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<p class="topic" style="text-align:center;font-size:350%;font-family:serif">Project Description</p>
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<p class="topic" style="text-align:center;font-size:350%;font-family:serif">Introduction</p>
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<p class="general"  style="text-align:center;font-size:170%;font-family:serif;">Many commercially important chemicals are manufactured with the help of microbes. These microbes are often genetically modified so that they are equipped with the necessary enzymes to produce those chemicals. However, there are millions of species in nature, each carrying their own version of enzymes. Which one should we choose to put into the microbes?
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To answer this questions, researchers normally test these enzymes – or combinations of enzymes – one by one to see if they work well in the microbe. As you may imagine, this is very labour-intensive and time-consuming, even with the help of automated systems. Therefore, we, the Homologics team, are developing a highly-adaptable method to speed up this process.</p>
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Biological synthesis and metabolic engineering have jointly emerged to provide an alternative to organic synthesis of drugs – given that most are derivatives of naturally found substances, meaning their synthetic pathway already exists in nature – and use of petroleum-derived fuels by offering a more sustainable and greener alternative for an energy source. For higher yield and lower cost, researchers have been optimising metabolic pathways by: </p>
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  <li>identifying the ideal environment for the organism</li>
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  <li>removing pathways that are unnecessary for the survival of the organism or the production of the product</li>
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  <li>removing negative feedback</li>
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  <li>codon optimisation</li>
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  <li>increasing expressivity by the choice of promoter and RBS</li>
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  <li>improving the methods for genetic modifications</li>
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  <li>determining the rate limiting step of a synthesis and testing homologues enzymes that have higher reaction rates.</li>
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Given the number of aspects that are taken into consideration, metabolic engineering is a strenuous and time-consuming procedure. One of the contributing reasons is that by today’s method all genetic modification are introduced separately into the system. Our project aims to address this issue by designing a genetic construct that would randomly introduce multiple homologues into a culture. This means if we have a 5-step synthetic pathway and each step is tested for 4 randomly-expressed enzyme homologues, a culture will contain 1024 varied combinations of the 5 enzymes and thus we will be able to detect which one is the optimal combination of enzyme homologues.  
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Introduction

Biological synthesis and metabolic engineering have jointly emerged to provide an alternative to organic synthesis of drugs – given that most are derivatives of naturally found substances, meaning their synthetic pathway already exists in nature – and use of petroleum-derived fuels by offering a more sustainable and greener alternative for an energy source. For higher yield and lower cost, researchers have been optimising metabolic pathways by:

  • identifying the ideal environment for the organism
  • removing pathways that are unnecessary for the survival of the organism or the production of the product
  • removing negative feedback
  • codon optimisation
  • increasing expressivity by the choice of promoter and RBS
  • improving the methods for genetic modifications
  • determining the rate limiting step of a synthesis and testing homologues enzymes that have higher reaction rates.

Given the number of aspects that are taken into consideration, metabolic engineering is a strenuous and time-consuming procedure. One of the contributing reasons is that by today’s method all genetic modification are introduced separately into the system. Our project aims to address this issue by designing a genetic construct that would randomly introduce multiple homologues into a culture. This means if we have a 5-step synthetic pathway and each step is tested for 4 randomly-expressed enzyme homologues, a culture will contain 1024 varied combinations of the 5 enzymes and thus we will be able to detect which one is the optimal combination of enzyme homologues.