Last week, I attended the Systems Biology Summit in Richmond, Virginia. The opening session in the Summit was entitled “the Systems Biology Challenge in 21st Century Biomedical Research”. It consisted of speakers from the Research Institute, the National Institute of Health, Academia, and the Pharmaceutical industry providing their various viewpoints of Systems Biology.
Dr. Leroy Hood began the session with his keynote lecture on systems approaches in Biology and Medicine. The following is Dr. Hood’s thoughts on where we are in systems biology:
The information we are finding represents the “parts” of the system, when we move into the realm of establishing functionality of the system we are determining the blueprints for these parts.
A later speaker, Dr. Keith Elliston of Genstruct, expanded the discussion with his research on biological causal networks and their use for diagnostic reasoning or predictive inference. The following was his entertaining quote on networks and pathways that was repeated throughout the weekend:
System biology is not pathways but networks…stupid. A pathways is a specific path through the network.
Another entertaining quote was from Dr. Burt Adelman, representing Industry’s perspective and their thoughts on the transition of animal research to human treatments.
We treat humans. They’re very complex not inbred… mostly. We have to find what aspects of human biology are animal research reproducing.
The session ended with a panel discussion on systems biology. The most intriguing of the topics covered was the current problems in systems biology:
- The peer review system for grant applications in the United States.
- Researchers fear of failure.
- Lack of effective collaborations.
- The lack of tools for non-elite scientists.
- The need of better leadership in the scientific community.
Overall I thought the summit was a great experience and I would go again if another opportunity arose. I got to network with different people and learned some new things that I will discuss on this blog in the next couple of weeks. My biggest gripe with the summit was that it was 90% presentations and 10% workshop. As a programmer coming into biology I know I should not expect anything like the WWDC, but if we are to build better collaborations and novel tools I just think the summit could have spent more time with people working together rather than gathering in a room and listening to one person talk. It would be interesting to put something like that together one day, what does everyone think?
The biologists in my lab study the effects of ethanol (alcohol) on the brain. To do this they have to come up with animal based experiments to model various alcohol based conditions. One of these models is known as the alcohol deprivation effect (ADE). What it models is the possible increase in alcohol craving or consumption after a period of withdrawal (deprivation).
One such experiment may expose mice to a volunteer intake of ethanol. Then after a measured amount of time (i.e. two weeks) the ethanol is taken away (i.e. another two weeks); this is known as the deprivation period. Once the deprivation period is over the mouse is reintroduced to choice bottle drinking of ethanol versus a plain solution. This gives the researcher a variety of things to study (i.e. average amount of ethanol consumed, ratio of ethanol versus plain solution consumed, etc).
My “bridging the gap” posts were intended to help teach other computer scientists biology jargon. If you’ve been here for a while you know I haven’t really been followed through (only two posts) with this concept, but starting today I’ll to give it another run.
Today I attended a seminar and found myself looking up various terms related to stem cell research. I’m sure you all have heard all the buzz going on about stem cell research the past couple of years. But I’m sure you didn’t know that there were two kinds branches of stem cells. More specifically there if a cell can differentiate into a mutre type then it is classified as either a pluripotent stem cells and multipotent stem cells [Stem Cell Research Foundation].
As I have very little knowledge in this field, does anyone care to share what they know about stem cells and the research?
I attended a seminar today where the speaker mentioned a gene whose name or function I’ve never heard of before. I used to use Wikipedia to look up a gene but that source is frowned upon by the scientific communtiy due to its unreliability. Now I use NCBI’s Online Mendelian Inheritance in Man (OMIM) which gives a nice condense summary of common knowledge on a gene.
Another one of the graduate students in my lab suggested iHOP that not only has a cool looking monkey on the front page but is also presents a page describing a gene that is loaded with links to various abstracts contained within PubMed.
What tools does everyone else out there use?
I found an interesting post on today explaining the genetic properties of eye color. The article describes how eye color is a polygenetic trait (i.e. more than one gene involved) and of the genes involved one particular gene, OCA2, has more of an influence than the rest.
Its a brief article but I thought it would be useful as it has some jargon that is commonly used in biology and bioinformatics.
Key Terms: single nucleotide polymorphisms (SNPs), gene expression [Wikipedia]
Here is an interesting quote form my school’s site that a professor recently pointed out in class:
… systems are more than a sum of the parts, and that nonlinear interactions of components and processes result in emergent properties that can not be predicted from knowledge of the individual components and their behavioral processes.
In lamen’s terms, the study of entire biological systems (i.e. looking at all the genes of a cell at once) provides more insight to properties of the system that could not be seen or identified with the old biological dogma of single gene studies.
This is what Bioinformatics has done to the study of Biology. It has transcended the study from a micro exploration of individual gene function to the macro examination of the system as a whole by observing all the parts simultaneously.
As a computer scientist coming into Bioinformatics I was faced with the heavy task of catching up on my Biology and Chemistry (I was a Physics minor in undergrad but that wasn’t applicable to my Bioinformatics catch up). This meant two semesters of General Chemistry, a semester of Organic Chemistry and a semester of Cell Biology. Though all this course work was very educational and useful for my degree I don’t think its all that necessary for a someone who may be interested in fooling around with Bioinformatics problems on the side.
Here is a very general overview of cell biology for Non-Biologists wanting to get involved in Bioinformatics:
- Proteins are the essential part of all living organisms. Proteins have a variety of functions and are involved in every process within our cells. [Wikipedia]
- DNA is the blueprint for proteins. Segments of DNA (genes) translate into proteins. For more detail look into the Translation and Transcription of DNA to proteins.
- Cell function is determined by which proteins are expressed and their quantity. This means that some kind of gene regulation must take place. Also one can argue if you know the amount of genes expressed in a cell you can possibly infer that cells function.
For a more specific overview, the following are some of the essential key points for biology and bioinformatics:
- Genome - all the DNA in a cell.
- DNA - a string of nucleic acids (i.e. GATCACTT…ATCG).
- Gene - a substring of DNA that encodes proteins.
- Proteins - a string of amino acids (i.e. ACDEF…RSTY).
- Gene expression is regulated by the product of other genes. It is a network of interactions.
- Post-translation modifications are an important regulation mechanism for gene expression.
You may notice that the above deals quite a bit with string manipulation, hence the strong emphasis for Perl experience in Bioinformatic job postings. You will find that string manipulation is not the only driving force for computer science in Bioinformatics. I will try to explain other topics in subsequent posts.
As for Biologists wanting to do Bioinformatics I can not provide the best advice since I didn’t come into Bioinformatics from that direction but I would imagine that you may want to look into the following:
- Learn how to program. You want to know how to use a scripting language (preferably Perl) for smaller every day tasks and an object-oriented language such as C, C++, or Java for larger projects.
- Learn how to use databases. Bioinformatics deals with very large datasets. At some point your are going to have to deal with either retrieving information from databases or building your very own database so you might as well begin playing with them now.
- Install and run a Unix/Linux OS (Optional). This might be my personal bias but I believe if you are going to be working in Bioinformatics and its large data sets eventually you will find yourself either maintaining a server or SSHing into one so you might as well become familiar with that type of environment. At the very least XP users should install Cygwin.
Useful Links:
- Bioinformatics intro offered at my university.
- Graduate level of the Bioinformatics intro course.
- Library of videos that cover a wide range of biological topics (theoretical and practical).
- RT-PCR a common molecular biology method practiced in the lab.
- Virtual lab which provides a virtual lab for non-biologists to actually work through basic molecular biologist techniques.
Finally I must say that I am far from an expert so any constructive suggestions to help clarify or expand the above is welcomed and appreciated.
Today in my Scientific Integrity classed we discussed the ethics behind human experimentation. I thought it was interesting that we happened to talk about this on Halloween so I decided to share a specific topic, the Tuskegee Experiment.
In 1972 a clinical study was conducted in Tuskegee, Albama, concerning the disease syphilis. The study was done on African American sharecroppers to examine the effects of syphilis on different ethnicities. For their participation these sharecroppers were assured a free treatment of mercury, which was of course was toxic but was the only available treatment for syphilis at the time.
By 1947, penecilin became the non-toxic treetment for syphilis. Knowing this fact, the scientists of the study still decided to press on with the toxic mercury as a treatment. They even went as far as preventing local hospitals from treating the sharecroppers with penecilin, arguing that an alternative treetment would ruin all their previous data and study.
This study was finally terminated in 1972 not by ethical or moral consideration but by a leak to the press.
Well, that’s my scary story for the evening. Happy Halloween.
Disclaimer: I have nothing against Doctors and admire the work they have to go through to become clinicians. In fact my girlfriend is a medical student and I believe she is going to be a great doctor because she is ethically sound. I just don’t approve of the character of some medical students and find the thought of them handling people’s lives in the future to be rather disturbing.
The definition of quantitative trait locus (QTL) is exactly what you would expect if you break down the term. A trait is something that distinguishes people from one another. They can be simple such as eye color or fairly complicated such as how anxious a person is. Lets think about this for a moment. Eye color has discrete values such as blue, brown, green, etc. but defining anxiety is a little more complicated. Lets just say we define anxiety numerically with a one to ten scale. One being that cool cousin that never worries about anything and ten being that crazy aunt that frantically runs around the house every holiday. Obviously there are all kinds of varitions in anxiety of people in between. Do you see how anxiety can be a quantitative trait? It is continuous and is not managed by a pool of discrete values.
We’ve defined the first part about QTL but what about the locus aspect. We know that locus is plural for loci and refers to some sort of position. Loci actually refers to the position of a single gene on the chromosome. So theoretically if the trait eye color was due to a single gene and we were interested in its position on a chromosme we would call this a trait loci. Since we are talking about a quantitative trait, several genes may be involved and located on several spots on the chromosome, hence locus.
When people think about steroids they usually think of the “juice†that athletes use to get that home run record. This is actually a misconception. Steroids exist in all of us. Their main function as hormones is to serve as a messenger between our cells.
The steroids used in sports refer to anabolic steroids. These steroids deliver a messege to cells promoting cell growth and division. The basic concept is that once the anabolic steroid is introduced to the body, the body has more steroids than usual and therefore faster muscle or bone growth occurs.
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