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

Cauliflower mosaic virus (CaMV) is typically spread from plant to plant through aphids acting as viral vectors. Once in the plant cell, CaMV exploits host transport mechanisms in order to spread. Neighbouring plant cells share cytoplasmic connections through connections called plasmodesmata, which are utilized by CaMV to spread to neighbouring cells. Additionally, viruses can also form tubules through cell walls to spread to adjacent cells. These two processes, however, are quite slow. An additional feature of the plant that the virus able to exploit is the plant’s vascular system, where it is able to spread quickly through the plant’s vascular system, leading to what is called systemic infection.

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

Model Formation

Biology of Viral Infection Spread

Intercellular

Cauliflower mosaic virus exploits host transport mechanisms in order to spread. Neighbouring plant cells share cytoplasmic connections through connections called plasmodesmata. These symplastic connections are exploited by CaMV to spread to neighbouring cells, but it is a fairly slow process. The virions must travel between the plasma membrane and the desmotubule (cytoplasmic channel) of the plasmodesmata, a very narrow passage. The size of molecule able to diffuse through the plasmodesmata is determined by the size exclusion limit (SEL), which is affected by the gating properties of the plasmodesmata. In another slow cell-to-cell process, the virus can move through tubules formed through the cell wall. However, CaMV is able to spread to different regions of the plant quite quickly using the plant’s phloem (sugar-transport network). Microtubules are a key component of intercellular transmission as well as intracellular motion. They form viral inclusions that encourage uptake by aphids (the major plant-to-plant transport agent) and of viral factories (Niehl et al., 2013) ****WE NEED TO FIND THIS REFERENCE*****. CaMV can induce the formation of microtubules in order to increase its rate of spread .

Systemic Infection

-Phloem/Vascular System

Plant Defense Mechanisms

Hypersensitive Response (HR)

Systemic Acquired Resistance (SAR)

Mechanisms

The systemic acquired resistance (SAR) defense mechanism, or immunization of plants, is a broad, long-term increased resistant to future infections. This is similar to the increased resistance against diseases in mammals, after having been infected. It is important to note that this resistance is not triggered by mechanical damage caused by factors such as herbivore attack. This mechanism is only activated after a pathogen is detected within the plant, triggering defenses through signals Salicylic acid (SA) has been identified as a molecule with an indispensable role in the pathway to systemic acquired resistance. Additionally, NO also has an important role in activating systemic acquired resistance. During the initial wave of defense after pathogen detection, there is an “oxidative burst” wherein levels of oxidative species suddenly increase. This is accompanied by cell wall protein linkage, the activation of kinase and increased gene expression defensive genes. A second wave of defense is found in the hypersensitive response, wherein lesions form (from programmed cell death). NO and SA play an interconnected role with each other, where reactive oxygen species (ROS) such as NO have been observed accumulating SA, and in turn, SA triggers ROS production (including NO and H2O2).

Signalling and Spread

Agent-Based Model Design

Plant Structure

Virus

Plant Response

Modelling Software Choice

The agent-based modelling software for the simulation had several different criteria needed to be fulfilled: the ability to create different kinds of agents, control over the connections between agents (e.g. plasmodesmata betweeen cells), an option to have complex rules within each agent, being able to have an arbitrary number of model states and keep track of time since infection, as well as being easy to use. Originally, three different software packages (MASON, MESA and Netlogo) were chosen out of several as the top candidates to create the viral spread simulation due to their fulfillment of the above criteria. All of the languages required were unfamiliar, and thus would have to be learned from scratch. Netlogo was chosen to create the final simulation due to a variety of its strengths as well as time constraints. Although Netlogo was the most unfamiliar-looking language, it presented itself as the simulation package with the smallest learning curve. Additionally, it had readily-available documentation and a built-in GUI. MESA was a Python-based simulation package and ultimately was not chosen due to its documentation not being as detailed or as readily available as MASON or Netlogo’s, as well as the level of familiarity with Python required to create the model being too high to achieve within the time constraints. MASON, a Java-based simulation package, despite having excellent, easily-found documentation did not have a built-in GUI, and did not have as many built in-functions as the Netlogo, thus requiring more work to get off the ground. Although in some cases the fewer amount of built-in functions could have proved to be an advantage (it would have added more flexibility and customization to the simulation), due to the time constraints and the demand of a level familiarity with Java (as with MESA) to create a simulation with MASON, Netlogo was thus chosen over MASON.

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