A methanotrophic bacterium to enable methane removal for climate mitigation.

The rapid increase of the potent greenhouse gas methane in the atmosphere creates great urgency to develop and deploy technologies for methane mitigation. One approach to removing methane is to use bacteria for which methane is their carbon and energy source (methanotrophs). Such bacteria naturally convert methane to CO and biomass, a value-added product and a cobenefit of methane removal.

A Computational Framework for Identifying Promoter Sequences in Nonmodel Organisms Using RNA-seq Data Sets.

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles.

Engineering Genomes with Genotype Specification Language.

High quality DNA design tools are becoming increasingly important as synthetic biology continues to increase the rate and throughput of building and testing genetic constructs. To make effective use of expanded build and test capacity, genotype design tools must not only be efficient enough to allow for many designs to be easily created, but also expressive enough to support the complex design patterns required by scientists on the frontier of genome engineering. Genotype Specification Language (GSL) is a language-based design tool invented at Amyris that enables scientists to quickly create DNA designs using a familiar syntax.

Genotype Specification Language.

We describe here the Genotype Specification Language (GSL), a language that facilitates the rapid design of large and complex DNA constructs used to engineer genomes. The GSL compiler implements a high-level language based on traditional genetic notation, as well as a set of low-level DNA manipulation primitives. The language allows facile incorporation of parts from a library of cloned DNA constructs and from the "natural" library of parts in fully sequenced and annotated genomes.