The Agile BioFoundry will unite and expand the capabilities of the national laboratories to develop a robust, agile biomanufacturing platform accessible to researchers across the private and public sectors.
The Agile BioFoundry will integrate industrially-relevant production microbes, advanced tools for biological engineering and data analysis, and robust, scaled up processes for integrated biomanufacturing.
ABF Technical Focus and Capabilities
The Agile BioFoundry (ABF) will unite the capabilities of the Department of Energy National Laboratories to integrate sophisticated synthetic biology tools including software for biological design, machine learning, high-throughput analytics, techno-economic and life cycle analyses, and expertise, into an agile and dynamic platform for biomanufacturing of microbes for production of bio-based fuels and chemicals.
The core of the ABF effort will be to develop the integrated Design-Build-Test-Learn cycle, where Design is informed by techno-economic analyses, life cycle assessments, and process considerations that allow for optimum host organism selection, and construction in Build. Test is represented by multiple arrows to indicate various dimensions of test, including bench-scale analysis (Test), integrated bioprocesses (PI), and scaling to relevant scales (S) for the bioprocess. Each of these dimensions of Test will provide valuable information for Learn and will be incorporated into the upfront TEA/LCA and process considerations for future rounds of Design.
Design is the ability to design bioprocesses for the desired target molecules, including the necessary workflows to build out pathways in a host organism and the needed testing to understand performance. R&D for design includes the development of computer assisted design software for biological pathways, databases of known expression systems, and the genetic tools available to build or enhance organisms. Design is further informed by iterations of techno-economic analyses (TEA –described in more detail below) and life cycle assessments (LCA – also see below for more detail) combined with overall process considerations that allow for ideal host organism selection.
Build encompasses the selection of the host organism for the designed pathways, liquid handling and other automated methods of assembling the genetic systems for the pathways, and transformation capabilities to insert pathways into the host organism.
Test includes the assays, instrumentation, and equipment necessary to understand how a designed pathway behaves in a host organism under specific growth conditions. Just as it is important to understand how a pathway performs in its host, it is also important to understand how that designed organism fits into an integrated process, including the feedstocks used and upstream and downstream unit operations. Additionally, understanding the performance at increasing scales is critical to understanding an organism’s performance in the environments of fermenters or other reactors.
Learn addresses one of the critical barriers in biological engineering–the ability to rationally improve upon design based upon data gathered in Test. Through machine learning, sophisticated statistical modeling, and metabolic flux analysis, this data will be translated into predictions that can be combined with techno-economic analyses and life cycle assessments, process considerations, and host organism parameters to improve and predict the design of future pathways and processes. The databases and information created through Learn will be made accessible to the community to share the knowledge gained and advance biomanufacturing broadly.
Techno-economic analyses (TEA) and life cycle assessments (LCA) enable researchers to understand impacts beyond the organism on process considerations. These analyses define criteria around pathway and host organism selection that allow for improved economic feasibility and minimized environmental impacts for a bioprocess and help direct research towards elements of a process that most influences cost or environmental performance goals.
Process considerations are key to designing a robust bioprocess. The selected process includes many points where understanding the host organism and designed pathway are critical. Unit operations such as fermentation conditions and separations technologies are determined understanding how a particular pathway will behave in a selected host. Design can take potential contaminants, toxic byproducts, or any other number of factors into consideration to reduce the challenges inherent in integrating a process.
Host organism selection is a key step in designing a bioprocess. Beyond impacting key production metrics like titer, rate, and yield, organism selection also affects pathway and process design. Organisms that perform well at high-temperature can offer savings on the energy required to maintain low temperatures in fermenters. Organisms that perform well at low or high pH can tolerate production of specific molecules. While there are well developed industrial host organisms, there is still a great need for further development, including genetic manipulation systems and growth conditions, of a range of host organisms that can act as chassis for a variety of diverse bioprocess pathways. The host selection for the ABF will focus on covering a range of product spaces through a diverse slate of organisms that have the potential for wide use in industry, or are already widely used but can use improvement.
Process integration and scaling are critical for understanding strain performance in the context of an overall bioprocess and its potential translation to an industrial setting. During process integration, the strain is grown at the appropriate scale using the relevant feedstock that has been formatted for the bioprocess and subjected to the appropriate pretreatment and starch conversion steps. Additionally, the downstream processing steps including product separation, purification, and upgrading are incorporated to understand and identify problems, as well as to provide data for TEA and LCA activities.
Integration of these activities is key to the mission, goal, and success of the ABF. Currently, Design, Build, and Test are implemented and integrated at varying levels in industry depending upon the mission, business model, and resources of a company. Learn still remains a critical barrier, with only a few companies investing in technologies to use machine learning or statistical methods to predict better organism and pathway design. A truly integrated DBTL platform that incorporates an understanding of overall process design, TEA/LCA, and scaling does not yet exist in private industry. The ABF will leverage and unite capabilities at the National Labs to offer an integrated platform that is accessible to companies and to researchers everywhere for rapid and efficient engineering of biology.