Reflection on Journal Club

WHOOO done with journal club!! :DD

Honestly though, I really enjoyed it. The paper I chose was on constructing logic gates in biological systems. Logic gates in synthetic biology was an area I have been interested in for a while, but haven't had the chance to explore it. The journal club presentation helped me figure out what logic gates in biology look like as I dug deep into how logic gates are constructed in E. Coli.

I'm also pretty lucky that I don't get too nervous before a presentation--speech and debate in high school really drilled the nerves out of me. Giving a scientific presentation is a different beast than debating a particular issue though--a complete understanding of the paper is a must. Can't BS your way though a journal club presentation like you could when arguing about US policy in Iraq.

The best part of the whole thing was definitely the audience. It was great presenting in front of people that really care. You know that feeling when you're trying to talk to someone when you're trying to say something, and they just keep texting? Or when you're trying to present, and you get this:

Not so in 109. Before the presentation, I was really apprehensive about the Q&A. What if someone asks me something I can't answer? But after the presentation, I realized that I really appreciate the questions. Even if I don't know the answer, the questions show that people were listening to what I've been preparing for a while. Plus, we all have the same goal: trying to understand and appreciate the cutting-edge research. No more fear of journal clubs. They're actually quite fun and enlightening.

Lack of sleep + anything = a bad combination. The importance of developing an assignment filter in order to apply all-nighters* effectively. “Why must life be so hard?”

* My definition of all-nighter is anything less than 3 hours of sleep between the hours of 12 and 8:30am.

First we’ll state a fundamental rule for having a productive day (adapted from an Amazon product description): sleep is important. More specifically, 7-8 hours nightly is good. Good? If that is considered good, then I don’t want to know how far below good I am on this spectrum.

Anyways, I’ve been 'pulling all-nighters' for the sake of purely turning in an assignment on time. The simple solution to this is properly managing time and progressively working on a project as the days go; I knew this, but I was finding it very difficult to motivate myself wrt academics.** Assignments were stressed over accordingly to the order in which they were due with no consideration at all of sleep required to perform well for the next day/major assignment due. The last few weeks were littered with these cycles. For example, an all-nighter token*** was spent on completing and polishing my module 1 lab notebooks (1.67%) which resulted in underperforming in or missing class the next day. Somehow something was then phosphorylated and another and then something happened down the signaling cascade (320 got in the way again) resulting in a clueless Xander who had no idea how to start and finish his methods (5%) and data and summary (15%). 

** I don’t want to say too much on here, but I’ve felt my worst in these last few weeks. Ask me about it when you get the chance after buying me dinner or snacks, and I just may tell you. There’s a lot of action and drama in the story. so light-weights beware. 

*** It’s fun to think of all-nighters as currency you spend from your end total pool of sleep.

Then the next week followed with using 5 all-nighter tokens on a design project (15%; honestly I chose to take my time with this project since I really enjoyed it). I just wanted to keep working at a steady pace and time just slipped passed me. After the assignment was turned in, I was faced with another all-nighter for my (self-declared) well-known, notorious journal club presentation (10%). It’s all in the past now, and I have a second chance to redeem myself come next presentation.

To sum everything up: perspectives have shifted for the better, going to sleep more, I’m happy to be alive, MIT is hard, and I will no longer have any all-nighter tokens by the age of 22.

Revisiting Scientific Writing, and Not Much Has Changed

Last week, I worked tirelessly writing the abstract and data summary for Module 1. I really wanted to turn in a quality piece of work, so I canceled plans, skipped sleep, and even punted a 20.320 pset to get it done. And where was I when I turned in the final product? Frantically making last minute changes before submitting it 30 minutes before the due date. Submitting the final product felt great, but I sacrificed a lot of work in my other classes to get it done. Now, I'm playing the catch up game while I take a short break from 109.

As a science fair person in high school, I've had my fair share of writing research papers. But the criteria for writing a good paper are so much more intense here. Back in high school, I had to make it a point to clearly delineate my hypothesis, control, variables, materials, and everything else on a poster board. The paper was an afterthought; something the judges can casually glance at. I thought I was really prepared for this class, as I have already done research writing in the past. I could not have been more wrong...

Back in high school, I had trouble fulfilling page requirements for papers. I developed a tendency to be the opposite of concise: I like to repeat things, add unnecessary colloquialisms, and repeat things. This tendency carried over to my writing drafts in 20.109. My most common form of feedback involved cutting out extraneous information. I cut down my original methods section from a 2.5 page beast to only a single page. To be fair, I still don't know my grade on it. Hopefully, downsizing my methods section did more good than harm.

Thinking back on it, it's not too surprising that I'm having a lot more difficulty in this class than I thought. After all, I put the majority of my high school science efforts in getting those posters done. Writing my first serious research papers in 20.109 has been quite humbling. I've had so much experience in science communication in the past, and I felt like a complete newbie when writing method and figure drafts. These past few weeks, I've learned more about science communication than I have during my entire science fair career in high school. On one hand, it's been a real struggle to improve. Even now, I still find it difficult to grasp the Module 2 paper- we have to write another 15 page monster??? But it has also been deeply rewarding, knowing that I am learning at a really fast pace. Even if that means a lot of short-term stress, I really do believe it will pay off in the end. That's what all the MIT graduates say, right?

Anyways, here's the poster for one of my science fair posters during high school. In hindsight, it is a truly hideous piece of work. But I was able to present it at a national convention!

Figures, a Story, and Working Incrementally

First, for some positives!

Writing the module 1 report took about 30 hours (and the entirety of my birthday). Nonetheless even as I was writing it, I felt the value inherent to 20.109. With every assignment, I can feel my technical communication skills improving. With goals of clarity, conciseness, and thoroughness constantly in mind, 20.109 is a rigorous yet well-focused class in teaching strong scientific communication skills.

Similarly, I thoroughly enjoyed delving into the literature to understand similar experiments and see how our experimentation relates. Dr. Samson’s multiplexed assay for DNA repair was particularly fascinating and I enjoyed reading about its development and validation. 

This holistic approach to data analysis has been challenging, but it has also pushed my understanding of the inner workings of each of our experiments. A classic example is the need to consider positive and negative controls when evaluating any experimental system. For example, a double digest will provide little information if the single digest cases did not work.

The difficulty of finding a common thread/story

The major challenge of scientific writing came, not in understanding individual experimental conditions, but in synthesizing different conditions to draw conclusions from an experiment and synthesizing different experiments to draw conclusions about the overall system. The ability to find and communicate a common thread in the experiments of Module 1—from the D32N-EGFP construction to the transfection of pCX-D32N-EGFP and pCX-D32C-EGFP into mouse embryonic stem cells—was truly a challenge. The large amount of information we learned and the amount of data we collected was difficult to manage, especially under time constraints. In order to handle this challenge more effectively for the next module, I plan to:

1.      Process all data first.

2.      Complete all figures, starting with data-oriented figures

3.      Draw conclusions from the figures

4.      Write a brief outline to understand the “story” underneath my scientific paper.

5.      Use the outline and conclusions to select details which are important to include in the introduction and implications/future work sections.

On the time spent creating figures

For the module 1 report, the creation of figures took about 16-18 hours, spread across an entire week. I am not sure if this amount of time to create figures was consistent across students but the time consumed by figures made writing the rest of the report difficult. For future modules, I will process/analyze data and create figures as soon as the data is available to us. That way once we near the module report deadline, I can focus on the overall story rather than data compilation and analysis.

I am so so so thankful that the FNW helped us begin on the Module 1 report. At the time, I didn’t understand the importance of starting the figures early. Oh how wrong I was.

For module 2, I plan to start the entire report earlier. By compiling and analyzing data as soon as I receive it, I hope to fully understand the experiments as I perform them. Such an understanding should also help cut down on the amount of time spent near the end of the module.

Looking forward

Overall, I am thoroughly enjoying 20.109. However given the junior year courseload, I need to find more effective ways to manage my time overall so that I can succeed in both 20.109 and all of my other courses. Working 

The big picture

I'm a big picture person.  I like to know why what I'm doing is important and what impact it may have someday.  I think this is ultimately why I chose course 20 as a major.  I like the idea of designing new biological systems so that in the end society benefits.

Unfortunately, I've found that this makes me mostly uninterested in pursuing research as a career.  Often times the result of what I'm working on is too obscure to have scientific significance for another century.  This summer I was working in a neuro-differentiation lab in Brussels.  My project was to design biological probes to mark a newly discovered type of progenitor cell in mammalian spinal cords.  After months of sitting in the lab waiting for centrifuges to finish, PCRs to go to completion and making slides in the cryostat, I had my probes.  I could look in the microscope and see the little spots where my probes had landed and marked our target.

It should have felt successful, should have been rewarding.  I had made something new that no one else had tried to produce. I help to advance the study of spinal cord development.  But in the end, it really didn't make much of an impact.  The cells we were studying aren't well classified.  It will be at least a decade before we really understand their function.  After that, it will be much much longer before the research actually makes an impact on health.

In 20.109, I'm conflicted about the projects.  I liked looking into how to make an EGFP homologous recombination assay because of it's potential for screening in the future.  It's interesting to learn how current biological assays actually can make a difference in health and how we study disease.  Looking forward, I imagine I'll be significantly less invested in the bacterial photography module.  While it's kind of fun, it's hard for me to imagine a practical use.  If I can't see the big picture at the end, somehow the details don't seem to matter.

20.109 Mod1 Report: The balancing act

Image credit: Left (http://www.jaykubassek.com/perspective/a-balancing-act); Right (http://www.diabetesmine.com/2013/02/ask-dmine-the-meaning-of-deviance-in-diabetes.html/balancing-act-elephant).

With anything new comes a learning curve. In 20.109, the curve was definitely steep for me, mostly in the written assignments. Each time I sat down at my laptop I felt as if I were that elephant trying to stand still on a beach ball, or perhaps balancing on the back legs of my chair. Although I still consider myself to be deep in the depths of this learning curve, throughout Module 1 I learned a thing or two about this so-called balancing act.

1.       Balanced Time: A little here and there pays off big in the long run.

a.       Although being short on time is nothing new to me as an MIT student, the difference with 109 is that there are a lot more subtleties to the assignments than with most classes I've taken before. It is very nice that the instructors of 109 help us realize this rather than letting us learn the hard way. Even still, I have spent more hours than I thought I would on 109 assignments; I have had to creatively fit 109 into my busy schedule each day. I have really benefited from the mindset that even 15 minutes is enough to make at least some progress.

2.       Balanced Writing: Can you be concise and thorough at the same time?

a.       Although concise and thorough are somewhat antonyms of each other, the long story short is yes. This is science, not literature and here the art lays not in the details but the ability to be blunt, concise and readable. Or better said, it is an art to be able to produce blunt but very coherent writing.  

3.       Balanced Experiments: It’s not science if you don’t have controls.

a.       In the first module of 20.109, something that has stuck out to me is the importance of controls. I came into this class definitely knowing what a control was and knowing that controls are important to have, but I have never put as much thought into my experiment controls as I have with 20.109. By the end of Module 1, I feel that I have a thorough appreciation of what constitutes a necessary collection of controls, and why they are so valuable. It’s important to have controls at every step for efficiency, legitimacy, peace of mind and/or unforeseen backtracking.

Overall, I love that in 20.109 we learn the theory behind everything and very little is left as a black box. If anything, there are some gray boxes, but the vast majority of techniques we apply in the lab are backed up with sufficient theoretical knowledge. Knowing the theory behind almost everything we do allows for us to produce quality work. Although it might still feel like a balancing act to communicate this work, I feel that major progress in scientific writing has been made throughout Module 1.

Conciseness, Conciseness, and Conciseness

My first draft of the M1D1 – D2 method section took up 2 entire pages with detailed explanations of how we obtained the pCX-EGFP sequence from which website and a huge table showing how much enzyme was used for each digestion reaction. In my final methods summary, I was able to fit in 5 days of lab protocol into less than a page and a half. My first attempt to analyse the recovery gel image listed every single lane individually and almost made an 800-word essay. In my final report, I was able to combine similar results and retell essentially the same story in 6 bullet points.

So what’s going on here?

I was never worried about writing assignments that have a minimum word count requirement. I mean, even though I am not a particularly talented writer, once I start writing, I can write a lot. I was trained to be a detailed-oriented programmer before “converting” to course 20 (another blog post to follow), so I am extremely cautious about conveying the technical details in any form of written or oral communication. For example, I wrote a lengthy tutorial about hosting your website online (http://sleepy-retreat-4174.herokuapp.com/blog/detail/1/True/) which contained every single piece of information you could possibly want to know. Now, as I look back at my most “proud” work after finishing the first module of 109, I feel deeply uncomfortable.

The most important lesson 109 has taught me so far is to communicate concisely. I realized that the three golden rule of communication is conciseness, conciseness, and conciseness. If three words can do, never use five. Just write no more than I should. Writing is a two-way process – choosing the right level of detail is showing the readers the respect that they deserve.

Like everyone else (judging from the high frequency with which “sleep deprivation” appeared in Mod1 blogs), I sat by my laptop 12 hours straight right before the report was due. But I was glad that the majority of that time was devoted to condensing lengthy expressions and getting rid of unnecessary details. The final report is still 12 pages long, but at least I tried my best to cut it down from 14 (not by reducing font!). I am sure my report is far from perfect, but now that I realized my tendency to ramble, and that I am making slow progress to correct it after receiving useful feedback from our beloved instructors, I believe I can do a better job in Module 2.

A few other goals for the upcoming challenges of 109:

1) Gain an understanding of the big picture: only until last Tuesday did I realize that we can actually use the assay for a variety of other applications like cancer screening – I need to better connect labs and lectures!

2) Discuss with my teammates more often: we shouldn’t start the discussion only at the last minute. Each time after lab, we should make sure everyone’s on the same page – that will also help each other do better in lab quizzes! Bria and Ashley, you heard me!

3) Start work early: yes, literally now! Mod2, here I am. 

Planning Ahead

I actually enjoyed writing the Module 1 Summary, in a sadistic sort of way. I was in the same boat as everyone else, having started the assignment later than I meant to because of that super fun 320 pset that was due on Friday. But I was weirdly excited to dedicate Friday and Saturday to Microsoft Office - special shoutout to PowerPoint, my new best friend. I started by making the figures and had a great time pretending to be a graphic artist, until I realized that my OCD had kicked in and I'd spent a solid half hour trying to perfectly match up two half circles (harder than it sounds, I swear).

The struggle began when I had to make the switch from Powerpoint to Excel. Excel and I have never gotten along; whenever I figure out how to graph something on it, I'll follow the exact same steps the next time and it won't work. Things started out well, I calculated all the averages and standard deviations, figured out confidence intervals, and then settled in to the chart making. I'm not going to say how much time that took because it's slightly embarrassing, so let's just say that eventually it worked and I was very happy, until...

www.lol-rofl.com

It was a sad moment. Luckily it didn't take me as long as I thought it would to recreate what I had lost, and soon I was ready to start writing. 

It was as a I planned out my approach to the summary that I promised myself to never complain about the FNW assignments ever again. Throughout the module I didn't fully understand the necessity of creating rough drafts of figures, parts of the summary, or even turning in the methods section separately, as that tended to add extra stress to already stressful weeks. However, having already completed the methods section going into the summary writing, I had a much better understanding of why we did each step, which made analyzing the results that much easier. The feedback on the figures we'd made for the gels after M1D2 were especially useful in deciding what information should be included in figure captions, and what was unnecessary. 

I'm really grateful that the teaching staff encouraged us to keep a lab notebook and made us start preparing for the culminating module summary through the FNW assignments; it made organizing and writing the paper so much easier than it would have been otherwise. Going into Module 2, I'm going to make sure I think ahead to the final assignment - and maybe start the battle with Excel a couple days earlier.

Well that happened….

Coming into 20.109, I felt that I had gotten a decent warning about how much work it would be, but still it didn’t prepare me for the DNA engineering summary that I (literally) just finished. I am a procrastinator by heart and although it’s come back to bite me in the butt a few times, generally it’s a successful way of life for me. Procrastination, however, means that it’s not the figures or analysis that’s the hardest for me; it’s just the motivation to get started in the first place. I wrote my methods section in around 24 hours and although it was tiring, I felt that I had devoted a good amount of time to it. With that mentality and also knowing this assignment was longer, I decided that 50 hours of time would suffice and therefore I would begin working on the data summary on Thursday afternoon. It is also important to note that I am traveling for the weekend, so I spent 9 hours of Friday sitting in an undersized Megabus seat. For some reason, it never dawned on me that perhaps given the circumstances, I should start the assignment earlier. Alas, my procrastination took over and I progressed through the assignment at a much slower speed than anticipated. As 5am on Saturday rolled around, I realized maybe a late day might be worth it. After giving myself this extra time, I was able to complete the assignment in a comfortable and non-stressful way.

There are definitely a few things I REALLY hope I’ve learned from this experience:

1.     Prepare figures and captions as the images and data become available; write a (very) rough draft of the results and discussion as soon as possible

2.     Complete edits and revisions as soon as comments are given back

3.     STOP PROCRASINATING.

Although I’m sure I’ll never truly embrace that last point, I’m hoping that I can at least adopt it for this class because I know it’ll save me a lot of anxiety and sleep deprivation in the future. Module 2 here I come!!!

Fun with module one!

        "Fun" might not be the most accurate description of writing the data summary for module 1. I just noticed that it rhymed in the title, so why not use it. Although working for a whole week straight without much sleep was not the most enjoyable thing in the world, it did teach me a lot of interesting things about writing scientific reports. I have written research reports before, but the longest one I ever wrote was with a partner for a science competition. This was the longest individual report I have ever written, and it taught me some very valuable lessons that I will try to follow from now on.

Lesson 1: Organize your data!
        If I had a nickel for every time I had to flip through several different papers to find the data I needed, then I probably could have had enough money to bribe someone to write my report (but I obviously wouldn't do that because I am an honest student). My point is, don't just shove your data somewhere and forget where you put it. You could save SO much time if you didn't...seriously. Organization is key, and I definitely overlooked that a bit until I got started crafting my results section.

Lesson 2: You cannot just "write" a research report. You have to develop it.
       Writing a good research report doesn't just involve having all the data and describing it. It requires making the results flow so that it makes sense to the readers. My strategy going into the paper was to first put all the data down and describe the data. After doing that, I realized that I made a huge mistake.

Not only was my data out of order in certain places, the paper just made no sense when reading it out loud. As the person who wrote the report, I couldn't even understand what I was trying to explain. This lead to many stressful nights, as I had to go through all the data and organize it so that it "flowed". Pro tip for future Mike Chen: make sure you know how and where you want to place your data before actually doing it. It will save time and effort.

Lesson 3: And that hardest part about the data summary was.....Being confident with your writing!
       As I started the paper, I did not even think that the most difficult part for me would have been just trusting my writing ability. Because the paper was worth 15% of my grade and I only had one chance to turn it in, it definitely was a little nerve-wracking to write the data summary. I second guessed myself way too much, and that cost me the most amount of time compared to any other setback I had. I was afraid of writing something that was incorrect or interpreting data in the wrong way. I think the better alternative would have been to outline the paper first before writing it. That would have taken away a lot of the second guessing that I did because it is easier to organize my thoughts in an outline first, so I would have been more confident when I actually wrote the paper.


Finally, with Module 1 complete, I can maybe relax for a day or two; get my head straight again. I am very excited to learn more in module 2 and to apply the lessons I learned in module 1.

Until next time, peace

One DNA engineering summary coming right up

     Module one taught me what it really means to be an engineer and a scientist. It also showed me why MIT is so hard, and the amount of work one can do in a straight set of 24 hours. When I began this class, I was merely an pile of facts and protocols. Of course, I could apply my knowledge to tests and experiments, but what I found out between three and five in the morning on Friday, while knee deep in what has been my most challenging assignment so far in college, was that I could not yet effectively communicate my knowledge. I currently am only half a true scientist.

     Working on the DNA engineering summary, I saw that my focus in the class had been skewed. It is not so much about understanding the protocols used and the experiments conducted (although it is essential to know these things) as it about learning to communicate to others the significance of this knowledge. If I had had this epiphany earlier, then I would have always kept my ultimate engineering summary in mind while carrying out my work. In retrospect, I should have been making as many of the figures as I possibly could for my summary before it was even officially assigned. This probably would have helped me save some time so that I could devote more time to data analysis and writing in general after finally obtaining data from the flow cytometry assays. And, maybe I would not have had to pull an all-nighter (although it was kind of fun since it was my first one, and now I feel like a real engineering student).

     

     As I continue with this class the main question I will ask myself is: How will I relate to others what I am doing now? (or, in context, how will this fit into my next big assignment?) Overall, this past module was an exciting roller coaster ride, and the summary (now that it is actually done) was fun. Now, I look forward to tackling Module 2 and becoming a well-rounded scientist.

   

When "Make it work" didn't work

As I’m guessing is the case with most of my 20.109 comrades, this is my first real lab class at MIT. While I’ve worked in labs for a while now, both outside of MIT and through UROPs here, those experiences certainly did not capture what I’ll call the “thoroughness” of this class. Sure, I’ve had to produce write-ups on my research, maintain lab notebooks, and more, but I’ve never really been taught how to efficiently and effectively do these things. Boy, do I know now.

I feel like my “tagline” on completing assignments so far at MIT has been, quoting the ever-eloquent Tim Gunn, “Make it work!” (Project Runway may or may not be a guilty pleasure…). All of my technical classes thus far have been pset and exam-based, for which “Make it work!” has worked! But Tim Gunn, sorry to disappoint, “Make it work!” simply won’t work for 20.109.

www.priorfatgirl.com

No more can I start assignments frighteningly close to the due date, in hopes that bucket-loads of caffeine and blasting motivational Eminem songs will get me through mountains of assignments and studying at breakneck speed. Unfortunately, I began the abstract and data summary thinking that strategy would still get me through.

giphy.com

Woops. I started off creating a hodgepodge of figures, including both schematics and graphs, only to realize later on that I wouldn’t end up using many of them anyways. After some fuddling around with those figures and writing out captions with no clear purpose, I decided to take a step back and map out my results section. Even at that stage, I struggled to make sense of all the data I was looking at. What finally helped me shape my thoughts was to take a break and write a background, solidifying the overall purpose of module one and directing my results section towards a clear goal. I then actually benefited from a mistake I had made earlier on during module one – what I’ll call a blessing in disguise. The initial draft of my module one schematic had been much more detailed than it needed to be, but eventually I followed this version to get a grasp on the order of data in my results section. I was definitely appreciative of having written the methods section already, and plan on doing so with future reports, as it helped me understand the reasoning behind each experiment we performed. Next up were the implications, a section that made me appreciate what we’d been working on, beyond how “cool” I thought each individual method had been. Writing this section made me wonder whether the therapeutic applications might actually show up in clinics sometime in the near future!

Finally, I was left with the abstract. While I normally dread writing abstracts given my experiences with them, I found that everything just came together as I tried to put my thoughts down. As much as this report made me suffer, mainly because of my own procrastination, I realized that the whole process forced me to gain a level of understanding that truly got me excited about the module and made me appreciate both the steps in experimentation and their implications, which is probably what made the abstract flow much more easily.

Now that Tim Gunn is dead to me (not really, you can’t not love the man), I am ready to embark on a new life of zero procrastination, eight hours of sleep per night, and zero coffee intake. Just kidding! Anyone who manages to do that at MIT is a wizard in my book. But I will definitely start any writing for 109 much, much earlier, and also write more efficiently, by mapping out my thoughts, and probably getting the big picture on paper from the get-go. 

Once upon a time...

My friends always tell me that I'm the worst at telling stories. I start from a odd point, end up making big leaps ahead or back because I can't remember if I mentioned certain details, and so the listeners just get confused or don't notice that I've reached the end. So when we were told during the introductory lecture that we were going to "tell stories" in 109, this is exactly how I felt:

I have written many papers before, and writing about something outside of my area of interest (like that one 10 page report I had to write on experimental Balinese music) has been pretty rough. But I was ready for 20.109! How hard could writing about your own field be? You just describe the experiments and report your results, right?

Well it turned out to be slightly more complicated than that. As our instructors mentioned, good scientific papers told a story; they used the IMRAD (introduction, methods, results and discussion) format in a cohesive manner, making transitions between sections smooth, avoiding repetition, and keeping things interesting until the ending (discussion) section. It was capturing that flow that was most difficult for me as I wrote our result summary for Module 1.

The great thing about 20.109 is how prepared I felt by the time I sat down to write my summary. The first draft assignment was great exercise, and the feedback I received on it gave me a good idea of things I had to watch out for in general. But when I decided to use my draft as a starting point, I made the mistake of starting the story from the middle.

I wrote out the rest of our experimental results, and prepared (and numbered) all my figures, and finished the results section. What I didn't take into account was why anybody would care about these results. So I started writing my "background and motivations" section, coming up with a useful application for the assay we developed in class. But when I reread my results section again, I noticed that the results I had presented didn't have anything to do with the motivation I suggested. In fact, they didn't seem to really point towards any kind of general conclusion at all!

And so, around 1AM Friday morning, I decided that I needed to reinterpret my data and rewrite a large portion of my results section. I somehow managed to do all of this in time, but I definitely learned the importance of developing a general flow -or story- for a scientific paper before starting any of the subsections. The data analysis we make had to be meaningful within the context of our proposed purpose.

I never thought I'd learn the ways of good storytelling from a lab class, but 20.109 has been full of great surprises so far.

Well it's finally submitted

I think it was at about 11 am Friday morning when I realized exactly how far down I had fallen.

I was decked out in sweat pants and a sweatshirt; my baggy eyes and a thermos of coffee in my hand were indicators to the general population of MIT that we had a rough week in Course 20: Module 1 methods, that 20.320 problem set, the abstract and data summary. The past 72 hours have taught me that this class is not one that anyone can afford to procrastinate in. I’ve also learned that organization is essential to this class.

The last time I was in any sort of lab setting was, no joke, 11^th grade AP Chemistry. I was pretty terrible at staying organized throughout Module 1, partly because I prefer writing on paper to keeping word docs and partly because I had no idea of the troubles to come. The data analysis and summary section took me much longer than it could have if I had been more organized about keeping in depth lab journals and resulting data all in one place, rather than flipping through my little notebook every time I needed to check something.

It’s not just about organization throughout the module, though. When I was writing my report, my first approach was to randomly start assembling figures I thought were at all relevant to the module and write up their captions. It wasn’t until later that I realized the degree of intricacy involved in planning out discussion sections that match with what the figures show. I ended up having to tailor my figures and captions to the demands of my discussion sections. I feel like it’d have been much easier to plan it all out first in a general outline before making my figures.

That being said, I’m impressed with everything we’ve managed to get through so far. Seeing it all laid out in my abstract and data summary gives a certain sense of accomplishment, and I’m sure I’ll appreciate that even more tomorrow when I wake up from hibernation.

reflection on the first major assignment

Once upon a time, a girl tried to write a data summary paper for her bio-engineering lab class, and very much underestimated the time it took to analyze her data, make her figures, and actually write relevant and concise information into about 12 pages of paper.

That girl was me.
I started three days before the assignment was due with my figures thinking that that was going to be the most time consuming part. Making the figures wasn't all that bad, I might even say I enjoyed it because it was a creative break from the usual psetting and problem solving that most of our homework entails. The second to worst part was making the figure captions, the worst part was my OCD forcing me to spend way to much time on unnecessary parts of the figures.
With two days left, I started my data analysis, effectively teaching myself how to use Excel, which was struggle because I'm not really what you would call computer savvy. So naturally that took me quite a while but I thought with one day and night left, I was sure to be able to do all the written portions no problem.
I was wrong.
24 hours later, and 4 hours of sleep later, I was still unfinished with my final paper and decided on taking a late day because I was sure with more sleep and time I could do a much better job than what I had done at that point.
After taking some of the pressure off I got some sleep and went through each section at a comfortable pace for me. The easiest part of the paper to write was background and motivation because I think that is the most interesting and relevant part of any research that people do, it is the answer to why is this important. I found the data sections to be grueling especially because I wasn't exactly sure what was relevant and not common knowledge enough to put into their based on figures as well as the data analysis that was done.
Pro-tip for future Katharina:
Start earlier, take better lab notes, and plan more.

looking before leaping (or running statistical analyses)

It’s the job that’s never started as takes longest to finish. Intent on completing the data summary long before the deadline, I mustered up some willpower and set to work. By the following day, I had color coded graphs, supportive statistical data, and captions. But something didn’t seem quite right. Had I analyzed the data correctly?

My questions were answered on Wednesday during office hours. It became evident that my hurry to finish this paper had led to errors through including particular data sets; I’d gotten a little ahead of myself. But no worries, I told myself. No problem. I’d just delete this data cluster, compare these two columns, and I’d be done. So I started (again).

It was only after relabeling the graphs and rerunning the statistical analyses at around 3 a.m. that I realized another thing I’d left out. The negative controls. I’d forgotten the negative controls.

So to say the least, I spent the majority of the later part of the week reworking graphs, rewriting captions, mulling over possible explanations for the results- whatever time wasn't spent on this was dedicated towards imbibing coffee.

My most major problem was a lack of forethought (how much of this was due to sleep deprivation- of that, I'm not certain); imprecise planning led to more work than necessary. It would have been much better to plan out what I intended to convey to readers in this study prior to processing data.

In the next module, I intend to follow a more organized method for tackling data summaries. What groups should I be comparing to each other? What kinds of data I want to include for the experiment to be more understandable? I think these aspects are a little clearer now, and I’m looking forward to making these changes in the next module.

The art of explaining

Never had to pull an almost-all-nighter and skip all of my classes to turn in a report, but it had to be done. The satisfaction of turning in the completed Mod 1 report (and a long nap afterwards) was worth it.

There was one part that I really struggled with though, and it took me forever to finish. There was an explanation that I thought would explain most of the data. Excited about it, I wrote on and on about it in the report, spelling it out in painful details. I thought it was crystal clear and made perfect sense. I asked the BE writing people to take a look, and lo and behold, they were confused. Absolutely befuddled.

I realized that my biggest challenge was how to explain a concept clearly but concisely. How do you make sure you've said enough for readers to follow your reasoning, but not so much that they get lost? How do you emphasize a point without being repetitive or going in circles? That was my big struggle. I did my best in the report, and I hope it at least makes some sense.

So far, I've realize that the organization of the explanation is most important: if it's written in an order that follows the logic, the reasoning makes a lot more sense. I tend to delve into trying to explain what's in my head instead of trying to map out my argument step-by-step before writing it all out. Besides that, I'm assuming that practice makes perfect... but other tips that people have would be greatly appreciated!

It's high time we catch up on sleep; see you guys next week, hopefully without the aid of coffee.

A Journey of a Thousand Miles...

          In chapter 64 of the Chinese classic text, Tao Te Ching, ancient philosopher and sage Laozi quips, “a journey of a thousand miles begins with a single step.” How inspirational! That is, until you realize that your average step is about 2.5 feet long, and a mile will take you approximately 2000 steps, which means in your journey of a thousand, you still have 1,999,999 steps left to go. That is why, even after inputting the activation energy to begin the abstract and data summary for Module 1, I had to dig deep within myself and find the mettle to finish the rest of the race strong.

          What I found particularly helpful was to break the assignment into parts. I first made every figure and schematic that was relevant to the project. (I have never used Microsoft Paint this much in my life before.) Then, I went back and wrote the bullet points for each image and discussed the significance of its results. At the end, I wrote my conclusion, and then introduction, last. For Module 2, I will continue to use this method for writing the full research report. I found this order to be particularly helpful for me, and I have diagrammed the reason why here:

Figure 1: Justin's function of Time vs. Fun for when he writes a scientific paper. Roller coaster car included to demonstrate concept of potential to kinetic energy.

          What I do know about myself now, though, is my ability to “tool₁” for hours and hours on end when I feel the pressure to complete a task. Given that I’ve managed to start the task at hand, I can set my mind to an assignment and do it to completion, with very little regard for sleep, food, sunlight, etc. Unhealthy, I know. But it works for me rather well. And now I must go on a caffeine purge.

1. For definition, refer to the verb form: http://mitadmissions.org/blogs/entry/speaking_mitese

A tirade on concision

Every person who has ever met me knows I'm talkative. My teacher evaluations throughout elementary and middle school usually went along the lines of, "Melodi is a great student, but she talks too much during class." I have grown and matured a lot since my primary school days, but that love of talking has stayed with me throughout the years. And with Module 1, it became my downfall.

The drafts for my Module 1 methods and results sections were chock-full of redundant information because I couldn't stop myself from writing. Looking back, it's almost as if for every place there should have been a period, there were 10 more words instead. Clearly, this habit is not one that is conducive for scientific writing.

Therefore, the most difficult part this module for me was not creating figures, doing statistical analysis, nor performing the experiments themselves, but rather deleting this extra information. This challenge was made harder by the fact that I have a fairly Type A personality and was constantly worried that every time I tried to shorten a sentence, I also deleted some crucial information about the experiment that was important to the paper. I attended almost every office hours that was held during the paper-writing parts of this module, and each session consisted of Shannon and Noreen showing me how to cut out words. It seemed like most of our meetings went something along these lines:

The most frustrating part of these meetings was that I usually could see their comments coming. I could tell that many of my sentences were superfluous -- it's why I went to office hours in the first place -- but without someone there to confirm my suspicions, I couldn't manage to get rid of anything. Whether that was due to fear or due to my innate tendency to keeping talking has yet to be determined. Going forward into Module 2, my goal is to learn how to top things off with a period.

Sleep Deprivation, Painful Exams, and Dumb Mistakes- Reflecting on the Past Week

Right now, I am writing this post at 4 AM. But why would anyone choose to stay up this late? Well, this past week has been quite eventful. I just survived my first "round" of assessments; a period of time in which concentrated exams, papers, and essays are all due for my classes. The good news: I survived! I can say that I am happy with the work I've put in on exam prep & writing. The bad news: my sleep cycle is completely devastated. Which is why I am still up at 4 AM after trying (and failing) to sleep for the past couple of hours.

What can I blame my insomnia on? The built up stress of so many classes, extracurriculars, projects, UROPs, and trying to balance it all? Nah, that's an everyday challenge. The root of my temporary sleep issues is something much smaller: the recent 20.320 exam. Most of the questions on that test are engineering-based, which involves a lot of data interpretation, model building, and equations. Four questions in 90 minutes: let's go! What this meant to me is that I had to commit these engineering concepts to muscle memory. We learn big themes in 20.320, such as enzyme inhibition, signal cascades, and a variety of experimental techniques. The only things that change between the practice exams and the real one are the specific molecules and reagents in question: EGFR, Raf, p38, or whatever. Just use the same equations, but switch out the proteins, ligands, and inhibitors! For the Tuesday exam, I ended up studying from Monday evening to 5 AM the next day, sleeping for 3 hours, and studying again at 8 AM. All for a 9:30 AM exam. I can only thank the wondrous powers of evolution (or whatever force you may believe in) for the creation of the coffee bean.

This adenine-like substance is my Facebook cover photo for a reason. Man, what am I going to do without you?

But how does this all relate to 20.109? I want to talk a bit about something differing: making stupid mistakes. A few hours after my all-night party with 20.320 and the subsequent exam, I walked into 20.109 lab feeling strangely energized. I walked right into that tissue culture room (M1D6) with a ton of confidence! Setting up the 17 reactions was totally easy, and I had a fun time laughing at my lab partner's shenanigans. As I began to clean the 17-well plate, I looked at the next few protocol steps in horror. "Pipette 95 uL of reaction into each well"? That totally doesn't add up. I only prepared enough to put in 50 uL for each well! And then it hit me: I forgot to add 50 uL of OptiMEM for the 17 reactions. Suddenly, all the aggregate exhaustion from the past 48 hours hit me as I completely lost my caffeine-addled energy and descended into a state of mental enervation. I cannot read protocols, I cannot follow protocols, and there's no way I'd be able to interpret statistical data from CometChip analysis.

Mistakes happen all the time, and sometimes it seems like life is just about learning from one mistake after another. With the help of our awesome TA's, (thank you Isaak!) Xander and I remade the reactions and completed our TC work without any further hitches. I learned to actually read protocols in advance instead of mindlessly skimming through them. And after I stepped out of the TC room, I made the decision to just go home and do the CometChip analysis when my brain recovered. After a really long nap. Speaking of naps, look at the time! I have a 20.320 lecture in about 5 hours. Time to submit this post and head to dreamland- good thing I took some melatonin. Let's hope I don't end up like Patrick for tomorrow's Flow Cytometry.