Better use of abundant, renewable substrates to make specialty chemicals via economically viable processes.

Engineering Biology

Industrial Biotechnology Challenge:

Enable next-generation production through sustainable, cost-competitive, flexible, and efficient manufacturing processes.

Better use of abundant, renewable substrates to make specialty chemicals via economically viable processes.

Engineering Biology Objectives & Technical Achievements

Modular systems (enzymes, communities, cell-free systems) that can adapt to different feedstocks and be easily modified to produce different target chemicals.

Engineering DNA	Biomolecular Engineering	Host Engineering	Data Science
Ability to edit genomes of microbial and fungal species that can rapidly degrade cellulosic biomass and other renewable feedstocks.	Rapid design and production of custom enzymes and enzyme pathways. Assembled sets of proteins that can completely degrade sustainable feedstocks. Regulatory components (including sensors and networks) that program the system to adapt to the feedstock, intermediates, and side products.	Engineered microbial consortia with predictable composition, dynamics and function, to feed off of sequential byproducts in an (almost) closed-loop system.	Novel analytics tools to enable prediction and manipulation of holistic microbial ecosystem function by incorporating both biological and environmental data. Analytical tools to determine and predict matching of organism, strain, or pathway with feedstock/substrate source for best productivity, yield, and lowest cost.

Engineering DNA

Biomolecular Engineering

Host Engineering

Data Science

Ability to edit genomes of microbial and fungal species that can rapidly degrade cellulosic biomass and other renewable feedstocks.

Rapid design and production of custom enzymes and enzyme pathways.

Assembled sets of proteins that can completely degrade sustainable feedstocks.

Regulatory components (including sensors and networks) that program the system to adapt to the feedstock, intermediates, and side products.

Engineered microbial consortia with predictable composition, dynamics and function, to feed off of sequential byproducts in an (almost) closed-loop system.

Novel analytics tools to enable prediction and manipulation of holistic microbial ecosystem function by incorporating both biological and environmental data.

Analytical tools to determine and predict matching of organism, strain, or pathway with feedstock/substrate source for best productivity, yield, and lowest cost.

New enzymes and cells that work synergistically to degrade biomass and process by-products, possibly in combination with new chemical innovations.

Engineering DNA	Biomolecular Engineering	Host Engineering	Data Science
Ability to synthesize, edit, assemble, and deliver many genes and regulatory components in a single cell.	Enzymes engineered to degrade renewable materials and process byproducts faster and more completely. Metabolic or protein engineering approaches to enable complete use of all substrate components and byproducts.	Increased protein secretion rates to enable on-demand enzyme synthesis and release. Engineered microbial consortia with predictable composition, dynamics and function, to feed off of sequential byproducts in an (almost) closed-loop system.	Prediction of protein and cell assemblies that will exhibit desired target production, considering both composition and how components are physically assembled. Prediction and modeling of microbial consortia functioning, specifically synergistic pathways and byproduct recycling.

Engineering DNA

Biomolecular Engineering

Host Engineering

Data Science

Ability to synthesize, edit, assemble, and deliver many genes and regulatory components in a single cell.

Enzymes engineered to degrade renewable materials and process byproducts faster and more completely.

Metabolic or protein engineering approaches to enable complete use of all substrate components and byproducts.

Increased protein secretion rates to enable on-demand enzyme synthesis and release.

Engineered microbial consortia with predictable composition, dynamics and function, to feed off of sequential byproducts in an (almost) closed-loop system.

Prediction of protein and cell assemblies that will exhibit desired target production, considering both composition and how components are physically assembled.

Prediction and modeling of microbial consortia functioning, specifically synergistic pathways and byproduct recycling.

Use of novel and lesser-used substrates/feedstocks for manufacturing processes that are more efficient and more environmentally-sustainable than currently available substrates.

This engineering biology objective is aimed at developing new substrate and feedstock systems based solely on biotechnology industry needs, rather than utilizing offshoots of systems created for other purposes (such as farming or animal feeds).

Engineering DNA	Biomolecular Engineering	Host Engineering	Data Science
	Improved methods for transformation of organisms with heterologous pathways.	Ability to engineer currently used organisms and hosts to work well with feedstocks deemed promising from environmental and cost standpoints.	Data analysis approaches combining sustainability analyses with strain/pathway/methodology product, yield, and efficiency to determine promise of lesser-known feedstocks from both industrial-productivity and environmental-sustainability standpoints. Modeling methods combining environmental and economic factors to determine the best ways to implement production of new feedstocks.

Engineering DNA

Biomolecular Engineering

Host Engineering

Data Science

Improved methods for transformation of organisms with heterologous pathways.

Ability to engineer currently used organisms and hosts to work well with feedstocks deemed promising from environmental and cost standpoints.

Data analysis approaches combining sustainability analyses with strain/pathway/methodology product, yield, and efficiency to determine promise of lesser-known feedstocks from both industrial-productivity and environmental-sustainability standpoints.

Modeling methods combining environmental and economic factors to determine the best ways to implement production of new feedstocks.