Engineering Biology
Industrial Biotechnology Challenge:

Scalable production of novel and existing products that are more sustainable and economically- and environmentally-friendly.

Better tools for rapidly translating desired products or process features into industrial workflows and accelerate time-to-market.

Engineering Biology Objectives & Technical Achievements

Predictive and generalized models that allow laboratory-scale results to be accurately projected to industrial scale processes and vice-versa.

Engineering DNA Biomolecular Engineering Host Engineering Data Science

Methods for creating variant libraries that can be used for validating models of genetic circuits and pathways.

Ability to predict function from sequence.

Ability to design sequences for desired functions.

Better methods for predicting interactions between components and for designing components and subsystems that behave as expected.

Higher-throughput data collection and analysis.

Better tools for predictive modeling across scales and environments.

Ability to estimate robustness of circuits and pathways to genetic, host, and environmental context.

Common (and reproducible) standards for biological components and subsystems that enable re-use and efficient component suppliers.

Engineering DNA Biomolecular Engineering Host Engineering Data Science

Methods for modular assembly of subsystems and replacement of components required to reconfigure subsystem interfaces.

Protein libraries that allow modular replacement of domains to mix and match functions required for circuit and pathway engineering.

Methods of defining “modules”, and flexible interconnection of modules, that maintain the desired function of the module independent of the operation of other parts of the circuit or genetic, host, environmental context.

Methods for modeling components/subsystems that allows better characterization and prediction of effects of interaction with other modules and cellular resources, as well as the effects of uncertainty (such as context or system noise).

Automated techniques for assembly and characterization of complex circuits consisting of thousands of individual elements (organized as interacting subsystems).

Increased rate of design-build-test-learn cycles that combine design, modeling, prototyping, implementing, and characterization of components, pathways/circuits, subsystems, cells, consortia, and multicellular organisms.

Engineering DNA Biomolecular Engineering Host Engineering Data Science

Faster, lower-cost methods for creating genome-length sequences that are generated by design tools.

Better methods for design and characterization of proteins and multi-protein complexes for performing a wide variety of functions required at component to subsystem scales.

Ability to design, implement, and characterize circuits/pathways, subsystems, cells, consortia and multicellular organisms consisting of hundreds to millions of individual components through a modular, hierarchical framework that enables reuse of interacting components.

Layered and modular design abstractions and the modeling, characterization, and testing tools required to support the creation and use of components, pathways/circuits, and subsystems to create engineered cells, consortia, and multicellular organisms.

Deployment and improved use of automation for both research and translational activities to increase throughput.

Engineering DNA Biomolecular Engineering Host Engineering Data Science

Well-defined libraries with associated assembly and editing methods for genomic sequences that support the output of compilers and other design tools.

Increased cross-talk between geneticists and automation experts to hone efficient, high-throughput, automated laboratory protocols and workflows for gene editing and assembly and genetic library creation and exploration.

Well-defined libraries with associated assembly and editing methods for protein domains and molecular machines that support the output of compilers and other design tools.

Engineered hosts with predictable composition, dynamics, and function.

Continually refined physical automation infrastructure and processes (including factories, robots, assembly lines, and workflows) to enable more efficient and modular high-throughput engineering of multiple different kinds of organisms, pathways, and product outputs.

Widely adopted methods for defining reproducible workflows that can be used by cloud laboratories to implement protocols for implementation, characterization, and verification and validation of components, pathways/circuits, sub-systems, cells, multicellular organisms, consortia, and automation platforms.

Last updated: June 19, 2019 Back