Difference between revisions of "Team:William and Mary/Interlab"

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Description
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Interlab
 
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<h3 style='padding-top: 50px; padding-bottom: 50px;'>Advisors</h3>
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Motivation
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<p class='large' style="color:#A9A9A9;">
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  Genetic circuits exist in great abundance in nature as complex metabolic pathways which interact
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  in various ways to perform vital cellular processes. Synthetic biologists aim to not only understand
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  naturally occurring circuit networks, but also to modify them or to conceptualize and build entirely
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  new circuits.
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<p class='large' style="padding-left:40px; text-indent: 50px;">
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The inherent versatility of synthetic genetic circuitry has lead to a vast array of diverse applications
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We participated in the Interlab Measurement Study using Flow Cytometry analysis on a FACS (Fluorescence-Activated Cell Sorter)
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in countless fields. However, the field remains fundamentally limited by the magnitude and specificity of
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machine following the protocol provided by iGEM. Our results are presented here:
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behavioral control over genetic circuits and circuit networks. These limitations can be boiled down to two
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essential problems: inherent constraints to behavior based on the nature of a circuit’s constituent genes,
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and the inefficiency of the “design-build-test” cycle which is relied upon for the construction of effective
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circuit models.
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The fundamental constraints of integral circuit components limit the ability to design and construct
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<img src="https://static.igem.org/mediawiki/parts/c/c3/T--William_and_Mary--Forum.jpg" width="600px">
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genetic circuits of arbitrary and highly specific behavior. When constructing a circuit with some intended
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behavior, design is limited by the available input-specific regulators to gene expression and their
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characteristic regulatory behavior. In order to achieve more precise behavioral control, the ability to
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tune expression levels of regulatory elements to some desired level is vital. This limitation highlights
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the need for genetic devices that can modify the behavior of arbitrary genetic circuits; implementing these
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devices would enable precise behavioral control invariant to the constraints of the constituent genes that
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make up the circuit in question [1].
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<p class='large' style="padding-left:40px; text-indent: 50px;">
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The other foundational limitation of genetic circuit construction addresses the inefficiency and
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In addition to participating in the Interlab Study, we also wanted to determine if there would be a noticeable difference
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unpredictability of the design and construction process itself. The progression from synthesizing parts
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between measurements taken at Midlog and measurements taken on those same samples at Saturation.
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into a circuit on a plasmid, to transformation and testing in vivo, is a lengthy and expensive process
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which furthermore is largely variable in terms of actual functionality of the final product [2].This often
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leads to a series of trial-and-error testing cycles whose products maintain a persistent level of uncertainty
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with regard to precise, predictable behavior. Although it is possible to achieve functional genetic circuits
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in this capacity, greater problems arise regarding the tunability of the product. The success of any genetic
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circuit relies on the ability to precisely tune a response to a range of input concentrations; it would therefore
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be desirable to obtain a reliable method for tuning circuit response, ideally without the need to rewire the
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internal workings of the circuit. This method would allow control over output expression to be implemented in
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a more rapid and predictable manner [3].
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The Project
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<p class='large' style="color:#A9A9A9;">
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Our project aims to provide a modular collection of genetic parts which can specifically and predictably
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tune the behavior of an arbitrary genetic circuit. This collection, which we have dubbed the “Circuit
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Control Toolbox,” consists of a suite of parts which can be added to the end of a given genetic circuit;
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each part provides a specific and independently tunable response which allows direct control over the
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ultimate output behavior of the circuit.
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<div class="description">
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<p class='large' style="padding-left:70px;">
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The overall input/output behavior of any genetic circuit can be represented by a graph known as a
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transfer function, which relates concentration of input molecule to output protein expression.
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Likewise, any modifications to the circuit affecting input/output behavior can be visualized by a
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transformation of the transfer function representing the circuit. The Circuit Control Toolbox consists
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of three distinct tools which prompt unique behavioral changes to the circuit’s output relative to its
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input, and therefore generate different transformations of the circuit’s original transfer function.
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<p class='large' style="padding-left:40px; text-indent: 50px;">
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<div class="description">
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We noticed that although there were differences between the midlog and saturation measurements, they were not very drastic
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<p class='large' style="padding-left:70px;">
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across the different devices when looking at bulk population-level mean measurements, with the exception of the negative control.
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The RBS library [link to page] provides a collection of ribosome binding sites of varying strength;
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</p>
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replacing the RBS within a circuit alters the translational efficiency of the output. This tool
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<p class='large' style="padding-left:40px; text-indent: 50px;">
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effectively allows for scaled changes in the magnitude of a circuit’s output response, thus adjusting
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However, because we measured the devices with flow cytometry, we were additionally able to assess the changes in population
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the amplitude of the transfer function.
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heterogeneity which occur with between midlog phase and saturation phase. We found that the midlog populations tended to be more
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unimodal than the saturation populations.
 
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<p class='large' style="padding-left:70px;">
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<p style="float: left; padding-top: 30px; padding-bottom: 30px;">
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The Decoy Binding Array [link to page] tool implements molecular titration to tune the circuit’s
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<img src="https://static.igem.org/mediawiki/parts/5/5e/T--William_and_Mary--LSBDemo.png" width="500px">
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sensitivity to input concentrations. This modification is accompanied by a shift in the threshold
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of the circuit’s transfer function.
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</p>
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<p class='large' style="padding-left:70px;">
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The Synthetic Enhancer Suite [link to page] exploits a synthetically modified enhancer/promoter
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system engineered to allow genetic circuits to generate multi-state responses. In other words,
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circuits are prompted to produce distinct levels of output based on the concentration of input molecule.
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This creates a staircase-like curve in the transfer function for the circuit.
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</p>
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<p class='large' style="padding-left:70px;">
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Each of these tools functions orthogonally to the activity of the other tools; furthermore,
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each tool is independently tunable to a specific degree. By implementing and adjusting multiple
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tools to the desired degree, a diverse range of circuit output behaviors can be achieved,
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generating a plethora of unique transfer function responses.
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</p>
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<p class='large' style="padding-left:70px;">
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The implementation of this Toolbox relies on its generalizability and consistency over any
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arbitrary genetic circuit. A circuit’s relative output behavior may be influenced by the
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coding sequence for the output which it controls. In order to ensure that behavior remains
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consistent across any range of coding sequences, we offer an additional Ribozyme Insulator
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[link to page] tool. This ribozyme part, known as RiboJ, insulates a circuit’s promoter
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activity from the genetic context of the coding sequence, allowing for consistency in the
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levels of relative expression across multiple coding sequence insertions. The addition of
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RiboJ as an insulator justifies the application of any number of Toolbox components to the
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end of an arbitrary genetic circuit, allowing for consistent and predictable behavioral
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modifications [4].
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</p>
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<p class='large' style="padding-left:70px; padding-bottom:50px;">
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Our Circuit Control Toolbox can easily be implemented in any project concerned with the
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behavior of genetic circuitry by working through the following sequence of events:
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</p>
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
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<b>1.</b> Visualize the original behavior of the circuit in question by constructing a characteristic transfer function.
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</p>
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
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<b>2.</b> Determine the appropriate Toolbox parts-to-use using our mathematical model. This model has been parameterized
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such that the model parameters correspond to actual physical variables (e.g. number of tetO arrays, plasmid backbone).
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</p>
 
</p>
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
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<p style="float: right; padding-top: 30px; padding-bottom: 30px;">
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<b>3.</b> Swap out the final protein coding sequence in the original circuit with a Ribo-J insulated repressor sequence
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<img src="https://static.igem.org/mediawiki/parts/5/5e/T--William_and_Mary--LSBDemo.png" width="500px">
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that is compatible with the Toolbox.
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</p>
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<p class='large' style="padding-left:70px; padding-bottom: 50px;">
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<b>4.</b> Apply the appropriate Toolbox parts to the end of your circuit, and express your original final output protein
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at the end of the series of Toolbox components.
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</p>
 
</p>
 
 
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<p class='large' style="padding-left:70px; padding-bottom: 100px;">
 
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In this manner, future iGEM teams and synthetic biologists will be able to easily obtain higher levels of precision
 
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and control over the behavior of their genetic networks.
 
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</p>
 
 
</div>
 
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<div align="center">
 
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<h3 style='padding-top: 50px; padding-bottom: 50px;'>References</h3>
 
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</div>
 
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
 
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<b>1.</b> Nielsen, A. A., Segall-Shapiro, T. H., & Voigt, C. A. (2013). Advances in genetic circuit design:
 
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Novel biochemistries, deep part mining, and precision gene expression. Current Opinion in Chemical Biology,
 
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17(6), 878-892. doi:<a>http://dx.doi.org/10.1016/j.cbpa.2013.10.003</a>
 
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</p>
 
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
 
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<b>2.</b> Sun, Z. Z., Yeung, E., Hayes, C. A., Noireaux, V., & Murray, R. M.Linear DNA for rapid prototyping of
 
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synthetic biological circuits in an escherichia coli based TX-TL cell-free system. ACS Synthetic Biology, (6), 387.
 
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doi:10.1021/sb400131a
 
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</p>
 
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
 
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<b>3.</b> Lucks, J. B., Qi, L., Whitaker, W. R., & Arkin, A. P. (2008). Toward scalable parts families for predictable
 
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design of biological circuits. Current Opinion in Microbiology, 11(6), 567-573. doi:<a>http://dx.doi.org/10.1016/j.mib.2008.10.002</a>
 
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</p>
 
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<p class='large' style="padding-left:70px; padding-bottom: 30px;">
 
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<b>4.</b> Lou, C., Stanton, B., Chen, Y., Munsky, B., & Voigt, C. A. (2012). Ribozyme-based insulator parts buffer
 
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synthetic circuits from genetic context. Nature Biotechnology, (30), 1137-1142.
 
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</p>
 
 
</div>
 
</div>
 
</div>
 
</div>

Revision as of 02:51, 20 October 2016


...

Interlab

We participated in the Interlab Measurement Study using Flow Cytometry analysis on a FACS (Fluorescence-Activated Cell Sorter) machine following the protocol provided by iGEM. Our results are presented here:

In addition to participating in the Interlab Study, we also wanted to determine if there would be a noticeable difference between measurements taken at Midlog and measurements taken on those same samples at Saturation.

We noticed that although there were differences between the midlog and saturation measurements, they were not very drastic across the different devices when looking at bulk population-level mean measurements, with the exception of the negative control.

However, because we measured the devices with flow cytometry, we were additionally able to assess the changes in population heterogeneity which occur with between midlog phase and saturation phase. We found that the midlog populations tended to be more unimodal than the saturation populations.