Team:ETH Zurich/Sensor Module

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AND Gate overall presentation:

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.

Description and Design

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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

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

Chemical species, reaction and equations

Reaction

NorR system: $$ \begin{align} &\rightarrow NorR\\ NO+NorR & \leftrightarrow NorR_{NO}\\ 2 NorR_{NO} &\leftrightarrow DNorR_{NO2}\\ 2 NorR & \leftrightarrow DNorR\\ DNorR+NO&\leftrightarrow DNorR_{NO1}\\ DNorR_{NO1}+NO&\leftrightarrow DNorR_{NO2}\\ DNorR_{NO2}+PnorV0&\leftrightarrow PnorV1\\ DNorR_{NO2}+PnorV1&\leftrightarrow PnorV2\\ DNorR_{NO2}+PnorV2&\leftrightarrow PnorV3\\ PnorV3;\rightarrow mRNA_{Bxb1}\\ NorR&\rightarrow \\ DNorR&\rightarrow \\ DNorR_{NO1}&\rightarrow\\ DNorR_{NO2}&\rightarrow\\ NorR_{NO} &\rightarrow\\ mRNA_{Bxb1} &\rightarrow\\ \end{align}$$

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Deterministic simulation

As a first approach, considering that the amount of nitric oxyde to detect in case of infection should be quite fast, we decided to deterministicaly simulate the system in order to have a quantitative idea of the behavior of the system

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.

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

Chemical species, reaction and equations

Deterministic simulation

first approach, high input -> enought for a good inderstanding of the system behavior

Stochastic simulation

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

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.

Deterministic simulation

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

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modular model

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