In the Young Lab, we engineer biology.
Our goal is to make catalysts and sensors for a sustainable future.
Therefore, we use genomics, synthetic biology, and metabolic engineering to make organisms that have important functions in microbiomes or interesting biosynthetic capacity.
Our goal is to make catalysts and sensors for a sustainable future.
Therefore, we use genomics, synthetic biology, and metabolic engineering to make organisms that have important functions in microbiomes or interesting biosynthetic capacity.
Why Catalysts?
Biosynthesis is a key part of ecological and industrial processes, and is a promising approach to a sustainable civilization.
However, the potential of biosynthesis can only be realized through engineering and design of "cell factories" that are more efficient and productive. Therefore, we focus on interesting organisms with desirable biosynthesis and engineer them to produce useful compounds like fuels and medicines.
Why Sensors?
Biological systems have incredible capacity to sense and respond to changes in the environment. It is possible to design genetic systems that sense and report a great deal of desired information. However, currently genetic circuits typically only work under laboratory conditions. Therefore, we focus on interesting organisms that function within important environments like the soil microbiome to build next-generation sensors. Our designs could be used to enhance agriculture or remediate pollution.
Why Genomics?
Genes and gene expression patterns are the source of interesting metabolism and physiology. Because we focus on organisms that are not well-studied or are entirely new to science, we have developed genomics and transcriptomics tools to get an accurate genome sequence, identify important genes, and derive synthetic biology parts. Many of these tools leverage machine learning and artificial intelligence algorithms. Our tools can also be used in traditional strain engineering workflows to confirm genome edits or to identify genetic engineering in metagenomic sequence data.
Why Synthetic Biology?
Synthetic biology is needed to control expression of biosynthetic genes and build genetic circuits. We specialize in deriving large numbers of genetic parts for the nonconventional organisms we engineer. We have shown that direct part transfer, inference using homology, and derivation from transcriptomics are each valid approaches to achieving gene expression control in various organisms. Coupled with laboratory automation, we can rapidly build and test genetic parts libraries for nearly any bacteria or yeast.
Why Metabolic Engineering?
Metabolic engineering is necessary to rewire biosynthesis to produce compounds of interest. Traditional metabolic engineering consists of gene knockouts and overexpressions to achieve biosynthesis of desired compounds. Combinatorial metabolic engineering builds on this concept to test many different expression levels and many different knockouts in one large, parallel experiment. Coupled with laboratory automation and machine learning, we can quickly iterate through many different pathway designs to find the optimum.
Biosynthesis is a key part of ecological and industrial processes, and is a promising approach to a sustainable civilization.
However, the potential of biosynthesis can only be realized through engineering and design of "cell factories" that are more efficient and productive. Therefore, we focus on interesting organisms with desirable biosynthesis and engineer them to produce useful compounds like fuels and medicines.
Why Sensors?
Biological systems have incredible capacity to sense and respond to changes in the environment. It is possible to design genetic systems that sense and report a great deal of desired information. However, currently genetic circuits typically only work under laboratory conditions. Therefore, we focus on interesting organisms that function within important environments like the soil microbiome to build next-generation sensors. Our designs could be used to enhance agriculture or remediate pollution.
Why Genomics?
Genes and gene expression patterns are the source of interesting metabolism and physiology. Because we focus on organisms that are not well-studied or are entirely new to science, we have developed genomics and transcriptomics tools to get an accurate genome sequence, identify important genes, and derive synthetic biology parts. Many of these tools leverage machine learning and artificial intelligence algorithms. Our tools can also be used in traditional strain engineering workflows to confirm genome edits or to identify genetic engineering in metagenomic sequence data.
Why Synthetic Biology?
Synthetic biology is needed to control expression of biosynthetic genes and build genetic circuits. We specialize in deriving large numbers of genetic parts for the nonconventional organisms we engineer. We have shown that direct part transfer, inference using homology, and derivation from transcriptomics are each valid approaches to achieving gene expression control in various organisms. Coupled with laboratory automation, we can rapidly build and test genetic parts libraries for nearly any bacteria or yeast.
Why Metabolic Engineering?
Metabolic engineering is necessary to rewire biosynthesis to produce compounds of interest. Traditional metabolic engineering consists of gene knockouts and overexpressions to achieve biosynthesis of desired compounds. Combinatorial metabolic engineering builds on this concept to test many different expression levels and many different knockouts in one large, parallel experiment. Coupled with laboratory automation and machine learning, we can quickly iterate through many different pathway designs to find the optimum.
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