Team:IISER-Mohali/Model

 

Modeling

The need for cohesive diffusion models

Modeling is a significant part of iGEM and synthetic biology as a whole. And for a project like FearOmone, where we cannot measure the rate of production of gases from yeast, we must look forward to a computational model for predicting the behavior of our biological system.

The rate of diffusion of the pheromone from the cell is instrumental in deciding the mode of application of the project. This model will also help us in determining the best conditions for maximal diffusion of the small molecule. To accomplish this, we simulated the diffusion of a small molecule from the insides of the cell into the environment using EFFUSE (EFflux of Fearomone for optimal USE). We then use this information to predict the optimal placement of our product in the storage area for maximal effect using RATTEL (RAT Timidity tester from Evidence based Learning). This software will supplement the Human practices aspect of our team.

Summary of software


EFFUSE - EFflux of Fearomone for optimal USE

  Aim:

  • To predict the diffusion rate of a small molecule from the cell.

  • To model the growth of yeast.

  • To use the above two to calculate the ouput of a colony of pheromone producing yeasts at any given point of time.

PyMOL simulation

  Method of Simulation:

  • A 3-Dimensional construct of the yeast cell was created. The different organelles and different parts of the cell can be modeled by compartments with different viscosity.

  • To simulate the membrane of the yeast, a outer layer of compartments with selective permeability was modeled.

  • In this model cell, a small particle was modeled with modified brownian motion. Step length of random walk was picked according to following function.

    Where N is normal distribution.

  • It was assumed that size of step is very large as compared to compartment size.

  • Average time tB required for a particle to cover distance equal to its diameter is given as follows.[1]

    The radius of gyration (r) in above equation is obtained from molecular visualization using PyMOL.[2]

  • Particles were assumed to be non-interacting. This allows us to model n-particle system by running single particle simulation n times.

  Results:

  • The result indicate that the rate of production of felinine is very low.

  • To increase the productioin of the chemical, the cell membrane was lysed, thereby releasing all the molecules inside a cell.



RATTEL - RAT Timidity tester from Evidence based Learning

  Aim:

  • To provide a software to complement experimental and hardware efforts and model the behavior of rats in response to external stimuli.

  • To model interaction of rats with FearOmone and predict localization of hardware deployment for most effective response of rats

  • To find the localization of multiple TOMCATs such that it drives the rats to particular location to facilitate trapping.

  Method of Simulation:

  • A 2-Dimensional Grid construct of the storage area floor was created.

  • A Static potential gradient was applied to the floor to simulate the rats intrinsic fear of open areas and attractiveness towards food.

  • Food rich areas were given a negative potential and regions with FearOmone product were given a positive potential. The video beside shows the dynamics FearOmone field with the passage of time.

  • The Rats were simulated as Active Brownian particles. This implies they will be attracted by negative potentials and will be averse to positive potentials,whilst having an internal drive to move in a particular direction.

  • A brute force algorithm was used to determine the an order parameter for each possible position of FearOmone box for maximum diffusion and minimum food spoilage.

  • We created a software that integrates all the aspects of the simulation and provides the position for optimal placement of the FearOmone product-TOMCAT.

  Results:

  • The software predicts the best location of TOMCAT Deployment for complementing experimental efforts and guides the final implementation of the hardware in the grain storehouses.

Simulation for optimum localization

Assisted trapping simulation

References:

  1. https://www.sciencedirect.com/topics/chemistry/brownian-motion

  2. https://pymol.org/2/

  3. American Journal of Physics 82, 659 (2014); doi: 10.1119/1.4870398

  4. Open Field Exploratory Behavior,Itai Fonio, Udi Fonio