Parallel laboratory evolution and rational debugging reveal genomic plasticity to S. cerevisiae synthetic chromosome XIV defects.

Synthetic chromosome engineering is a complex process due to the need to identify and repair growth defects and deal with combinatorial gene essentiality when rearranging chromosomes. To alleviate these issues, we have demonstrated novel approaches for repairing and rearranging synthetic Saccharomyces cerevisiae genomes. We have designed, constructed, and restored wild-type fitness to a synthetic 753,096-bp version of S. cerevisiae chromosome XIV as part of the Synthetic Yeast Genome project. In parallel to the use of rational engineering approaches to restore wild-type fitness, we used adaptive laboratory evolution to generate a general growth-defect-suppressor rearrangement in the form of increased TAR1 copy number. We also extended the utility of the synthetic chromosome recombination and modification by loxPsym-mediated evolution (SCRaMbLE) system by engineering synthetic-wild-type tetraploid hybrid strains that buffer against essential gene loss, highlighting the plasticity of the S. cerevisiae genome in the presence of rational and non-rational modifications.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments.

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Blueprints for Biosensors: Design, Limitations, and Applications.

Biosensors are enabling major advances in the field of analytics that are both facilitating and being facilitated by advances in synthetic biology. The ability of biosensors to rapidly and specifically detect a wide range of molecules makes them highly relevant to a range of industrial, medical, ecological, and scientific applications. Approaches to biosensor design are as diverse as their applications, with major biosensor classes including nucleic acids, proteins, and transcription factors. Each of these biosensor types has advantages and limitations based on the intended application, and the parameters that are required for optimal performance. Specifically, the choice of biosensor design must consider factors such as the ligand specificity, sensitivity, dynamic range, functional range, mode of output, time of activation, ease of use, and ease of engineering. This review discusses the rationale for designing the major classes of biosensor in the context of their limitations and assesses their suitability to different areas of biotechnological application.