

DOSE RESPONSE

However, the output of the NO module is the number of PnorV promoter activated by the NO. This number, at a cell level is between 1 and 15, so noise may play an important role in the system behavior, that is why a stochastic simulation may, in case of low NO level, be interested in order to get deeper insight on the system response to NO.

Figure 6:Plotting the concentration corresponding to the maximum of the derivative of the previous dose response curve we compute the limit of detection of the system as a function of the transcription rate, assuming a degradation rate respecting the previous ratio constrain.

Figure 7:With regards to all the previous simulation it appears that a promoter strength of 3 nM for example is enough to see a 4 fold promoter activation under 200 uM of NO system stimulation. We wanted to determine which concentration of the NO species this promoter strength would represent. It appears that the NOrR concnetration remains quite low and similar to the concentration of native NorR in the E.Coli [1]. In order to make the circuit as easy to implement as possible. It was suggested to thus only use the native NorR naturally present in the cell. This would simplify the circuit to reducing the amount of sequence to inject inside the plasmids.

PARAMETER ESTIMATION

In order to provide the biologists more accurate information for an efficient system tuning, we decided to estimate the parameters of the real system. As the NO sensor already worked pretty well and shows a nice behaviour on the plate reader tests, we decided to fit the parameters of our model based on one of those plate reader experiments.

Figure 7:curve fitting for the NO sensor. Each curve correspond to a time response to a certain concentration of nitric oxide induction. we used MEIGO for the fitting. MEIGO uses metaheuristic and Bayesian methods to fit data to a system of differential equations.

Figure 17: Dose response of the NO sensor with the estimated parameters.

Figure 8: AHL Sensor overview

AHL SENSOR

In the absence of AHL, EsaR is constitutively produced, dimerizes and bind as a dimer to the esaBox situated downstream the promoter, preventing transcription as a roadblock. When a higher than normal amount of AHL is present in the gut, it binds to the EsaR dimer, and free the promoter, allowing transcription. Later on, several EsaBox can be added, in order to tune the sensor sensitivity.

ASSUMPTION

We assume a very fast dimerization of EsaR

REACTIONS

EsaR Hybrid Promoter System:

\begin{align*} &\rightarrow EsaR\\ 2 EsaR & \rightleftharpoons DEsaR\\ AHL+DEsaR &\rightleftharpoons DEsaR_{AHL1}\\ AHL+DEsaR_{AHL1}&\rightleftharpoons DEsaR_{AHL2}\\ Pesar1+AHL&\rightleftharpoons Pesar1_{AHL1}\\ Pesar1_{AHL1}+AHL&\rightleftharpoons Pfree +DEsaR_{AHL2}\\ Pfree &\rightarrow mRNA_{GFP}\\ EsaR&\rightarrow\\ DEsaR&\rightarrow \\ DEsaR_{AHL1}&\rightarrow\\ DEsaR_{AHL2}&\rightarrow\\ mRNA_{GFP} &\rightarrow\\ \end{align*}

Esar Reporter System:

\begin{align*} &\rightarrow EsaR\\ 2 EsaR & \rightleftharpoons DEsaR\\ AHL+DEsaR&\rightleftharpoons DEsaR_{AHL1}\\ AHL+DEsaR_{AHL1} & \rightleftharpoons DEsaR_{AHL2}\\ Pesar2+AHL&\rightleftharpoons Pesar2_{AHL1}\\ Pesar2_{AHL1}+AHL&\rightleftharpoons Pout+DEsaR_{AHL2}\\ Pout &\rightarrow mRNA_{GFP}\\ EsaR&\rightarrow \\ DEsaR&\rightarrow \\ DEsaR_{AHL1}&\rightarrow\\ DEsaR_{AHL2}&\rightarrow\\ mRNA_{GFP} &\rightarrow\\ \end{align*}

Species	Description
AHL	Acyl Homocerine Lactone introduced in the medium
EsaR	EsaR constitutively produced insideE. coli cells
DEsaR	Dimer of EsaR , regulatory protein binding to Esaboxes situated downstream the promoter
DEsaR _AHL1	Dimer with one AHL bound to one of its site
DEsaR _AHL2	Dimer with two AHL bound to one of its site
DNorR _NO2	Dimer two NO bound to it
Pesar _i	Pesar1 correspond to the hybrid promoter. Pesar1 is the reporter promoter. They are independant
Pfree Pout respectively	promoter freed from the road block constituted by the EsaR bound to the downstream esaboxes

Paramters Values

RESULTS

The sensor module must be able to finely sense the different species, and in the rigth amount of concentrations. In this section we will explain how the model was used to provide useful insights for the biological system parameters.

REQUIREMENTS

AHL sensor sensitivity range = [10 nM - 1 uM] Dynamic range : the system must be as fast as possible

KEY IDEA

We want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so it propagates information relative to the inflammatory and candidate species to the switch and thus to the reporter. Under FACS and fluorescence distribution analysis the level of inflammation could then be inferred

PARAMETERS OF INTEREST

transcription rate of NorR
translation rate of NorR (RBS concentration)
Degradatioin rate of NorR

those parameters will allow us to set with the kinetic and the steady-state concentration of NorR in the system.

SENSITIVITY ANALYSIS

HEAT MAP

Figure 9:The activation of the promoter was simulated under a constant AHL simulation of 100 nM with varying translation and transcription rates of Esar. As we can see it seems that they have similar impact on the circuit behaviour

As we can see on the graph below, translation and transcription have similar effect on promoter activation. Thus we decided to play with promoter strength rather than rbs level inside each cells. Later on we decided to only play with the promoter strength (transcription rate), as a entire collection of biobrick promoters is available, and thus spare to the lab to work on a rbs library in order to modify the cell translation rate.

Figure 10:We simulated the effect on the transcription and degradation rate on the AHL sensor promoter activity under 100 nM (ideal lower limit of detection) As before we want to match the detection and dynamic range of the sensor to propagate the level of inflammation through the genetic circuit. Here it implies that the ratio (Kd) must stay between 0.1 nM and 0.5 nM

Figure 11:The same analysis performed on the AHL sensor system with the ideal upper detection limit 1 uM diplays the heatmap above. To guarantee a activation superior to 90% at the input level, we need a Kd < 0.66 nM

DOSE RESPONSE

However the output is a amount of freed promoter at a cell level. As our cells only contain around 15 plasmid so stochastic modelling may be interesting.

Figure 12:The same dose response analysis was performed on the AHL sensor to finely tune our system in order to make it behave as ideally as possible. a range of different Esar production rate were tested on the circuit while simulated the dose response, assuming the ration constrained respected.

PARAMETERS ESTIMATION

Using the previous analysis, we now want to be able to give insights for the real system we have. The first step consists in estimated the actual parameters of our circuit. Actually we can only play with a few parameters, as most of them are chemical reactions rate on which we do not have any impacts. We used MEIGO, a optimisation tool, to infer the parameters.

Figure 13: We performed a parameters deterministic estimation using MEIGO. The Estimation was perform using facs datas. Meigo is an optimization toolbox that includes metaheuristic methods and a Bayesian inference method for parameter estimation.

We have some trouble with the plate reader test during the first part of our lab project. In particular with the AHL sensor that presented an unexpected behaviour at some concentration of AHL. Therefore, we decided to perform this parameter estimation in order to get a deeper understanding of the chemical mechanism, and to try to find an explanation and give to the biologists some clues to improve the system. As before the number of parameters the biologists can play with is rather restricted. We thus decided to focus on the two following parameters : EsaR production and degradation rate, which are easily tunable.

Figure 14: A plate reader experiment presenting "unexpected behaviour"

Figure 15: Simulation of the same plate reader experiment. As we can see, there is a "fall" of GFP activity for AHL concentrations of 10 and 100 nM. See the dose response plots below for more explanations

The number of parameters the biologists can play with is rather restricted. We thus decided to focus on the two following parameters : EsaR production and degradation rate, which are easily tunable. On the graphs below we simulated the influence of degradation and production rate of EsaR on the dose response.

Figure 14: Influence of EsaR production rate on the dose response of the system. As stated above our ideal range of sensitivity to AHL is between 10 nM and 10 uM. The "bump" at low concnetration must be attenuated in order to avoid false positive. The results above suggests the the bump is attenuated at low production rate, but then the lack of road block preventing the GFP transcription decrease the limit of detection, And the system became sensitive to 1 nM of AHL. Another solution would be to increase EsaR production rate, but as shown on the graph, activation disappear even at very high AHL concentration

Figure 15: Influence of EsaR degradation rate on the dose response of the system. Unlike Esar production rate, when Esar degradation rate decreases, the "low-concentration bump" decreases as well, but the activation at higher AHL concentration is not decreased. Therefore, the model tends to suggest that decreasing EsaR degradation rate could improve the circuit accuracy

EXPLANATION AND OUTLOOK

This behaviour was totally unexpected as we never witnessed any information about it on litterature describing Esar and esaboxes systems. Therefore, our biologists spend quite along time trying to figure out the reason of this bumpy like dose response, thinking they did something wrong during experiment settings. However once we set a proper FACS analysis, we managed to estimate the parameters and plugged them in the model. Then, it appears that all our experoiment were totally fine, just displaying a counter-intuitive behaviour. To explain it, one has to take a closer look to the equation: bothe AHL and Esar are involved in different equations : AHL presence shift forward the equilibrium of DEsaR_{AHL} production and \begin{align*} Pesar1_{AHL1}+AHL&\rightarrow Pfree +DEsaR_{AHL2}\\\end{align*}. However an increase of EsaR concentration also increase the production of DEsaR_{AHL} which shifts the last reaction on the opposite direction. Therefore there a particular range of AHL/EsaR ratio for which the activation is stronger than the repression, which explains the "bumpy behavior" for intermediate AHL concentrations.

Figure 13: Lactate Sensor overview

LACTATE SENSOR

The promoter if flanked of two LldR specific binding sites : O1 and O2. In the absence of of lactate, LldR and LldD are constitutively produced. LldR then binds to O1 and O2 as a dimer, forms a DNA loop and preventing transcription. When Lactate (Lac) is present, it binds to the LldR complex and free the promoter. LldD lowers the concentration of Lactate inside the cell by catalyzing its transformation into pyruvate. The idea is to set a tunable treshold to the Lactate sensor, as this species, just like AHL, is anyway always present in the gut, and we only want to sense abnormal concentration.

ASSUMPTIONS

LldR exists as a dimer in solution. 2 molecules of lactate bind to one LldR dimer (L2). Lldr dimer bind to the two operator sites when no LldR is present. Lactate releases the binding of LldR dimer to the operators.

Reaction

Lactate system:

\begin{align*} &\rightarrow LldD\\ &\rightarrow LldR\\ LldD+Lac&\rightleftharpoons Pyr+LldD\\ 2LldR&\rightleftharpoons DLldR\\ DLldR+ G_on&\rightleftharpoons G_off\\ DLldR + Lac&\rightleftharpoons DLldR_{Lac1}\\ DLldR_{Lac1}+Lac&\rightleftharpoons DLldR_{Lac2}\\ G_off + Lac&\rightleftharpoons G_off_1\\ G_off_1 + Lac&\rightleftharpoons G_on + DLldR_{Lac2}\\ G_on&\rightleftharpoons mRNA_{GFP}\\ LldD&\rightarrow\\ LldR&\rightarrow\\ DLldR&\rightarrow\\ DLldR_{Lac1}&\rightarrow\\ DLldR_{Lac2}&\rightarrow\\ \end{align*}

Species	Description
LldR	regulatory protein of the Lac system, acts as a repressor
DLldR	Dimer of LldR
Lac	Lactate introduced in the medium. Forms a complex with LldR, preventing it from repressing the Promoter. Acts thus as an activatorE. coli cells
Pyr _NO	Pyruvate, inactive form of lactateE. coli cells
LldD	Regulatory protein, catalyse the oxydation of Lactate into Pyruvate
G_on _NO1	Active promoter
G_off _NO2	Promoter repressed by LldR binding
G_off_1 _NO2	Repressed promoter with 1 lactate molecule bound
DLldR_Lac1 _i	DLldR with one Lactate molecule bound _NO2
DLldR_Lac2 ₃	DLldR with two Lactate molecule bound

Paramters Values

REQUIREMENTS

NO sensor sensitivity range = [100 uM - 100 mM] Dynamic range : the system must be as fast as possible

KEY IDEA

As before, we want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so it propagates information relative to the inflammatory and candidate species to the switch and thus to the reporter. Under FACS and fluorescence distribution analysis the level of inflammation could then be inferred. ETH-Zurich previous team already worked on a Lactate sensor. Thus we tried to improve their work and adapt the sensor sensitivity to our system. It appears that their system is too sensitive for our purpose. As a consequence we decided to add to the plasmid LldR which is constitutively produced and, by inactivating Lactate, artificially lower the concentration of active Lactate that would be able to bind the LLdR complex that inhibits gene transcription.

Figure 13: Lactate Sensor overview

PARAMETERS OF INTEREST

transcription rate of LldD
transcription rate of LldR
Degradation rate of LldD

those parameters will allow us to set with the kinetic and the steady-state concentration of Lactate in the system. However in term of modularity, it turns out it is easier for the biologist to play with the constitutive promoters involved in LldR and LldD production.

Figure 14:The behavior of the lactate sensor with LldD was simulated. The lactate input was set on the lower bound of the ideal sensitive range, namely 100 uM. For this concentration we wish to set the proper LldD concentration that allow a small activation (ideally <30%), so there is no risk of false negative.

Figure 15:The same analysis performed, with the ideal upper detection limit 100mM. To guarantee this concentration is very high, and we can see we have full activation, whatever are the production rates values.

Figure 13: In the graph above, we plotted the dose response curve for a large range of LldR production rate. This shows that one can easily tune the system and adjust the limit of detection, by increasing or decreasing the production rate of the LldR protein.

Figure 13: As for the LldR plot, the LldD production rate can modify the limit of detection and the range of sensitivity of the lactate sensor. Therefore for a more precise tuning, playing with the LldD production rate is a possible way to tune the circuit.

To quickly summarize the difficulties met with the lactate sensor: We based our model and design on the ETH_Zurich iGem team 2015. In their project, they also use a lactate sensitive circuit, So we decided to use the datas they already have for our sensor. However, last year sensor proved to be by far too sensitive for our purpose: they were able to sense up to only a few nM of Lactate in the system while we need tosense between 100 uM and 100 mM. Thus we needed to tune the circuit such that it meet our requirements. To do that we propose the following method: As the more accessible parameters biologists can play with are protein production and degradation rate, we decided to simulate the influence of those parameters on the limit of sensitivity and dynamic range of our system. It appears that aas LldR is a repressor protein to the promoter, increasing its production rate and/or decreasing its degradation rate shift the limit of detection to the right, decreasing the sensitivity of our circuit. However the amount of available promoter is limited, and do is their strength. We came up with the idea of introducing the LldD specie in the design. LldD catalyse the degradation of lactate into Pyruvate. So its presence artificially decrease the concentration of Lactate available to activate the reporter inside the cell. Based on parameters found on litterarure, we figured out that the more you increase its production rate the less sensitive is the sensor. There fore playing with the different constitutive promoter strength allows the biologists to finely tune the system and make it fit our sensitivity requirements.

Figure 13: Plotting the maximum of the derivative of the dose response curves for the range of LldD production rate, we are able to define the limit of detection of the lactate sensor.

Figure 8: Full AND Gate overview

Full AND Gate

Now it is time to link the two previous modules together in order to create the full AND Gate. Ideally, we would like to keep the model as modular as possible. In a first part, our way to proceed in order to recreate the hybrid promoter behavior from the two simple PnorV+Esabox promoter will be described. Then we propose a second model which takes into account all the different states of the promoter under NO and AHL/lactate binding, that can be stochastically simulated.

We developed two models to simulate the and gate. The first one is based on the modular model and consider both systems (NO sensor part and AHL sensor part) as independent. It was developed in the idea to keep everything inter-operable, interchangeable and modular. However, this require a strong assumption, namely that there is no influence of one of the module on the other one. In order to keep the precision of our model, we developed a second model formed of 61 equation that takes into account all the interactions between in the AND gate. Therefor, we have two models for the AND gate, the first one which allow modularity and gives you a quick but slightly approximated overview of the full AND gate circuit, and a Second model, more complex, and longer to implement and to simulate, but that keep the high precision in term of mass action equations, and which is interesting in term of tuning, but sacrifices modularity.

MODULAR MODEL

Figure 16:AND gate simulation using the modular model. As we can see, the rage of sensitivity is not totally the expected one. However, this corresponds to our expectations, since we decided to assume the NO sensor set of reaction and the AHL one independent. We obtain the above results by multiplying the fraction of active promoters of each sensor part, and consider the resulting fraction as the fraction of active hybrid promoter, leading to mRNA and protein production.

FULL STATE MODEL

Figure 17: T plot was obtain using a more detailed model, taking into account all the interaction and overlapping between the NO sensor and the AHL sensor. As describe above, it is composed of 61 equations and takes into account all the binding/unbinding reaction of the different species to and from the hybrid promoter ( PnorV + esaboxes ) As you can see above, it displays for our ideal AND Gate, a better range of sensitivity.

AND GATE SIMULATION WITH THE ESTIMATED PARAMETERS

In order to be able to predict our AND Gate behaviour, we need to simulate it with the fitted parameters. Thus we are able to compare the "real" AND gate dose response with the ideal system. The fitted AND Gate simulation provide allow us to predict our system behaviour, to confirm their experiments and provide some insights in order to improve the circuit and stick closer to the ideal range of input species we want to detect.

MODULAR MODEL

Figure 16:AND gate simulation using the modular model, using the fitted parameters. Here, we decided to use the parameters estimated from the test of the NO and AHL sensor separately tested and characterized. For simplicity, we assume that those parameters are not modified by the presence of the other sensor ( AHL and NO sensor respectively ). As you can see, the AND Gate present an activation at low concentration of AHL. Although it doesnt behave as an AND gate in the full range of possible input, it shows consistency with the previously characterized sensor. This "early activation" actually corresponds to teh bumpy dose response of the AHL sensor for the current parameters.

FULL STATE MODEL

Figure 17:AND gate simulation using the full model, with the fitted parameters. As stated above, while the modular model provide flexibility and reasonably consistent results, it still lacks precision due to the assumptions. As the behaviour of our AND Gate is unusual, we decided to run the simulation using the full model, which provides more accurate results. As we can see the bump is attenuated. This could be explained by the fact that the shifting of equilibrium due to the AHL/EsaR ratio is attenuated by the presence of the NO sensor part. For more information please refer to the AHL sensor module part.

ADDITIONAL LINKS AND INFORMATIONS

FULL-MODEL

For additional information about the full model, please check the link below.

Full Mass Action model for the AND Gate

DETERMINISTIC OVER STOCHASTIC MODEL

At the beginning of the project, we wanted to a model as precise as possible that would allow us to tune finely our system in order to get a response as close to the ideal one as possible. In addition, we also decided to go first for a full stochastic model. Thus FACS analysis and comparison as well as fitting would assure us a good parameter estimation. Moreover, we wished that looking at the reporter distribution would help us to get some additional information about the input of the circuit. As the system is thought as a detector, and its application (travelling through the gut which is seen as a black box to get activated eventually by the presence of both NO and specific AHL) is to express a reporter, we wished that by simply looking at he distribution of mean and variance of the reporter could give us enough information in order to reconstitute the input. Thus in a medical application, a simple FACS analysis of the remaining E.Coli harvesting from the faeces, would be enough to determine the acuteness of the inflammation and a quantitative data on the microbiota disbalance. However, it appeared after several staochastic simulation that the fluctuation of the ratio of activated promoter, for one trajectory, was to fast to have any influence of the reporter protein expression, as binding and unbinding of protein and ligand to and from DNA strand are quick reaction compared to mRNA and protein production and folding. As shown on the graph below. Thus we decided to go for simple deterministic simulation for the AND gate characterization.

Figure 18:We compare the behavior of the model under deterministic and stochastic simulations. As we can see the mean behavior remains similar. The fluctuations around the mean value due to the stochasticity can be neglected at a promoter scale because protein-ligand binding-unbinding reaction is extremely fast compare to protein/mRNA production and folding. Thus choosing a deterministic approach here does not represent a loss of information neither for the kinetic nor for the concentration at steady state.

ADDITIONAL LINKS AND INFORMATIONS

MODEL-BRICK

For additional information, please check our public Git repository. All our codes are available there. As the spirit of iGem is information and knowledge sharing, we tried to make our codes as easily usable as possible.

Our Git repository

REFERENCES

[1] Tucker, N. P. et al. "Essential Roles Of Three Enhancer Sites In 54-Dependent Transcription By The Nitric Oxide Sensing Regulatory Protein Norr". Nucleic Acids Research 38.4 (2009): 1182-1194.

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+            <h2>OVERVIEW</h2>
+            <p>
+                Our idea was to identify bowel infection and its possible causes based on the intestine level of nitric oxyde (NO), which
+                is infection specific, and of N-acyl homoserine-lactone (AHL), which is microbiota specific. Thus, the main goal was to detect the simultaneous presence of those two chemicals in an abnormal amount.
+            </p>
+            <p>
+                Additionally, we found out in our interview with Prof. Christophe Lacroix that lactate is also a molecule of interest in inflammatory bowel disease (IBD) research: lactate plays an important role in microbiome metabolism and recent studies suggest its presence in high amounts in certain cases of severe IBD.
+            </p>
+            <p>
+                For this reasons, we decided that two type of sensors are interesting to develop in order to investigate the causes of IBD: One sensor to associate the presence of both AHL and NO, and another sensor to associate lactate
+                and NO.
+            </p>
+            <h3>SENSOR MODULE</h3>
-			<h2>OVERVIEW</h2>
+            <div class="image_box full_size">
-			<p>
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--sensor2design.svg">
-				Our idea was to identify bowel infection and its possible causes based on the intestine level of nitric oxyde (NO), which
+                    <img src="https://static.igem.org/mediawiki/2016/6/67/T--ETH_Zurich--sensor2design.svg">
-				is infection specific, and of N-acyl homoserine-lactone (AHL), which is microbiota specific. Thus, the main goal was
+                </a>
-				to detect the simultaneous presence of those two chemicals in an abnormal amount.
+                <p><b>Figure 1:</b> Two alternative designs for the sensor module. <b>Left:</b> A sensor module that associates the simultaneous presence of the inflammatory marker nitric oxide (NO) and the microbiotic marker AHL. <b>Right:</b> Association of NO with the microbiotic marker lactate.</p>
-			</p>
+            </div>
-			<p>
-				Additionally, we found out in our interview with Prof. Christophe Lacroix that lactate is also a molecule of interest in
-				inflammatory bowel disease (IBD) research: lactate plays an important role in microbiome metabolism and recent studies
-				suggest its presence in high amounts in certain cases of severe IBD.
-			</p>
-			<p>
-				For this reasons, we decided that two type of sensors are interesting to develop in order to investigate the causes of IBD:
-				One sensor to associate the presence of both AHL and NO, and another sensor to associate lactate and NO.
-			</p>
-			<h3>SENSOR MODULE</h3>
-			<div class="image_box full_size">
+        </div>
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--sensor2design.svg">
+    </div>
-					<img src="https://static.igem.org/mediawiki/2016/6/67/T--ETH_Zurich--sensor2design.svg">
+    <div class="sec light_grey">
-				</a>
+        <div>
-				<p><b>Figure 1:</b> Two alternative designs for the sensor module. <b>Left:</b> A sensor module that associates the simultaneous
+            <h3>GOALS</h3>
-					presence of the inflammatory marker nitric oxide (NO) and the microbiotic marker AHL. <b>Right:</b> Association of NO
-					with the microbiotic marker lactate.</p>
-			</div>
-		</div>
+            <ul>
-	</div>
+                <li>To get an overall overview of the behavior and to compute a dose response curve.</li>
-	<div class="sec light_grey">
+                <li>To identify how the biological design may influence the the behavior of our system.</li>
-		<div>
+                <li>To identify sensitive parameters that can be tuned.</li>
-			<h3>GOALS</h3>
+                <li>To compare alternative designs.</li>
+            </ul>
-			<ul>
+            <p>
-				<li>To get an overall overview of the behavior and to compute a dose response curve.</li>
+            </p>
-				<li>To identify how the biological design may influence the the behavior of our system.</li>
+        </div>
-				<li>To identify sensitive parameters that can be tuned.</li>
+    </div>
-				<li>To compare alternative designs.</li>
-			</ul>
-			<p>
+        <div class="sec white" id="nosensor">
-			</p>
+        <div>
-		</div>
-	</div>
-	<div class="sec white" id="nosensor">
+            <div class="image_box" style="max-width: 500px;">
-		<div>
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--NorRSystem.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/3/34/T--ETH_Zurich--NorRSystem.svg">
+                </a>
+                <p><b>Figure 1:</b> NorR overview.</p>
+            </div>
-			<div class="image_box" style="max-width: 500px;">
+            <div class="sec white">
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--NorRSystem.svg">
+                <h2>Nitric Oxyde Sensor</h2>
-					<img src="https://static.igem.org/mediawiki/2016/3/34/T--ETH_Zurich--NorRSystem.svg">
+                <p>
-				</a>
+                    In the absence of NO, NorR is produced constitutively and binds to the PnorV promoter, which leads to a repression of the gene transcription.
-				<p><b>Figure 1:</b> NorR overview.</p>
+                    When NO is present in the medium, it binds cooperatively to the hexameric form of NorR and activates the promoter.
-			</div>
+                </p>
+            </div>
-			<div class="sec white">
+            <div class="sec white">
-				<h2>Nitric Oxyde Sensor</h2>
+                <h3>ASSUMPTIONS</h3>
-				<p>
+                <p> We assume here that the binding of NorR to the promoter PnorV does not affect the binding between NO and NorR. Thus, the reactions \begin{align*} NorR+NO&amp;\rightleftharpoons NorR_{NO}\\ \end{align*} and \begin{align*}
-					In the absence of NO, NorR is produced constitutively and binds to the PnorV promoter, which leads to a repression of the
+                    PnorV_{NorR}+NO&amp;\rightleftharpoons PnorV1\\ \end{align*} have the same reaction rate (PnorV1 is the complex consisting of PnorV, NO and NorR). Under this
-					gene transcription. When NO is present in the medium, it binds cooperatively to the hexameric form of NorR and activates
+                    assumption, the system of equation can be simplified as follows:
-					the promoter.
+                </p>
-				</p>
+            </div>
-			</div>
-			<div class="sec white">
-				<h3>ASSUMPTIONS</h3>
-				<p> We assume here that the binding of NorR to the promoter PnorV does not affect the binding between NO and NorR. Thus,
-					the reactions \begin{align*} NorR+NO&amp;\rightleftharpoons NorR_{NO}\\ \end{align*} and \begin{align*} PnorV_{NorR}+NO&amp;\rightleftharpoons
-					PnorV1\\ \end{align*} have the same reaction rate (PnorV1 is the complex consisting of PnorV, NO and NorR). Under this
-					assumption, the system of equation can be simplified as follows:
-				</p>
-			</div>
+        </div>
+    </div>
+    <div class="sec white two_columns">
+        <div>
-		</div>
-	</div>
-	<div class="sec white two_columns">
-		<div>
+            <h4>REACTIONS</h4>
-			<h4>REACTIONS</h4>
+            <div>
+                <p>NOrR SYSTEM:</p>
-			<div>
+                \begin{align*} &amp;\rightarrow NorR\\ NO+NorR&amp;\rightleftharpoons NorR_{NO}\\ 2NorR_{NO}&amp;\rightleftharpoons DNorR_{NO2}\\
-				<p>NOrR SYSTEM:</p>
+NorR &amp;\rightleftharpoons DNorR\\ DNorR+NO&amp;\rightleftharpoons DNorR_{NO1}\\ DNorR_{NO1}+NO&amp;\rightleftharpoons
+                DNorR_{NO2}\\ DNorR_{NO2}+PnorV0&amp;\rightleftharpoons PnorV1\\ DNorR_{NO2}+PnorV1&amp;\rightleftharpoons
+                PnorV2\\ DNorR_{NO2}+PnorV2&amp;\rightleftharpoons PnorV3\\ PnorV3&amp;\rightarrow mRNA_{Bxb1}\\ NorR&amp;\rightarrow\\
+                DNorR&amp;\rightarrow \\ DNorR_{NO1}&amp;\rightarrow\\ DNorR_{NO2}&amp;\rightarrow\\ NorR_{NO}&amp;\rightarrow\\
+                mRNA_{Bxb1}&amp;\rightarrow\\ \end{align*}
-				\begin{align*} &amp;\rightarrow NorR\\ NO+NorR&amp;\rightleftharpoons NorR_{NO}\\ 2NorR_{NO}&amp;\rightleftharpoons DNorR_{NO2}\\
+            </div>
-NorR &amp;\rightleftharpoons DNorR\\ DNorR+NO&amp;\rightleftharpoons DNorR_{NO1}\\ DNorR_{NO1}+NO&amp;\rightleftharpoons
-				DNorR_{NO2}\\ DNorR_{NO2}+PnorV0&amp;\rightleftharpoons PnorV1\\ DNorR_{NO2}+PnorV1&amp;\rightleftharpoons PnorV2\\ DNorR_{NO2}+PnorV2&amp;\rightleftharpoons
-				PnorV3\\ PnorV3&amp;\rightarrow mRNA_{Bxb1}\\ NorR&amp;\rightarrow\\ DNorR&amp;\rightarrow \\ DNorR_{NO1}&amp;\rightarrow\\
-				DNorR_{NO2}&amp;\rightarrow\\ NorR_{NO}&amp;\rightarrow\\ mRNA_{Bxb1}&amp;\rightarrow\\ \end{align*}
-			</div>
-			<div>
+            <div>
-				<table>
+                <table>
-					<tr>
+                    <tr>
-						<th>Species </th>
+                        <th>Species </th>
-						<th>Description </th>
+                        <th>Description </th>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>NO</td>
+                        <td>NO</td>
-						<td> Nitric Oxyde produced from DETA/NO reaction </td>
+                        <td> Nitric Oxyde produced from DETA/NO reaction </td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>NorR </td>
+                        <td>NorR </td>
-						<td>NorR constitutively produced inside <i>E. coli </i> cells </td>
+                        <td>NorR constitutively produced inside  <i>E. coli </i>  cells </td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>NorR
+                        <td>NorR
-							<SUB>NO</SUB> </td>
+                            <SUB>NO</SUB> </td>
-						<td>NorR with one NO molecule bound</td>
+                        <td>NorR with one NO molecule bound</td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>DNorR</td>
+                        <td>DNorR</td>
-						<td>NorR dimer, regulatory protein PnorV operon</td>
+                        <td>NorR dimer, regulatory protein PnorV operon</td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>DNorR
+                        <td>DNorR
-							<SUB>NO1</SUB>
+                            <SUB>NO1</SUB>
-						</td>
+                        </td>
-						<td>NorR dimer with one NO molecule bound</td>
+                        <td>NorR dimer with one NO molecule bound</td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>DNorR
+                        <td>DNorR
-							<SUB>NO2</SUB>
+                            <SUB>NO2</SUB>
-						</td>
+                        </td>
-						<td>NorR dimer with two NO molecules bound</td>
+                        <td>NorR dimer with two NO molecules bound</td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>PnorV
+                        <td>PnorV
-							<SUB>i</SUB>
+                            <SUB>i</SUB>
-						</td>
+                        </td>
-						<td>PnorV promoter with i sites occupied by DNoR
+                        <td>PnorV promoter with i sites occupied by DNoR
-							<SUB>NO2</SUB>
+                            <SUB>NO2</SUB>
-						</td>
+                        </td>
-					</tr>
+                    </tr>
-					<tr>
+                    <tr>
-						<td>PnorV
+                        <td>PnorV
-							<SUB>3</SUB>
+                            <SUB>3</SUB>
-						</td>
+                        </td>
-						<td>Active promoter, PnorV promoter with 3 sites occupied by DNoR
+                        <td>Active promoter, PnorV promoter with 3 sites occupied by DNoR
-							<SUB>NO2</SUB>
+                            <SUB>NO2</SUB></td>
-						</td>
+                    </tr>
-					</tr>
+                </table>
-				</table>
 				<div class="quicklinks">
 					<div>
@@ Line 184: / Line 177: @@
 						</div>
 					</div>
-				</div>
 			</div>
-		</div>
+            </div>
-	</div>
+        </div>
+    </div>
+    <div class="sec white">
+        <div>
+            <h3>RESULTS</h3>
+            <p>
+               The sensor module must be able detect the different species with high specificity and a sensitivity that lies in the physiological concentration range. In this section we will
+explain how the we applied our model to provide useful insights for the biological implementation of the system.
+            </p>
+        </div>
+    </div>
+    <div class="sec white">
+        <div>
+            <h4>REQUIREMENTS</h4>
+            <p>
+              <ul>
+               <li>NO sensor sensitivity range = [2 uM - 200 uM]</li>
+               <li>The system must be as fast as possible.</li>
+             </ul>
+            </p>
+            <h5>KEY IDEA</h5>
+            <p>
+Ideally, we need a dose response curve alignment for the sensor activation and the activation of the hybrid promoter. This ensures that the information on the inflammatory and candidate markers propagates to the switch and finally to the reporter. Based on flow cytometry data, the level of inflammation can
+then be inferred.
+            </p>
+            <h5>PARAMETERS OF INTEREST</h5>
+            <p>
+            <ul>
+                <li>Production rate NorR </li>
+                <li>Degradatioin rate of NorR</li>
+            </ul>
+            </p>
+            <p>
+            These parameters are tunable and will allow us to tune the kinetics and the steady-state concentration of NorR in the system.
+            </p>
+        </div>
+    </div>
 	<div class="sec white">
-		<div>
+	    <div> <h5>PARAMETER TUNING</h5>
-			<h3>RESULTS</h3>
+			<p> We aim for a medium promoter activation (20-60% activation) for NO concentrations around 2 uM and high promoter activation (>70% activation) for NO concentrations around 200 uM. Since the NorR production and degradation rates are the tunable parameters, we investigated their impact on the promoter activation at [NO] = 2 uM and [NO] = 200 uM. The model shows that the ratio between the degradation and production rate needs to lie between 0.65 - 1.20 (see <b> Fig. 3 </b>) and that the degradation rate needs to be faster than 2.5 nM/min (see <b> Fig. 4 </b>).
-			<p>
-				The sensor module must be able detect the different species with high specificity and a sensitivity that lies in the physiological
-				concentration range. In this section we will explain how the we applied our model to provide useful insights for the
-				biological implementation of the system.
 			</p>
-		</div>
+	    </div>
-	</div>
+    </div>
-	<div class="sec white">
+    <div class="sec white two_columns">
+	<div>
+	    <div>
+            <div>
+                <div class="image_box full_size">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorProdVSdeg2uM.svg">
+						<img src="https://static.igem.org/mediawiki/2016/a/a6/T--ETH_Zurich--heatMapKnorProdVSdeg2uM.svg">
+					</a>
+					<p><b>Figure 3:</b> This simulation was run with an input concentration of NO = 2 uM, the ideal lower limit of the sensor's dynamic. As explained before, we would like to achieve dose response alignment between the sensor and the hybrid promoter, in order to ensure optimal information transmission through the genetic circuit. In order to avoid false negative results during the conditioning phase, we aim for an activation between 20% and 60% at [NO] = 2 uM. To achieve this, the model suggests that we need to keep the ratio of the degradation and production rate between 0.65 - 1.20. </p>
+				</div>
+			</div>
+        </div>
 		<div>
-			<h4>REQUIREMENTS</h4>
-			<p>
+		    <div>
-				<ul>
+				<div class="image_box full_size">
-					<li>NO sensor sensitivity range = [2 uM - 200 uM]</li>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorProdVSdeg200uM.svg">
-					<li>The system must be as fast as possible.</li>
+						<img src="https://static.igem.org/mediawiki/2016/f/f1/T--ETH_Zurich--heatMapKnorProdVSdeg200uM.svg">
-				</ul>
+					</a>
-			</p>
+					<p><b>Figure 4:</b> This simulation was run with an input concentration of NO = 200 uM, the ideal upper limit of the sensor's dynamic range. At this
-			<h5>KEY IDEA</h5>
+					point we want a full activation of the sensor, which means at least 70% activation of the promoter. To achieve this, the model suggests that the NorR production rate needs to be faster than 2.5 nM/min. </p>
-			<p>
+				</div>
-				Ideally, we need a dose response curve alignment for the sensor activation and the activation of the hybrid promoter. This
+            </div>
-				ensures that the information on the inflammatory and candidate markers propagates to the switch and finally to the reporter.
-				Based on flow cytometry data, the level of inflammation can then be inferred.
+        </div>
-			</p>
+    </div>
-			<h5>PARAMETERS OF INTEREST</h5>
-			<p>
-				<ul>
-					<li>Production rate NorR </li>
-					<li>Degradatioin rate of NorR</li>
-				</ul>
-			</p>
-			<p>
-				These parameters are tunable and will allow us to tune the kinetics and the steady-state concentration of NorR in the system.
-			</p>
-		</div>
 	</div>
      <div class="sec white">
-		<div>
+	        <div>
-	<h5>PARAMETER TUNING</h5>
+                <div class="image_box full_size">
-	<p>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--doseReponseNorR.svg">
-		We aim for a medium promoter activation (20-60% activation) for NO concentrations around 2 uM and high promoter activation
+						<img src="https://static.igem.org/mediawiki/2016/2/22/T--ETH_Zurich--doseReponseNorR.svg">
-		(>70% activation) for NO concentrations around 200 uM. Since the NorR production and degradation rates are the tunable
+					</a>
-		parameters, we investigated their impact on the promoter activation at [NO] = 2 uM and [NO] = 200 uM. The model shows that
+					<p><b>Figure 5:</b> Dose response of the NO sensor for different NorR production rates. For each production rate, the degradation rate has been adjusted such that it meets the criteria identified in Fig. 3 and 4.</p>
-		the ratio between the degradation and production rate needs to lie between 0.65 - 1.20 (see <b> Fig. 3 </b>) and that
+				</div>
-		the degradation rate needs to be faster than 2.5 nM/min (see <b> Fig. 4 </b>).
-	</p>
-	<div>
-		<div>
-			<div class="image_box full_size">
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorProdVSdeg2uM.svg">
-					<img src="https://static.igem.org/mediawiki/2016/a/a6/T--ETH_Zurich--heatMapKnorProdVSdeg2uM.svg">
-				</a>
-				<p><b>Figure 3:</b> This simulation was run with an input concentration of NO = 2 uM, the ideal lower limit of the sensor's
-					dynamic. As explained before, we would like to achieve dose response alignment between the sensor and the hybrid promoter,
-					in order to ensure optimal information transmission through the genetic circuit. In order to avoid false negative results
-					during the conditioning phase, we aim for an activation between 20% and 60% at [NO] = 2 uM. To achieve this, the model
-					suggests that we need to keep the ratio of the degradation and production rate between 0.65 - 1.20. </p>
 			</div>
-		</div>
-	</div>
+            <div>
+				<h4>DOSE RESPONSE</h4>
-	<div>
+				<p>
-		<div>
+                However, the output of the NO module is the number of PnorV promoter activated by the NO. This number, at a cell level is
-			<div class="image_box full_size">
+                between 1 and 15, so noise may play an important role in the system behavior, that is why a stochastic simulation
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorProdVSdeg200uM.svg">
+                may, in case of low NO level, be interested in order to get deeper insight on the system response to NO.
-					<img src="https://static.igem.org/mediawiki/2016/f/f1/T--ETH_Zurich--heatMapKnorProdVSdeg200uM.svg">
+				</p>
-				</a>
-				<p><b>Figure 4:</b> This simulation was run with an input concentration of NO = 200 uM, the ideal upper limit of the sensor's
-					dynamic range. At this point we want a full activation of the sensor, which means at least 70% activation of the promoter.
-					To achieve this, the model suggests that the NorR production rate needs to be faster than 2.5 nM/min. </p>
 			</div>
-		</div>
+    </div>
-	</div>
+    <div class="sec white two_columns">
+	    <div>
-	<div class="sec white two_columns">
+           <div>
-		<div>
+            <div>
-			<div class="image_box full_size">
+                <div class="image_box full_size">
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--doseReponseNorR.svg">
-					<img src="https://static.igem.org/mediawiki/2016/2/22/T--ETH_Zurich--doseReponseNorR.svg">
-				</a>
-				<p><b>Figure 5:</b> Dose response of the NO sensor for different NorR production rates. For each production rate, the degradation
-					rate has been adjusted such that it meets the criteria identified in Fig. 3 and 4.</p>
-			</div>
-		</div>
-	</div>
-	<div>
-		<h4>DOSE RESPONSE</h4>
-		<p>
-			However, the output of the NO module is the number of PnorV promoter activated by the NO. This number, at a cell level is
-			between 1 and 15, so noise may play an important role in the system behavior, that is why a stochastic simulation may,
-			in case of low NO level, be interested in order to get deeper insight on the system response to NO.
-		</p>
-	</div>
-	</div>
-    	</div>
-	</div>
-	<div class="sec white two_columns">
-		<div>
-			<div>
-				<div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--maxde.png">
 						<img src="https://static.igem.org/mediawiki/2016/1/16/T--ETH_Zurich--maxde.png">
 					</a>
-					<p><b>Figure 6:</b>Plotting the concentration corresponding to the maximum of the derivative of the previous dose response
+					<p><b>Figure 6:</b>Plotting the concentration corresponding to the maximum of the derivative of the previous dose response curve
-						curve we compute the limit of detection of the system as a function of the transcription rate, assuming a degradation
+					we compute the limit of detection of the system as a function of the transcription rate, assuming a degradation rate respecting
-						rate respecting the previous ratio constrain.</p>
+					the previous ratio constrain.</p>
 				</div>
 			</div>
 		</div>
+		<div>
-		<div>
+		    <div>
-			<div>
 				<div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--levelOfNorRforThisPromoterStrengthValue.png">
 						<img src="https://static.igem.org/mediawiki/2016/4/48/T--ETH_Zurich--levelOfNorRforThisPromoterStrengthValue.png">
 					</a>
-					<p><b>Figure 7:</b>With regards to all the previous simulation it appears that a promoter strength of 3 nM for example
+					<p><b>Figure 7:</b>With regards to all the previous simulation it appears that a promoter strength of 3 nM for example is enough to see a 4 fold promoter activation under
-						is enough to see a 4 fold promoter activation under 200 uM of NO system stimulation. We wanted to determine which concentration
+uM of NO system stimulation. We wanted to determine which concentration of the NO species this promoter strength would represent. It appears that the NOrR concnetration
-						of the NO species this promoter strength would represent. It appears that the NOrR concnetration remains quite low
+					remains quite low and similar to the concentration of native NorR in the E.Coli [1]. In order to make the circuit as easy to implement as possible. It was suggested to thus
-						and similar to the concentration of native NorR in the E.Coli [1]. In order to make the circuit as easy to implement
+					only use the native NorR naturally present in the cell. This would simplify the circuit to reducing the amount of sequence to inject inside the plasmids.</p>
-						as possible. It was suggested to thus only use the native NorR naturally present in the cell. This would simplify the
-						circuit to reducing the amount of sequence to inject inside the plasmids.</p>
 				</div>
-			</div>
+				</div>
+            </div>
 		</div>
 	</div>
 	<div class="sec white">
 		<div>
-			<h3>PARAMETER ESTIMATION
+			<h3>PARAMETER ESTIMATION<h3>
-				<h3>
+						<p>In order to provide the biologists more accurate information for an efficient system tuning, we decided to estimate the parameters of the
-					<p>In order to provide the biologists more accurate information for an efficient system tuning, we decided to estimate
+		real system. As the NO sensor already worked pretty well
-						the parameters of the real system. As the NO sensor already worked pretty well and shows a nice behaviour on the plate
+						and shows a nice behaviour on the plate reader tests, we decided to fit the parameters of our model based on one of those plate reader
-						reader tests, we decided to fit the parameters of our model based on one of those plate reader experiments.
+		experiments.
-					</p>
+						</p>
 		</div>
 	</div>
 	<div class="sec white two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<div class="image_box full_size">
+            <div>
+                <div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--T--ETH_Zurich--paramfittingNOpart.png">
 						<img src="https://static.igem.org/mediawiki/2016/8/83/T--ETH_Zurich--paramfittingNOpart.png">
 					</a>
-					<p><b>Figure 7:</b>curve fitting for the NO sensor. Each curve correspond to a time response to a certain concentration
+					<p><b>Figure 7:</b>curve fitting for the NO sensor. Each curve correspond to a time response to a certain concentration of nitric oxide induction. we used MEIGO for the fitting. MEIGO uses metaheuristic and Bayesian methods to fit data to a system of differential equations. </p>
-						of nitric oxide induction. we used MEIGO for the fitting. MEIGO uses metaheuristic and Bayesian methods to fit data
-						to a system of differential equations. </p>
 				</div>
-			</div>
+            </div>
-		</div>
+        </div>
 		<div>
-			<div>
+		    <div>
 				<div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseNOsensorFittedParam.png">
@@ Line 359: / Line 345: @@
 					<p><b>Figure 17:</b> Dose response of the NO sensor with the estimated parameters.</p>
 				</div>
-			</div>
+            </div>
+        </div>
+    </div>
+ </div>
-		</div>
-	</div>
+    <div class="sec light_grey" id="ahlsensor">
+        <div>
+            <div class="image_box" style="max-width: 500px;">
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--AHLSystem.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/f/f7/T--ETH_Zurich--AHLSystem.svg">
+                </a>
+                <p><b>Figure 8:</b> AHL Sensor overview</p>
+            </div>
+            <div>
+                <h2>AHL SENSOR</h2>
+                <p>
+                    In the absence of AHL, EsaR is constitutively produced, dimerizes and bind as a dimer to the esaBox situated downstream the
+                    promoter, preventing transcription as a roadblock. When a higher than normal amount of AHL is present
+                    in the gut, it binds to the EsaR dimer, and free the promoter, allowing transcription. Later on, several
+                    EsaBox can be added, in order to tune the sensor sensitivity.
+                </p>
+            </div>
+            <div>
+                <h4>ASSUMPTION</h4>
+            </div>
+            <p>
+                We assume a very fast dimerization of EsaR
+                </p>
+        </div>
+    </div>
+    <div class="sec light_grey two_columns">
+        <div>
+            <h4>REACTIONS</h4>
+            <div>EsaR Hybrid Promoter System:
+                <p></p>
-	<div class="sec light_grey" id="ahlsensor">
+                \begin{align*} &amp;\rightarrow EsaR\\ 2 EsaR &amp; \rightleftharpoons DEsaR\\ AHL+DEsaR &amp;\rightleftharpoons DEsaR_{AHL1}\\
-		<div>
+                AHL+DEsaR_{AHL1}&amp;\rightleftharpoons DEsaR_{AHL2}\\ Pesar1+AHL&amp;\rightleftharpoons Pesar1_{AHL1}\\
-			<div class="image_box" style="max-width: 500px;">
+                Pesar1_{AHL1}+AHL&amp;\rightleftharpoons Pfree +DEsaR_{AHL2}\\ Pfree &amp;\rightarrow mRNA_{GFP}\\ EsaR&amp;\rightarrow\\
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--AHLSystem.svg">
+                DEsaR&amp;\rightarrow \\ DEsaR_{AHL1}&amp;\rightarrow\\ DEsaR_{AHL2}&amp;\rightarrow\\ mRNA_{GFP} &amp;\rightarrow\\
-					<img src="https://static.igem.org/mediawiki/2016/f/f7/T--ETH_Zurich--AHLSystem.svg">
+                \end{align*}
-				</a>
-				<p><b>Figure 8:</b> AHL Sensor overview</p>
-			</div>
-			<div>
+            </div>
-				<h2>AHL SENSOR</h2>
-				<p>
-					In the absence of AHL, EsaR is constitutively produced, dimerizes and bind as a dimer to the esaBox situated downstream the
-					promoter, preventing transcription as a roadblock. When a higher than normal amount of AHL is present in the gut, it
-					binds to the EsaR dimer, and free the promoter, allowing transcription. Later on, several EsaBox can be added, in order
-					to tune the sensor sensitivity.
-				</p>
-			</div>
-			<div>
-				<h4>ASSUMPTION</h4>
-			</div>
-			<p>
-				We assume a very fast dimerization of EsaR
-			</p>
-		</div>
-	</div>
-	<div class="sec light_grey two_columns">
+            <div>Esar Reporter System:
-		<div>
+                <p></p>
-			<h4>REACTIONS</h4>
-			<div>EsaR Hybrid Promoter System:
-				<p></p>
-				\begin{align*} &amp;\rightarrow EsaR\\ 2 EsaR &amp; \rightleftharpoons DEsaR\\ AHL+DEsaR &amp;\rightleftharpoons DEsaR_{AHL1}\\
+                \begin{align*} &amp;\rightarrow EsaR\\ 2 EsaR &amp; \rightleftharpoons DEsaR\\ AHL+DEsaR&amp;\rightleftharpoons DEsaR_{AHL1}\\
-				AHL+DEsaR_{AHL1}&amp;\rightleftharpoons DEsaR_{AHL2}\\ Pesar1+AHL&amp;\rightleftharpoons Pesar1_{AHL1}\\ Pesar1_{AHL1}+AHL&amp;\rightleftharpoons
+                AHL+DEsaR_{AHL1} &amp; \rightleftharpoons DEsaR_{AHL2}\\ Pesar2+AHL&amp;\rightleftharpoons Pesar2_{AHL1}\\
-				Pfree +DEsaR_{AHL2}\\ Pfree &amp;\rightarrow mRNA_{GFP}\\ EsaR&amp;\rightarrow\\ DEsaR&amp;\rightarrow \\ DEsaR_{AHL1}&amp;\rightarrow\\
+                Pesar2_{AHL1}+AHL&amp;\rightleftharpoons Pout+DEsaR_{AHL2}\\ Pout &amp;\rightarrow mRNA_{GFP}\\ EsaR&amp;\rightarrow
-				DEsaR_{AHL2}&amp;\rightarrow\\ mRNA_{GFP} &amp;\rightarrow\\ \end{align*}
+                \\ DEsaR&amp;\rightarrow \\ DEsaR_{AHL1}&amp;\rightarrow\\ DEsaR_{AHL2}&amp;\rightarrow\\ mRNA_{GFP} &amp;\rightarrow\\
+                \end{align*}
-			</div>
-			<div>Esar Reporter System:
+            </div>
-				<p></p>
-				\begin{align*} &amp;\rightarrow EsaR\\ 2 EsaR &amp; \rightleftharpoons DEsaR\\ AHL+DEsaR&amp;\rightleftharpoons DEsaR_{AHL1}\\
+            <table>
-				AHL+DEsaR_{AHL1} &amp; \rightleftharpoons DEsaR_{AHL2}\\ Pesar2+AHL&amp;\rightleftharpoons Pesar2_{AHL1}\\ Pesar2_{AHL1}+AHL&amp;\rightleftharpoons
+                <tr>
-				Pout+DEsaR_{AHL2}\\ Pout &amp;\rightarrow mRNA_{GFP}\\ EsaR&amp;\rightarrow \\ DEsaR&amp;\rightarrow \\ DEsaR_{AHL1}&amp;\rightarrow\\
+                    <th>Species </th>
-				DEsaR_{AHL2}&amp;\rightarrow\\ mRNA_{GFP} &amp;\rightarrow\\ \end{align*}
+                    <th>Description </th>
+                </tr>
+                <tr>
+                    <td>AHL</td>
-			</div>
+                    <td> Acyl Homocerine Lactone introduced in the medium </td>
+                </tr>
-			<table>
+                <tr>
-				<tr>
+                    <td>EsaR </td>
-					<th>Species </th>
+                    <td>EsaR constitutively produced inside<i>E. coli </i> cells </td>
-					<th>Description </th>
+                </tr>
-				</tr>
+                <tr>
-				<tr>
+                    <td>DEsaR </td>
-					<td>AHL</td>
+                    <td>Dimer of EsaR , regulatory protein binding to Esaboxes situated downstream the promoter</td>
-					<td> Acyl Homocerine Lactone introduced in the medium </td>
+                </tr>
-				</tr>
+                <tr>
-				<tr>
+                    <td>DEsaR
-					<td>EsaR </td>
+                        <SUB>AHL1</SUB>
-					<td>EsaR constitutively produced inside<i>E. coli </i> cells </td>
+                    </td>
-				</tr>
+                    <td>Dimer with one AHL bound to one of its site </td>
-				<tr>
+                </tr>
-					<td>DEsaR </td>
+                <tr>
-					<td>Dimer of EsaR , regulatory protein binding to Esaboxes situated downstream the promoter</td>
+                    <td>DEsaR
-				</tr>
+                        <SUB>AHL2</SUB>
-				<tr>
+                    </td>
-					<td>DEsaR
+                    <td>Dimer with two AHL bound to one of its site </td>
-						<SUB>AHL1</SUB>
+                </tr>
-					</td>
+                <tr>
-					<td>Dimer with one AHL bound to one of its site </td>
+                    <td>DNorR
-				</tr>
+                        <SUB>NO2</SUB>
-				<tr>
+                    </td>
-					<td>DEsaR
+                    <td>Dimer two NO bound to it </td>
-						<SUB>AHL2</SUB>
+                </tr>
-					</td>
+                <tr>
-					<td>Dimer with two AHL bound to one of its site </td>
+                    <td>Pesar
-				</tr>
+                        <SUB>i</SUB>
-				<tr>
+                    </td>
-					<td>DNorR
+                    <td>Pesar1 correspond to the hybrid promoter. Pesar1 is the reporter promoter. They are independant</td>
-						<SUB>NO2</SUB>
+                </tr>
-					</td>
+                <tr>
-					<td>Dimer two NO bound to it </td>
+                    <td>Pfree Pout respectively</td>
-				</tr>
+                    <td>promoter freed from the road block constituted by the EsaR bound to the downstream esaboxes</td>
-				<tr>
+                </tr>
-					<td>Pesar
+            </table>
-						<SUB>i</SUB>
-					</td>
-					<td>Pesar1 correspond to the hybrid promoter. Pesar1 is the reporter promoter. They are independant</td>
-				</tr>
-				<tr>
-					<td>Pfree Pout respectively</td>
-					<td>promoter freed from the road block constituted by the EsaR bound to the downstream esaboxes</td>
-				</tr>
-			</table>
 			<div class="quicklinks">
-				<div>
+					<div>
-					<div class="outline_item">
+						<div class="outline_item">
-						<a href="https://2016.igem.org/Team:ETH_Zurich/Parameters">
+							<a href="https://2016.igem.org/Team:ETH_Zurich/Parameters">
 							<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Paramters Values</a>
+						</div>
 					</div>
-				</div>
 			</div>
-		</div>
+        </div>
-	</div>
+    </div>
+    <div class="sec light_grey">
+        <div>
+            <h3>RESULTS</h3>
+            <p>
+               The sensor module must be able to finely sense the different species, and in the rigth amount of concentrations. In this section we will
+explain how the model was used to provide useful insights for the biological system parameters.
+            </p>
+        </div>
+    </div>
+    <div class="sec light_grey">
+        <div>
+            <h4>REQUIREMENTS</h4>
+            <p>
+               AHL sensor sensitivity range = [10 nM - 1 uM]
+               Dynamic range : the system must be as fast as possible
+            </p>
+            <h5>KEY IDEA</h5>
+            <p>
+We want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so it propagates information relative to the
+inflammatory and candidate species to the switch and thus to the reporter. Under FACS and fluorescence distribution analysis the level of inflammation could
+then be inferred
+            </p>
+            <h5>PARAMETERS OF INTEREST</h5>
+            <p>
+            <ul>
+                <li>transcription rate of NorR</li>
+                <li>translation rate of NorR (RBS concentration)</li>
+                <li>Degradatioin rate of NorR</li>
+            </ul>
+            </p>
+            <p>
+            those parameters will allow us to set with the kinetic and the steady-state concentration of NorR in the system.
+            </p>
+            <p>
+            <h5>SENSITIVITY ANALYSIS</h5>
+        </div>
+    </div>
 	<div class="sec light_grey">
-		<div>
+        <div>
-			<h3>RESULTS</h3>
+		<h5>HEAT MAP</h5>
-			<p>
-				The sensor module must be able to finely sense the different species, and in the rigth amount of concentrations. In this
+		    <div>
-				section we will explain how the model was used to provide useful insights for the biological system parameters.
-			</p>
-		</div>
-	</div>
-	<div class="sec light_grey">
-		<div>
-			<h4>REQUIREMENTS</h4>
-			<p>
-				AHL sensor sensitivity range = [10 nM - 1 uM] Dynamic range : the system must be as fast as possible
-			</p>
-			<h5>KEY IDEA</h5>
-			<p>
-				We want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so it propagates
-				information relative to the inflammatory and candidate species to the switch and thus to the reporter. Under FACS and
-				fluorescence distribution analysis the level of inflammation could then be inferred
-			</p>
-			<h5>PARAMETERS OF INTEREST</h5>
-			<p>
-				<ul>
-					<li>transcription rate of NorR</li>
-					<li>translation rate of NorR (RBS concentration)</li>
-					<li>Degradatioin rate of NorR</li>
-				</ul>
-			</p>
-			<p>
-				those parameters will allow us to set with the kinetic and the steady-state concentration of NorR in the system.
-			</p>
-			<p>
-				<h5>SENSITIVITY ANALYSIS</h5>
-		</div>
-	</div>
-	<div class="sec light_grey">
-		<div>
-			<h5>HEAT MAP</h5>
-			<div>
 				<div class="image_box" style="max-width: 800px;">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaptranslaVStranscrip.svg">
 						<img src="https://static.igem.org/mediawiki/2016/9/9b/T--ETH_Zurich--heatMaptranslaVStranscrip.svg">
 					</a>
-					<p><b>Figure 9:</b>The activation of the promoter was simulated under a constant AHL simulation of 100 nM with varying
+					<p><b>Figure 9:</b>The activation of the promoter was simulated under a constant AHL simulation of 100 nM with varying translation and transcription rates of Esar.
-						translation and transcription rates of Esar. As we can see it seems that they have similar impact on the circuit behaviour</p>
+					As we can see it seems that they have similar impact on the circuit behaviour</p>
 				</div>
-			</div>
+		    </div>
-			<div>
+		    <div>
 				<p>
-					As we can see on the graph below, translation and transcription have similar effect on promoter activation. Thus we decided
+As we can see on the graph below, translation and transcription have similar effect on promoter activation. Thus we decided to play with promoter strength
-					to play with promoter strength rather than rbs level inside each cells. Later on we decided to only play with the promoter
+rather than rbs level inside each cells.
-					strength (transcription rate), as a entire collection of biobrick promoters is available, and thus spare to the lab
+Later on we decided to only play with the promoter strength (transcription rate), as a entire collection of biobrick promoters  is available, and thus spare to the lab to work on a rbs library
-					to work on a rbs library in order to modify the cell translation rate.
+in order to modify the cell translation rate.
-				</p>
+                </p>
-			</div>
+		   </div>
 		</div>
-	</div>
+    </div>
-	<div class="sec light_grey two_columns">
+    <div class="sec light_grey two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaptranscriptVSdegraFor10nMofAHL.svg">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaptranscriptVSdegraFor10nMofAHL.svg">
-							<img src="https://static.igem.org/mediawiki/2016/3/3e/T--ETH_Zurich--heatMaptranscriptVSdegraFor10nMofAHL.svg">
+						<img src="https://static.igem.org/mediawiki/2016/3/3e/T--ETH_Zurich--heatMaptranscriptVSdegraFor10nMofAHL.svg">
-						</a>
+					</a>
-						<p><b>Figure 10:</b>We simulated the effect on the transcription and degradation rate on the AHL sensor promoter activity
+					<p><b>Figure 10:</b>We simulated the effect on the transcription and degradation rate on the AHL sensor promoter activity under 100 nM (ideal lower limit of detection)
-							under 100 nM (ideal lower limit of detection) As before we want to match the detection and dynamic range of the sensor
+					As before we want to match the detection and dynamic range of the sensor to propagate the level of inflammation through the genetic circuit. Here it implies that the
-							to propagate the level of inflammation through the genetic circuit. Here it implies that the ratio (Kd) must stay
+					ratio (Kd) must stay between 0.1 nM and 0.5 nM</p>
-							between 0.1 nM and 0.5 nM</p>
-					</div>
 				</div>
 			</div>
-			<div>
+        </div>
-				<div>
+		<div>
-					<div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaptranscriptVSdegraFor1uMofAHL.svg">
+		    <div>
-							<img src="https://static.igem.org/mediawiki/2016/0/04/T--ETH_Zurich--heatMaptranscriptVSdegraFor1uMofAHL.svg">
+				<div class="image_box full_size">
-						</a>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaptranscriptVSdegraFor1uMofAHL.svg">
-						<p><b>Figure 11:</b>The same analysis performed on the AHL sensor system with the ideal upper detection limit 1 uM diplays
+						<img src="https://static.igem.org/mediawiki/2016/0/04/T--ETH_Zurich--heatMaptranscriptVSdegraFor1uMofAHL.svg">
-							the heatmap above. To guarantee a activation superior to 90% at the input level, we need a Kd
+					</a>
-							< 0.66 nM</p>
+					<p><b>Figure 11:</b>The same analysis performed on the AHL sensor system with the ideal upper detection limit 1 uM diplays the heatmap above. To guarantee
-					</div>
+					a activation superior to 90% at the input level, we need a Kd < 0.66 nM</p>
 				</div>
+            </div>
+        </div>
+    </div>
+    </div>
-			</div>
+    <div class="sec light_grey">
-		</div>
+        <div>
-	</div>
+           <h5>DOSE RESPONSE</h5>
+            <p>
+                However the output is a amount of freed promoter at a cell level. As our cells only contain around 15 plasmid so stochastic
+                modelling may be interesting.
+            </p>
+        </div>
+        <div class="image_box full_size">
+	    <a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponse.svg">
+	        <img src="https://static.igem.org/mediawiki/2016/5/5a/T--ETH_Zurich--doseResponse.svg">
+	         </a>
+		<p><b>Figure 12:</b>The same dose response analysis was performed on the AHL sensor to finely tune our system in order to make it behave as ideally as possible.
+		a range of different Esar production rate were tested on the circuit while simulated the dose response, assuming the ration constrained respected.</p>
+       </div>
+    </div>
 	<div class="sec light_grey">
-		<div>
+        <div>
-			<h5>DOSE RESPONSE</h5>
+           <h5>PARAMETERS ESTIMATION</h5>
-			<p>
+            <p>
-				However the output is a amount of freed promoter at a cell level. As our cells only contain around 15 plasmid so stochastic
+               Using the previous analysis, we now want to be able to give insights for the real system we have. The first step consists in estimated the actual parameters
-				modelling may be interesting.
+			   of our circuit. Actually we can only play with a few parameters, as most of them are chemical reactions rate on which we do not have any impacts.
-			</p>
+			   We used MEIGO, a optimisation tool, to infer the parameters.
-		</div>
+            </p>
-		<div class="image_box full_size">
+        </div>
-			<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponse.svg">
+        <div class="image_box full_size">
-				<img src="https://static.igem.org/mediawiki/2016/5/5a/T--ETH_Zurich--doseResponse.svg">
+	    <a href="https://2016.igem.org/File:T--ETH_Zurich--AHLPartsensorFitting.svg">
-			</a>
+	        <img src="https://static.igem.org/mediawiki/2016/f/f3/T--ETH_Zurich--AHLPartsensorFitting.svg">
-			<p><b>Figure 12:</b>The same dose response analysis was performed on the AHL sensor to finely tune our system in order to
+	         </a>
-				make it behave as ideally as possible. a range of different Esar production rate were tested on the circuit while simulated
+		<p><b>Figure 13:</b> We performed a parameters deterministic estimation using MEIGO. The Estimation was perform using facs datas. Meigo is an optimization toolbox that includes
-				the dose response, assuming the ration constrained respected.</p>
+metaheuristic methods and a Bayesian inference method for parameter estimation.		</p>
-		</div>
+       </div>
-	</div>
+    </div>
+<div class="sec light_grey">
-	<div class="sec light_grey">
+<div>
-		<div>
+<p> We have some trouble with the plate reader test during the first part of our lab project. In particular with the AHL sensor that presented an unexpected behaviour at some
-			<h5>PARAMETERS ESTIMATION</h5>
+concentration of AHL. Therefore, we decided to perform this parameter estimation in order to get a deeper understanding of the chemical mechanism, and to try to find an explanation
-			<p>
+and give to the biologists some clues to improve the system. As before the number of parameters the biologists can play with is rather restricted. We thus decided to focus on
-				Using the previous analysis, we now want to be able to give insights for the real system we have. The first step consists
+the two following parameters : EsaR production and degradation rate, which are easily tunable.</p>
-				in estimated the actual parameters of our circuit. Actually we can only play with a few parameters, as most of them are
+</div>
-				chemical reactions rate on which we do not have any impacts. We used MEIGO, a optimisation tool, to infer the parameters.
+</div>
-			</p>
-		</div>
-		<div class="image_box full_size">
-			<a href="https://2016.igem.org/File:T--ETH_Zurich--AHLPartsensorFitting.svg">
-				<img src="https://static.igem.org/mediawiki/2016/f/f3/T--ETH_Zurich--AHLPartsensorFitting.svg">
-			</a>
-			<p><b>Figure 13:</b> We performed a parameters deterministic estimation using MEIGO. The Estimation was perform using facs
-				datas. Meigo is an optimization toolbox that includes metaheuristic methods and a Bayesian inference method for parameter
-				estimation. </p>
-		</div>
-	</div>
-	<div class="sec light_grey">
-		<div>
-			<p> We have some trouble with the plate reader test during the first part of our lab project. In particular with the AHL sensor
-				that presented an unexpected behaviour at some concentration of AHL. Therefore, we decided to perform this parameter
-				estimation in order to get a deeper understanding of the chemical mechanism, and to try to find an explanation and give
-				to the biologists some clues to improve the system. As before the number of parameters the biologists can play with is
-				rather restricted. We thus decided to focus on the two following parameters : EsaR production and degradation rate, which
-				are easily tunable.</p>
-		</div>
-	</div>
 	<div class="sec light_grey two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--platereaderAHLEsaR.png">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--platereaderAHLEsaR.png">
-							<img src="https://static.igem.org/mediawiki/2016/a/ab/T--ETH_Zurich--platereaderAHLEsaR.png">
+						<img src="https://static.igem.org/mediawiki/2016/a/ab/T--ETH_Zurich--platereaderAHLEsaR.png">
-						</a>
+					</a>
-						<p><b>Figure 14:</b> A plate reader experiment presenting "unexpected behaviour" </p>
+					<p><b>Figure 14:</b> A plate reader experiment presenting "unexpected behaviour" </p>
-					</div>
 				</div>
 			</div>
-			<div>
+        </div>
-				<div>
+		<div>
-					<div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--ModelPlateReaderAHLEsaR_V4.png">
+		    <div>
-							<img src="https://static.igem.org/mediawiki/2016/0/08/T--ETH_Zurich--ModelPlateReaderAHLEsaR_V4.png">
+				<div class="image_box full_size">
-						</a>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--ModelPlateReaderAHLEsaR_V4.png">
-						<p><b>Figure 15:</b> Simulation of the same plate reader experiment. As we can see, there is a "fall" of <b>GFP</b> activity
+						<img src="https://static.igem.org/mediawiki/2016/0/08/T--ETH_Zurich--ModelPlateReaderAHLEsaR_V4.png">
-							for AHL concentrations of 10 and 100 nM. See the dose response plots below for more explanations</p>
+					</a>
-					</div>
+					<p><b>Figure 15:</b> Simulation of the same plate reader experiment. As we can see, there is a "fall" of <b>GFP</b> activity for AHL concentrations
+					of 10 and 100 nM. See the dose response plots below for more explanations</p>
 				</div>
+            </div>
-			</div>
-		</div>
+        </div>
-	</div>
+    </div>
+    </div>
 	<div class="sec light_grey">
-		<div>
+	<div>
-			<p> The number of parameters the biologists can play with is rather restricted. We thus decided to focus on the two following
+	<p> The number of parameters the biologists can play with is rather restricted. We thus decided to focus on
-				parameters : EsaR production and degradation rate, which are easily tunable. On the graphs below we simulated the influence
+	the two following parameters : EsaR production and degradation rate, which are easily tunable. On the graphs below we simulated the influence of degradation and production rate
-				of degradation and production rate of EsaR on the dose response.</p>
+	of EsaR on the dose response.</p>
-		</div>
 	</div>
+	</div>
 	<div class="sec light_grey two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseAHLPartWithFittedParam.png">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseAHLPartWithFittedParam.png">
-							<img src="https://static.igem.org/mediawiki/2016/8/87/T-ETH_Zurich--doseResponseAHLPartWithFittedParam.png">
+						<img src="https://static.igem.org/mediawiki/2016/8/87/T-ETH_Zurich--doseResponseAHLPartWithFittedParam.png">
-						</a>
+					</a>
-						<p><b>Figure 14:</b> Influence of EsaR production rate on the dose response of the system. As stated above our ideal range
+					<p><b>Figure 14:</b> Influence of EsaR production rate on the dose response of the system. As stated above our ideal range of sensitivity to AHL is between 10 nM and 10 uM.
-							of sensitivity to AHL is between 10 nM and 10 uM. The "bump" at low concnetration must be attenuated in order to avoid
+The "bump" at low concnetration must be attenuated in order to avoid false positive. The results above suggests the the bump is attenuated at low production rate, but then the lack of road block
-							false positive. The results above suggests the the bump is attenuated at low production rate, but then the lack of
+preventing the <b>GFP</b> transcription decrease the limit of detection, And the system became sensitive to 1 nM of AHL. Another solution would be to increase EsaR production rate, but as shown on the graph,
-							road block preventing the <b>GFP</b> transcription decrease the limit of detection, And the system became sensitive
+activation disappear even at very high AHL concentration</p>
-							to 1 nM of AHL. Another solution would be to increase EsaR production rate, but as shown on the graph, activation
-							disappear even at very high AHL concentration</p>
-					</div>
 				</div>
 			</div>
-			<div>
+        </div>
-				<div>
+		<div>
-					<div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseDegRateAHLPartFittedParam.png">
+		    <div>
-							<img src="https://static.igem.org/mediawiki/2016/4/49/T--ETH_Zurich--doseResponseDegRateAHLPartFittedParam.png">
+				<div class="image_box full_size">
-						</a>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseDegRateAHLPartFittedParam.png">
-						<p><b>Figure 15:</b> Influence of EsaR degradation rate on the dose response of the system. Unlike Esar production rate,
+						<img src="https://static.igem.org/mediawiki/2016/4/49/T--ETH_Zurich--doseResponseDegRateAHLPartFittedParam.png">
-							when Esar degradation rate decreases, the "low-concentration bump" decreases as well, but the activation at higher
+					</a>
-							AHL concentration is not decreased. Therefore, the model tends to suggest that decreasing EsaR degradation rate could
+					<p><b>Figure 15:</b> Influence of EsaR degradation rate on the dose response of the system. Unlike Esar production rate, when Esar degradation rate decreases, the "low-concentration bump" decreases as well, but the activation at higher AHL concentration is not decreased.
-							improve the circuit accuracy</p>
+Therefore, the model tends to suggest that decreasing EsaR degradation rate	could improve the circuit accuracy</p>
-					</div>
 				</div>
+            </div>
-			</div>
+        </div>
-		</div>
 	</div>
+    </div>
 	<div class="sec light_grey">
 		<div>
-			<h3>EXPLANATION AND OUTLOOK</h3>
+		<h3>EXPLANATION AND OUTLOOK</h3>
-			<p>This behaviour was totally unexpected as we never witnessed any information about it on litterature describing Esar and
+		<p>This behaviour was totally unexpected as we never witnessed any information about it on litterature describing Esar and esaboxes systems. Therefore, our biologists spend quite along time trying to figure out the reason of this
-				esaboxes systems. Therefore, our biologists spend quite along time trying to figure out the reason of this bumpy like
+bumpy like dose response, thinking they did something wrong during experiment settings. However once we set a proper FACS analysis, we managed to estimate the parameters and plugged them in the model. Then, it appears that all our experoiment were totally fine, just displaying a counter-intuitive behaviour.
-				dose response, thinking they did something wrong during experiment settings. However once we set a proper FACS analysis,
+To explain it, one has to take a closer look to the equation: bothe AHL and Esar are involved in different equations : AHL presence shift forward the equilibrium of DEsaR_{AHL} production and \begin{align*} Pesar1_{AHL1}+AHL&amp;\rightarrow Pfree +DEsaR_{AHL2}\\\end{align*}. However an increase of EsaR concentration also increase the
-				we managed to estimate the parameters and plugged them in the model. Then, it appears that all our experoiment were totally
+production of DEsaR_{AHL} which shifts the last reaction on the opposite direction. Therefore there a particular range of AHL/EsaR ratio for which the activation is stronger than the repression, which explains the "bumpy behavior" for intermediate AHL concentrations.
-				fine, just displaying a counter-intuitive behaviour. To explain it, one has to take a closer look to the equation: bothe
+		</p>
-				AHL and Esar are involved in different equations : AHL presence shift forward the equilibrium of DEsaR_{AHL} production
-				and \begin{align*} Pesar1_{AHL1}+AHL&amp;\rightarrow Pfree +DEsaR_{AHL2}\\\end{align*}. However an increase of EsaR concentration
-				also increase the production of DEsaR_{AHL} which shifts the last reaction on the opposite direction. Therefore there
-				a particular range of AHL/EsaR ratio for which the activation is stronger than the repression, which explains the "bumpy
-				behavior" for intermediate AHL concentrations.
-			</p>
 		</div>
-	</div>
+    </div>
+    <div class="sec white" id="lactsensor">
+        <div>
+            <div class="image_box" style="max-width: 500px;">
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--LactSystem.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/b/bf/T--ETH_Zurich--LactSystem.svg">
+                </a>
+                <p><b>Figure 13:</b> Lactate Sensor overview</p>
+            </div>
+            <div>
+                <h2>LACTATE SENSOR</h2>
+                <p>
+                    The promoter if flanked of two LldR specific binding sites : O1 and O2. In the absence of of lactate, LldR and LldD are constitutively
+                    produced. LldR then binds to O1 and O2 as a dimer, forms a DNA loop and preventing transcription. When
+                    Lactate (Lac) is present, it binds to the LldR complex and free the promoter. LldD lowers the concentration
+                    of Lactate inside the cell by catalyzing its transformation into pyruvate. The idea is to set a tunable
+                    treshold to the Lactate sensor, as this species, just like AHL, is anyway always present in the gut,
+                    and we only want to sense abnormal concentration.
+                </p>
+            </div>
+            <div class="sec white">
+                <h3>ASSUMPTIONS</h3>
+                <p>
+                    LldR exists as a dimer in solution. 2 molecules of lactate bind to one LldR dimer (L2). Lldr dimer bind to the two operator
+                    sites when no LldR is present. Lactate releases the binding of LldR dimer to the operators.
+                </p>
+            </div>
+        </div>
+    </div>
-	<div class="sec white" id="lactsensor">
+    <div class="sec white two_columns">
-		<div>
+        <div>
+            <div>Reaction
+                <p>Lactate system:</p>
-			<div class="image_box" style="max-width: 500px;">
+                \begin{align*} &amp;\rightarrow LldD\\ &amp;\rightarrow LldR\\ LldD+Lac&amp;\rightleftharpoons Pyr+LldD\\ 2LldR&amp;\rightleftharpoons
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--LactSystem.svg">
+                DLldR\\ DLldR+ G_on&amp;\rightleftharpoons G_off\\ DLldR + Lac&amp;\rightleftharpoons DLldR_{Lac1}\\ DLldR_{Lac1}+Lac&amp;\rightleftharpoons
-					<img src="https://static.igem.org/mediawiki/2016/b/bf/T--ETH_Zurich--LactSystem.svg">
+                DLldR_{Lac2}\\ G_off + Lac&amp;\rightleftharpoons G_off_1\\ G_off_1 + Lac&amp;\rightleftharpoons G_on + DLldR_{Lac2}\\
-				</a>
+                G_on&amp;\rightleftharpoons mRNA_{GFP}\\ LldD&amp;\rightarrow\\ LldR&amp;\rightarrow\\ DLldR&amp;\rightarrow\\
-				<p><b>Figure 13:</b> Lactate Sensor overview</p>
+                DLldR_{Lac1}&amp;\rightarrow\\ DLldR_{Lac2}&amp;\rightarrow\\ \end{align*}
-			</div>
+            </div>
-			<div>
+        </div>
-				<h2>LACTATE SENSOR</h2>
-				<p>
-					The promoter if flanked of two LldR specific binding sites : O1 and O2. In the absence of of lactate, LldR and LldD are constitutively
-					produced. LldR then binds to O1 and O2 as a dimer, forms a DNA loop and preventing transcription. When Lactate (Lac)
-					is present, it binds to the LldR complex and free the promoter. LldD lowers the concentration of Lactate inside the
-					cell by catalyzing its transformation into pyruvate. The idea is to set a tunable treshold to the Lactate sensor, as
-					this species, just like AHL, is anyway always present in the gut, and we only want to sense abnormal concentration.
-				</p>
-			</div>
-			<div class="sec white">
-				<h3>ASSUMPTIONS</h3>
-				<p>
-					LldR exists as a dimer in solution. 2 molecules of lactate bind to one LldR dimer (L2). Lldr dimer bind to the two operator
-					sites when no LldR is present. Lactate releases the binding of LldR dimer to the operators.
-				</p>
-			</div>
-		</div>
-	</div>
-	<div class="sec white two_columns">
-		<div>
-			<div>Reaction
-				<p>Lactate system:</p>
-				\begin{align*} &amp;\rightarrow LldD\\ &amp;\rightarrow LldR\\ LldD+Lac&amp;\rightleftharpoons Pyr+LldD\\ 2LldR&amp;\rightleftharpoons
-				DLldR\\ DLldR+ G_on&amp;\rightleftharpoons G_off\\ DLldR + Lac&amp;\rightleftharpoons DLldR_{Lac1}\\ DLldR_{Lac1}+Lac&amp;\rightleftharpoons
-				DLldR_{Lac2}\\ G_off + Lac&amp;\rightleftharpoons G_off_1\\ G_off_1 + Lac&amp;\rightleftharpoons G_on + DLldR_{Lac2}\\
-				G_on&amp;\rightleftharpoons mRNA_{GFP}\\ LldD&amp;\rightarrow\\ LldR&amp;\rightarrow\\ DLldR&amp;\rightarrow\\ DLldR_{Lac1}&amp;\rightarrow\\
-				DLldR_{Lac2}&amp;\rightarrow\\ \end{align*}
-			</div>
-		</div>
-		<div>
-			<div>
-				<table>
-					<tr>
-						<th>Species </th>
-						<th>Description </th>
-					</tr>
-					<tr>
-						<td>LldR</td>
-						<td> regulatory protein of the Lac system, acts as a repressor </td>
-					</tr>
-					<tr>
-						<td>DLldR</td>
-						<td> Dimer of LldR </td>
-					</tr>
-					<tr>
-						<td>Lac</td>
-						<td>Lactate introduced in the medium. Forms a complex with LldR, preventing it from repressing the Promoter. Acts thus
-							as an activator<i>E. coli </i> cells </td>
-					</tr>
-					<tr>
-						<td>Pyr
-							<SUB>NO</SUB> </td>
-						<td>Pyruvate, inactive form of lactate<i>E. coli </i> cells </td>
-					</tr>
-					<tr>
-						<td>LldD</td>
-						<td>Regulatory protein, catalyse the oxydation of Lactate into Pyruvate</td>
-					</tr>
-					<tr>
-						<td>G_on
-							<SUB>NO1</SUB>
-						</td>
-						<td>Active promoter </td>
-					</tr>
-					<tr>
-						<td>G_off
-							<SUB>NO2</SUB>
-						</td>
-						<td>Promoter repressed by LldR binding </td>
-					</tr>
-					<tr>
-						<td>G_off_1
-							<SUB>NO2</SUB>
-						</td>
-						<td>Repressed promoter with 1 lactate molecule bound </td>
-					</tr>
-					<tr>
-						<td>DLldR_Lac1
-							<SUB>i</SUB>
-						</td>
-						<td>DLldR with one Lactate molecule bound
-							<SUB>NO2</SUB>
-						</td>
-					</tr>
-					<tr>
-						<td>DLldR_Lac2
-							<SUB>3</SUB>
-						</td>
-						<td>DLldR with two Lactate molecule bound</td>
-					</tr>
-				</table>
+        <div>
+            <div>
+                <table>
+                    <tr>
+                        <th>Species </th>
+                        <th>Description </th>
+                    </tr>
+                    <tr>
+                        <td>LldR</td>
+                        <td> regulatory protein of the Lac system, acts as a repressor </td>
+                    </tr>
+                    <tr>
+                        <td>DLldR</td>
+                        <td> Dimer of LldR </td>
+                    </tr>
+                    <tr>
+                        <td>Lac</td>
+                        <td>Lactate introduced in the medium. Forms a complex with LldR, preventing it from repressing the Promoter.
+                            Acts thus as an activator<i>E. coli </i> cells </td>
+                    </tr>
+                    <tr>
+                        <td>Pyr
+                            <SUB>NO</SUB> </td>
+                        <td>Pyruvate, inactive form of lactate<i>E. coli </i> cells </td>
+                    </tr>
+                    <tr>
+                        <td>LldD</td>
+                        <td>Regulatory protein, catalyse the oxydation of Lactate into Pyruvate</td>
+                    </tr>
+                    <tr>
+                        <td>G_on
+                            <SUB>NO1</SUB>
+                        </td>
+                        <td>Active promoter </td>
+                    </tr>
+                    <tr>
+                        <td>G_off
+                            <SUB>NO2</SUB>
+                        </td>
+                        <td>Promoter repressed by LldR binding </td>
+                    </tr>
+                    <tr>
+                        <td>G_off_1
+                            <SUB>NO2</SUB>
+                        </td>
+                        <td>Repressed promoter with 1 lactate molecule bound </td>
+                    </tr>
+                    <tr>
+                        <td>DLldR_Lac1
+                            <SUB>i</SUB>
+                        </td>
+                        <td>DLldR with one Lactate molecule bound
+                            <SUB>NO2</SUB>
+                        </td>
+                    </tr>
+                    <tr>
+                        <td>DLldR_Lac2
+                            <SUB>3</SUB>
+                        </td>
+                        <td>DLldR with two Lactate molecule bound</td>
+                    </tr>
+                </table>
 				<div class="quicklinks">
 					<div>
 						<div class="outline_item">
 							<a href="https://2016.igem.org/Team:ETH_Zurich/Parameters">
-								<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Paramters Values</a>
+							<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Paramters Values</a>
 						</div>
 					</div>
-				</div>
 			</div>
-		</div>
-	</div>
+            </div>
-	<div class="sec white">
+        </div>
-		<div>
+    </div>
-			<h4>REQUIREMENTS</h4>
+    <div class="sec white">
-			<p>
+        <div>
-				NO sensor sensitivity range = [100 uM - 100 mM] Dynamic range : the system must be as fast as possible
+            <h4>REQUIREMENTS</h4>
-			</p>
+            <p>
-			<h5>KEY IDEA</h5>
+               NO sensor sensitivity range = [100 uM - 100 mM]
-			<p>
+               Dynamic range : the system must be as fast as possible
-				As before, we want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so
+            </p>
-				it propagates information relative to the inflammatory and candidate species to the switch and thus to the reporter.
+            <h5>KEY IDEA</h5>
-				Under FACS and fluorescence distribution analysis the level of inflammation could then be inferred. ETH-Zurich previous
+            <p>
-				team already worked on a Lactate sensor. Thus we tried to improve their work and adapt the sensor sensitivity to our
+As before, we want to make the sensitivity range of the sensor and the activation range of the hybrid promoter match, so it propagates information relative to the
-				system. It appears that their system is too sensitive for our purpose. As a consequence we decided to add to the plasmid
+inflammatory and candidate species to the switch and thus to the reporter. Under FACS and fluorescence distribution analysis the level of inflammation could
-				LldR which is constitutively produced and, by inactivating Lactate, artificially lower the concentration of active Lactate
+then be inferred. ETH-Zurich previous team already worked on a Lactate sensor. Thus we  tried to improve their work and adapt the sensor sensitivity to our system.
-				that would be able to bind the LLdR complex that inhibits gene transcription.
+It appears that their system is too sensitive for our purpose. As a consequence we decided to add to the plasmid LldR which is constitutively produced and, by inactivating
-			</p>
+Lactate, artificially lower the concentration of active Lactate that would be able to bind the LLdR complex that inhibits gene transcription.
+            </p>
-			<div>
+<div>
-				<div class="image_box" style="max-width: 500px;">
+            <div class="image_box" style="max-width: 500px;">
-					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100uMlactate_v2.svg">
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100uMlactate_v2.svg">
-						<img src="https://static.igem.org/mediawiki/2016/0/09/T--ETH_Zurich--heatMap100uMlactate_v2.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/0/09/T--ETH_Zurich--heatMap100uMlactate_v2.svg">
+                </a>
+                <p><b>Figure 13:</b> Lactate Sensor overview</p>
+            </div>
+            </div>
+            <h5>PARAMETERS OF INTEREST</h5>
+            <p>
+            <ul>
+                <li>transcription rate of LldD</li>
+                <li>transcription rate of LldR</li>
+                <li>Degradation rate of LldD</li>
+            </ul>
+            </p>
+            <p>
+            those parameters will allow us to set with the kinetic and the steady-state concentration of Lactate in the system. However in term of modularity,
+			it turns out it is easier for the biologist to play with the constitutive promoters involved in LldR and LldD production.
+            </p>
+        </div>
+    </div>
+   <div class="sec white two_columns">
+	<div>
+	    <div>
+            <div>
+                <div class="image_box full_size">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100uMlactate.svg">
+						<img src="https://static.igem.org/mediawiki/2016/5/5c/T--ETH_Zurich--heatMap100uMlactate.svg">
 					</a>
-					<p><b>Figure 13:</b> Lactate Sensor overview</p>
+					<p><b>Figure 14:</b>The behavior of the lactate sensor with LldD was simulated. The lactate input was set on the lower bound of the ideal sensitive range, namely 100 uM. For this concentration we wish to set the proper LldD concentration that allow a small activation (ideally <30%), so there is no risk of false negative.  </p>
 				</div>
 			</div>
-			<h5>PARAMETERS OF INTEREST</h5>
-			<p>
+        </div>
-				<ul>
-					<li>transcription rate of LldD</li>
-					<li>transcription rate of LldR</li>
-					<li>Degradation rate of LldD</li>
-				</ul>
-			</p>
-			<p>
-				those parameters will allow us to set with the kinetic and the steady-state concentration of Lactate in the system. However
-				in term of modularity, it turns out it is easier for the biologist to play with the constitutive promoters involved in
-				LldR and LldD production.
-			</p>
-		</div>
-	</div>
-	<div class="sec white two_columns">
 		<div>
-			<div>
+		    <div>
-				<div>
+				<div class="image_box full_size">
-					<div class="image_box full_size">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100mMlactate.svg">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100uMlactate.svg">
+						<img src="https://static.igem.org/mediawiki/2016/f/f9/T--ETH_Zurich--heatMap100mMlactate.svg">
-							<img src="https://static.igem.org/mediawiki/2016/5/5c/T--ETH_Zurich--heatMap100uMlactate.svg">
+					</a>
-						</a>
+					<p><b>Figure 15:</b>The same analysis performed, with the ideal upper detection limit 100mM. To guarantee
-						<p><b>Figure 14:</b>The behavior of the lactate sensor with LldD was simulated. The lactate input was set on the lower
+					this concentration is very high, and we can see we have full activation, whatever are the production rates values.</p>
-							bound of the ideal sensitive range, namely 100 uM. For this concentration we wish to set the proper LldD concentration
-							that allow a small activation (ideally
-							<30%), so there is no risk of false negative. </p>
-					</div>
 				</div>
+            </div>
-			</div>
+        </div>
-			<div>
+    </div>
+    </div>
-				<div>
-					<div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMap100mMlactate.svg">
-							<img src="https://static.igem.org/mediawiki/2016/f/f9/T--ETH_Zurich--heatMap100mMlactate.svg">
-						</a>
-						<p><b>Figure 15:</b>The same analysis performed, with the ideal upper detection limit 100mM. To guarantee this concentration
-							is very high, and we can see we have full activation, whatever are the production rates values.</p>
-					</div>
-				</div>
-			</div>
-		</div>
-	</div>
 	<div class="sec white two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseLactateLldR.svg">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseLactateLldR.svg">
-							<img src="https://static.igem.org/mediawiki/2016/b/bd/T--ETH_Zurich--doseResponseLactateLldR.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/b/bd/T--ETH_Zurich--doseResponseLactateLldR.svg">
-						</a>
+                </a>
-						<p><b>Figure 13:</b> In the graph above, we plotted the dose response curve for a large range of LldR production rate.
+                <p><b>Figure 13:</b> In the graph above, we plotted the dose response curve for a large range of LldR production rate. This shows that one can easily tune the system and adjust the limit of detection,
-							This shows that one can easily tune the system and adjust the limit of detection, by increasing or decreasing the
+				by increasing or decreasing the production rate of the LldR protein.</p>
-							production rate of the LldR protein.</p>
-					</div>
-				</div>
-			</div>
-			<div>
-				<div>
-					<div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseLactate.svg">
-							<img src="https://static.igem.org/mediawiki/2016/6/62/T--ETH_Zurich--doseResponseLactate.svg">
-						</a>
-						<p><b>Figure 13:</b> As for the LldR plot, the LldD production rate can modify the limit of detection and the range of
-							sensitivity of the lactate sensor. Therefore for a more precise tuning, playing with the LldD production rate is a
-							possible way to tune the circuit.</p>
-					</div>
 				</div>
 			</div>
+        </div>
+		<div>
+		    <div>
+				<div class="image_box full_size">
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--doseResponseLactate.svg">
+                    <img src="https://static.igem.org/mediawiki/2016/6/62/T--ETH_Zurich--doseResponseLactate.svg">
+                </a>
+                <p><b>Figure 13:</b> As for the LldR plot, the LldD production rate can modify the limit of detection and the range of sensitivity of the lactate sensor. Therefore for a more precise
+				tuning, playing with the LldD production rate is a possible way to tune the circuit.</p>
+                </div>
+            </div>
 		</div>
+    </div>
 	</div>
 	<div class="sec white two_columns">
+	<div>
+	    <div>
+            <p>
+			To quickly summarize the difficulties met with the lactate sensor: We based our model and design on the ETH_Zurich iGem team 2015. In their  project, they also use a lactate sensitive circuit, So we decided to
+			use the datas they already have for our sensor. However,  last year sensor proved to be by far too sensitive for our purpose: they were able to sense up to only a few nM of Lactate in the system while we need tosense between
+uM and 100 mM. Thus we needed to tune the circuit such that it meet our requirements. To do that we propose the following method: As the more accessible parameters biologists can play with are protein production and degradation rate,
+			we decided to simulate the influence of those parameters on the limit of sensitivity and dynamic range of our system. It appears that aas LldR is a repressor protein to the promoter, increasing its production rate and/or decreasing its degradation rate
+			shift the limit of detection to the right, decreasing the sensitivity of our circuit. However the amount of available promoter is limited, and do is their strength. We came up with the idea of introducing the LldD specie in the design.
+			LldD catalyse the degradation of lactate into <i>Pyruvate</i>. So its presence artificially decrease the concentration of Lactate available to  activate the reporter inside the cell.
+			Based on parameters found on litterarure, we figured out that the more you increase its production rate the less sensitive is the sensor. There fore playing with the different constitutive promoter strength allows the biologists to finely tune the system and make it fit our sensitivity requirements.</p>
+        </div>
+		<div>
 		<div>
-			<div>
+		    <div class="image_box full_size">
-				<p>
+				 <a href="https://2016.igem.org/File:T--ETH_Zurich--limitDetec.svg">
-					To quickly summarize the difficulties met with the lactate sensor: We based our model and design on the ETH_Zurich iGem team
+                    <img src="https://static.igem.org/mediawiki/2016/2/24/T--ETH_Zurich--limitDetec.svg">
-. In their project, they also use a lactate sensitive circuit, So we decided to use the datas they already have
+                </a>
-					for our sensor. However, last year sensor proved to be by far too sensitive for our purpose: they were able to sense
+                <p><b>Figure 13:</b> Plotting the maximum of the derivative of the dose response curves for the range of LldD production rate, we are able to
-					up to only a few nM of Lactate in the system while we need tosense between 100 uM and 100 mM. Thus we needed to tune
+				define the limit of detection of the lactate sensor.</p>
-					the circuit such that it meet our requirements. To do that we propose the following method: As the more accessible parameters
+            </div>
-					biologists can play with are protein production and degradation rate, we decided to simulate the influence of those
-					parameters on the limit of sensitivity and dynamic range of our system. It appears that aas LldR is a repressor protein
-					to the promoter, increasing its production rate and/or decreasing its degradation rate shift the limit of detection
-					to the right, decreasing the sensitivity of our circuit. However the amount of available promoter is limited, and do
-					is their strength. We came up with the idea of introducing the LldD specie in the design. LldD catalyse the degradation
-					of lactate into <i>Pyruvate</i>. So its presence artificially decrease the concentration of Lactate available to
-					activate the reporter inside the cell. Based on parameters found on litterarure, we figured out that the more you increase
-					its production rate the less sensitive is the sensor. There fore playing with the different constitutive promoter strength
-					allows the biologists to finely tune the system and make it fit our sensitivity requirements.</p>
 			</div>
+        </div>
-			<div>
+    </div>
-				<div>
+    </div>
-					<div class="image_box full_size">
+    <!-- <div class="sec white">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--limitDetec.svg">
-							<img src="https://static.igem.org/mediawiki/2016/2/24/T--ETH_Zurich--limitDetec.svg">
-						</a>
-						<p><b>Figure 13:</b> Plotting the maximum of the derivative of the dose response curves for the range of LldD production
-							rate, we are able to define the limit of detection of the lactate sensor.</p>
-					</div>
-				</div>
-			</div>
-		</div>
-	</div>
-	<!-- <div class="sec white">
          <div>
              <div class="image_box full_size">
@@ Line 1,009: / Line 969: @@
-	<div class="sec light_grey">
+    <div class="sec light_grey">
-		<div>
+        <div>
-			<div class="image_box" style="max-width: 500px;">
+<div class="image_box" style="max-width: 500px;">
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--andGate.png">
+                <a href="https://2016.igem.org/File:T--ETH_Zurich--andGate.png">
-					<img src="https://static.igem.org/mediawiki/2016/1/1e/T--ETH_Zurich--andGate.png">
+                    <img src="https://static.igem.org/mediawiki/2016/1/1e/T--ETH_Zurich--andGate.png">
-				</a>
+                </a>
-				<p><b>Figure 8:</b> Full AND Gate overview</p>
+                <p><b>Figure 8:</b> Full AND Gate overview</p>
-			</div>
+            </div>
-			<h2>Full AND Gate</h2>
+            <h2>Full AND Gate</h2>
-			<p>
+            <p>
-				Now it is time to link the two previous modules together in order to create the full AND Gate. Ideally, we would like to
+                Now it is time to link the two previous modules together in order to create the full AND Gate. Ideally, we would like to
-				keep the model as modular as possible. In a first part, our way to proceed in order to recreate the hybrid promoter behavior
+                keep the model as modular as possible. In a first part, our way to proceed in order to recreate the hybrid
-				from the two simple PnorV+Esabox promoter will be described. Then we propose a second model which takes into account
+                promoter behavior from the two simple PnorV+Esabox promoter will be described. Then we propose a second model
-				all the different states of the promoter under NO and AHL/lactate binding, that can be stochastically simulated.
+                which takes into account all the different states of the promoter under NO and AHL/lactate binding, that
-			</p>
+                can be stochastically simulated.
-		</div>
+            </p>
-		<div>
+        </div>
-			<p>We developed two models to simulate the and gate. The first one is based on the modular model and consider both systems
+        <div>
-				(NO sensor part and AHL sensor part) as independent. It was developed in the idea to keep everything inter-operable,
+         <p>We developed two models to simulate the and gate. The first one is based on the modular model and consider both systems (NO sensor part and AHL sensor part) as independent. It was developed in the idea to keep everything inter-operable, interchangeable and modular. However, this  require a strong assumption, namely that there is no influence of one of the module on the other one.
-				interchangeable and modular. However, this require a strong assumption, namely that there is no influence of one of the
+In order to keep the precision of our model, we developed a second model formed of 61 equation that takes into account all the interactions between in the AND gate.
-				module on the other one. In order to keep the precision of our model, we developed a second model formed of 61 equation
+Therefor, we have two models for the AND gate, the first one which allow modularity and gives you a quick but slightly approximated overview of the full AND gate circuit, and a Second model, more complex, and longer to implement and to simulate, but that keep the high precision in term of mass action equations, and which is interesting in term of tuning, but sacrifices modularity.<p/>
-				that takes into account all the interactions between in the AND gate. Therefor, we have two models for the AND gate,
+    </div>
-				the first one which allow modularity and gives you a quick but slightly approximated overview of the full AND gate circuit,
+    </div>
-				and a Second model, more complex, and longer to implement and to simulate, but that keep the high precision in term of
-				mass action equations, and which is interesting in term of tuning, but sacrifices modularity.
-				<p/>
-		</div>
-	</div>
 	<div class="sec light_grey two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<h5>MODULAR MODEL</h5>
+		    <h5>MODULAR MODEL</h5>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorR3nM.svg">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapKnorR3nM.svg">
-							<img src="https://static.igem.org/mediawiki/2016/c/ca/T--ETH_Zurich--heatMapKnorR3nM.svg">
+						<img src="https://static.igem.org/mediawiki/2016/c/ca/T--ETH_Zurich--heatMapKnorR3nM.svg">
-						</a>
+					</a>
-						<p><b>Figure 16:</b>AND gate simulation using the modular model. As we can see, the rage of sensitivity is not totally
+					<p><b>Figure 16:</b>AND gate simulation using the modular model. As we can see, the rage of sensitivity is not totally the expected one. However, this corresponds
-							the expected one. However, this corresponds to our expectations, since we decided to assume the NO sensor set of reaction
+					to our expectations, since we decided to assume the NO sensor set of reaction and the AHL one independent. We obtain the above results by multiplying the fraction of active promoters
-							and the AHL one independent. We obtain the above results by multiplying the fraction of active promoters of each sensor
+of each sensor part, and consider the resulting fraction as the fraction of active hybrid promoter, leading to mRNA and protein production.					</p>
-							part, and consider the resulting fraction as the fraction of active hybrid promoter, leading to mRNA and protein production.
-							</p>
-					</div>
 				</div>
 			</div>
-		</div>
+        </div>
+	 </div>
 		<div>
-			<h5>FULL STATE MODEL</h5>
+		    <h5>FULL STATE MODEL</h5>
-			<div>
+		    <div>
 				<div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMapFullModelAndGate.svg">
 						<img src="https://static.igem.org/mediawiki/2016/e/ee/T--ETH_Zurich--heatMapFullModelAndGate.svg">
 					</a>
-					<p><b>Figure 17:</b> T plot was obtain using a more detailed model, taking into account all the interaction and overlapping
+					<p><b>Figure 17:</b> T plot was obtain using a more detailed model, taking into account all the interaction and overlapping between the NO sensor and the AHL sensor.
-						between the NO sensor and the AHL sensor. As describe above, it is composed of 61 equations and takes into account
+				As describe above, it is composed of 61 equations and takes into account all the binding/unbinding reaction of the different species to and from the hybrid promoter ( <i>PnorV</i> + <i> esaboxes</i> )
-						all the binding/unbinding reaction of the different species to and from the hybrid promoter ( <i>PnorV</i> + <i> esaboxes</i>						) As you can see above, it displays for our ideal AND Gate, a better range of sensitivity.
+				As you can see above, it displays for our ideal AND Gate, a better range of sensitivity.
-					</p>
+				 </p>
 				</div>
-			</div>
+            </div>
-		</div>
+        </div>
-	</div>
+    </div>
 	<div class="sec light_grey two_columns">
 		<div>
-			<h3>AND GATE SIMULATION WITH THE ESTIMATED PARAMETERS</h3>
+		<h3>AND GATE SIMULATION WITH THE ESTIMATED PARAMETERS</h3>
-			<p>In order to be able to predict our AND Gate behaviour, we need to simulate it with the fitted parameters. Thus we are
+		<p>In order to be able to predict our AND Gate behaviour, we need to simulate it with the fitted parameters. Thus we are able to compare the "real" AND gate dose
-				able to compare the "real" AND gate dose response with the ideal system. The fitted AND Gate simulation provide allow
+		response with the ideal system. The fitted AND Gate simulation provide allow us to predict our system behaviour, to confirm their experiments and provide some insights in
-				us to predict our system behaviour, to confirm their experiments and provide some insights in order to improve the circuit
+		order to improve the circuit and stick closer to the ideal range of input species we want to detect.</p>
-				and stick closer to the ideal range of input species we want to detect.</p>
 		</div>
-	</div>
+    </div>
 	<div class="sec light_grey two_columns">
-		<div>
+	<div>
-			<div>
+	    <div>
-				<h5>MODULAR MODEL</h5>
+		    <h5>MODULAR MODEL</h5>
-				<div>
+            <div>
-					<div class="image_box full_size">
+                <div class="image_box full_size">
-						<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaprealparam.png">
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--heatMaprealparam.png">
-							<img src="https://static.igem.org/mediawiki/2016/c/c3/T--ETH_Zurich--heatMaprealparam.png">
+						<img src="https://static.igem.org/mediawiki/2016/c/c3/T--ETH_Zurich--heatMaprealparam.png">
-						</a>
+					</a>
-						<p><b>Figure 16:</b>AND gate simulation using the modular model, using the fitted parameters. Here, we decided to use
+					<p><b>Figure 16:</b>AND gate simulation using the modular model, using the fitted parameters. Here, we decided to use the parameters estimated from the test of the NO and AHL sensor separately tested and characterized.
-							the parameters estimated from the test of the NO and AHL sensor separately tested and characterized. For simplicity,
+					For simplicity, we assume that those parameters are not modified by the presence of the other sensor ( AHL and NO sensor respectively ). As you can see, the AND Gate present an activation at low
-							we assume that those parameters are not modified by the presence of the other sensor ( AHL and NO sensor respectively
+concentration of AHL. Although it doesnt behave as an AND gate in the full range of possible input, it shows consistency with the previously characterized sensor. This "early activation" actually corresponds to teh bumpy dose response of the AHL sensor
-							). As you can see, the AND Gate present an activation at low concentration of AHL. Although it doesnt behave as an
+for the current parameters.					</p>
-							AND gate in the full range of possible input, it shows consistency with the previously characterized sensor. This
-							"early activation" actually corresponds to teh bumpy dose response of the AHL sensor for the current parameters. </p>
-					</div>
 				</div>
 			</div>
-		</div>
+        </div>
+	 </div>
 		<div>
-			<h5>FULL STATE MODEL</h5>
+		    <h5>FULL STATE MODEL</h5>
-			<div>
+		    <div>
 				<div class="image_box full_size">
 					<a href="https://2016.igem.org/File:T--ETH_Zurich--FullANdGatewithfittedparamModularfullmodelV2.png">
 						<img src="https://static.igem.org/mediawiki/2016/0/0d/T--ETH_Zurich--FullANdGatewithfittedparamModularfullmodelV2.png">
 					</a>
-					<p><b>Figure 17:</b>AND gate simulation using the full model, with the fitted parameters. As stated above, while the modular
+					<p><b>Figure 17:</b>AND gate simulation using the full model, with the fitted parameters. As stated above, while the modular model provide flexibility and reasonably
-						model provide flexibility and reasonably consistent results, it still lacks precision due to the assumptions. As the
+consistent results, it still lacks precision due to the assumptions. As the behaviour of our AND Gate is unusual, we decided to run the simulation using the full model, which provides more accurate results.
-						behaviour of our AND Gate is unusual, we decided to run the simulation using the full model, which provides more accurate
+As we can see the bump is attenuated. This could be explained by the fact that the shifting of equilibrium due to the AHL/EsaR ratio is attenuated by the presence of the NO sensor part. For more information please refer to the AHL sensor module part.</p>
-						results. As we can see the bump is attenuated. This could be explained by the fact that the shifting of equilibrium
-						due to the AHL/EsaR ratio is attenuated by the presence of the NO sensor part. For more information please refer to
-						the AHL sensor module part.</p>
 				</div>
-			</div>
+            </div>
-		</div>
+        </div>
-	</div>
+    </div>
-	<div class="sec light_grey">
+    <div class="sec light_grey">
-		<div>
+        <div><h3>ADDITIONAL LINKS AND INFORMATIONS</h2>
-			<h3>ADDITIONAL LINKS AND INFORMATIONS</h2>
+		<h4>FULL-MODEL</h4>
-				<h4>FULL-MODEL</h4>
+			<p> For additional information about the full model, please check the link below.</p>
-				<p> For additional information about the full model, please check the link below.</p>
+			<div class="quicklinks">
-				<div class="quicklinks">
 					<div>
 						<div class="outline_item">
 							<a href="https://static.igem.org/mediawiki/2016/e/ef/T--ETH_Zurich--FULL_MASS_ACTION_MODEL_FOR_AND_GATE_MODULE.pdf">
-								<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Full Mass Action model for the AND Gate</a>
+							<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Full Mass Action model for the AND Gate</a>
 						</div>
 					</div>
-				</div>
+			</div>
 		</div>
 	</div>
 	<div class="sec white">
+        <div>
+            <h2>DETERMINISTIC OVER STOCHASTIC MODEL</h2>
+            <p>
+                At the beginning of the project, we wanted to a model as precise as possible that would allow us to tune finely our system in order to get
+				a response as close to the ideal one as possible. In addition, we also decided to go first for a full stochastic model. Thus FACS analysis and comparison
+				as well as fitting would assure us a good parameter estimation. Moreover, we wished that looking at the reporter distribution would help us
+				to get some additional information about the input of the circuit. As the system is thought as a detector, and its application (travelling through the gut
+				which is seen as a black box to get activated eventually by the presence of both NO and specific AHL) is to express a reporter, we wished that by simply
+				looking at he distribution of mean and variance of the reporter could give us enough information in order to reconstitute the input. Thus in a medical
+				application, a simple FACS analysis of the remaining E.Coli harvesting from the faeces, would be enough to determine the acuteness of the inflammation and
+				a quantitative data on the microbiota disbalance.
+				However, it appeared after several staochastic simulation that the fluctuation of the ratio of activated promoter, for one trajectory, was to fast to have any
+				influence of the reporter protein expression, as binding and unbinding of protein and ligand to and from DNA strand are quick reaction compared to mRNA and protein
+				production and folding. As shown on the graph below. Thus we decided to go for simple deterministic simulation for the AND gate characterization.
+            </p>
+        </div>
 		<div>
-			<h2>DETERMINISTIC OVER STOCHASTIC MODEL</h2>
+				<div class="image_box full_size">
-			<p>
+					<a href="https://2016.igem.org/File:T--ETH_Zurich--comparisonStochasVSDeterm.svg">
-				At the beginning of the project, we wanted to a model as precise as possible that would allow us to tune finely our system
+						<img src="https://static.igem.org/mediawiki/2016/1/1e/T--ETH_Zurich--comparisonStochasVSDeterm.svg">
-				in order to get a response as close to the ideal one as possible. In addition, we also decided to go first for a full
+					</a>
-				stochastic model. Thus FACS analysis and comparison as well as fitting would assure us a good parameter estimation. Moreover,
+					<p><b>Figure 18:</b>We compare the behavior of the model under deterministic and stochastic simulations. As we can see the mean behavior remains similar. The fluctuations around the mean value due to the stochasticity can be neglected at a promoter scale because protein-ligand binding-unbinding reaction is extremely fast compare to protein/mRNA production and folding. Thus choosing a deterministic approach here does not represent a loss of information neither for the kinetic nor for the concentration at steady state. </p>
-				we wished that looking at the reporter distribution would help us to get some additional information about the input
+				</div>
-				of the circuit. As the system is thought as a detector, and its application (travelling through the gut which is seen
+            </div>
-				as a black box to get activated eventually by the presence of both NO and specific AHL) is to express a reporter, we
+    </div>
-				wished that by simply looking at he distribution of mean and variance of the reporter could give us enough information
-				in order to reconstitute the input. Thus in a medical application, a simple FACS analysis of the remaining E.Coli harvesting
-				from the faeces, would be enough to determine the acuteness of the inflammation and a quantitative data on the microbiota
-				disbalance. However, it appeared after several staochastic simulation that the fluctuation of the ratio of activated
-				promoter, for one trajectory, was to fast to have any influence of the reporter protein expression, as binding and unbinding
-				of protein and ligand to and from DNA strand are quick reaction compared to mRNA and protein production and folding.
-				As shown on the graph below. Thus we decided to go for simple deterministic simulation for the AND gate characterization.
-			</p>
-		</div>
-		<div>
-			<div class="image_box full_size">
-				<a href="https://2016.igem.org/File:T--ETH_Zurich--comparisonStochasVSDeterm.svg">
-					<img src="https://static.igem.org/mediawiki/2016/1/1e/T--ETH_Zurich--comparisonStochasVSDeterm.svg">
-				</a>
-				<p><b>Figure 18:</b>We compare the behavior of the model under deterministic and stochastic simulations. As we can see the
-					mean behavior remains similar. The fluctuations around the mean value due to the stochasticity can be neglected at a
-					promoter scale because protein-ligand binding-unbinding reaction is extremely fast compare to protein/mRNA production
-					and folding. Thus choosing a deterministic approach here does not represent a loss of information neither for the kinetic
-					nor for the concentration at steady state. </p>
-			</div>
-		</div>
-	</div>
 	<div class="sec light_grey">
-		<div>
+        <div><h2>ADDITIONAL LINKS AND INFORMATIONS</h2>
-			<h2>ADDITIONAL LINKS AND INFORMATIONS</h2>
+		<h3>MODEL-BRICK</h3>
-			<h3>MODEL-BRICK</h3>
+			<p> For additional information, please check our public Git repository. All our codes are available there. As the spirit of iGem is information and
-			<p> For additional information, please check our public Git repository. All our codes are available there. As the spirit of
+		knowledge sharing, we tried to make our codes as easily usable as possible.	</p>
-				iGem is information and knowledge sharing, we tried to make our codes as easily usable as possible. </p>
 			<div class="quicklinks">
-				<div>
+					<div>
-					<div class="outline_item">
+						<div class="outline_item">
-						<a href="https://bitbucket.org/mattiagollub/igem_2016">
+							<a href="https://bitbucket.org/mattiagollub/igem_2016">
 							<img src="https://static.igem.org/mediawiki/2016/7/72/T--ETH_Zurich--arrow.svg">Our Git repository</a>
+						</div>
 					</div>
-				</div>
 			</div>
 		</div>
 	</div>
-	<div class="sec white">
+<div class="sec white">
-		<div>
+<div><h3>REFERENCES</h3>
-			<h3>REFERENCES</h3>
+<p>[1] Tucker, N. P. et al. "Essential Roles Of Three Enhancer Sites In  54-Dependent Transcription By The Nitric Oxide Sensing Regulatory Protein Norr". Nucleic Acids Research 38.4 (2009): 1182-1194.</p>
-			<p>[1] Tucker, N. P. et al. "Essential Roles Of Three Enhancer Sites In 54-Dependent Transcription By The Nitric Oxide Sensing
+</div>
-				Regulatory Protein Norr". Nucleic Acids Research 38.4 (2009): 1182-1194.</p>
+</div>
-		</div>
-	</div>
 </body>
 </html>
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Difference between revisions of "Team:ETH Zurich/Sensor Module"

Revision as of 19:39, 19 October 2016

SENSOR MODULE

OVERVIEW

SENSOR MODULE

GOALS

Nitric Oxyde Sensor

ASSUMPTIONS

REACTIONS

RESULTS

REQUIREMENTS

KEY IDEA

PARAMETERS OF INTEREST

PARAMETER TUNING

DOSE RESPONSE

PARAMETER ESTIMATION

AHL SENSOR

ASSUMPTION

REACTIONS

RESULTS

REQUIREMENTS

KEY IDEA

PARAMETERS OF INTEREST

SENSITIVITY ANALYSIS

HEAT MAP

DOSE RESPONSE

PARAMETERS ESTIMATION

EXPLANATION AND OUTLOOK

LACTATE SENSOR

ASSUMPTIONS

REQUIREMENTS

KEY IDEA

PARAMETERS OF INTEREST

Full AND Gate

MODULAR MODEL

FULL STATE MODEL

AND GATE SIMULATION WITH THE ESTIMATED PARAMETERS

MODULAR MODEL

FULL STATE MODEL

ADDITIONAL LINKS AND INFORMATIONS

FULL-MODEL

DETERMINISTIC OVER STOCHASTIC MODEL

ADDITIONAL LINKS AND INFORMATIONS

MODEL-BRICK

REFERENCES

Thanks to the sponsors that supported our project: