User talk:Stefan Tintea

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Week 2 - Exponential Growth
Generally, the simple exponential function was approximated well by the iterative multiplication. k values below 1 resulted in decay and above 1 resulted in growth. The logistic correction fixed the plot between 0 and 1. It generally worked well for low values of k, but when k was raised above roughly 1.5 choppy oscillations resulted. The poor approximation was due to too few iterations being used.

Week 3 - DNA Analysis
This problem set led me to become much more familiar with the python environment than the first one did. While I still would not call myself fluid in the language, looking up all of the necessary commands led me to be much more comfortable with it than I was before. The ability for python to perform repetitive tasks like DNA transcription seems like it promises to be a very useful tool. Also, it was interesting to see what would happen in the DNA mutation simulations and how they are elaborated into protein changes. I think an interesting way to follow up would be to include frameshift mutations and to check if any silent mutations occured.

Week 4 - Human 2.0
Exponential increases in genomic technology have allowed us to understand the human genome on levels people didnt think was possible a few decades ago. The idea of Human 2.0 involves us taking advantage of this newfound understanding and using it to our advantage. An important first step to this would be to generate an even deeper understanding of how the genome affects human traits. As DNA sequencing costs continue to fall exponentially, we can create a so called catalog of genomes from representative populations. By comparing large numbers of genomes and seeing where the sequences vary we can glean a better understanding of the genetic basis for traits such as intelligence, longevity, specific types of health traits, and survival in different conditions. Allowing humanity to better understand the roots of its strengths will allow us to take further advantage of them.