Team:MIT/Motivation

From 2012.igem.org

(Difference between revisions)
Line 10: Line 10:
     </div><!--end block-content-->
     </div><!--end block-content-->
<h1>Motivation</h1>
<h1>Motivation</h1>
-
<p>Synthetic biology has grown as a field over the past few decades to feature increasingly complex circuit topology, modular parts, and a myriad of application spaces. However, the increase in complexity is limited by the number of specialized parts available to the community. The proteins used for cellular processing must be orthogonal to one another and cannot place too high a metabolic load on the host cell. </p>
+
<p>Our project aims to combine the <b>modular parts</b> of synthetic biology with the exponential growth in <logic circuit complexity</b> of nucleic-acid based molecular computation to create <b>RNA circuits in mammalian cells</b>.</p>
-
<p>At the same time, a field of similar origins, nucleic-acid based molecular computation, has seen an exponential growth in the ability to make more and more complex structures and logic circuits. In particular, recent publications feature square-root calculators and neural network implementations using strands of DNA and their natural thermodynamic reactions to process information and make calculations. </p>
+
-
<p>
+
-
<p><b>We are motivated by the advances made in both synthetic biology and molecular computation and aim to bring the best of both together into one project.</b></p>
+
<p>Nucleic-acid based circuitry has several advantages over traditional transcription-based topologies:</p>
<p>Nucleic-acid based circuitry has several advantages over traditional transcription-based topologies:</p>
<ul>
<ul>
-
<li type=circle>a much smaller nucleotide footprint
+
<li type=circle><b>No need for specialized parts.</b>
-
<li type=circle>no need for specialized parts
+
We can design circuit parts from the ground up instead of searching for suitable orthogonal proteins.
-
<li type=circle>a huge combinatorial space which is easily parametrized
+
<li type=circle><b>Large combinatorial space.</b>
-
<li type=circle>minimal metabolic load; DNA parts don’t need to be translated into proteins
+
We can generate and iterate unlimited parts and filter for constraints, such as percentage of Cs or Gs.
-
<li type=circle>direct interface with mRNA, miRNA, etc.
+
<li type=circle><b>Minimal metabolic load.</b>
-
</ul><p>By using mammalian cells, we have access various levels of regulation, for example being able to use the RNA-interference (RNAi) pathway.</p>
+
Nucleic acid parts are fully functional and do not need to be translated into proteins.
 +
<li type=circle><b>Much smaller nucleotide footprint.</b>
 +
RNA parts require much fewer bases than the mRNAs coding for protein parts.
 +
<li type=circle><b>Direct interfacing with mRNA, miRNA, etc.</b>
 +
The use of mammalian cells provides us with access to various levels of regulation, such as the RNA-interference pathway.
 +
 
 +
 
<p>Let's look at a potential synthetic biology application: <b>detecting cancer state in mammalian cells</b>. What would this require?  </p>
<p>Let's look at a potential synthetic biology application: <b>detecting cancer state in mammalian cells</b>. What would this require?  </p>
<p>You can imagine the first step, a cancer cell sensor, can be achieved by creating an mRNA sensor. The state of the art circuit with this function, (1 Multi-Input RNAi-Based Logic Circuit for Identification of Specific Cancer Cells. Xie et al. Science 2011)  requires at least five composite parts for sensing high and low mRNA concentrations. The next part would need to process information from these five separate inputs, invert some of it, and send a signal to up regulate fluorescent protein production. This would require another set of promoters for each sensed mRNA and a repression system to produce the correct logic. The last step would be the induced expression of the signal protein; another unique promoter and protein pairing.</p><p>We'll compare traditional, promoter-based synbio logic with a novel strategy from the field of DNA computing: strand displacement.
<p>You can imagine the first step, a cancer cell sensor, can be achieved by creating an mRNA sensor. The state of the art circuit with this function, (1 Multi-Input RNAi-Based Logic Circuit for Identification of Specific Cancer Cells. Xie et al. Science 2011)  requires at least five composite parts for sensing high and low mRNA concentrations. The next part would need to process information from these five separate inputs, invert some of it, and send a signal to up regulate fluorescent protein production. This would require another set of promoters for each sensed mRNA and a repression system to produce the correct logic. The last step would be the induced expression of the signal protein; another unique promoter and protein pairing.</p><p>We'll compare traditional, promoter-based synbio logic with a novel strategy from the field of DNA computing: strand displacement.

Revision as of 02:51, 3 October 2012

iGEM 2012

Motivation

Our project aims to combine the modular parts of synthetic biology with the exponential growth in of nucleic-acid based molecular computation to create RNA circuits in mammalian cells.

Nucleic-acid based circuitry has several advantages over traditional transcription-based topologies:

  • No need for specialized parts. We can design circuit parts from the ground up instead of searching for suitable orthogonal proteins.
  • Large combinatorial space. We can generate and iterate unlimited parts and filter for constraints, such as percentage of Cs or Gs.
  • Minimal metabolic load. Nucleic acid parts are fully functional and do not need to be translated into proteins.
  • Much smaller nucleotide footprint. RNA parts require much fewer bases than the mRNAs coding for protein parts.
  • Direct interfacing with mRNA, miRNA, etc. The use of mammalian cells provides us with access to various levels of regulation, such as the RNA-interference pathway.

    Let's look at a potential synthetic biology application: detecting cancer state in mammalian cells. What would this require?

    You can imagine the first step, a cancer cell sensor, can be achieved by creating an mRNA sensor. The state of the art circuit with this function, (1 Multi-Input RNAi-Based Logic Circuit for Identification of Specific Cancer Cells. Xie et al. Science 2011) requires at least five composite parts for sensing high and low mRNA concentrations. The next part would need to process information from these five separate inputs, invert some of it, and send a signal to up regulate fluorescent protein production. This would require another set of promoters for each sensed mRNA and a repression system to produce the correct logic. The last step would be the induced expression of the signal protein; another unique promoter and protein pairing.

    We'll compare traditional, promoter-based synbio logic with a novel strategy from the field of DNA computing: strand displacement.

    Parts required (see below for explanation):

    • 5 input sensor modules
    • 5 processor modules (one for each input)
    • 1 actuation module

    Promoter-based logic

    Maximum number of promoters found in published synbio circuits, over time.     		Strand displacement-based logic

    Maximum number of gates found in published strand displacement-like systems, over time.
    Promoter-protein pairs required: 11 (one for each module)
    Max achieved in literature: 6     		Strands of nucleic acid required: 80
    Size of each promoter-protein pair: ~1,600 bp Length of each strand: ~40 bp
    Total size of circuit: ~17,600 bp Total size of circuit: ~3,200 bp

Retrieved from "http://2012.igem.org/Team:MIT/Motivation"