Team:IISER-Mohali/Measurement
Measurement
Importance of measurement in our project
Measurement is an integral part of our project as the implementation of the entire project hinges on the diffusion of the pheromone into the environment. So it is crucial for us to have precise measurements of the diffusion rate and capacity of the product. The Modelling of this diffusion rate has been performed by the insilico simulations. The model for this diffusion can be tweaked for a variety of measurement applications.
Novelty of the Measurement model
- Although Diffusion mechanisms are vastly studied and exploited by scientists, there is no cohesive model to predict the diffusion rate of an autoinducer produced from a synthetic micro-organism.
- Our software provides an inbuilt extensive list of diffusible molecules in different cells and a few cell types. This reduces the need to model different molecules and different cell types.
- Our computational model is flexible and can be used for a variety of application by extending the model with feedback loops
Possible Applications of the Diffusion model:
Quorum sensing is a process of cell–cell communication that allows bacteria to share information about cell density and adjust gene expression accordingly. This process enables bacteria to express energetically expensive processes as a collective only when the impact of those processes on the environment or on a host will be maximized. Among the many traits controlled by quorum sensing is the expression of virulence factors by pathogenic bacteria.
Auto-Inducers accumulate in the environment as the bacterial population density increases, and bacteria monitor this information to track changes in their cell numbers and collectively alter gene expression.
If Modelling of Quorum sensing is accomplished, features of micoorganisms such as bioluminescence, sporulation, competence, antibiotic production, biofilm formation, and virulence factor secretion can be predicted.This will enable synthetic Biologists to be able to predict their biological systems more accurately.
Aim
- To create a simulation which gives the diffusion data of any AHL in a set of microorganisms.
- To Model Quorum sensing feedback loops if they exhibit Quorum sensing utilizing the newly introduced AHL.
Measurement Method
- The exploratory modelling done to simulate the diffusion of pheromone was utilized to simulate the diffusion of Autoinducers from and into the cell.
- The relation between the production rate of Auto-inducers and the Auto-inducer concentration was established.
- The population of the colony at any given time was calculated.
Underlying Algorithm
- The diffusion algorithm is identical to the one used in the modelling section. A schematic representation of the Workflow is given below.
- The relation between the production rate of Auto-inducers and the Auto-inducer concentration can be established by:
- In the above equation, A(t) is the Concentration of the molecule outside the cell, αr, γr ar and βr are constants unique to the properties of the cell.
Results
- The database of AHLs was prepared through literature search and their diffusion properties were analysed.
- The Software for diffusion of pheromone was extended to apply for Diffusible molecules given below.
The Databse of AHLs:
| S No | Type of Cell | Type of the Chemical | Name of the Chemical | Radius of Gyration (nm) |
|---|---|---|---|---|
| 1 | Pseudomonas aeruginosa | AHL | acyl-homoserine lactone | 1.98 |
| 2 | Vibrio cholerae | NA | cholera autoinducer-1 ( (S)-3-hydroxytridecan-4-one ) | 4.72 |
| 3 | Escherichia coli | Autoinducer | AI-2/LsrR | |
| 4 | Salmonella typhimurium | Autoinducer | AI-2/LsrR | |
| 5 | Vibrio fischeri | AHL | N-(3-oxohexanoyl)-homoserine lactone | 3.51 |
| 6 | Pantoea stewartii | AHL | 3-oxo-C6-HSL | |
| 7 | Agrobacterium tumefaciens | AHL | 3-oxo-C8-HSL | 4.22 |
| 8 | Nitrobacter winogradskyi | AHL | 7, 8-trans-N-(decanoyl) homoserine lactone (C10:1-HSL) | |
| 9 | Burkholderia mallei | AHL | C8-HSL | 4.3 |
| 10 | Burkholderia mallei | AHL | 3-hydroxy-C8-HSL | 4.23 |
| 11 | Pseudomonas aeruginosa | AHL | C4-HSL | 2.84 |
| 12 | Pseudomonas aeruginosa | AHL | 3-oxo-C12-HSL | 5.76 |
| 13 | Rhodopseudomonas palustris | AHL | p-coumaroyl-HSL (pC-HSL) | 4.09 |
| 14 | Nitrosomonas europaea | AHL | C6-HSL | 3.56 |
| 15 | Nitrosomonas europaea | AHL | C8-HSL | 4.3 |
| 16 | Nitrosomonas europaea | AHL | C10-HSL | 5.13 |