Team:Waterloo/Modeling/Intercellular Spread
Viral Spread Model
Motivation
The spread of infection between cells and among populations has been modelling extensively, but the middle ground of intercellular spread is relatively unexplored. We investigated whether our defense system would protect individual Arabidopsis plants using an agent-based approach. Briefly, each plant cell is treated an agent that may become infected by CaMV or resistant to infection due to plant defense signalling. Infected cells run an instance ODE model that was created to study viral replication and then may pass infection to their neighbours. The spread through the stems and leaves of the plant can be tracked with and without the CRISPR/Cas9 system.
[insert screenshot of the simulation] [insert diagram of the plant’s plasmodesmata and vascular system]As a reaction to the presence of the virus, the plant cells will destroy infected cells through apoptosis as well as signal to the rest of the plant to begin production of defensive chemicals in preparation for attack. This signaling causes systemic required resistance, a broad, long-term increased resistant to future infections.
- Explain and link this to the parameters in the model once the parameters are finalized
- Explain reasons for the parameters
Model Formation
Biology of Viral Infection Spread
Intercellular
For information on this, please see the Intercellular Spread section of the CaMV biology page here.
Systemic Infection
Phloem/Vascular System
Plant Defense Mechanisms
Agent-Based Model Design
Include Model Assumptions!!!
Plant Structure
- Plasmodesmata
- Phloems and Vascular System
Virus
- Initial Infection Sites
- Founder Population
- Viral Spread Rates
- Viral Spread Chance
- Viral Assembly
Plant Response
- SA Chance (Plant Defense)
- Lysis Chance
- SAR
- SAR Spread Chance
Agent-Based Modelling Software
We identified several requirements for the software used in our agent-based model:
- Ability to create different kinds of agents.
- Control over connections between agents (e.g. plasmodesmata betweeen cells)
- Support for complex rules (e.g. ODE simulations) within each agent
- Arbitrary number of model states
- Tracking of time since infection
- Ease of use
- Photo © Uri Wilensky
- Original Photo
Three different software packages (MASON, MESA and Netlogo) were considered in depth. MESA is a Python-based simulation package and was not used due to its poor documentation and the level of familiarity with Python required to create the model. MASON, a Java-based simulation package, has excellent, easily-found documentation but no GUI and fewer built-in functions. Although fewer built-in functions could be an advantage (adding more flexibility and customization to the simulation), time constraints and unfamiliarity with Java pushed us to use NetLogo instead of MASON. NetLogo was easy to learn, well-documented and had a built in GUI.