# The Small of Synthetic Biology: Engineering Strategies for Aroma Compound Production in Yeast
## 5 Future Outlook
\begin{table}
\begin{tabular}{l l l l} \hline
**Aroma Compound** & **Molecule(s) Sensed** & **Description** & **Reference** \\ \hline \multirow{6}{*}{\(\beta\)**-phenylethanol**} & \multirow{6}{*}{Aromatic amino acids} & _Shikimate pathway_ & \\  & & _Alosteric transcription factor sensor. Transcriptional_ & \\  & & regulation _LacZ_ reporter gene by the aromatic amino & \\  & & acid responsive _AR_O9 promoter. Increased & \\  & & \(\beta\)-galactosidase activity correlated with elevated & \\  & & \(\beta\)-phenylethanol levels. & \\ \hline \multirow{6}{*}{**Precursor**} & \multirow{6}{*}{Betaaxanthin} & Enzyme-coupled sensor. Highly yeast-active & \\  & & heterologous \(\mathrm{t}\)-tyrosine hydroxylases were & \\  & & identified, based on increased bataxanthin & \\  & & fluorescence intensities in yeast expressing the & \\  & & plant DOPA dioxygenase. & \\ \hline \multirow{6}{*}{\(p\)**-Coumaric acid**} & \multirow{6}{*}{\(p\)-Coumaric acid} & Exogenous bacterial sensor. Droplet sorting of & \\  & & encapsulated \(p\)-coumaric acid producing & \\  & & yeast cells and \(p\)-coumaric acid sensing _E. coli_ cells, & \\  & & to select producers based on bacterial & \\  & & YFP-fluorescence output. & \\ \hline \multirow{2}{*}{**Precursor**} & \multirow{2}{*}{Muconic acid} & Heterologous allosteric transcription factor. Used an & \\  & & _Acintebacter_ sp. transcriptional regulator to drive & \\  & & GFP expressing in the presence of muoconic acid. & \\ \hline \multirow{6}{*}{**Precursor**} & \multirow{6}{*}{Manolyl-CoA} & _Mecanonate pathway_ & \\  & & Recombinant allosteric transcription factor sensor. & \\  & & Used a bacterial FapR transcription factor and FapO & \\  & & operator pair to identify strains from a genome-wide & \\  & & overexpression library that produce high levels of & \\  & & malonyl-CoA. & \\ \hline \multirow{6}{*}{**Precursor**} & \multirow{2}{*}{Isopentenyl diphosphate} & Synthetic transcription factor to allow & \\  & & feedback-regulated evolution of phenotype. Higher & \\  & & intracellular IPP concentrations resulted in increased & \\  & & _GAL10_ transcription, generating an evolvable growth & \\  & & phenotype on galactose & \\ \hline \end{tabular}
\end{table} 
Table 1: Examples of yeast biosensors to detect phenylpropanoids and terpenoids produced in _Saccharomyces cerevisiae_. aims to streamline and accelerate the iterative process of improving a biological system as well as to minimise human input to eliminate bias in the interpretation of the output. As mentioned by [PERSON] et al. [84], the testing part is often reliant on a high-throughput screen which makes this component--if available--often the most time-consuming and costly. This is particularly true for the determination of most aroma compounds, as the screening of millions of cells and assessing individual cells for aroma compound production is not yet feasible. This is why the development of aroma biosensors with appropriate sensitivity and specificity is most desired. A desired attribute of the biosensor would be for it to have a 'dynamic range,' that is, besides detecting aroma compounds it would have the ability to provide a differing signal depending on the concentration of the aroma. This might be achieved, although not yet tested in such a capacity, by coupling either a bioluminescence resonance energy transfer (BRET)-based sensor [85] or metal oxide [86] sensor, developed to detect volatile organic compounds, within a high-throughput screening process to test a yeast library. Figure 4: An example of how DBTL could be applied to build a yeast with increased production levels of raspberry ketone. During the “design” part of the project, researchers would need to plan all the experiments with a particular focus on the types of metabolic engineering tactics that will be employed to alter the target pathway within the yeast. The raspberry ketone synthesis pathway has already been introduced in _S.