Organizing genome engineering for the gigabase scale

Abstract

Engineering the entire genome of an organism enables large-scale changes in organization, function, and external interactions, with significant implications for industry, medicine, and the environment. Improvements to DNA synthesis and organism engineering are already enabling substantial changes to organisms with megabase genomes, such as Escherichia coli and Saccharomyces cerevisiae. Simultaneously, recent advances in genome-scale modeling are increasingly informing the design of metabolic networks. However, major challenges remain for integrating these and other relevant technologies into workflows that can scale to the engineering of gigabase genomes.In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will likely be required for gigabase genome engineering. We recommend that the community address these challenges by 1) adopting and extending existing standards and technologies for representing and exchanging information at the gigabase genomic scale, 2) developing new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing new legal and contractual infrastructure to better enable collaboration across multiple institutions.

The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering

“…SBOL supports the representation of abstraction hierarchies across multiple scales of bioengineering, from individual molecules to multi-cellular compositions and complete synthetic genomes (Bartley et al, 2020 ). The SBOL data model supports a wide variety of important use cases for synthetic biology and bioengineering, including visualization (McLaughlin et al, 2016 ), sequence design automation (Zhang et al, 2017 ), sharing of genetic design information (McLaughlin et al, 2018 ), metabolic engineering (Kuwahara et al, 2017 ), and generation of dynamical models from sequence representations (Misirli et al, 2018 ).…”

Current State and Future Directions of Genetics and Genomics of Endophytic Fungi for Bioprospecting Efforts

“…Synthetic biology offers vast opportunity to investigate the function of BGCs, even cryptic ones or those identified in metagenomic assemblies, in a heterologous host. The possibility to generate long synthetic DNA fragments (Eisenstein, 2020 ) and advanced DNA assembly strategies (Bartley et al, 2020 ) allow for synthesis of entire clusters including de novo design of BGCs with host promoters and regulatory elements for better heterologous expression. Selection of a host and the design of DNA constructs suitable for the host (presence/absence of introns, codon usage, choice of vectors, and burden of foreign DNA) are the most important success limiting factors in heterologous expression, but they can be overcome with new design tools.…”

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

“…In recent years, advances in genomic measurement technologies for data generation, the establishment of data repositories, and the development of WCM simulation platforms have significantly facilitated the derivation of WCMs [see (Goldberg et al, 2018) for a review]. Nevertheless, the implementation of WCM-based design-build-test cycles for genome-scale engineering requires further challenges to be addressed (Bartley et al, 2020).…”

“…We foresee that automation will play a fundamental role in the derivation of WCMs for eukaryotic organisms and in their application to design complex processes. Ideally, we would like to introduce automation at different stages, such as data extraction from the literature, model derivation, and model/data integration both within the model fitting and validation steps, and when comparing in silico design prediction with in vivo tests (Bartley et al, 2020). This, in turn, will require the adoption of standards for both data and model repositories.…”