genetic-code-supremacy

Is the universal genetic code the best one possible? I created this code to evaluate the hypothesis that the universal genetic code is optimized for minimizing codon neighbor distances, which reduces the impact of mutations.

Objective

This report evaluates the hypothesis that the universal genetic code is optimized for minimizing codon neighbor distances, which reduces the impact of mutations.

Process

SETUP: Created a new file "Raees_Report1_v1.0.R" in myScripts folder.

PART 1: Generation and Evaluating Genetic Codes

Steps that must be taken by the program to generate and evaluate 1000 random genetic codes

• Defined evaluateCode() to calculate the sum of distances using the aaSim() function for each code by comparing codon neighbors using the neighCodons() function.

• Defined evaluateRandomCodes() to generate 1000 random genetic codes (randomization seed 1011304214) using the rGC() function, and then computed the sum of distances for each using the evaluateCode() function

• The evaluateRandomCodes() function writes each of sum of distance to a created file named "raees_report1_code_evaluations_v1.0.csv"

PART 2: Plotting Histogram

• Generated a histogram by reading data from "raees_report1_code_evaluations_v1.0.csv" with the distributions of sum distances on x-axis and the probability density on the y-axis. This indicates how likely certain sum distances are to occur, rather than just the number of occurrences.
• Overlaid normal distribution curve over the histogram.

PART 3: Calculating Results

• To assess the observed standard genetic code's distance against the random codes, the following were calculated:

• mean of sum distances

• standard deviation of sum distances

• z-score

• p-value

Results

1. Mean Sum of Distances of Random Codes: 1848.625

2. Standard Deviation of Sum Distances: 64.0324

3. Z-Score: -8.990674

4. P-Value: 2.457198e-19

Interpretation 

The results demonstrate the standard genetic code exhibits a sum of distances significantly lower than the mean of random genetic codes. With a z-score of about  -8.99, indicating nearly 9 standard deviations below the mean, and a p-value of approximately 2.46e-19, the likelihood of this occurring by chance is virtually zero. 

Image produced by Chat-GPT-4o to "visualize a graph of a normal distribution with a datapoint 9 standard deviations (labeled as 9 sigmas) to the left."

The distance of this data point away from the curve emphasizes the sheer significance of the z-score of our observed value. 

Conclusion

This computational experiment suggests a remarkable level of optimization in the standard genetic code, far beyond what would be expected from random processes. The analysis strongly supports the idea that the standard genetic code is likely shaped by evolutionary pressures for stability against mutations.