Advanced Algal Biofoundries for the Production of Polyurethane Precursors
[1];
- University of California, San Diego, CA (United States)
- University of California, Davis, CA (United States)
- Georgia Institute of Technology, Atlanta, GA (United States)
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
The primary goal of the BEEPs project was to develop a process that could accelerate the development of algae as bioproduction platforms, from initial chemical product concept to an economically viable market supply. Under this program we elected to develop strains of algae that could generate polyurethane precursors, while simultaneously developing basic genetic tools to enable improved algal production systems. This program was specifically designed to incorporate National Laboratories as a means to utilize the expertise and facilities for new bio-production platforms. To that end, we designed a program to collaborate with the Agile BioFoundry at Lawrence Berkeley National Laboratory (LBNL) and computational platforms at Pacific Northwest National Laboratory (PNNL). In addition to these National Laboratory partners, we also had academic partners from UC Davis and Georgia Tech, as well as commercial partners Algenesis Materials and BASF. To achieve these goals, we initially focused on developing the genetic tools and high throughput screening technologies necessary to generate and assess production of polymer precursors (succinic acid) in algae and cyanobacteria, including advanced promoters and biosensors. In parallel, we computationally identified potential production bottlenecks and then used the developed genetic tools to increase production rates and yields. Constant feedback of data was used in conjunction with machine learning, high-throughput cell sorting, and synthetic biology, for additional targeted metabolic engineering. Multiple rounds of tool design, building, testing, and learning were supplied to partners at PNNL and LBNL to develop new models and tools that could expedite bioproduction platform development and increase yield performance. We targeted, and achieved, the FOA requirement yield metric of 20 g/L, as a milestone and deliverable from at least one of our engineered strains for the production of succinic acid.
- Research Organization:
- Univ. of California, San Diego, CA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO)
- DOE Contract Number:
- EE0008491
- OSTI ID:
- 2217593
- Report Number(s):
- DOE-UCSD-0008491-1
- Country of Publication:
- United States
- Language:
- English
Similar Records
Accelerating Engineered Microbe Optimization through Machine Learning and Multi-Omics Datasets [Abstract]
Application of Machine Learning to Improve Biobased Glucaric Acid Production (CRADA 504)