David+Zeng


 * __WEEK3__**
 * __Orphan Drugs__**

Although I did not go on the trip to Johnson-Johnson, the topic of the trip made me want to write about something related the pharmaceutical issue. Also, our activity with the debate over whether or not people should be allowed to take a pill to modify their body made me want to write about something that has something to be debated about.

There are many rare diseases in the world, with the number of people affected by the disease numbered in the thousands or sometimes in the tens or hundreds. For the most part, these diseases are rare because they are linked to a rare genetic mutation. Even though these rare diseases may be life-threatening, development and research into drugs has to be helped along by usually the government for progress to occur. This happens for several reasons. The simplest reason is that the lack of a market for the drug makes it economically discouraging for pharmaceutical companies. Research and development is extremely costly, with the many years to even decades of drug trials that the company must perform. With little chance of recouping the cost, companies would normally just turn to other more profitable diseases to treat. In addition, just finding people who are willing to undergo clinical trials of experimental drugs is challenging. For example, typically, phase three clinical trials are expected to have at least a thousand patients who are tested on. Yet, there may not even be a thousand patients who have that rare disease.



For some info on clinical trials: []



Obviously, there is a moral issue with letting money dictate which diseases get research into their treatment. Thus, many governments have stepped in to encourage the development of these "orphan drugs". Generally, governments use two approaches. They either allow the company to make more profits should they successfully develop an orphan drug or help to lower costs of developing the drug. One common way is to extend the number of years that the company that develops a drug holds a monopoly over production. For most normal drugs, after a certain number of years, external companies are allowed to produce generic versions of that drug, lowering profits of the original company. This is mostly to make sure that the original company isn't allowed to extract huge amounts of money from patients indefinitely and generally lowers health care costs. By extending the number of years, the company has a higher chance of recouping costs.

I believe that we as a society have a responsibility to help. Whether or not you have a particular rare genetic disease is completely up to chance. People with rare genetic diseases have to deal with doctors who can't help them simply because they've never seen that particular disease before. Even if they are properly diagnosed, they are often faced with the lack of potential treatment options. Providing funding for research into rare diseases is one way that we can help. In the end, we are bettering the human race's resources. Genetic diseases usually don't just magically go away, nor do they appear spontaneously that often. If over many decades of research, we can build up a solid library of drugs and information into these genetic diseases, we are creating a better future.


 * __WEEK2__**
 * __Computers in Biology__**

Since I wasn't in class this week, my topic this week will be related to what I was doing this week, mostly working with computers and specifically, computer algorithms. There is a whole field that consists of the combination of biology and computer science, called computational biology or sometimes bioinformatics.

Perhaps the most well known example is with DNA analysis. Since DNA strands are millions of characters long, it is often helpful to employ computers to analyze the DNA. However, when doing something such as sequence alignment, it is a challenge to get the computer to figure out what parts of the DNA sequence from two different organisms are supposed to align, especially if the two species are not closely related. People have created many different algorithms to solve the problem both with accuracy and with decent speed so that it won't take excessive amounts of time for the best alignment to be found(especially for things such as blast where many thousands of organisms who each have thousands of genes are searched through to find the best fit).

In the end, things such as BLAST have been created to provide genomic alignment tools to the public.

Another aspect that is related to biology is the modeling of various biological molecules. For example, figuring out how exactly proteins fold is a big part challenge. Even though we know various attractions and repulsions amino acids in a protein will have, it is very difficult to predict what the proteins structure will turn out to be since there are so many possible variations in structure and positioning the protein will have. Here is a video of what it looks like when a computer tries to figure out how a protein folds. []. Although there are many approaches to protein folding, generally, computers will try to make small adjustments to the protein in an attempt to get the protein to a more "stable" state(aka maximizing attractions and minimizing repulsions.

Finally, a topic that really isn't related to actual computer algorithms but more about computers in general is the work towards trying to modeling a human brain. An article here gives a introduction to the topic. [] Basically, with the rapid increase in computer processing power, the possibility of using a computer the "model" a human brain by simulating the various actions the neurons in a brain would perform in an actual living brain has become closer and closer. The benefits of this are huge. If we can simulate the brain on a computer, than we will be able to better understand how the human brain works. This of course has some obvious implications and benefits. Many neurological diseases will be better understood. In addition, aspects of psychology and understanding of how things such as learning and memory work will be improved.

Overall, I hope this has helped shed some light into where biology can go in the future.
 * __WEEK1__**
 * __Human Carrying Capacity__**

The video we watched in class, "Shift Happens", reminded me of the many charts that I have seen depicting human population growth over the past thousands of years. In the video, statistics about how many people are being born or how many people were in various asian countries was given.



The interesting thing about humans is how even though they are k-strategists, meaning that they provide more nurturing to a fewer number of offspring, lending them to be successful when there is a limited carrying capacity, the true carrying capacity of the human race on Earth is a mystery. This idea of human's reaching their carrying capacity is a very well explored topic, with the question rooted in both social science as well as biology. In the following paragraphs, I'll be trying to provide a general overview of the current thoughts and background knowledge behind this topic.

First, the mechanisms behind a carrying capacity need to be examined.

For humans, the limiting factor that has been most discussed is food output. In the past, we've managed to escape the supposed carrying capacity of humans based on the total possible agricultural output by advancing agriculture technology with things such as mechanization, selective breeding/GMO, fertilizers, and pesticides. There is a theoretical limit based on the amount of usable land and the percent of energy absorbed from sunlight that can be converted by plants into usable energy but we are far from it. Here is some more info on this aspect: []

However, there is much more to human carrying capacity than just food. In a way, our exploitation of natural resources is perhaps allowing us to temporarily exceed the carrying capacity that agriculture alone could sustain. For example, by using fossil fuels, a limited and non renewable resource, we are able to power farm machinery that helps us increase farm output. These natural resources are also used to create many of the other objects that inhabitants of developed countries have gotten used to.

A bigger picture needs to be taken into account. A measure developed to account for three main factors related to human carrying capacity is I=PAT, which states that environmental impact by humans equals the population times the affluence level(how much people consume) times the technological factor. While increases in population and affluence increase the ecological impact, advances in certain types of technology generally reduce the environmental impact. A detailed explanation can be found here: []. An important example of affluence that is connected to the earlier topics about agriculture is the food that many people in developed nations eat. The amount of energy it takes to produced a pound of meat is much greater than the energy it takes to produce a pound of vegetable.

Thus, the three primary solutions to the problem are to lower population(which is difficult), lower living standards(which some people will object to), or develop new technologies that make things more efficient(which is generally what has happened in the past).

Below is an entertaining summary of this topic. It goes over the history of overpopulation as well as many current examples that help explain the problem. media type="custom" key="26018224"