Design paradigms and software.

Engineering biology is a complex discipline, requiring the balancing of many interdependent design choices. Despite this, current computer aided design tools are focused at the “DNA level” that require the manual connection of DNA parts to build up a design. We are developing new design paradigms embedded in software environments that allow a designer to work at a higher level and design automation is applied to convert it to a DNA sequence. These tools integrate across platforms to enable the integration of pathway engineering with genome and regulatory engineering.

Sequencing data to chemical diversity.

There is vast potential in the DNA sequence databases, including new materials, chemicals, and pharmaceuticals. The US government has invested $3.8 billion in DNA sequencing, which has populated the databases with >500 billion bp from >260,000 species. Since 1982, this has doubled every 18 months. There is no means to re-access these functions that scales with this growth. We have established a genome-to-molecule pipeline that systematically identifies pathways of interest, rebuilds them for a new host, and screens them for their products.

Highly-parallelized DNA construction.

It is technically challenging to build long strands of DNA containing many genetic parts with high fidelity. We are applying automation and manufacturing principles to the construction of large designs that require many genes and genetic parts. Many constructs are built simultaneously with multi-megabase libraries being routine.

Debugging living systems.

Engineers need a complete picture of how their designs impact a cell. Techniques from systems biology can measure all proteins, RNA, and metabolites, but they are expensive and are usually performed only on the top designs. Our methods apply these simultaneously in single cells for a large design library, providing a more complete picture of how design choices influence the cell.

Learning from big data.

Engineers are deluged with terabytes of data from screens, growth assays, and –omics measurements. Integrating these datasets to deduce why a particular design works is slow, requiring complex software platforms and user expertise. Thus, the data is typically used to understand a design “after the fact” as opposed to fully informing the next round of design. We have developed learning algorithms that automate the integration and analysis of big data sets and make actionable design decisions.

Rapid prototyping for difficult hosts.

In some organisms, it is impossible to screen the many genetic designs produced by the Foundry. Rapid prototyping closes this gap. Designs are pre-screened for proper part function before selecting the most valuable for evaluation in the desired host. This capability is offered for organisms that are difficult to transform with DNA and grow, such as crop plants and industrial microorganisms, including fungi and bacteria.