Team:BostonU/Characterization

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JUMP TO...<br>
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<a href="#bu-wellesley_wiki_content">Top</a><br>
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<a href="#CharIntro">Introduction</a><br>
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<a href="#FlowC">Flow Cytometry</a><br>
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<a href="#CharApp">Our Approach</a><br>
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<h4>Characterization</h4>
<h4>Characterization</h4>
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<h9>An Introduction to Characterization</h9>
<h9>An Introduction to Characterization</h9>
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Much of the synthetic biology community builds novel genetic constructs from novel DNA parts. The qualitative function of many commonly used parts is well known, but the quantitative behavior of most parts is not well known or documented. There have been recent pushes in the community to develop a standard for quantitative characterization of DNA parts, but there still exists no standard for doing so.  
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<p dir="ltr">Much of the synthetic biology community builds novel genetic constructs from novel DNA parts. The qualitative function of many commonly used parts is well known, but the quantitative behavior of most parts is not well documented. There have been recent pushes in the community to develop a standard for quantitative characterization of DNA parts, but there still exists no standard for doing so.  
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The part characterization problem is multi-dimensional - for one, it is rarely possible to have a single DNA part carry out an observable function without the assistance of other parts.  Additionally, it is not widely agreed how to acquire measurements and what conditions are best for characterizing function. There is a need to develop such a methodological standard so that DNA part information can be understood and applied by multiple groups.
The part characterization problem is multi-dimensional - for one, it is rarely possible to have a single DNA part carry out an observable function without the assistance of other parts.  Additionally, it is not widely agreed how to acquire measurements and what conditions are best for characterizing function. There is a need to develop such a methodological standard so that DNA part information can be understood and applied by multiple groups.
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Canton et. al 2008 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ellis et. al 2009 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Beal et. al 2012
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Canton et. al 2008 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Ellis et. al 2009 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Beal et. al 2012
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Recent work has been done to accomplish this goal. Some groups have worked towards setting a standard for measuring and sharing part characterization data (Canton et. al 2008) and others have used characterization data for model-guided design (Ellis et. al 2009). Other have explored the ability to get complete transfer curves for chemical induction of various promoters (Beal et. al). However, characterization data is often collected and analyzed in different ways making it difficult to compare and re-use.
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Recent work has been done to accomplish this goal. Some groups have worked towards setting a standard for measuring and sharing part characterization data (Canton et. al 2008) and others have used characterization data for model-guided design (Ellis et. al 2009). Other have explored the ability to get complete transfer curves for chemical induction of various promoters (Beal et. al 2012). However, characterization data is often collected and analyzed in different ways making it difficult to compare and re-use.
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<h9>Flow Cytometry</h9>
<h9>Flow Cytometry</h9>
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Flow cytometry is a technology for recording single-cell measurements of fluorescence. These measurements are taken by passing cells one-at-a-time through the path of a laser and recording the refracted light at with a series of band-pass filters. This allows multiple fluorophores to be measured simultaneously at a single-cell resolution.  
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<p dir="ltr">Flow cytometry is a technology for recording single-cell measurements of fluorescence. These measurements are taken by passing cells one-at-a-time through the path of a laser and recording the refracted light with a number of band-pass filters. This allows quantitative measurement of multiple fluorophores simultaneously at a single-cell resolution.  
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A more detailed description of the technology can be found on the <a href="http://www.bdbiosciences.com/support/training/itf_launch.jsp">BD Biosciences website</a>
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A more detailed description of the technology can be found on the <a href="http://www.bdbiosciences.com/support/training/itf_launch.jsp">BD Biosciences website.</a>
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<h9>Our Approach</h9>
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We approach the characterization problem with a standard characterization workflow. The process begins after plasmids have already been transformed into E. coli in a frozen liquid stock. Cells containing the plasmid of interest are plated on agar plates for overnight growth at 37C. Three colonies are picked from each plate and grown in spinning liquid culture in triplicate at 37C, 300rpm for ~6 hours. These cultures are then diluted 200:1 into fresh media including a chemical inducer if appropriate. Afterwards, these cultures are again incubated at 37C, 300rpm but for ~15 hours. Finally, cultures are diluted 10:1 into PBS for flow cytometry.
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Although this process is not experimentally proven to be optimal, it is an effort to standardize the workflow within the lab.
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<h8>References</h8>
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[1] T. Ellis et al. “Diversity-­‐based, model-­‐guided construction...” Nature Biotech 27(5). 2009.
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[2] B. Canton et al. “Refinement and standardization...” Nature Biotech 26(7). 2008.
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[3] J Beal et al. "A Method for Fast, High-Precision Characterization..." MIT-CSAIL-TR-2012-008
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Latest revision as of 21:17, 26 October 2012

BostonU iGEM Team: Welcome


Characterization




An Introduction to Characterization

    Much of the synthetic biology community builds novel genetic constructs from novel DNA parts. The qualitative function of many commonly used parts is well known, but the quantitative behavior of most parts is not well documented. There have been recent pushes in the community to develop a standard for quantitative characterization of DNA parts, but there still exists no standard for doing so.

    The part characterization problem is multi-dimensional - for one, it is rarely possible to have a single DNA part carry out an observable function without the assistance of other parts. Additionally, it is not widely agreed how to acquire measurements and what conditions are best for characterizing function. There is a need to develop such a methodological standard so that DNA part information can be understood and applied by multiple groups.


    Canton et. al 2008                          Ellis et. al 2009                                                               Beal et. al 2012

    Recent work has been done to accomplish this goal. Some groups have worked towards setting a standard for measuring and sharing part characterization data (Canton et. al 2008) and others have used characterization data for model-guided design (Ellis et. al 2009). Other have explored the ability to get complete transfer curves for chemical induction of various promoters (Beal et. al 2012). However, characterization data is often collected and analyzed in different ways making it difficult to compare and re-use.

Flow Cytometry

    Flow cytometry is a technology for recording single-cell measurements of fluorescence. These measurements are taken by passing cells one-at-a-time through the path of a laser and recording the refracted light with a number of band-pass filters. This allows quantitative measurement of multiple fluorophores simultaneously at a single-cell resolution.


    A more detailed description of the technology can be found on the BD Biosciences website.

Our Approach

    We approach the characterization problem with a standard characterization workflow. The process begins after plasmids have already been transformed into E. coli in a frozen liquid stock. Cells containing the plasmid of interest are plated on agar plates for overnight growth at 37C. Three colonies are picked from each plate and grown in spinning liquid culture in triplicate at 37C, 300rpm for ~6 hours. These cultures are then diluted 200:1 into fresh media including a chemical inducer if appropriate. Afterwards, these cultures are again incubated at 37C, 300rpm but for ~15 hours. Finally, cultures are diluted 10:1 into PBS for flow cytometry.



    Although this process is not experimentally proven to be optimal, it is an effort to standardize the workflow within the lab.

    References
    [1] T. Ellis et al. “Diversity-­‐based, model-­‐guided construction...” Nature Biotech 27(5). 2009.
    [2] B. Canton et al. “Refinement and standardization...” Nature Biotech 26(7). 2008.
    [3] J Beal et al. "A Method for Fast, High-Precision Characterization..." MIT-CSAIL-TR-2012-008



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