Author Archives: Brian Teague

My dad flies his own airplane, a Cessna 182 Skylane in which I have occasionally traveled with him.  I've always been impressed by how safe a pilot he is -- he plans conscientiously, he doesn't cut corners, and he doesn't get in over his head.  Each time he takes off, he performs a full preflight checklist and if there's anything that doesn't check out, you will not go flying with him today.

I was thinking about my dad's checklist recently in a different context.  You see, one of my roles around the Weiss lab for the last year or so has been to help "expedite"  manuscripts.  Ron is an excellent science communicator, but his time is limited and once he becomes involved in preparing a manuscript for submission, he rapidly becomes the rate-limiting step.  The more I can help my colleagues get things in order before he gets involved, the faster we can go from draft to submission.

I've been involved in this role for three manuscripts thus far, and it has been amazing to me how careful, otherwise detail-oriented scientists who have been reading papers for many years miss some of the most basic things about science communication.  Not subtleties about narrative structure or debatable questions about the work's larger import -- I'm talking about "did you label this axis" kinds of missing pieces.

So, in the spirit of my dad's checklist, I present a preflight checklist for manuscripts.  Are your seatbelts buckled?

...continue reading

Wow.  SEED came and went in the blink of an eye -- what a whirl-wind!  The following are some only lightly organized reflections on what went well, what didn't, and what might change next year.

Goals

For me, the core of synthetic biology is the ability to solve real-world problems by creating new organisms.  So that's what I wanted the students to do:

  • Choose a real-world problem they were interested in that could be addressed with a new organism
  • Describe that organism's new behavior and how it would address the problem
  • Plan a plasmid or two that implements the new behavior
  • Build those plasmids out of reusable genetic parts (BioBricks)
  • Test their plasmids
  • Communicate their results

....over the course of 8 Saturdays together.  The difficulty, as I outlined in the previous post, is that there's an enormous amount of domain knowledge and operational skill involved in this.  Did it work? Read on!

Project Selection

I left the choice of an interesting problem up to the students.  They each came up with a problem, then wrote it on the whiteboards around the classroom so they could think about their classmates' proposed problems.  Then, they self-assorted into 5 groups around five different problems.

What worked: The students were all really stoked to be working on projects of their own choosing. This was reflected again and again in the feedback we got from the students.

What didn't work: While I had asked the students to develop their own ideas based on a list of parts from the Parts Registry that could be useful in solving them, I'm not sure they understood quite what I was asking for.  Thus, we ended up with 5 cool problems to work on -- only one of which had the pieces we'd need available to us.  Due to an incomplete understanding of the relevant biology, we also ended up with several projects that were biologically impossible.

What could change: I am still thinking about how to balance the authenticity and engagement that you get from allowing the students to choose their own project, with the predictability and improved success rate you get from constraining their choices. I think one way to go about it would be to choose a project area for them to work on, one that had good parts support and a number of "obvious" good projects, then allow the students some choice within the project space.

Circuit Design

I did some reading and came up with a set of related questions that the students could feasibly build plasmids to answer.  But how to give them experience actually doing the plasmid design? I ended up hacking together some "datasheets" for the parts and backbones that would actually be useful, then putting them in folders for each of the projects.  This gave the students a chance to practice thinking about what each part did, how it related to the other parts, and how they would be assembled into transcriptional units.

What worked: There was a lot of engagement with this activity -- by far the best non-lab activity we did.  I think we lost some of the students; but there was also a lot of them that really got it by the end, which was really rewarding.

What didn't work: This was also a significant amount of work on my end -- there was a lot of content to create for what was essentlly a one-off lesson.

What could change: I think that constraining the projects (as above) could ameliorate this substantially.  You could give each group the same set of parts and have them come up with different designs.

Plasmid Constuction

We completed a single BioBricks build cycle:

  • Digest the parts
  • Ligate the digested parts
  • Transform the ligations
  • Miniprep

All this was pretty straightforward on their end.

What worked: They loved (loved!) the lab work.  The more pipetting, the better -- miniprep day was the best, followed by the day they did transformations.

What didn't work: On my end, it required preparing a pretty massive number of parts from the Registry distribution plates.  I also designed and synthesized a number of parts (usually promoters for which repressors were available in the Registry but for which there were no promoters on the plates) -- with all of the attendant issues for a cloning cycle.  In the end, each group tried to build 2 or 3 plasmids -- but I think each group only managed to get one to go together.

Finally, there was at least one group that was quite large, and I was really sad to read in the feedback that at least one student felt like they were stuck watching everyone else pipette but didn't have a chance to themselves.  No bueno.

What could change: More constrained projects mean fewer parts to synthesize (or none at all??), higher success rates, and the possibility for the instructors to build things behind-the-scenes.  Also, I'd love to think about ways to spend more time in the lab.  We were about 50-50 this year, and even though I think the classroom stuff was important, it was clearly less engaging. (One alternative: make the classroom stuff more engaging?)

Experimental Design and Characterization

I asked the students to design their own experiments to test the plasmids.  This worked out okay -- but again, we ran into the trouble of not enough biology background.  We also ran into issues with plasmids they had designed but hadn't managed to build (and that I couldn't get to go together in the intervening week.)  At the end of the day, I pretty much had to propose experiments that were related to the problems they wanted to solve.

The actual experiments were pretty straightforward -- most were growth-rate measurements under different conditions.  (For example: some of the students wanted to work on lead bioremediation for drinking water.  They had a plasmid that constitutively expressed a lead-binding protein.  The experiment: do the engineered coli grow in media containing heavy metals better than an un-engineered strain?)

What worked: For the most part, the students had experiments that could directly test the functionality of at least one of the plasmids they had built. And connecting that experiment back to their system and their problem was good practice.

What didn't work: Sometimes that connection wasn't super-solid. A lot of the disconnect came from not having managed to build all the plasmids that they proposed to make. Changes to improve the success rate would make this more straightforward.

What could change: One of the hardest connections to make in the plasmid design phase is with the experiments you want to do to test them. I'd like to think of some way to de-couple these a little bit, which could make both the design and the subsequent experiments more straightforward.

Science Communication

The last day, the students are expected to give presentations, posters or demonstrations to their parents. This year I asked each group to give a 10-minute talk. I demonstrated what I wanted using an example project we had been discussing all semester. We spent most of the last session together (before the wrap-up session) doing talk planning, then actually working on the slides.

What worked: They all gave talks. They all spoke pretty fluently about the projects they were working on and why they were interesting and what their approach was. The best talks took a deep dive into the human practice implications of their project and did a solid job with data interpretation.

What didn't work: We spent a long time talking about science communication, and even so I thought the structure of the talks was pretty universally shaky.

What could change: I'd love an opportunity for them to communicate more about their science -- learning happens via practice and feedback, and I'd love them to have more opportunities for both.  The question is ... when?

Takeaways

There are clearly things that could change next year. I think it's also really clear that the students got a lot out of SEED this last semester.  There were at least two students who told me they wanted to continue to study bioengineering in college, which I count as a success.  (-:  And maybe some of them will get involved in iGEM, which seems like an obvious extension of this....

I am super-excited to be teaching SEED this semester! The SEED Academy is a program run out of MIT's Office for Engineering Outreach Programs whose goal is to give under-represented and under-resourced students exposure to various engineering disciplines. The spring semester of senior year, the topic is Synthetic Biology.

The rest of the SEED experiences are very project-focused; for example, in the Aero/Astro semester, they build (and then shoot off) model rockets. I've spoken to previous SEED synbio instructors, and while the students did lab work, they didn't really have a project focus in the way that other SEED modules do.

I think this semester it's time to change that. Between BioBuilder and iGEM, it's pretty clear that highschoolers can do "real" synthetic biology. More on how I'd like to structure the course later, but I've been giving some thought to "content" -- ie, "what do new practitioners of synthetic biology need to know to get started?"

I've done a bit of mind-mapping and have come up with four broad categories of "content" knowledge.  If the goal of a synthetic biologist is to "program cells with DNA", then we can attack that goal with four questions:

  1. What does a cellular program look like? This is a set of knowledge and skills based around the idea of a specification.  In terms of learning goals, I want my students to be able to take a problem (like "is this water safe to drink?") and turn it into a specific cellular behavior that they want ("detect arsenic in the water and turn red if it's found.") Being able to do that successfully depends on knowning ...
  2. What are the pieces of a cellular program?  Promoters and RBSes and genes oh my!  I want my students to be able to choose pieces to put together that implement the specification.  This depends on knowing what the pieces are, and what they do.  The "what they do" part, in turn, depends on knowning...
  3. How does a cell "run" a program? Here is where the cellular and molecular biology comes in.  The most important parts are basically the central dogma, but with an engineering twist.  For example, DNA is transcribed to RNA.  What controls whether, and how much, RNA is made from a gene? The RNA is transcribed to protein; how can we moderate or control this process? What effect does the protein have on the cell? What effect does the protein have on the circuit? I want students to be able to predict how a circuit will behave based on some relatively basic cellular and molecular biological knowledge.
  4. How do we build a gene circuit? This is the "biotechnology" part -- manipulating DNA.  I want students to be able to build a gene circuit based on a particular assembly technology (in this case, biobricks.) This means learning about, and using, restriction enzymes and ligases and chemically competent cells and sequencing.  (Oh yes, and using pipettors and thermocyclers and other things.) Interestingly enough, this is where many peoples' minds go when they think about "learning synthetic biology."  And sure, it's important -- but only one of the building blocks.

The difficulty here, I think, is the interrelatedness of the four areas. How to sequence learning opportunities so that they all build on eachother? Stay tuned...

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Third in an occasional series on iGEM.

iGEM recruiting season has ended. At MIT, we advertise and hold info sessions, then ask that applicants send us an up-to-date resume and answer three questions with a paragraph each:

  • Why do you want to be on the MIT iGEM team?
  • What project do you think would be appropriate and exciting for the MIT iGEM team?
  • What other non-science skills can you contribute to the team?

Along with the resume, these questions try to get at a student's enthusiasm, creativity, maturity, and diversity. (Pre-existing lab skills are a bonus; domain-specific knowledge we can (and do!) teach.) In past years, I have read the applications and taken notes, then looked back over the notes and picked the 10-12 applicants that I liked the best.

And then, over the last few months, I went to two really eye-opening talks.  The first was an event as a part of Boston HUBweek 2016, about designing inclusive organizations. The second was sponsored by the MIT Institute Community and Equity Office on probing ones' hidden biases, a fascinating workshop run by Harvard Prof. Mahzarin Banaji who studies implicit biases at Harvard. These workshops discussed recent studies in social psychology, whose upshot is that we like people who look like us, who think like us, who share our values. Well, that's no surprise -- but it's a problem when you're hiring.

Or, say, choosing iGEMers. I want a diverse, inclusive team, not a team that looks like me and thinks like me.  Diverse groups do better science and have better educational outcomes -- and the teams we've had that do the best have students who are from different majors, different classes, different backgrounds, and have different interests. If I only pick the students that I "like", I'm likely to end up with a team that ... looks and thinks alot like me. Even if that's not the intent.

(Side note -- this is how we ended up with a tech industry full of white cis guys. Companies "hire for fit" and the people that get hired are the people that look like everyone else and think like everyone else.)

So what to do? There are a couple of things. Blind evaluations are a great start -- this is why, for example, many top orchestras are having applicants audition from behind a screen. Unfortunately, that's a pain in this case -- I'd have to get someone to receive the applications, then edit the resumes to remove identifying information for gender, race, etc. I'm a bit of a one-man show at the moment. (Hearteningly, there is evidence that being aware of your own biases can help you account for them.)

Another way to fight implicit bias is to make your review structured.  If you're interviewing job candidates, decide what is important to elicit from the candidates, then ask all the candidates the same questions. Not only will this make the evaluations of different candidates more comparable, but deciding up front what to ask helps make sure that the evaluation is actually relevant to the job you're hiring for. (Ie, you can make sure upfront that the interview questions are relevant to the job's requirements.)

I applied this strategy to the problem of choosing applicants for this year's iGEM team by coming up with a rubric before I started reading applications. I scored each candidate on enthusiasm, experience, creativity, diversity, maturity, and availability. Each category was scored 1 to 3, with exemplars as follows:

  • Enthusiasm
    1. What's this "synbio" thing? It sounds cool, but I don't know enough about
      it to say.
    2. I know what synbio is and I'm pretty stoked. Maybe I even took a class
      on it or have done some independent research.
    3. I've been interested in iGEM since forever. Maybe I participated in a
      team in highschool, or I tried to start one. I think synbio is AMAZING
      and would REEEEALY like to join this year's team.
  • Experience
    1. This will be my first research experience.
    2. I've had some research experience elsewhere.
    3. I've had extensive research experience, or some synbio research experience.
  • Creativity
    1. Ideas are poorly thought out or wildly impractical. Eg, "Let's terraform mars!"
    2. Ideas are practical but a little ho-hum. Eg, "Let's cure cancer!"
    3. Ideas are creative and impactful. Eg, "Let's make a tunable timer for time-release drugs."
  • Diversity
    1. Biology or bioengineering; no or few "extra skills"
    2. Electrical engineer or computer science; extra skills include organizing teams and planning events.
    3. Artists, musicians, architects, mathematicians; other engineering majors; other skills including web dev, design, etc.
  • Maturity (yes, I know, but it really does make a difference)
    1. Freshman
    2. Sophomore
    3. Junior
  • Availability
    1. I can only give you the summer.
    2. I can give you the summer, but I have regular conflicts spring or fall (or I'm gone IAP)
    3. I have no current conflicts

I was pleased to see a diversity of scores in each of these categories, indicating that they're ... measuring something, maybe? And when I summed them together, I got a nice range of "total" scores. All in all, I thought it worked well, and took a lot of the "I'm the only person reading these things what if I screw it up??" anxiety out of the process.

Also ... I am really excited about our team this year.  I think it's the most diverse, creative, interesting team I've ever been involved in helping choose, and I think it portends a great year for MIT iGEM!

One last thing.  I'm not sure that there's even anyone reading this. If you are, and you are an iGEM mentor involved in choosing your team, drop a note in the comments about what you do, or what you do differently. (Or just to say hi (-; ).

 

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(Part 2 in a series about teaching and iGEM; see part 1 here.)

Communication is one of the major themes of iGEM.  So for the last few iGEM seasons I've been thinking pretty closely about how to help my students communicate their science clearly.  And one of the things they always seem to have trouble with is the structure: what (and how much!) background to present, how to contextualize each result in the context of the larger project, how to emphasize the big-picture takeaway from among the less-important details.  How to take a bunch of disparate bits and tell a story.

That's not the focus of this post.  It will come, though, trust me.

On the other side of the communications coin, my students also frequently struggle with reading scientific literature.  Their first attempt usually looks like they read the introduction and the conclusion, skimmed the results, and pretty much took the authors' word for what they found.  It takes them a long time to understand that there's narrative structure in a piece of primary literature, too, exactly the same narrative structure as they will one day eventually use to tell their own story.

And not only is the narrative structure the same between a talk and a paper, but it serves the same purpose: to help the audience understand what is going on.  The details, the individual experiments and results, make much more sense when they're integrated into a scientific story.  So much so that if you look for the narrative when reading a paper, you can frequently gloss over the experimental and domain-specific details and still retain the thrust of the authors' argument.  And that, of course, is the key to both reading literature in a domain that's not familiar, and to presenting your work to a room full of otherwise intelligent non-specialists.

For me, the narrative arc of a scientific story is divided into five pieces:

  1. What is the question? Why did you do what you did? If you're "successful", what new knowledge will you have gained?  What are you trying to convince your audience of?
  2. What did you do? (And why did you do it that way?) What experiments were performed? How did you go about trying to answer the question you posed? Why did you choose that particular approach over some other approach?
  3. What did you see?  What were the "raw" results?
  4. What does it mean? What is your interpretation of the results?  Does it answer the question from #1?  If not, why not?  Does it raise any new questions?
  5. What's next? What's the next study? The next experiment? The next question to ask?

What I particularly like about this structure is that it applies to many different levels of scientific discourse.  At the whole paper level, it looks like the following:

  1. What is the question?  This is covered in the Introduction. There should be enough information here to situate the current work in the broader field and convince the reader that the question being asked is interesting and important.  It also gives a broad introduction to the approach the authors took to answer the question.
  2. What did you do? And why?  You might say "oh yes of course this is the Methods section."  Frankly, I (and most other scientists I know) pretty much skip the methods section, because it answers the "what did you do" question in laborious detail without addressing the "why did you do it that way?" aspect.  A well-written Results section, on the other hand, interleaves the actual experimental results with enough experimental detail to allow you to interpret them without necessarily referring back to the Methods section; and more importantly, they frequently discuss the rationale for choosing the experimental approach.
  3. What did you see?  The Results section are, well, the results.
  4. What does it meanand 5. What's next? are the domain of the Conclusion section. Did the study answer the question posted in the Introduction? Does it raise more questions? How does it move the field forward?

However, the same structure applies to an individual experiment or a "result" in the Results section of a paper or a talk.  Or it should!  Sometimes you have to infer the answers to some of the questions:

  1. What is the question?  What specifically were the authors trying to learn with this one particular experiment?  And how does that relate to the larger question they're trying to ask?
  2. What did they do? And why?  The question from #1 motivates the experimental approach.  For example, if I'm looking for whether protein A binds to protein B, I might choose to do a co-immunoprecipitation: use an antibody against protein A from a crude cell lysate, run the bound proteins on a gel, then do a Western blot and probe with an antibody to protein B.  Is this the only possible approach?  No, of course not.  Why use this approach over, say, something mass-spec based? Or immunofluorescence and co-localization? Or surface-plasmon resonance?
  3. What did they see?  If the investigators ran a Western blot, here's the actual blot to look at.  If it's not in the main text, check the Supplemental Info.
  4. What does it mean?  Without context, a Western blot is just lanes and bands. Do the presence or absence of particular bands at particular molecular weights actually answer the question that was asked?  What other explanations are there for the data?
  5. What's next?  If the experiment raises other questions, let's go test them.  If there are multiple explanations for the observed data, then let's go rule them out with additional experiments.

The reason this structure works is it explicitly relates every piece of the paper to its context. By and large, humans don't learn by remembering random facts; instead, they learn by relating new material to what they already know. (That's the basis of constructivism.)  And sometimes a paper is poorly written and some of the context is implicit!  All too frequently a paper reads as if the authors did one thing after another with no rhyme or reason.  Looking at a paper this way forces you into the authors' shoes and makes you ask "why did they choose this approach, this experiment, this strategy?"

And that is where things get really interesting. Much of the primary literature on teaching with primary literature (heh) emphasizes critical thinking (and rightly so.)  But all too often, I feel like that criticism gets bogged down in the details of the experiments: niggling questions about experimental details, sample sizes, confidence intervals.  Don't get me wrong!  Technical correctness is important.  But I think it's much more interesting to focus on the bigger structure: why did the authors answer their question using this approach instead of some other one?  Are they asking questions that build on each other logically?  Is there some other explanation for these results?  This kind of lateral thinking, reasoning with information other than what was explicitly presented to you, is at the core of what it means to do good science.

 

And finally --- thinking about and presenting other peoples' science in this way will get my students used to the structure so that when it comes time to present their own work, doing so in a similar narrative arc will be much more natural.

PS - I am well aware that this take is not the first take on teaching students to read primary literature, or on science as storytelling. I doubt it's the first place they've been synthesized, either; if you know of another example, leave a comment below!  This structure also draws heavily from my very favorite treatise on scientific communication, The Science of Scientific Writing.  Seriously, if you haven't read it, go do so -- it's a long read, but so so worth it.

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It's the most wonderful time of the year!  The beginning of a new iGEM team!

iGEM Logo

For those of you not in the loop, iGEM is a synthetic biology competition: teams build biological systems using reusable genetic parts, then compete for fame and trophies at the iGEM Giant Jamboree in Boston each fall.  iGEM started at MIT twelve years ago, and has since grown to over 350 teams (primarily undergraduate) from all over the world.

Oh, and I've run the MIT team for the last few years.  This year, I figured I'd try posting a few things from the perspective of a team instructor.  I think there's a lot to be had out there on the experience of the team participants, and maybe a little less on the challenges (and rewards!) of being an instructor.

Today's topic: choosing a project.

I and my boss are trying to provide the most authentic research experience for these students that we possibly can.  And so we let the team choose their own project.  This has some advantages and some challenges:

  • The team owns the project.  This is huge.  You get a huge boost in engagement (and, I suspect, learning!) when the team is working on something that they want to work on, a problem they think is important.  It gets a lot easier to log the late nights (especially towards the end of the summer and into the fall as we get ready for the Jamboree) when it's your baby from start to finish.  When we talk to past MIT iGEM students about what their experience means to them (or listen to them talk about iGEM to prospective iGEMers) this comes up over and over and over.
  • The team doesn't know what's not possible, and they come up with great ideas.  This has resulted, a number of times, in projects that have since become research foci for the lab. Are the kids going to turn out a publication's worth of results in a summer?  Not likely.  Is there enough to put as preliminary data in a grant application?  You bet.
  • They consistently overestimate how much they're going to be able to get done in a summer.  And so we have to help them scope the project.  Unfortunately, we as advisors sometimes get it wrong too.
  • Sometimes they want to do something that we as a lab don't have much experience with.  That got us into trouble last year: we let them choose a project involving both some DNA manipulations and some analytical techniques that we as a lab didn't have much experience in, and it took us a long time to get up to speed.
Image credit: Shinjini Saha

Unfortunately, choosing a research project is hard.  Even when you've been in the field for years, it's hard to pick a project that is exciting, important, and feasible.  How can we expect these poor students, usually completely new to research science, to get it right?

No, that's the wrong question.  The right question is, how can we help them learn to do so?

Ah yes.  Much better.  Now I can apply all those good pedagogy skills I've been acquiring.  If choosing a project is something we want the students to be able to do, then what kind of knowledge will they need to do so?  What skills?  What scaffolding?  What support?  What opportunities for practice and feedback?

I've been working with some amazing students from previous years' iGEM teams on this problem.  The plan is to do most of this over MIT's Independent Activities Period (IAP), the four weeks in January where students are on campus but not taking classes.  (Whenever you hear about MIT students doing something crazy, it likely happened over IAP.)  I think we've got it broken down into a couple of pieces:

  1. Get a sense for iGEM.  We're going to start with a deep dive into a single exemplar iGEM project (either the 2014 MIT project or the 2013 Paris Bettencourt project) as a way to get a handle on what an iGEM project generally looks like, and all the deliverables.  I think both of these teams did things that were interesting, important, and feasible, and I would love to see something similarly scoped from this year's MIT team.
  2. Get a feel for how we do synthetic biology.  The Weiss lab tends to think of synthetic biology in terms of sensing the cell's environment, computing some sort of function on a set of inputs, and actuating some output whereby the cell modifies its environs.  The plan is to spend a day on each of these, choosing various past iGEM projects we think did each of these things particularly well for the students to explore.
  3. Capture ideas. This is the point, right?  Each of these discussions will include explicit brainstorming about how this year's team could apply a similar strategy or approach to problems they find interesting.
  4. Refine those ideas.  This is the last few days.  Hopefully, between asking the students to propose things in their submissions, and the discussions we've had as we explore past projects together, we'll have a huge whiteboard (or a wiki page) full of interesting things to think about.  We'll start with one person per idea, and they'll go off and try to build a project proposal around that idea.  Then they'll all present, and we can offer feedback.  Eventually we'll start winnowing down to the ones that are interesting and feasible; I think that will happen organically, but others have suggested that we'll need some structure.  We'll see.
  5. Foster ownership.  This is something we're still working on.  In the past, we've always come down to two or three ideas and a team vote: and that always leaves someone disappointed.  This year, I want to do something to try to address that: maybe we'll round-robin people and projects, and say "okay yesterday those people worked on this project, today you work on it and see what you can come up with."  That may dilute out expertise, though.... ?
Credit: Shinjini Saha

I think this plan has a few nice pedegogical things going for it:

  • Demonstrate expert thinking.  The case studies of previous well-structured and well-executed iGEM projects will be chosen by the instructors.  I think we all have a pretty good feel for what a "good" iGEM project looks like, and even if we say "focus on aspect X of this team's work for tomorrow" I think that repeated exposure to generally good projects will hep steer the students in the right direction.
  • Provide structure for learning It's easy to say "go look at a few previous teams' projects!" -- but there were 360 teams last year; if we go back 4 years or so, we're at well over 1000.  I think we'll have much better success with "this team did a fantastic job at building a new sensor; go learn about building sensors from that team."
  • Provide resources for missing knowledge.  There's an immense amount of domain-specific knowledge required: from basic biology (who remembers the central dogma from highschool biology?) to research techniques (quick! what's the difference between a Southern Blot, a Northern Blot, and a Western Blot?) to a basic feel for the state of the field (who wants to do something fun with CRISPR?)  I think the only good answer here, for such an ill-defined question, is to have people around that can answer these questions in a high-throughput way.  If we make them go look everything up in Wikipedia or Alberts, they'll never get anything done.
  • Provide opportunities for practice and feedback.  The general structure for the first few days will be "go off and look at this team's project, focusing on (say) how they applied information processing ... and then come back tomorrow and tell us about it."  Later in the week, it will be "go do some research on this idea that you had ... and then come back tomorrow and tell us about it."  I think this opportunity to try their hand at understanding someone else's work, or at thinking critically about their own ideas, and then having some close-to-immediate feedback to help them refine their thinking, is really going to be key.
  • Provide structure for learning, part 2.  The tasks here ("research this project idea") can be awfully ill-defined; our answer is to use templates and rubrics.  If the task is "tell us about Paris Bettencourt's design of a new TB drug screen" it's not necessarily obvious which parts we want them to pay attention to.  If we say "give us a brief presentation tomorrow on this project idea" it's not clear what will help us, and the team, evaluate that as a possible iGEM project.  So the plan is to offer rubrics and templates to help them do so.  Questions to answer; maybe a PowerPoint template to fill in.  The risk here is that a template puts the students in "minimum effort" mode -- "I have answered these questions, now I'm done."
  • Keep the students focused on their goal.  The goal, remember, is to choose a project idea!  So, if we spend a day talking about other teams' biological sensors, that discussion will have explicitly threaded through it a discussion on how similar ideas, or similar approaches, could be used on problems the team is interested in.
  • Keep the students engaged.  I want to absolutely minimize the amount of time I or the other instructors spend talking.  This is not knowledge transmission: this is a set of skills to learn by practicing them, with support and feedback, on interesting problems, in a context that will actually matter because the end result (an iGEM project) is something we're going to be working on the for next nine months.

So I'm pretty excited about this plan.  I think there's some sound teaching philosophy here, and the authenticity of actually starting iGEM (instead of "let's do some basic lab work that you won't remember in three months!") is going to be a heck of a motivator.

Thoughts?  Leave a comment, I'd love some feedback.  Want to follow along at home?  The syllabus and all of the knowledge we capture is going to be posted on the MIT iGEM wiki.  At the moment it still has instructions for applying, but that will be changing soon.

Python tools for quantitative, reproducible flow cytometry analysis

Welcome to a different style of flow cytometry analysis. For a quick demo, check out an example IPython notebook.

What's wrong with other packages?

Packages such as FACSDiva and FlowJo are focused on primarily on identifying and counting subpopulations of cells in a multi-channel flow cytometry experiment. While this is important for many different applications, it reflects flow cytometry's origins in separating mixtures of cells based on differential staining of their cell surface markers.

Cytometers can also be used to measure internal cell state, frequently as reported by fluorescent proteins such as GFP. In this context, they function in a manner similar to a high-powered plate-reader: instead of reporting the sum fluorescence of a population of cells, the cytometer shows you the distribution of the cells' fluorescence. Thinking in terms of distributions, and how those distributions change as you vary an experimental variable, is something existing packages don't handle gracefully.

What's different about CytoFlow?

A few things.

An emphasis on metadata. CytoFlow assumes that you are measuring fluorescence on several samples that were treated differently: either they were collected at different times, treated with varying levels of inducers, etc. You specify the conditions for each sample up front, then use those conditions to facet the analysis.

Cytometry analysis conceptualized as a workflow. Raw cytometry data is usually not terribly useful: you may gate out cellular debris and aggregates (using FSC and SSC channels), then compensate for channel bleed-through, and finally select only transfected cells before actually looking at the parameters you're interested in experimentally. CytoFlow implements a workflow paradigm, where operations are applied sequentially; a workflow can be saved and re-used, or shared with your coworkers.

Easy to use. Sane defaults; good documentation; focused on doing one thing and doing it well.

Good visualization. I don't know about you, but I'm getting really tired of FACSDiva plots.

Versatile. Built on Python, with a well-defined library of operations and visualizations that are well separated from the user interface. Need an analysis that CytoFlow doesn't have? Export your workflow to an IPython notebook and use any Python module you want to complete your analysis. Data is stored in a pandas.DataFrame, which is rapidly becoming the standard for Python data management (and will make R users feel right at home.)

Extensible. Adding a new analysis module is simple; the interface to implement is only four functions.

Statistically sound. Ready access to useful data-driven tools for analysis, such as fitting 2-dimensional Gaussians for automated gating and mixture modeling.

Sound like your kind of thing?  Join us.

1 Comment

I deeply appreciate good design in data visualization, and this jumped out of my news queue today.

Conflicting views: Public versus scientists

I'm not going to comment on the content, except to say that for the most part I align myself with "AAAS scientists" -- no surprise, right?  But imagine, for a moment, this data presented as a bar graph: "public" in red and "science" in blue.  Doesn't this do a much better job conveying both "magnitude" and "difference"?

All living organisms face the same problem: their DNA is much longer than their cells.  If you took the DNA from a single human cell and stretched it all out end-to-end, it would be about 1 meter long!  Not only do the cells have to fit all that DNA in there, they have to be able to access it - to transcribe it, to copy it, etc.

Prokaryotes and eukaryotes solve these problems in different ways (as you might expect: remember, one of the ways prokaryotes and eukaryotes are different is that prokaryotic cells don't have a nucleus.)  Prokaryotes solve the problem by supercoiling their DNA: imagine taking a piece of rope, pinning down one end and then twisting the other.  Eventually the rope starts wrapping around itself; and as you continue to add twists, the wrapping gets tighter and the end-to-end length gets shorter.  Prokaryotes have a set of enzymes that supercoil DNA to pack it tightly, and another set that selectively uncoils it when it needs to be accessed or copied.  Many of these proteins are present only in prokaryotes and not eukaryotes, which makes them a good target for antibiotics.

Eukaryotes solve the problem differently, wrapping their DNA around tetrameric protein cores called histones into a 10 nm-wide fibre that, close up, looks like "beads on a string."

DNA beads on a string.  Image: Figure 31-19, Biochemistry, 6th ed, Stryer

These chromatin fibers are further squeezed together into higher-order structures, the sum of which is called chromatin: the gooey mass of DNA and proteins that together hold each cell's genetic information intact.  Far from being random, these higher-order structures form something akin to a fractal globule, a self-organizing structure that achieves tight packing without becoming knotted.  Oh, and it's quite visually striking too:

Fractal globule genome.
Fractal globule genome. Ashok Cutkosky, Najeeb Tarazi, Erez Lieberman-Aiden, via BioTechniques

 

Two things to note.  First, the fact that the DNA reproducibly self-organizes at this level explains the phenomenon of DNA transregulatory elements, where a spot on the genome regulates gene expression at loci many millions of bases away: just because they're distant in linear "genome" space, doesn't mean that they're far away in actual space.

Second, genome architecture provides another layer of regulation for gene control.  Some parts of the DNA hairball are open, accessible for transcription (these genes are "on"), and some parts of the DNA hairball are closed, compacted, inaccessible (these genes are "off").  What I find particularly wacky, and what got me thinking about this in the first place, is that these structural changes seem directly related to cell type.  That is, the DNA in a skin cell and a liver cell may have exactly the same sequence, the same genetic "program", but because the DNA is arranged differently different parts of the program are "running."

And yes, this means that if I could take a skin cell and change the parts of the DNA that are on and off, I might be able to make it into a liver cell, or a brain cell, or a heart cell.  This is one of the hottest areas of regenerative medicine research right now.  Soon, if you get hepatitis and need a new liver, you won't have to wait for someone to die and take theirs -- you'll donate some skin cells (or some fat cells) and three months later you'll have a new liver (well, some liver-like tissue) waiting for you in a jar.

This is also (one of) the reason(s) why biomedical science didn't end when the human genome was sequenced.  (Not that it's finished, even a decade after it was declared finished.)  Not only do we still not know what all that DNA does; there are several layers of regulation that determine whether a piece of genome is active or not, and sorting out all those relationships will provide graduate projects for a long time yet.