# Overview

We hope that this project will eventually be applied to natural water bodies, such as small lakes.

We designed a model to better guide our approach in predicting the outcome of algal phagocytes acting on cyanobacteria cells. In our design, the key chassis is a virus. Therefore, in our solution, the process of "cracking cyanobacteria and degrading toxins by releasing cyanophages" is actually very similar to the process of virus infecting biological population.

In the area of cyanobacteria bloom with very high cell density, the infectious disease model cannot describe the process of water surface covered and recovered by cyanobacteria very vividly. Therefore, we refer to the cellular automata model to simulate the process of cyanophages acting on cyanobacteria cells, and predict and qualitatively analyze the influence of cyanobacteria reproduction and survival ability on the results.

# Our model

Cellular automata uses discrete spatial layout and discrete time intervals to divide cells into finite states, and the evolution of individual states of cells is only related to their current state and the state of some local neighborhood.

Our model simulated the effect of cyanophage on the cyanobacterial cells, and carried out the prediction and qualitative analysis of the effect of the phage reproduction and viability on the results.

**State quantity in the system changing with time:**

the blue parts shows the solution & the cells that are broken down

the green parts represents cyanobacterial cells

the yellow parts is the random distribution of phages

**Objective:**
Obtain the above three state quantities, given the initial conditions, changes with time.

**Assumptions:**
A phage infects a cell, so the decrease in the number of uninfected cyanobacteria is the same as the decrease in the number of phage infected and the decrease in the number of phages free in the solution.

Furthermore, phage infection rate is a number related to the number of uninfected cells and the number of phages free in the solution.

Under these assumptions, we set up the model simulation results of the viability (continuous action) and diffusion probability of phagocytes with different parameters. The information obtained from the simulation map should not be used as a quantitative prediction, but as a rough estimation criterion to identify high-impact targets of algal phagocytes infecting cyanobacteria cells.

# Result Analysis Section

Let's first look at the results of the final phagocytic action. It is mainly determined by two parameters. By adjusting the spread chance & continue function chance of algal phagocytes, we can get the system state after algal phagocytes enter the system and the same action time under different initial parameters.

It was found that the diffusion ability dominated the effect of cyanobacteria removal, and the persistence of cyanobacteria was not significant to the removal of cyanobacteria. However, when algophagoides are controlled by us, the probability value of their continuous action is low, leading to their unsatisfactory action results.

# Biological significance

The model successfully proved that the number of cyanobacteria could be increased by controlling the diffusion probability of cyanobacteria. When the introduction of non-natural amino acid control part of the alga body, will be completely controlled in the natural water.

In order to remove cyanobacteria better, we need to find a way to improve the diffusion probability. At the same time, although the effect of diffusion probability was only obtained, the relative increase in the number of cleared cells indicated that our proposed design had a significant effect on the cyanobacteria infected by the phagocytes, which will be quantified through experiments.

# Algorithm&Code