Life on Earth is incredibly diverse. Yet, underneath that diversity, there are a number of constants and highly conserved processes: all life is based on DNA and RNA; the genetic code is universal; biology is limited to a small subset of potential chemistries. A vast amount of knowledge has been accrued through describing and characterizing enzymes, biological processes and organisms. Nevertheless, much remains to be understood about the natural world. One of the goals in Synthetic Biology is to recapitulate biological complexity from simple systems made from biological molecules–gaining a deeper understanding of life in the process. Directed evolution is a powerful tool in Synthetic Biology, able to bypass gaps in knowledge and capable of engineering even the most highly conserved biological processes. It encompasses a range of methodologies to create variation in a population and to select individual variants with the desired function–be it a ligand, enzyme, pathway or even whole organisms. Here, we present some of the basic frameworks that underpin all evolution platforms and review some of the recent contributions from directed evolution to synthetic biology, in particular methods that have been used to engineer the Central Dogma and the genetic code.

The Saccharomyces cerevisiae strain JAY270/PE2 is a highly efficient biocatalyst used in the production of bioethanol from sugarcane feedstock. This strain is heterothallic and diploid, and its genome is characterized by abundant structural and nucleotide polymorphisms between homologous chromosomes. One of the reasons it is favored by many distilleries is that its cells do not normally aggregate, a trait that facilitates cell recycling during batch-fed fermentations. However, long-term propagation makes the yeast population vulnerable to the effects of genomic instability, which may trigger the appearance of undesirable phenotypes such as cellular aggregation. In pure cultures of JAY270, we identified the recurrent appearance of mutants displaying a mother-daughter cell separation defect resulting in rough colonies in agar media and fast sedimentation in liquid culture. We investigated the genetic basis of the colony morphology phenotype and found that JAY270 is heterozygous for a frameshift mutation in the ACE2 gene (ACE2/ace2-A7), which encodes a transcriptional regulator of mother-daughter cell separation. All spontaneous rough colony JAY270-derived isolates analyzed carried copy-neutral loss-of-heterozygosity (LOH) at the region of chromosome XII where ACE2 is located (ace2-A7/ace2-A7). We specifically measured LOH rates at the ACE2 locus, and at three additional chromosomal regions in JAY270 and in a conventional homozygous diploid laboratory strain. This direct comparison showed that LOH rates at all sites were quite similar between the two strain backgrounds. In this case study of genomic instability in an industrial strain, we showed that the JAY270 genome is dynamic and that structural changes to its chromosomes can lead to new phenotypes. However, our analysis also indicated that the inherent level of genomic instability in this industrial strain is normal relative to a laboratory strain. Our work provides an important frame of reference to contextualize the interpretation of instability processes observed in the complex genomes of industrial yeast strains.

This work presents the use of Raman spectroscopy and chemometrics for on-line control of the fermentation process of glucose by Saccharomyces cerevisiae. In a first approach, an on-line determination of glucose, ethanol, glycerol, and cells was accomplished using multivariate calibration based on partial least squares (PLS). The PLS models presented values of root mean square error of prediction (RMSEP) of 0.53, 0.25, and 0.02% for glucose, ethanol and glycerol, respectively, and RMSEP of 1.02 g L(-1) for cells. In a second approach, multivariate control charts based on multiway principal component analysis (MPCA) were developed for detection of fermentation fault-batch. Two multivariate control charts were developed, based on the squared prediction error (Q) and Hotelling's T(2) . The use of the Q control chart in on-line monitoring was efficient for detection of the faults caused by temperature, type of substrate and contamination, but the T(2) control chart was not able to monitor these faults. On-line monitoring by Raman spectroscopy in conjunction with chemometric procedures allows control of the fermentative process with advantages in relation to reference methods, which require pretreatment, manipulation of samples and are time consuming. Also, the use of multivariate control charts made possible the detection of faults in a simple way, based only on the spectra of the system.

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