# Genetic algorithm for cell culture optimization

Munroe, S., K. Sandoval, D. E. Martens, D. Sipkema, and S. A. Pomponi. 2019. Genetic algorithm as an optimization tool for the development of sponge cell culture media. In Vitro Cellular & Developmental Biology - Animal 55:149–158.

# R Based Genetic Algorithm

genalg R package documentation

## Example code

```
# Load the genalg package
library(genalg)
# Define the objective function to minimize
# The objective function should take a vector representing concentrations as input
<- function(concentrations) {
objective_function # Simulate the effect of concentrations on a specific metric (e.g., cell growth)
# You will need to replace this with your own experimental data and evaluation logic
<- simulated_metric(concentrations)
metric_value return(metric_value)
}
# Set GA parameters
<- 50
population_size <- 100
generations <- 0.1
mutation_rate
# Define the range of concentrations for each component
# This can be customized based on your specific components
<- matrix(c(0, 1), nrow = 1, ncol = 10)
concentration_range
# Create an initial random population of concentrations within the defined range
<- rbga.bin(size = 10, popSize = population_size, iters = 1,
initial_population mutationChance = mutation_rate, levels = concentration_range)
# Run the genetic algorithm
<- rbga.bin(size = 10, popSize = population_size, iters = generations,
result mutationChance = mutation_rate, evalFunc = objective_function,
levels = concentration_range, pType = "real-valued", initial = initial_population$solutions)
# Print the best solution found (optimized concentrations)
cat("Best solution (optimized concentrations):", result$solution, "\n")
cat("Objective value (metric value):", result$evaluations, "\n")
```

### Brainstorming the objective to feed the algorithm

Note that it is possible to have a multi-objective optimization framework, however this can be more challenging than a single-objective optimization. The optimization algorithm will seek a set of solutions that represent trade-offs between these objectives. The solutions are known as the Pareto front.

If you do more than one objective, you will need to assign weights or priorities to each objective function.

Cell density of at least 10

^{6}, the minimum required amount to conduct gene expression work.Frequency of monolayer formation for epithelial tissue from Botryllus schlosseri.

Mitochondrial activity assessed using the 96 well plate reader.

### Brainstorming factors that are present in the media and what would potentially be changed

Fetal Bovine Serum

L-15 Media Basal

HEPES Buffer

Penicillin-Streptomycin

Amphotericn B

Artificial Seawater