Team:ETH Zurich/Sensor Module
SENSOR MODULE
INTRODUCTION:
Our idea was to recognize bowel infection and its possible cause based on the intestine level of Nitric Oxyde (NO) which is infection specific, and of Acyl Homoserine-Lactone (AHL) which is microbiota specific. Thus, the simultaneous presence of those two chemicals in an abnormal amount can de detected, and later associated.
Lactate is also a molecule of interest in IBD research : non only is it playing an important role in metabolism, but recent studies tend to show that it is present in high amount in certain cases of severe IBD.
Thus it turns out that two type of sensors are interesting to devellop in order to investigate the causes of IBD. The first AND Gate will be able to detect the presence of both AHL and NO, while the second one will detect Lactate and NO.
SENSOR MODULE
Figure 2:two alternative design for the sensor module
GOALS
- Have an overall overview of the behavior and characteristic of our system
- Discuss the specification of our model and see how the design may influence the equations and this the output behavior
- Define the parameters that can be tuned and that can impact the output of our system so we can control our system range of working
- Compare the different design
- Infer the input state from the output signal analysis
Figure 1:NorR overview
Nitric Oxyde sensor
In the absence of NO, NorR is produced constitutively and binds repressively to the PnorV promoter, preventing gene transcription. When NO is present in the medium, it binds cooperatively to the hexameric form of NorR,and activate the promoter.
ASSUMPTION
We considered here that the binding of NO to NorR and PnorV_{i} does not affect the other species binding. Thus the reactions \begin{align*} NorR+NO&\rightleftharpoons NorR_{NO}\\ \end{align*} and \begin{align*} PnorV_{NorR}+NO&\rightleftharpoons NorR_{NO}\\ \end{align*} have the same reaction rate. Under those assumption, the system of equation can thus 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 insideE. coli cells |
| NorR NO | NorR with No boundE. coli cells |
| DNorR | Dimer of NorR , regulatory protein PnorV operon |
| DNorR NO1 | Dimer with one NO bound to one of its site |
| DNorR NO2 | Dimer two NO bound to it |
| PnorV i | PnorV promoter with i sites occupied by DNoR NO2 |
| PnorV 3 | PnorV3 is the active promoter |
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
NO sensor sensitivity range = [2 uM - 200 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
DOSE RESPONSE
Stochastic simulation
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 2: 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
| 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 | prmoter freed from the road block constituted by the EsaR bound to the downstream esaboxes |
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 1:NorR overview
As we can see on the grapf 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.
Figure 1:NorR overview
Figure 1:NorR overview
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 modeling may be interesting
Figure 1:NorR overview
Figure 3: 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.
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 |
RESULTS
high input, nice behavior.
Stochastic simulation
always the output issue.
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.
Full state model
-- inset image and equation --
modular model
-- inset image and equation --
Thanks to the sponsors that supported our project:
