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{% block title %}Project Description{% endblock %}
{% block lead %}Describe how and why you chose your iGEM project.{% endblock %}

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        <h1>PROJECT<br>DESCRIPTION</h1>
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      <p class="subtitles">Motivations</p>
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        Lactococcus lactis (L. lactis) is a ‘Generally Regarded As Safe’ (GRAS) organism. It is naturally found in milk products and is also known to colonize the human gut. Unlike E. coli, it does not have an endotoxin layer, which requires extra measures for purification. Due to its ability to use a wide range of substrates and tolerance for a wide range of conditions (pH, temperature, solvent concentration), L. lactis serves as an alternate bacterial model for metabolic and bioprocess engineering.
        The range of products it can be engineered to produce include bioplastics, biofuels, biopolymers, polyols, and food flavors. However, the absence of genetic and regulatory libraries for this organism make genetic circuits design and assembly challenging in this chassis.
        The ability to fine-tune gene expression forms a cornerstone for the design and operation of genetic circuits. This optimization has not been fully carried out in non-traditional chasses like L. lactis.
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        Synthetic biology envisions a bioengineering domain for designing new genetic parts and systems, or redesigning of existing ones. Until now, the most versatile workhorse of synthetic biology has been Escherichia coli. However, there is a need for exploring new chassis which can be naturally adapted to unique traits or metabolic pathways.
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      <p class="subtitles">Background</p>
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      <p class="subtitles">Project</p>
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        <b>Our project is aimed at the construction and testing of a library of synthetic 5’ untranslated regions (UTR) containing ribosome binding sites (RBS) in L. lactis.</b><br><br>
        In addition to our lab work, we also plan to study the drawbacks of existing models and come up with potential improvements. This would include studying the effect of temperature on mRNA folding, the interaction between the mRNA and the ribosomal S1 protein, the difference in optimal RBS-AUG spacing in Gram-positive and Gram-negative bacteria, and the effect of RNAse-mediated degradation of the transcript. Combining the model with an optimisation algorithm, we aim to rationally design the ‘best’ RBS for a given gene, in a given organism.
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