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  <h1>Project<br>Description</h1>
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  <p style="font-size: 32px"><strong>What is the problem we are trying to solve?</strong></p>
  <p>The fundamental thought process behind this project is exploration beyond conventionally-used methods and how these
    can be made accessible to the everyday researcher. <i>E. coli</i> emerged as a feasible chassis organism in the early days
    of synthetic biology, but this led to a sunken cost feedback loop in which research investigating <i>E. coli</i> was
    favored disproportionately over looking for new and better chassis. Over time, simply because the body of
    information on <i>E. coli</i> aggregated to be too large for any investigative effort outside it to be practically
    unfavorable, synthetic biology fell into a rut of having a singular chassis for the overwhelming majority of its
    workflow.</p>
  <p style="font-size: 32px"><strong>How are we solving it?</strong></p>
  <p>As students, driven by an interest in synthetic biology, we have relatively more freedom to question the
    fundamentals of the nature of our work, explore novel chassis and develop better genetic engineering techniques. Our
    laboratory efforts have been driven towards characterizing Lactococcus lactis better, which requires significantly
    less effort with regards to downstream processing as there is no additional purification required to remove an
    endotoxin layer. The most significant hurdles encountered when working with a new organism is an absence of genetic
    and regulatory information, making circuit design and fine-tuning gene expression challenging and extremely labor
    intensive. Our wet lab team constructed a library of randomized RBS sequences characterized by their expression
    levels, providing a solid base of initial information for anyone using this chassis.</p>
  <p style="font-size: 32px"><strong>Our RBS Predictor-Optimizer: Synthopedia!</strong></p>
  <p>Our predictor-optimizer addresses the issue of fine tuning protein expression on a broader level, and contributes
    to the groundwork to an in silico solution to better control over expression dynamics. Harnessing machine learning
    principles over a wider set of parameters, including both thermodynamic and biophysical data points, our team has
    designed a machine learning model for protein expression, leveraging which, we are able to predict expression levels
    for RBS sequences as well as optimize them for a particular expression level. The results of this model have been
    cross-tested against state of the art models.</p>
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