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.

This leads to our second goal which is to integrate the intracellular replication and intercellular spread models to fully understand impact of CRISPR/Cas9. Altogether, we are attempting to demonstrate the feasibility of our anti-viral system and use our findings to direct the project design.

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

For information on this, please see the Intercellular Spread section of the CaMV biology page here.

Agent-Based Model Design

Include Model Assumptions!!!

Model Assumptions

• Viral Spread Chance - before this was added to the model, the virus would infect 100% of the plant at an unreasonably fast pace, in opposition to laboratory observations. Complete infection of the plant would be very unlikely to happen, even with plants being intentionally bombarded with viruses to infect it. Unfortunately, no number for this could be found in literature, so a reasonable approximation of this value was implemented in order to emulate normal viral spread.
• Cas9 is already at a steady state at time of infection

Plant Structure

• Plasmodesmata
• Phloems and Vascular System

Virus

• Initial Infection Sites is a user-selected integer representing the number of lesions on a plant leaf for the application of the virus
• Founder Population represents the number of viruses in each of the lesions, or the multiplicity of infection, which should be between 2 and 13
• Viral Spread Rates the virus spreads at a rate of about 3 cells per day, but it takes longer for the cell to begin actively producing and exporting virions
• Viral Spread Chance
• Viral Assembly
• Modify Virus "On" runs the simulation with Cas9, "Off" is without

Plant Response

• SAR or Systemic Acquired Response, is a form of acquired immunity. The plant produces signalling molecules and enables cells to become resistant or lyse themselves
• Resistance Threshold is the level of SA signalling molecule required for a cell to become resistant to the pathogen
• Lysis Threshold is the level of SA signalling molecule required for a cell to undergo apoptosis to protect the rest of the organism
• Signalling Molecule is generated by resistant or susceptible cells neighbouring infected or resistant cells at a rate of one per minute, after those neighbours have been infected/resistant for 8hrs. The model has been simplified to have both HR and SAR spread through the plant using one signalling chemical instead of the complex interactions between several different signalling molecules separately .

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

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• 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.

Results

Discussion

References