I had met Milan Chheda at an outreach event a few weeks earlier, and my final day at the Broad started with a tour of his workspace. Milan, a neurologist, works in William Hahn's Lab, which focuses on how human cells transform into cancer cells. Milan's work is to optimize a technique that is currently applied to study the development of glioblastomas, a particularly virulent type of brain tumor.
The technique in question uses RNA interference to knock down the expression of certain genes. Using a lentivirus, the researcher introduces a hairpin structure to candidate nerve cells to reduce the expression of a specific gene in vitro. After culturing the nerve cells that have received this structure, the researcher typically uses a microscope to check for glioblastomas or other types of cells that may form.
What does it mean to optimize this technique? Milan is part of an effort to create methods that would enable RNAi experiments to scale up the way the Broad's sequencing center has industrialized gene sequencing. In one example, members of his group are working to use software that automatically checks microscope images for glioblastomas and other features. By identifying trouble spots in the experimental pipeline, the hope is that these experiments can be conducted at a larger scale, resulting in reliable and larger quantities of data to analyze.
I left the Broad with a greater appreciation of the interplay between data gathering and data analysis. Writing about my discussions with other researchers greatly enhanced my ability to gain this appreciation. With this in mind, expect further updates to this blog once I return to Berkeley.
Showing posts with label broad institute. Show all posts
Showing posts with label broad institute. Show all posts
Sunday, August 26, 2007
Wednesday, August 8, 2007
Science on Wednesday
During junior high, I attended a weekly lecture series at the Princeton Plasma Physics Laboratory designed to introduce students to science research. The program was called Science on Saturday, and it featured scientists in fields as diverse as cosmology and forensics talking about their work to a largely non-technical audience that consisted of students and their parents.
The Broad Institute had a similar program this summer called Midsummer Nights' Science, an apt name given this year's Shakespeare on the Common production. For the four Wednesdays following Independence Day, scientists at the Broad would describe their work to the greater Boston community. Each Wednesday featured a different researcher describing his or her work. While the projects and interests described each week were quite different, all of them implicitly promoted the idea that large databases of data can enable new kinds of research.
The first talk featured David Reich, who discussed how he and his colleague conducted a comparative analysis of the DNA of humans, chimpanzees, and gorillas, which have led them to a new model for the evolution of these species from a common ancestor. The way I first learned about evolution was that it starts when two groups of the same species are physically isolated from one another. Then, under appropriate environmental conditions, the two groups would eventually evolve into different species, after which any hybrids between these two species would be less fertile and die out. This is called allopatric speciation. If this is true, then one can model the DNA sequences as following a branching process, so the evolution of species would look like a tree, where each fork in the tree indicates one species dividing into two. Reich and his colleagues discovered that this model is not a great one to describe the evolution of humans, chimps, and gorillas. In fact, if one constructs a phylogenetic tree for these three species using DNA from one section of the genome, one tree emerges indicating that the most recent split was between humans and chimps split, but if the same analysis is performed using a sequence from another section, which comprises between a fifth and a third of the genome, a different tree emerges, indicating the most recent split was between humans and gorillas. An alternate hypothesis the group proposed was that hybridization among these species took place, and by a careful analysis of the sequence data they had, they were able to confirm this was a better model by which to describe the speciation of these three species. Indeed, the study probably would not have been possible without all the DNA sequence information available for these three species.
During the following week, Pardis Sabeti explained how the HapMap project, another data gathering effort, is enabling researchers to determine the role natural selection has played in humans and pathogens. The HapMap project collects DNA samples from different populations around the world. The samples of DNA they collect account for 90% of the genetic variation among humans. These samples are divided into haplotypes, which represent sections of DNA that are inherited as a group. If there is no selective pressure on an organism, one would expect the prevalence of a particular haplotype to decay as its size gets larger. By similar reasoning, if a larger haplotype is highly prevalent in a population, then there is evidence that the corresponding section of DNA is under selective pressure. Sabeti explained how this has allowed researchers to track lactose tolerance in European populations, who domesticated cattle relatively early, and link the sickle cell trait to malaria resistance. Once again, the availability of this data enabled such an analysis.
The third talk, given by Todd Golub, was about cancer research in the era of genomics. He started the talk by describing two patients, both the same age, both diagnosed with the same type of leukemia in a similar stage of progression, and both given similar doses chemotherapy. However, Patient A lived and Patient B did not. Golub then explained how the mutations that had occurred at the genomic level for these patients were actually quite different, and if one were to look at patient survival by isolating these two different mutations, the group with the same mutation as Patient A had a survival rate much closer to 1 and those treated with the same mutation as Patient B had a survival rate close to 0 within a few years of diagnosis. Golub then went on to describe a treatment that had been customized to target the mutation in groups with Patient B's mutation. The result led to Gleevec, a drug now available to patients with this version of the disease. Since it's introduction, patients diagnosed with this specific mutation have had a 100% survival rate with minimal side effects from the medication. Golub appeared optimistic that similar treatments could be developed for most of these mutations that results in cancer.
Unlike the the preceeding talks, the final speaker barely mentioned genomics in his talk. Vamsi Mootha described mitochondria and his group's research efforts on understanding them. Mitochondria are found inside the cell and produce much of the energy a cell uses. Unlike most other organelles, mitochondria contain their own DNA. However, proteins found in mitochondria are mix of genes derived from within the mitochondrial DNA and the cell's nuclear DNA. It turns out that metabolic diseases are closely related to problems with mitochondrial function, which are apparent in changes to their protein composition. Mootha's group is building an atlas of the protein content of mitochondria in different parts of the body. Their hope is that this data will enable researchers to characterize the specific problems associated with certain metabolic diseases. In this instance, the hope of a future payoff inspired his group to procure a large data set.
Midsummer Nights' Science showcased how biological and medical research have benefited and can continue to do so when certain kinds of data are available in significant quantities. Hopefully this message reached the students who attended and will capture the imagination of those who decide to pursue research in the future.
The Broad Institute had a similar program this summer called Midsummer Nights' Science, an apt name given this year's Shakespeare on the Common production. For the four Wednesdays following Independence Day, scientists at the Broad would describe their work to the greater Boston community. Each Wednesday featured a different researcher describing his or her work. While the projects and interests described each week were quite different, all of them implicitly promoted the idea that large databases of data can enable new kinds of research.
The first talk featured David Reich, who discussed how he and his colleague conducted a comparative analysis of the DNA of humans, chimpanzees, and gorillas, which have led them to a new model for the evolution of these species from a common ancestor. The way I first learned about evolution was that it starts when two groups of the same species are physically isolated from one another. Then, under appropriate environmental conditions, the two groups would eventually evolve into different species, after which any hybrids between these two species would be less fertile and die out. This is called allopatric speciation. If this is true, then one can model the DNA sequences as following a branching process, so the evolution of species would look like a tree, where each fork in the tree indicates one species dividing into two. Reich and his colleagues discovered that this model is not a great one to describe the evolution of humans, chimps, and gorillas. In fact, if one constructs a phylogenetic tree for these three species using DNA from one section of the genome, one tree emerges indicating that the most recent split was between humans and chimps split, but if the same analysis is performed using a sequence from another section, which comprises between a fifth and a third of the genome, a different tree emerges, indicating the most recent split was between humans and gorillas. An alternate hypothesis the group proposed was that hybridization among these species took place, and by a careful analysis of the sequence data they had, they were able to confirm this was a better model by which to describe the speciation of these three species. Indeed, the study probably would not have been possible without all the DNA sequence information available for these three species.
During the following week, Pardis Sabeti explained how the HapMap project, another data gathering effort, is enabling researchers to determine the role natural selection has played in humans and pathogens. The HapMap project collects DNA samples from different populations around the world. The samples of DNA they collect account for 90% of the genetic variation among humans. These samples are divided into haplotypes, which represent sections of DNA that are inherited as a group. If there is no selective pressure on an organism, one would expect the prevalence of a particular haplotype to decay as its size gets larger. By similar reasoning, if a larger haplotype is highly prevalent in a population, then there is evidence that the corresponding section of DNA is under selective pressure. Sabeti explained how this has allowed researchers to track lactose tolerance in European populations, who domesticated cattle relatively early, and link the sickle cell trait to malaria resistance. Once again, the availability of this data enabled such an analysis.
The third talk, given by Todd Golub, was about cancer research in the era of genomics. He started the talk by describing two patients, both the same age, both diagnosed with the same type of leukemia in a similar stage of progression, and both given similar doses chemotherapy. However, Patient A lived and Patient B did not. Golub then explained how the mutations that had occurred at the genomic level for these patients were actually quite different, and if one were to look at patient survival by isolating these two different mutations, the group with the same mutation as Patient A had a survival rate much closer to 1 and those treated with the same mutation as Patient B had a survival rate close to 0 within a few years of diagnosis. Golub then went on to describe a treatment that had been customized to target the mutation in groups with Patient B's mutation. The result led to Gleevec, a drug now available to patients with this version of the disease. Since it's introduction, patients diagnosed with this specific mutation have had a 100% survival rate with minimal side effects from the medication. Golub appeared optimistic that similar treatments could be developed for most of these mutations that results in cancer.
Unlike the the preceeding talks, the final speaker barely mentioned genomics in his talk. Vamsi Mootha described mitochondria and his group's research efforts on understanding them. Mitochondria are found inside the cell and produce much of the energy a cell uses. Unlike most other organelles, mitochondria contain their own DNA. However, proteins found in mitochondria are mix of genes derived from within the mitochondrial DNA and the cell's nuclear DNA. It turns out that metabolic diseases are closely related to problems with mitochondrial function, which are apparent in changes to their protein composition. Mootha's group is building an atlas of the protein content of mitochondria in different parts of the body. Their hope is that this data will enable researchers to characterize the specific problems associated with certain metabolic diseases. In this instance, the hope of a future payoff inspired his group to procure a large data set.
Midsummer Nights' Science showcased how biological and medical research have benefited and can continue to do so when certain kinds of data are available in significant quantities. Hopefully this message reached the students who attended and will capture the imagination of those who decide to pursue research in the future.
Wednesday, August 1, 2007
Cultural Learnings
When I told a friend I would be interning this summer, he was surprised.
"Why are you doing an internship?" he asked.
"The idea," I responded, "is to get introduced to a new environment, so I return to grad school with a broader perspective."
"Sounds like Borat."
Like a foreign correspondent reporting to his home country, I gave an informal talk to the Stochastic Systems Group about my summer project. The resulting feedback helped me improve my results this summer. However, once the problem was described, there were a lot of similarities with problems familiar to the group. It was hardly Borat.
That said, there are practices at the Broad outside of my work that I would be surprised to see in my own research community. Perhaps the most surprising thing I have discovered is that people are willing to share their ongoing research with people at the Broad. Weekly seminars feature researchers from outside the Broad discussing their as yet unpublished work. Broadies see data that has yet to be made public. I was particularly surprised by this since there is some controversy that Watson and Crick's paper about the structure of DNA used unpublished data from Rosalind Franklin.
There is a catch. Attendees of the seminar must agree not to work on anything they pick up during the course of the presentation. This understanding and the honor system are what make people comfortable enough to discuss work they might otherwise keep private.
The presentations may also be a way to start collaborations. In a field driven by data, if someone provides the data for a figure on a paper, that person frequently becomes an author, even if the idea of the paper came from others. Thus, advertising results before they are published might allow other researchers to avoiding running the same experiments.
A consequence of this practice is that one rarely finds single authored papers and often finds papers with four or more authors. How does one delineate the contributions of each author? Author ordering may only give a coarse indication of an individual's contribution. An existing solution in some journals is to include an author contributions section. This section typically follows the acknowledgments and may read some like the following:
While some biologists I spoke to joked about some of these practices (one described how an author contributions section might read if each individual's contribution were described honestly), almost all of them were comfortable with the idea that providing data is a legitimate way to become an author on a paper. The same might not be true for my community, but I wonder if any of these practices would transfer well.
"Why are you doing an internship?" he asked.
"The idea," I responded, "is to get introduced to a new environment, so I return to grad school with a broader perspective."
"Sounds like Borat."
Like a foreign correspondent reporting to his home country, I gave an informal talk to the Stochastic Systems Group about my summer project. The resulting feedback helped me improve my results this summer. However, once the problem was described, there were a lot of similarities with problems familiar to the group. It was hardly Borat.
That said, there are practices at the Broad outside of my work that I would be surprised to see in my own research community. Perhaps the most surprising thing I have discovered is that people are willing to share their ongoing research with people at the Broad. Weekly seminars feature researchers from outside the Broad discussing their as yet unpublished work. Broadies see data that has yet to be made public. I was particularly surprised by this since there is some controversy that Watson and Crick's paper about the structure of DNA used unpublished data from Rosalind Franklin.
There is a catch. Attendees of the seminar must agree not to work on anything they pick up during the course of the presentation. This understanding and the honor system are what make people comfortable enough to discuss work they might otherwise keep private.
The presentations may also be a way to start collaborations. In a field driven by data, if someone provides the data for a figure on a paper, that person frequently becomes an author, even if the idea of the paper came from others. Thus, advertising results before they are published might allow other researchers to avoiding running the same experiments.
A consequence of this practice is that one rarely finds single authored papers and often finds papers with four or more authors. How does one delineate the contributions of each author? Author ordering may only give a coarse indication of an individual's contribution. An existing solution in some journals is to include an author contributions section. This section typically follows the acknowledgments and may read some like the following:
S.B.C. conceived and designed the experiments. B.S. conducted the experiments. S.B.C. and B.S. performed the analysis. S.B.C. and B.S. wrote the manuscript.What happens if the work is primarily by two authors? The practice described to me for these instances is called co-first authorship. To do this, one simply places an asterisk next to each author's name with a footnote that reads: "These authors contributed equally to the work."
While some biologists I spoke to joked about some of these practices (one described how an author contributions section might read if each individual's contribution were described honestly), almost all of them were comfortable with the idea that providing data is a legitimate way to become an author on a paper. The same might not be true for my community, but I wonder if any of these practices would transfer well.
Friday, July 27, 2007
Genome Factory
About ten years ago, I spent a summer with other high school students for a summer program at the Waksman Institute of Microbiology. The program's goal was to introduce us to protocols to extract plant DNA and isolate regions of interest for sequencing. We learned how to use restriction enzymes to cut the DNA into smaller fragments, bacterial transformations to make copies of the DNA within E. coli, PCR to make copies of DNA without the help of E. coli, and gel electrophoreses to separate the DNA fragments by size and isolate the one(s) we wanted. Finally, the DNA had to be sequenced, and for this, we were introduced to the Sanger method, developed in 1975 by Frederick Sanger and his colleagues.
The Sanger method involves adding modified nucleotides called dideoxynucleotides, which can only form bonds at one end. Think of a Lego piece with a flat top. Thus, a DNA chain that has such a nucleotide will immediately terminate. If these nucleotides are mixed in with regular nucleotides during a process like PCR, it creates fragments of the DNA sequence with the same starting point and varying endpoints. If only a particular type of dideoxynucleotide such as dideoxyadenine (ddATP) is used, then all the resulting fragments terminate with an 'A'. If these fragments are then separated by gel electrophoresis, one can get a rough idea of the positions where 'A' shows up in the DNA sequence of interest. If 'C', 'G', and 'T' wells are adjacent to the one for 'A', one can just read off the DNA sequence from the gel electrophoresis. This is the basic principle of the Sanger method.
By the time school started again, we had become familiar with the techniques and protocols. We continued to return to the Waksman Institute periodically and apply these techniques. We would eventually use the sequence data from these visits to construct a phylogenetic tree of the Allium (i.e. onion) genus. Unfortunately, the data collection process could often be slow and annoying. There were many stages in which something could go wrong, and I would have to return to the beginning. All of this work produced just a tiny fraction of sequence information from these genomes.
A lot can happen in ten years. Thanks to my friends in the Broad's Outreach Program, I had a chance to visit 320 Charles St., the location of the Broad Institute's DNA sequencing facility. It is sometimes called a high-throughput production facility because of the rate at which they manage to sequence DNA. The facility was responsible for many of the sequences that were part of the Human Genome Project, and I was about to find out how they did it.
We entered 320 Charles St. and sat down for a presentation. Before we could start our tour of the facility, one of the scientists wanted to describe the process. To my surprise, she described the Sanger method. How could this be the process of a high-throughput production facility? Once the tour started, it became clear how: they industrialized the process. We had entered a factory, complete with conveyor belts, robotic arms, and computers. A group of technicians oversaw that the work on this genome assembly line went smoothly. Others, including the scientist leading the tour, were working on ways to industrialize new and improved sequencing methods developed by Solexa and 454.
It was interesting to learn that part of the rate increase has come from engineering solutions to scale up production. The amount of sequence data now available is enabling some researchers to ask questions that may previously have been too time-consuming to answer. I have talked to biologists this summer that have told me how challenging data collection can be, and I am starting to realize how those difficulties play a role in the questions they ask. How might these questions change if other protocols for data gathering were similarly industrialized?
The Sanger method involves adding modified nucleotides called dideoxynucleotides, which can only form bonds at one end. Think of a Lego piece with a flat top. Thus, a DNA chain that has such a nucleotide will immediately terminate. If these nucleotides are mixed in with regular nucleotides during a process like PCR, it creates fragments of the DNA sequence with the same starting point and varying endpoints. If only a particular type of dideoxynucleotide such as dideoxyadenine (ddATP) is used, then all the resulting fragments terminate with an 'A'. If these fragments are then separated by gel electrophoresis, one can get a rough idea of the positions where 'A' shows up in the DNA sequence of interest. If 'C', 'G', and 'T' wells are adjacent to the one for 'A', one can just read off the DNA sequence from the gel electrophoresis. This is the basic principle of the Sanger method.
By the time school started again, we had become familiar with the techniques and protocols. We continued to return to the Waksman Institute periodically and apply these techniques. We would eventually use the sequence data from these visits to construct a phylogenetic tree of the Allium (i.e. onion) genus. Unfortunately, the data collection process could often be slow and annoying. There were many stages in which something could go wrong, and I would have to return to the beginning. All of this work produced just a tiny fraction of sequence information from these genomes.A lot can happen in ten years. Thanks to my friends in the Broad's Outreach Program, I had a chance to visit 320 Charles St., the location of the Broad Institute's DNA sequencing facility. It is sometimes called a high-throughput production facility because of the rate at which they manage to sequence DNA. The facility was responsible for many of the sequences that were part of the Human Genome Project, and I was about to find out how they did it.
We entered 320 Charles St. and sat down for a presentation. Before we could start our tour of the facility, one of the scientists wanted to describe the process. To my surprise, she described the Sanger method. How could this be the process of a high-throughput production facility? Once the tour started, it became clear how: they industrialized the process. We had entered a factory, complete with conveyor belts, robotic arms, and computers. A group of technicians oversaw that the work on this genome assembly line went smoothly. Others, including the scientist leading the tour, were working on ways to industrialize new and improved sequencing methods developed by Solexa and 454.
It was interesting to learn that part of the rate increase has come from engineering solutions to scale up production. The amount of sequence data now available is enabling some researchers to ask questions that may previously have been too time-consuming to answer. I have talked to biologists this summer that have told me how challenging data collection can be, and I am starting to realize how those difficulties play a role in the questions they ask. How might these questions change if other protocols for data gathering were similarly industrialized?
Wednesday, July 18, 2007
Cat's Cradle in a Hard-Boiled Wonderland and the End of the Brave New World
During the ITA workshop in January, Desmond Lun and I had the following exchange.
I had a taste of the future of biology research during lunch when Desmond described his project with George Church's lab. The goal of the project is to study ways to use biology to produce renewable fuel sources. One fuel source is ethanol, and there is a well-known biological recipe to produce it. Add yeast to a sugar solution. Mix. Let it ferment.
The approach Desmond described was a little different. It turns out one can modify the E. coli genome and use the modified E. coli to produce ethanol. Driven by this success, there is an effort to see if alkanes or other fuels can be created by hacking the genome. Indeed, some start-ups are trying to capitalize on this idea.
The technology that enables such genome hacking falls into the field of synthetic biology. What is synthetic biology? The answer can vary depending on who answers, but to my understanding, synthetic biology is the study of how to design and fabricate living systems that do not exist in nature. In addition to adding and removing genes from a genome, Desmond said there exist techniques that allow one to increase the mutation rate of certain organisms. Once enough mutations accrue over the population, a researcher can then create conditions that select the mutations most suited to a task of interest. This may be the only truly parallel implementation of a genetic algorithm.
Of course, such technology also generates concern. The ETC Group is a public watch-dog for synthetic biology. They have been vocal in challenging Craig Venter's attempt to patent synthetic life and oppose the idea of scientists creating synthetic life without regulations. "Playing God in the Galapagos," the title of one of their publications, reflects this position.
These concerns are also in the public consciousness. Desmond mentioned a recent online poll asking about such technologies. The response choices ranged from complete opposition to regulations to complete opposition to the research. How do scientists feel? It turns out Church's lab took a similar poll. Surprisingly, the group was in favor of more regulations.
Me: So what are you doing these days? Are you a post doc?By the time I started looking for internships, I knew what the Broad Institute was and sent Desmond my resume. I have been at the Broad now for two months, and Desmond and I work in the same group. It helps working with someone here who hails from the same research community, and our conversations span topics that include information theory and biology research.
Desmond: Actually, I'm at the Broad Institute.
Me: What's the Broad Institute?
I had a taste of the future of biology research during lunch when Desmond described his project with George Church's lab. The goal of the project is to study ways to use biology to produce renewable fuel sources. One fuel source is ethanol, and there is a well-known biological recipe to produce it. Add yeast to a sugar solution. Mix. Let it ferment.
The approach Desmond described was a little different. It turns out one can modify the E. coli genome and use the modified E. coli to produce ethanol. Driven by this success, there is an effort to see if alkanes or other fuels can be created by hacking the genome. Indeed, some start-ups are trying to capitalize on this idea.
The technology that enables such genome hacking falls into the field of synthetic biology. What is synthetic biology? The answer can vary depending on who answers, but to my understanding, synthetic biology is the study of how to design and fabricate living systems that do not exist in nature. In addition to adding and removing genes from a genome, Desmond said there exist techniques that allow one to increase the mutation rate of certain organisms. Once enough mutations accrue over the population, a researcher can then create conditions that select the mutations most suited to a task of interest. This may be the only truly parallel implementation of a genetic algorithm.
Of course, such technology also generates concern. The ETC Group is a public watch-dog for synthetic biology. They have been vocal in challenging Craig Venter's attempt to patent synthetic life and oppose the idea of scientists creating synthetic life without regulations. "Playing God in the Galapagos," the title of one of their publications, reflects this position.
These concerns are also in the public consciousness. Desmond mentioned a recent online poll asking about such technologies. The response choices ranged from complete opposition to regulations to complete opposition to the research. How do scientists feel? It turns out Church's lab took a similar poll. Surprisingly, the group was in favor of more regulations.
Thursday, July 12, 2007
Outreach
I had been at the Broad for a little over a month, but I had yet to meet the co-worker standing next to me in the elevator. To avoid my tendency to shift between staring awkwardly at the elevator doors and the lighted floor number, I introduced myself. "I'm Megan," she responded, and we started a conversation.
Megan Rokop is Director of the Broad Institute's Educational Outreach Program. In addition to the research that goes on at the Broad, the Institute also sponsors a series of programs to engage with students, teachers, and the general public in the Boston area. A main feature of the program is the opportunity for high school classes to visit the Broad, where students get to conduct experiments using Broad facilities.
Megan wasn't always interested in biology. She started college at Brown as a foreign languages major, but a scheduling error placed her in a biology class. Unlike her previous experiences with the subject, which primarily involved memorizing a list of facts, the professor for this class presented the material in a way that inspired Megan's interest in the subject. "I wanted to be like him," she said of the professor.
Sure enough, Megan switched majors and eventually received her PhD in biology from MIT. After teaching at MIT for a few years, a fellow biology instructor told her about an opening for the Outreach position at the Broad Institute. Although she enjoyed teaching undergraduates, Megan recognized that not everyone benefits from a scheduling error, and saw the position as an opportunity to reach students while they were still exploring interests. When I told her I was interested in learning more biology, she was more than happy to oblige.
Our first lab involved identifying and mating different strains of Caenorhabditis elegans, a worm that serves as a model organism for investigators with interests ranging from genetics to neuroscience. C. elegans are only a millimeter long, so we needed a microscope to observe them. Once under the microscope, the distinguishing characteristics of mutant strains and sexes were clearly visible.
C. elegans are divided into two sexes: male and hermaphrodite. Mating two of the mutant strains requires the transfer of a male and hermaphrodite onto the same dish. The offspring can later be counted to determine whether their traits were dominant or recessive. After a few false starts, I was able to use a special hook to transfer a wild-type (WT) male onto the same dish as an uncoordinated (UNC) hermaphrodite. While we couldn't see the worms without a microscope, we could see the tracks the wild-type was making as he searched for his uncoordinated partner.
The second lab involved running a gel electrophoresis with an application to paternity testing. Not all DNA code for proteins, and in the non-coding regions, certain strings repeat. The number of times these strings repeat can be used to distinguish individuals and determine heredity.
One way to distinguish the number of repeats is via gel electrophoresis. The idea is to load the DNA into different wells on one side of a gel and run a current through the gel. Since DNA is negatively charged, this current causes the strands to move across the gel towards the positively charged end. Since longer sequences diffuse more slowly, sequences with more repeats don't travel as far away from the negative end.
Unlike the first lab, I worked on the second lab with a group of high school students. They were visiting from the National Youth Leadership Forum on Medicine, a summer program for aspiring doctors. After the lab, I had a chance to talk to some of the students, who were curious what a non-biologist was doing at the Broad. In turn, it was interesting to hear from the students, some of whom weren't completely set on a career in medicine. While I wasn't sure whether their experiences that week would increase their interest in medicine, mine certainly increased my curiosity about biology.
Megan Rokop is Director of the Broad Institute's Educational Outreach Program. In addition to the research that goes on at the Broad, the Institute also sponsors a series of programs to engage with students, teachers, and the general public in the Boston area. A main feature of the program is the opportunity for high school classes to visit the Broad, where students get to conduct experiments using Broad facilities.
Megan wasn't always interested in biology. She started college at Brown as a foreign languages major, but a scheduling error placed her in a biology class. Unlike her previous experiences with the subject, which primarily involved memorizing a list of facts, the professor for this class presented the material in a way that inspired Megan's interest in the subject. "I wanted to be like him," she said of the professor.
Sure enough, Megan switched majors and eventually received her PhD in biology from MIT. After teaching at MIT for a few years, a fellow biology instructor told her about an opening for the Outreach position at the Broad Institute. Although she enjoyed teaching undergraduates, Megan recognized that not everyone benefits from a scheduling error, and saw the position as an opportunity to reach students while they were still exploring interests. When I told her I was interested in learning more biology, she was more than happy to oblige.
Our first lab involved identifying and mating different strains of Caenorhabditis elegans, a worm that serves as a model organism for investigators with interests ranging from genetics to neuroscience. C. elegans are only a millimeter long, so we needed a microscope to observe them. Once under the microscope, the distinguishing characteristics of mutant strains and sexes were clearly visible.
C. elegans are divided into two sexes: male and hermaphrodite. Mating two of the mutant strains requires the transfer of a male and hermaphrodite onto the same dish. The offspring can later be counted to determine whether their traits were dominant or recessive. After a few false starts, I was able to use a special hook to transfer a wild-type (WT) male onto the same dish as an uncoordinated (UNC) hermaphrodite. While we couldn't see the worms without a microscope, we could see the tracks the wild-type was making as he searched for his uncoordinated partner.
The second lab involved running a gel electrophoresis with an application to paternity testing. Not all DNA code for proteins, and in the non-coding regions, certain strings repeat. The number of times these strings repeat can be used to distinguish individuals and determine heredity.
One way to distinguish the number of repeats is via gel electrophoresis. The idea is to load the DNA into different wells on one side of a gel and run a current through the gel. Since DNA is negatively charged, this current causes the strands to move across the gel towards the positively charged end. Since longer sequences diffuse more slowly, sequences with more repeats don't travel as far away from the negative end.
Unlike the first lab, I worked on the second lab with a group of high school students. They were visiting from the National Youth Leadership Forum on Medicine, a summer program for aspiring doctors. After the lab, I had a chance to talk to some of the students, who were curious what a non-biologist was doing at the Broad. In turn, it was interesting to hear from the students, some of whom weren't completely set on a career in medicine. While I wasn't sure whether their experiences that week would increase their interest in medicine, mine certainly increased my curiosity about biology.
Saturday, July 7, 2007
Concept
In August of 2005, I volunteered to be the Faculty Interview Coordinator for the EEGSA at UC Berkeley. While it is not standard practice in all departments, the EECS department brings graduate students into the faculty interview process. Student involvement consists of a time slot during which graduate students may interview each faculty candidate. One of my friends, who had co-organized the student interviews the previous year, was leaving Berkeley, so I signed up for the vacant position. Interviews started in the spring, and I would have help from the other co-organizer.
That was the plan. Near the end of January, I received an e-mail that took me by surprise. My co-organizer, who had been involved in the process the previous year, would not be actively involved with the interview process that spring. I quickly recruited a friend to help out with the interviews, but neither of us had any experience. To handle this problem, I arranged a meeting with the previous co-organizer to run me through the process. While most of the issues we discussed at that meeting were logistical, I had a concern. How should I handle a faculty candidate whose expertise was in an area where I knew nothing?
His answer was in some of the advice he gave me. "My favorite question to ask is what they consider important research questions over the next ten years. The answers are usually pretty interesting. Plus, it works on any candidate, regardless of how much you know about their work."
Armed with this advice, I began interviewing prospective faculty. The list of interviewees ranged from graduate students wrapping up their dissertations to senior faculty at other universities, one of whom was considered a contender for the Nobel Prize. While there were some logistical headaches, the interview experience itself was a positive one. It gave me a window into research outside my direct area of interest and gave me perspective on larger questions in electrical engineering.
Why stop at electrical engineering? The intent of this blog is to summarize conversations with graduate students, faculty, and others about their fields of interest. Since faculty candidate interviews are confidential, I will have to look elsewhere for content. Hopefully my summer at the Broad Institute, where the focus is on biomedical research, will prove to be a useful starting point.
That was the plan. Near the end of January, I received an e-mail that took me by surprise. My co-organizer, who had been involved in the process the previous year, would not be actively involved with the interview process that spring. I quickly recruited a friend to help out with the interviews, but neither of us had any experience. To handle this problem, I arranged a meeting with the previous co-organizer to run me through the process. While most of the issues we discussed at that meeting were logistical, I had a concern. How should I handle a faculty candidate whose expertise was in an area where I knew nothing?
His answer was in some of the advice he gave me. "My favorite question to ask is what they consider important research questions over the next ten years. The answers are usually pretty interesting. Plus, it works on any candidate, regardless of how much you know about their work."
Armed with this advice, I began interviewing prospective faculty. The list of interviewees ranged from graduate students wrapping up their dissertations to senior faculty at other universities, one of whom was considered a contender for the Nobel Prize. While there were some logistical headaches, the interview experience itself was a positive one. It gave me a window into research outside my direct area of interest and gave me perspective on larger questions in electrical engineering.
Why stop at electrical engineering? The intent of this blog is to summarize conversations with graduate students, faculty, and others about their fields of interest. Since faculty candidate interviews are confidential, I will have to look elsewhere for content. Hopefully my summer at the Broad Institute, where the focus is on biomedical research, will prove to be a useful starting point.
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