20.109(F13): DNA engineering summary

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(Hypothesis testing: DNA repair assay revised)
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**For repeated samples, does the class-wide data mark any of your own data as suspect?
**For repeated samples, does the class-wide data mark any of your own data as suspect?
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===Implications and future work: potential topics and figures<font color=red>revised</font color>===
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===Implications and future work: potential topics and figures===
*Topic: Based on the results, whether they matched your expectations or not, what experiments might you recommend next? Follow-up experiments could distinguish between competing explanations of a given outcome or broaden the sample set for a question you already asked, to give just two examples.
*Topic: Based on the results, whether they matched your expectations or not, what experiments might you recommend next? Follow-up experiments could distinguish between competing explanations of a given outcome or broaden the sample set for a question you already asked, to give just two examples.
*Topic: How might this assay be improved?
*Topic: How might this assay be improved?
*Topic: How might this assay be used as a research tool? In the clinic? In the pharmaceutical industry?
*Topic: How might this assay be used as a research tool? In the clinic? In the pharmaceutical industry?
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*Topic: If you could "see" any protein you wanted by getting the cells to fluoresce (that is, if you expressed a fusion protein such as actin-GFP), what protein would you pick and what would you predict the relationship would be between that protein and this assay? <font color=red>Actually realized I'm not sure what you're driving at here</font color>
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*Topic: If you were studying two cell lines that showed different levels of HR with this assay, what more could you learn by tagging BRCA2 protein with RFP (red fluorescent proteins) in the same two cell populations?

Revision as of 11:56, 16 August 2013

20.109(F13): Laboratory Fundamentals of Biological Engineering

Home        Schedule Fall 2013        Assignments       
DNA Engineering        System Engineering        Biomaterials Engineering              

Contents

Overview

The culminating assignment for Module 1 will consist of two elements: an abstract that succinctly describes your DNA engineering investigation, and a thorough summary of your data in figures and supporting text – including context for understanding the work’s broader implications.

The target audience for this report is a scientifically literate reader who is unfamiliar with your specific field. Thus, you can assume rapid comprehension – but not a priori knowledge – of technical information, and consequently should strive to present your work in a logical, step-by-step fashion.

Logistics

You will complete this assignment in pairs. Be sure to review the 20.109 statement on collaboration and integrity as you proceed.

Method of Submission

Please submit your completed summary on Stellar, with filename TeamColor _LabSection_Mod1.doc (for example, Rainbow_TR_Mod1.doc).

Be sure to review the class late policy (link)

First Draft Submission: Oct 10th/11th

The first draft of your abstract and summary is due by 11 am on October 10th (Thursday) or 11th (Friday), according to which day you have lab.

Professor Engelward will comment on your submissions and assign them a draft grade. Additionally, your section-specific writing instructor will give feedback about abstract structure and comprehensibility.

Revised Document Submission: October 29th/30th

Your commented first draft will be returned on October 22nd (Tuesday) or 23nd (Wednesday). You will then have the opportunity to revise your work for up to a one and one-third letter grade improvement. In other words, a C can be revised up to an B+, a C+ to an A-, a B- to an A, etc. ) The final draft is due by 11 am on October 29th (Tuesday) or 30th (Wednesday), according to which day you have lab.

Please re-submit your marked up report (with Prof. Engelward’s comments) so she can compare the old and new versions side by side. AND MAYBE? Please briefly highlight any substantial revisions to your text in the “notes” section of the slide. (For example, “this slide was substantially revised to clarify the figure and deepen the analysis.)

Late Policy Clarification

Penalties for a late draft are direct. A late draft that is not revised will have the penalized grade recorded. For example, a B paper that is one day late and not revised will be recorded as a B-. If submitted on time, the B paper could go up to an A+, while the penalized B- paper can go up "only" to an A.

Penalties for a late revision affect the maximum possible grade. A revision that is one day late can only go up one full letter grade, one that is two days late can only increase by two-thirds of a letter grade, etc. For example, a B paper that is not late can earn up to an A+, that is one day late can earn an A, that is two days late can earn an A-, that is three days late can earn a B+, and that is four days late cannot improve on the B.

Guidelines on Formatting and Length

We recommend that you prepare your document in a drawing program such as PowerPoint, using a portrait rather than landscape layout. This approach will allow you to create your figures with minimal hassle but maintain the look of a document rather than a presentation.

Core document length should be about 10 pages, and certainly not exceed 12 pages. Though somewhat variable, typical section lengths (including both text and figures) might be:

  • Background and motivation: ~2 pages
  • Data presentation and interpretation: ~5-8 pages
  • Implications and future work: ~1-2 pages

The first page of the document should include an informative title and author information (section/color/names); the second page should include just an abstract. These two pages will not count toward the suggested 10-12 pages. A typical context page will be enhanced by a supporting figure, though some might include only text. A typical data page should include a figure or two and its caption on the top half, and a few bullet points/short blocks of text interpreting that piece of data below. (Reminder: Your figure captions themselves should avoid interpretation.) More detailed suggestions for content (as opposed to style) are below.

Content Guidelines

Begin by reading the general guidelines for scientific writing. In particular, the sections on Title, Abstract, Figures, and Holistic View of Data are particularly applicable to this assignment.

A few prompts to get you started are below, but note that this list is not exhaustive and also that several elements could reasonably be included in more than one section.

Background and motivation: potential topics and figures

  • Topic: Why is measuring HR interesting and/or useful?
  • Topic: How does HR work?
  • Figure: Depiction of HR
  • Topic: How does the HR assay work?
  • Schematic: HR assay approach
    • You may prepare something similar to the assay depiction from Bevin’s lecture notes, but should NOT copy and insert it directly. Your goal should be to make a figure tailored specifically to this assignment and audience. What elements might be cut or added? How can you modify the figure to best highlight key takeaways?
  • Topic: What kinds of questions can the HR assay address?

Data: potential topics and figures

Figures and topics are listed below according to two major phases of your experiment. Within each phase, you should look for sub-groupings of interest, rather than treat each piece of data in isolation. In other words, try to both interpret and communicate outcomes holistically.

Keep in mind that you are describing the detailed methods in a separate assignment. Therefore, figure captions and/or supporting text should include only the most relevant aspects of the methods, such as the names of the diagnostic enzymes, a clear description of any normalization or statistics done on the flow cytometry data, etc.

System construction: making and verifying plasmid

  • Schematic: Overall approach
    • You may prepare something similar to the M1D1 Intro figure, but should NOT copy and insert it directly.
  • Figure: Gel of digested DNA prior to cloning
  • Figure: Recovery gel of purified, digested DNA
  • Topic: Apparent success of PCR, digestion, and recovery, including role of controls when applicable
  • Table: Colony counts after ligation and transformation
  • Topic: Apparent success of ligation and transformation, including role of controls when applicable
  • Schematic and/or Table: Diagnostic digest plan, for example in marked up plasmid map form
  • Figure: Diagnostic digest gel
  • Topic: Apparent success of cloning, explicitly including predicted versus observed sizes, extraneous bands, and role of controls when applicable

Hypothesis testing: DNA repair assay

  • Schematic: Overall approach and question(s) being asked
    • You may further modify the background section figure (or create a new one) to emphasize specific samples, if you did not do so before
  • Figure: Sample raw flow cytometry data from own experiment
    • at a minimum, include one FSC-SSC plot, as well as fluorescence plots for a negative control, a positive control, and one experimental sample
  • Topic: Rationale for gating choices, particularly on FL1-FL2 plot.
  • Topic: Understanding controls: What is the purpose of the negative control? The positive control? The two single plasmid controls?
  • Figure: Processed individual flow cytometry data (e.g., bar chart)
  • Figure: Processed class-wide flow cytometry data (e.g., bar chart and error bars)
    • please use a separate plot or dual-axis plot to keep the positive control from dwarfing the other data
  • Topic: Flow data analysis: for the sub-topics below, be as specific as you can be. When possible, use supporting statistics to make your argument.
    • In more depth than you may have described above, what can you learn from the positive control? If two groups have different GFP+ values (say, 60 and 80%), what does that outcome mean and how might it influence your later data analysis?
    • Did the outcomes for the single plasmid controls match your expectations? How might you explain any discrepancies?
    • Did the relative amounts of HR (both individual and classwide) match your expectations/hypotheses? How might you explain any discrepancies between outcomes and expectations?
    • For repeated samples, does the class-wide data mark any of your own data as suspect?

Implications and future work: potential topics and figures

  • Topic: Based on the results, whether they matched your expectations or not, what experiments might you recommend next? Follow-up experiments could distinguish between competing explanations of a given outcome or broaden the sample set for a question you already asked, to give just two examples.
  • Topic: How might this assay be improved?
  • Topic: How might this assay be used as a research tool? In the clinic? In the pharmaceutical industry?
  • Topic: If you were studying two cell lines that showed different levels of HR with this assay, what more could you learn by tagging BRCA2 protein with RFP (red fluorescent proteins) in the same two cell populations?
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