Engineering Biological Systems

Strategic Vision Team Update

Laurel Miner

NIST Program Coordination Office 
The team 

Elizabeth Strychalski (MML)

Mandy Esch (CNST) David Hoogerheide (NCNR)

Thomas Perkins (PML) Anne Plant (MML)

Jim Booth (CTL)

Kelley Rogers (MML) Vijay Srinivasan (EL)

Laurel Miner (PCO) Mary Brady (ITL)
NIST Biological Sciences Strategic Priorities

 Complex Biotherapeutics

Develop measurement science,

standards and tools to support the

quantitative definition of complex

biologic therapeutics and correlation

of their structural differences with

clinical outcomes.

 Engineering Biology 

Develop the measurements and models

for engineering biology to map the

fundamental principles that drive the

development of the next generation of

bio-based products.

 Microbial Metrology 

Develop measurement infrastructure

for microbial measurements in health

and environmental applications.

 Precision Medicine 

Develop measurement science and

standards to ensure confidence in

clinical decision-making, and ultimately

enable adoption of precision medicine.

 Reproducibility of Biomedical Research 

Establish NIST as the agency for measurement

assurance to enable reproducibility of biomedical

research results.

https://mmlstrategy.nist.gov/

Previously
Why Now? 

Preparing for Future Products of Biotechnology, National Academies of Science, 2017

• Industry can deliberately alter cell-based platforms to produce biologically synthesized products, 

from high-value biopharmaceuticals to commodity chemicals.

• Technology is increasingly accessible and advancing rapidly, including, next-generation DNA 

sequencing, ‘omics’ technology, and targeted gene-editing tools such as CRISPR/Cas. 
Why NIST?

• Existing capabilities include

– genomic and biomolecular 

measurement assurance, 

– imaging technologies, 

– sensors and nanoscale devices, 

– data and AI technology

• Existing partnerships

– industrial biomanufacturing

– academic laboratories

– stakeholder communities

NIST Provides a 

Foundation of 

Trust 
Vision and Strategies 

Biological systems can be intentionally and efficiently engineered to produce robust, safe, 

and trusted outcomes, enabling a growing bioeconomy.

• Develop a measurement toolkit to quantitatively 

define critical attributes and performance metrics for 

engineered biological systems and associated 

biomanufactured products. 

• Support the development of foundational knowledge 

of enabling engineering principles that harness the 

controlling forces of biological systems. 

Measurement 

Toolkit

Predictive 

Models

Informs needs

Ensures quality of data
Measurement Toolkit

• Required measurements will be highthroughput, multimodal, non-destructive, 

and include and bridge molecular and 

cellular-system scales

• NIST must provide methods, products, and 

tools for measurement assurance

• NIST should leverage current strengths and 

build new capabilities through partnerships

Measurement 

Toolkit

Predictive 

Models

Informs needs

Ensures quality of data
Predictive Models

• Models linking genotype and other 

perturbations to intermediates and outcomes in 

different contexts will allow intentional design

• New AI and machine learning tools will leverage 

data variety and volume

• NIST should partner with stakeholders to curate 

and share high-quality data and models for 

integration, mining, and discovery

• NIST’s measurement toolkit will enable partners 

to ensure the quality of data shared

Measurement 

Toolkit

Predictive 

Models

Informs needs

Ensures quality of data
What does success look like? 

Measurement Toolkit

• NIST has broader capabilities in cutting-edge bioengineering tools and techniques

• NIST’s unique sensor and imaging capabilities provide new tools for measuring 

cellular systems

• NIST-developed tools and protocols for improving assurance in engineering biology 

practices frequently used by academic and industrial stakeholders

Predictive Models

• Standards for data curation and sharing, including natural language ontologies

• Stakeholders are actively sharing data and models in open-source facility

• A suite of multiscale models and AI tools are available for predicting complex 

cellular response
Questions for Discussion

• Even with a focus on engineering biological systems, the problem 

space could still be all-encompassing. Are there particular problem 

areas that NIST should target above others?

• How does NIST ensure relevancy in a field with so many other large 

Government players?

• What new tools and approaches can NIST offer to provide 

measurement assurance in biology?