Multipart and modular DNA part libraries and assembly standards have become common tools in synthetic biology since the publication of the Gibson and Golden Gate assembly methods, yet no multipart modular library exists for use in bacterial systems. Building upon the existing MoClo assembly framework, we have developed a publicly available collection of modular DNA parts and enhanced MoClo protocols to enable rapid one-pot, multipart assembly, combinatorial design, and expression tuning in Escherichia coli. The Cross-disciplinary Integration of Design Automation Research lab (CIDAR) MoClo Library is openly available and contains promoters, ribosomal binding sites, coding sequence, terminators, vectors, and a set of fluorescent control plasmids. Optimized protocols reduce reaction time and cost by >80% from that of previously published protocols.

D-Gluconate which is primarily catabolized via the Entner-Doudoroff (ED) pathway, has been implicated as being important for colonization of the streptomycin-treated mouse large intestine by Escherichia coli MG1655, a human commensal strain. In the present study, we report that an MG1655 ⌬edd mutant defective in the ED pathway grows poorly not only on gluconate as a sole carbon source but on a number of other sugars previously implicated as being important for colonization, including L-fucose, D-gluconate, D-glucuronate, N-acetyl-D-glucosamine, D-mannose, and D-ribose. Furthermore, we show that the mouse intestine selects mutants of MG1655 ⌬edd and wild-type MG1655 that have improved mouse intestinecolonizing ability and grow 15 to 30% faster on the aforementioned sugars. The mutants of MG1655 ⌬edd and wild-type MG1655 selected by the intestine are shown to be nonmotile and to have deletions in the flhDC operon, which encodes the master regulator of flagellar biosynthesis. Finally, we show that ⌬flhDC mutants of wild-type MG1655 and MG1655 ⌬edd constructed in the laboratory act identically to those selected by the intestine; i.e., they grow better than their respective parents on sugars as sole carbon sources and are better colonizers of the mouse intestine.

This Supporting Information document contains method detail for the paper "An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications." In particular, it details:• The computational tools used and how they were executed.• Protocols used for DNA preparation and assembly• Protocols used for cell culture and FACS• Details of optical microscopy• Methods for analysis of FACS data Computational ToolsThe compilation process used a standard Release 6 installation of the Proto p2b stand-alone compiler, 1 configured with neocompiler option. The Proto BioCompiler 2 was coded in C++ as a Proto plug-in. Both were executed on a standard MacBook laptop, with negligible execution time ( 1 second). The compilation process was executed using input configurations stored in Proto code files and invoked for each platform via command-line arguments. Intermediate models were recorded by means of standard Proto logging facilities. The final Proto BioCompiler AGRN output was encoded in a custom XML format based on SBOL 1.0. 3 MatchMaker was coded in Java as a stand-alone application, and also wrapped to produce a Clotho 4 application, for Clotho 2.0 ("tasbe"). The Clotho application version has interfaces to initialize feature, signal and part databases from Clotho databases. The algorithms and mathematical formulation of the feature matching, signal matching and part matching steps explained in detail in a stand-alone paper which is currently under review for publication. 5 MatchMaker was executed on a standard MacBook laptop, with negligible running time (under 1 second).

Molecular biologists routinely clone genetic constructs from DNA segments and formulate plans to assemble them. However, manual assembly planning is complex, error prone and not scalable. We address this problem with an algorithm-driven DNA assembly planning software tool suite called Raven (http://www.ravencad.org/) that produces optimized assembly plans and allows users to apply experimental outcomes to redesign assembly plans interactively. We used Raven to calculate assembly plans for thousands of variants of five types of genetic constructs, as well as hundreds of constructs of variable size and complexity from the literature. Finally, we experimentally validated a subset of these assembly plans by reconstructing four recombinase-based 'genetic counter' constructs and two 'repressilator' constructs. We demonstrate that Raven's solutions are significantly better than unoptimized solutions at small and large scales and that Raven's assembly instructions are experimentally valid.

Diatoms are genetically diverse unicellular photosynthetic eukaryotes that are key primary producers in the ocean. Many of the over 100 extant diatom species in the cosmopolitan genus Thalassiosira are difficult to distinguish in mixed populations using light microscopy. Here, we examine shifts in Thalassiosira spp. composition along a coastal to open ocean transect that encountered a 3-month-old Haida eddy in the northeast Pacific Ocean. To quantify shifts in Thalassiosira species composition, we developed a targeted automated ribosomal intergenic spacer analysis (ARISA) method to identify Thalassiosira spp. in environmental samples. As many specific fragment lengths are indicative of individual Thalassiosira spp., the ARISA method is a useful screening tool to identify changes in the relative abundance and distribution of specific species. The method also enabled us to assess changes in Thalassiosira community composition in response to chemical and physical forcing. Thalassiosira spp. community composition in the core of a 3-month-old Haida eddy remained largely (>80%) similar over a 2-week period, despite moving 24 km southwestward. Shifts in Thalassiosira species correlated with changes in dissolved iron (Fe) and temperature throughout the sampling period. Simultaneously tracking community composition and relative abundance of Thalassiosira species within the physical and chemical context they occurred allowed us to identify quantitative linkages between environmental conditions and community response.