Extending Federal Stewardship of the U.S. Bioeconomy

In 2020, the National Academies of Sciences, Engineering, and Medicine released the Safeguarding the Bioeconomy Consensus Study Report, which provided a recommended guiding definition for the U.S.: 

Activities encompassed by the bioeconomy include agricultural, biomedical, bioindustrial applications along with tools, kits, and services. Some of the most dynamic bioeconomy-based innovations include replacing nitrogenic, topical fertilizers with reprogrammed microbes; using enzymes to infinitely recycle all types of plastic- and textile-based polyethylene terephthalate waste; transforming mycelium into high quality leather alternatives; creating 3D printed authentic meat from plant-based products; and emulating hookworms through biomimicry-based design of long-term drug delivery systems. 

Protecting Critical and Emerging U.S. Technologies from Foreign Threats Factsheet, bioeconomy development is enabled by life science, artificial intelligence (AI) and machine learning (ML), high-performance and next-generation computing, and effective cybersecurity. Coordinated federal action in these sectors is required to fully harness the bioeconomy to advance health, climate, and the overall economy, while protecting national security. 

Based on original research and analysis conducted, here are our recommendations:

Research and development in the bioeconomy is undergoing a paradigm shift as the translation of research to end products becomes a central driver, making extensive knowledge in a variety of increasingly complex fields —including quality, safety, and regulatory science—even more essential to success. Moreover, the increasing velocity of discovery emphasizes the need to be familiar with data generation, sharing, and provenance; high-throughput data analysis; and strategic allocation of resources, which are this era’s hallmark innovations in technology and standards for experimental design.

The federal government should create interagency fellowship programs at leading biotechnology agencies to train diverse cohorts of experts who will catalyze future, transdisciplinary advancements as bioeconomy generalists. To guarantee talent is available and equitably distributed to fill the bioeconomy jobs of the future, the government must develop regional public-private partnerships for workforce upskilling. Finally, the government must increase funding to recruit and retain the bioeconomy-based knowledge of its own workforce to ensure innovations are safely developed, properly regulated, protected, and utilized.

Life sciences and biotechnology research is fueled by extracting knowledge, detecting complicated patterns, and making predictions from increasingly larger volumes and varieties of data. Artificial intelligence and machine learning catalyze connections between our understanding of proteins, organisms, ecosystems, and biomes more rapidly than possible in isolation. Therefore, to accelerate the comprehensive development of the bioeconomy, national AI capabilities need to be developed and integrated into every facet of research. Data sets must be ethically sourced, trustworthy, well-defined, and accessible. They must also cover the key domains of the bioeconomy: from human health records for developing clinical care improvements to plant and microbial genomics for agriculture and energy applications. 

The U.S. government must invest in initiatives for propelling bioeconomy research and development through the accessibility of impactful data sets and democratization of AI/ML use. Data sets should be developed through government research programs, created via synthetic data generators, and made available through sophisticated privacy-preserving data mining techniques. For example, the federal government should continue to champion innovative research programs like the National Institutes of Health’s All of Us Research Program, while simultaneously funding additional grand initiatives across life sciences and biotechnology so that bioeconomy-related data is available to researchers. 

Finally, the widespread application of AI/ML to these data sets should be enabled by the greater availability of scalable compute resources (e.g., cloud computing environments) and AI as a service and ML as a service platforms. These platforms should provide AI operations tools and processes to reduce the development time and increase the likelihood of success for delivering AI solutions.

Today, scientific data is being produced at a scale and rate never seen before. To unlock breakthrough insights across diverse data types and datasets, scalable computing environments (e.g., cloud computing) and specialized hardware (e.g., graphics processing units) are needed. 

Projects that look to increase the efficiency of laboratory data operations or to expedite the development of new, synthetic small molecules for drug design via protein folding will directly benefit bioeconomy-related research and development. 

These can accelerate informatics and advanced analytics tools, kits, and services capable of extracting granular knowledge. Continued development and greater use of next-generation computing techniques, including edge computing and quantum computing, will further disrupt the status quo of life science research—for example, through platforms that can transform smart devices into data-collection devices capable of transmitting, processing, and sharing information in the field independent of the cloud or a network for data processing. In addition to addressing cybersecurity risks posed by these next-generation computing techniques, the U.S. government must prioritize bioeconomy research and development applications. 

Maintaining and protecting intellectual property, research, and large and sensitive health data sets, requires robust security measures. Indeed, intellectual-property theft of bioeconomy assets has increased dramatically in the last decade. 

Hacking and data leakage also put privacy at risk. For example, genetic databases could be hacked, opening up daunting possibilities for advanced identity theft, genetic surveillance, or societal repression. 

Scientific research is intrinsically collaborative, requiring data sharing across networks and the cloud. Standardized enterprise solutions often lack the nuance necessary to meet the specialized needs of secured scientific environments. Thus, protecting valuable bioeconomy information assets will require new and innovative approaches such as zero trust and AI-enhanced cybersecurity.

The federal government should prioritize embedding cybersecurity subject matter experts into all life-sciences-based organizations and agencies so that native cyber solutions can be created precisely for the unique needs of the bioeconomy. In addition, bioeconomy experts should be employed by federal cyber agencies to ensure that bioeconomy-specific cyber threats are being appropriately assessed and triaged from the top.