Difference between revisions of "Harvard:Biophysics 101/2009/Project"

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'''Interesting Links:'''
'''Interesting Links:'''
*[http://genomecommons.org/ Genome Commons] provides some good resources about interpretation of human genome variations.
*[http://genomecommons.org/ Genome Commons] provides some good resources about interpretation of human genome variations.
*Interesting paper on [[Media:Palsson_PNAS_2006.pdf|Reconstruction of Human Metabolic Network]] which may be helpful for the pharmacogenomics project idea. [[User:Harriswang|Harriswang]] 20:20, 8 October 2009 (EDT)

Revision as of 16:20, 8 October 2009

Biophysics 101: Genomics, Computing, and Economics


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Class Project Topics:

"SNPCupid" // Anna Turetsky & Joe Torella

  • We decided to build on Anna's project idea for a genetic testing service which would allow couples to assess, before having children, what phenotypes those children might inherit (and with what probability). These phenotypes range from the medically relevant (i-cell disease, diabetes) to the cosmetic (male pattern baldness, eye color), to the beneficial (intelligence, athletic ability).
  • A starting point for this kind of work is to cross-reference the SNPedia, which catalogs easily/cheaply-testable human SNPs associated with some phenotype, and OMIM, which contains a wealth of information on the heritability of genes associated with those SNPs. GeneTests[1] can also be used to gather up-to-date information about medically relevant genetic tests currently able to be offered. The program would focus mostly on recessive inheritance patterns, with X-linked recessive traits providing an additional layer of complexity. In addition, due to the non-standard formatting of heritability information in OMIM, parsing the data in a systematic way would provide an interesting (and I think "do-able") challenge.
  • Check out our talk page for an example of this idea using male-pattern baldness, and for a discussion of how something like this might be implemented in a systematic way.

In Response to SNPCupid //Ben Leibowicz

Your idea of a genetically oriented "dating service" sparked my interest in our last class meeting and I think it's a good jumping-off point. It also seems like something we could do given what we have, which is really just computers and access to some existing genomes. I wrote a bit more about this idea in my talk page and I'm curious whether you think such an idea is a significant enough departure from our current situation to be considered Human 2.0, as what it would be doing is simply to allow a group of individuals to manipulate human evolution in a seemingly positive way through advanced information collection and processing. Doesn't this beg the question: should we instead be focusing on technologies that will allow us to manipulate the genome of an offspring in a way that prevents inheritance of genetic disorders where it would naturally occur? This seems to be realistic in the not-so-distant future and might make such a genetic dating service obsolete. What do you think?

JT: I should say first that I'm not really behind the idea of this as some sort of "dating service" (although, admittedly, the project name implies it pretty strongly). I think it's more appropriate to think of this as a tool for inferring F1 phenotypes from very complex parental genotypes, in human populations. At any rate, there's a lot here, so I'll just focus on two questions: (1) is this enough of a departure for "human 2.0," and (2) shouldn't we be focused on interventions rather than informatics? Here are my answers:

  1. If we consider the cloud of ideas that has gone around regarding human 2.0, they are primarily concepts in which some human ability is enhanced by virtue of greater information. Personalized medicine is something we think of as obviously "human 2.0," but it is not fundamentally different from present-day medicine; it simply updates modern medical treatment with personal (genetic) information. Similarly, we find romantic partners largely through instinct, and it seems logical that part of "human 2.0" would be to incorporate the new information we have (again, genetic) into partner-finding decisions - and that's the idea here. In conclusion, since we think of personalized medicine as "human 2.0," I think it is fair to consider this "SNPCupid" idea similarly "human 2.0" in character.
  2. Your second question is whether we should focus on interventions, rather than informatics. But I feel that question sets up a false dichotomy; I'm not sure the boundary between informatics and intervention is so great. For instance, some countries are beginning to approve preimplantation genetic screening for in vitro fertilization, to avoid undesirable medical problems: Spain allows preimplantation genetic screening for cancer. Basically, they used in vitro fertilization to produce fertilized embryos, and implanted only those not carrying a gene greatly increasing the risk of breast and ovarian cancer. Since reliable genetic manipulation of human embryos is (seemingly) a long way off, such in vitro selection methods are the best way of avoiding or encouraging the inheritance of certain traits. In order to do this, however, we require knowledge of what traits the child is likely to inherit, and ways of testing for it, before we can rationally select which embryos are "best" (and while I'm aware this drifts into that dark and stormy 'eugenics' category, I think most parents would jump at the chance to prevent their child from inheriting an 80% probability of breast cancer!).

Medicine 2.0 // Filip Zembowicz, Zach Frankel, Alex Ratner, Alex Lupsasca, Ben Leibowicz

More on Medicine 2.0 // Jackie Nkeube and Brett Thomas We also really liked the idea of a drug metabolism tool. We had some additional ideas we wanted to add:

  • We made a high level design of the research implications of such a tool here. We identified differences in CYP540 expression across populations as an area that data from Filip's tool could really advance. Factors to consider include race, gender, age, any others? (Brett wonders if diet and activity are relevant too)
  • In general, we think that any project like this should be designed with research implications in mind, as the success of a personalized drug recommendation engine depends on the underlying research.
  • On that note, we think that the drug metabolism tool should strive to be self learning, which would have major implications to the underlying architecture. The way it could be self learning is to track user observations about their responses to drugs. This would require some sort of feedback mechanism for users to optionally tell us how the dosage worked.
  • There are some websites that provide drug interaction services - maybe we can experiment with an addon to one of them instead of reinventing the wheel.

Interesting Links: