IARPA-RFI-22-06_C.pdf

Request for Information (RFI) on Characterization of Biomanufactured Materials and Production Pipelines

IARPA-RFI-22-06

IARPA is seeking information on methods, databases, approaches, and appropriate metrics for characterizing the structure, function, and synthesis pipelines of biomanufactured materials. This RFI is issued for planning purposes only and does not constitute a formal solicitation for proposals or suggest the procurement of any material, data sets, etc.

Definitions

• Biomanufacturing: The use of a living organism, or component elements from a living organism, to produce or generate a product.

• Biomanufactured material (BMM): A product, material, subcomponent, or precursor derived from biomanufacturing processes.

• Chassis Systems: The biological factories used in biomanufacturing broadly broken down by – prokaryotic, eukaryotic, and cell-free.

Background Biomanufacturing offers unprecedented flexibility and access to the production of numerous types of materials, potentially replacing up to 60% of traditionally derived material inputs and half of the industrial chemical feed stocks supporting US defense needs by 2050. [1, 2, 3, 4] Pharmaceuticals, industrial chemicals, energetics, petroleum and petroleum by-products, and living materials have been produced cheaper, more efficiently, and through alternative supply sources using biomanufacturing. [5, 6, 7, 8, 9] Additionally, the processes and technologies, including the chassis systems, present unique challenges to secure as it takes only a small initial quantity to grow into full production capability, offering routes to illegally circumvent intellectual property laws. [10, 11] As the role and impact of biomanufacturing expands, new approaches and predictive capabilities are essential to secure national security interests.

Historically, new manufacturing approaches across industries has often led to the development of new ways to characterize products/materials and their manufacturing pipelines. [12, 13, 14] Commonly termed reverse engineering, these approaches, in general, follow a similar assessment process. This process includes characterization of the product or material being analyzed, characterization of the matrix solute or structure surrounding the material, and reconstruction of the material, using experimental and theoretical processes. The possibility to develop similar capabilities, adapting to the complexities of living factories, numerous feedstocks, and varying production scales may now be possible using a combination of data-driven machine learning, explicitly programmed statistical approaches, and wet-lab analytics.

Leveraging recent advances in more sensitive analytical instruments – both destructive and non-destructive, analytic processes utilizing these instruments, molecular engineering tools, and data-driven machine learning modeling approaches combined with an ever growing and expanding reference literature associated with biomanufacturing will inform towards the RFI questions below. This includes expanding knowledge of successfully engineered and characterized chassis systems and metabolic pathways, a database which may serve as a foundation of metabolic pathway and multi-omic knowledge. [15, 16] New instrumentation and methodologies, such as 2-Dimensional nuclear magnetic resonance, cryo-electron microscopy, and mass spectrometry data independent acquisition enable more accurate and/or complete characterization of material structure and matrix composition, continuing to expand reference databases. [17, 18, 19] Novel in silico approaches, RoseTTAFold, AlphaFold 2, and ProtENN, enable prediction structure, function, and metabolites using machine learning with accuracies similar to experimental approaches. [20, 21, 22, 23, 24, 25] The combination of these advances may enable the research objectives this RFI seeks to identify.

Scope

For IARPA to evaluate if there have been sufficient advancements to support a research effort aligning with BMM characterization and synthesis pipeline prediction, it is necessary to ask the following questions:

1. What emergent analytical approaches and/or instruments are most likely to enable comprehensive characterization of a BMM and its matrix?

2. What approaches enable extensible and accurate structure and function prediction across numerous BMM types?

3. What confounders – equipment, chassis, feedstocks, metabolites, purification approaches, encapsulation approaches - are likely to obfuscate efforts to predict synthesis pipelines?

4. What signatures are known or are likely linked to variations between chassis, scale, feedstock, growth, purification processes, or other elements of the biomanufacturing process?

5. What metrics are most appropriate for evaluating improvements for BMM structure prediction, BMM function prediction, and synthesis pipeline prediction?

a. What metrics are currently used by industry, government regulators, and academia?

6. What curated databases exist which could inform towards BMM characterization, functional annotation, and pipeline synthesis prediction?

Preparation Instructions to Respondents:

IARPA requests that respondents submit responses to the above questions for use by the Government in formulating a potential program. IARPA requests that submittals address one or more of the above questions and include a brief technical description of potential approach(es) or concept, outline critical technical issues/obstacles, describe how the approach may address those issues/obstacles and comment on the expected performance and robustness of the proposed approach. If appropriate, respondents may also choose to provide a non-proprietary rough order of magnitude (ROM) estimate regarding what such approaches might require in terms of funding and other resources for one or more years to execute the respondent’s vision for structural and functional characterization of BMMs and/or synthesis pipelines. This announcement contains all of the information required to submit a response. No additional forms, kits, or other materials are needed.

IARPA welcomes responses from all capable and qualified sources including academia, industry, government, national laboratories, and other federally funded research entities from within and outside of the U.S. This includes perspectives and associated capabilities for effectively evaluating potential research approaches.

In response to submissions to this RFI, IARPA may organize an invitation-only workshop for the purpose of reviewing and discussing relevant current and future research. Should a workshop occur, participants may be asked to make formal presentations based on their RFI submissions or associated research. Written submissions and information discussed at a workshop may assist in the formulation of future U.S. Government research.

Reponses must meet the following formatting requirements:

1. A one-page cover sheet that identifies the title, organization(s), respondent's technical and administrative points of contact - including names, addresses, phone and fax numbers, and email addresses of all co-authors, and clearly indicating its association with IARPA-RFI-22- 06;

2. A substantive, focused, one-half page executive summary;

3. A response to one, some, or all of the questions posed above with a maximum of two pages;

4. A description (limited to 5 pages) of the technical challenges associated with the above topic and potential research approaches to address the interests described;

a. A single quad-chart depicting the described approach, key ideas, and potential impact

– may include supportive figures or illustrations. This quad chart can be submitted as a single slide Power Point or single page PDF. This does not count against the 5-page limit.

5. A list of references (any significant claims or reports of success must be accompanied by citations – citations do not count against the 5-page limit; and

6. All documents are limited to 12-point Times New Roman font, appropriate for single-sided, single-spaced 8.5 by 11-inch paper with 1-inch margins.

Submission Instructions to Respondents:

Responses to this RFI are due no later than 5:00 p.m., Eastern Time, May 06, 2022. All submissions must be electronically submitted to dni-iarpa-rfi-22-06@iarpa.gov as a PDF document. Inquiries to this RFI must be submitted to dni-iarpa-rfi-22-06@iarpa.gov. Do not send questions with proprietary content. No telephone inquiries will be accepted.

Disclaimers and Important Notes:

This is an RFI issued solely for information and planning purposes and does not constitute a solicitation or authority to enter into negotiations for a contract. Respondents are advised that IARPA is under no obligation to acknowledge receipt of the information or to provide feedback to respondents with respect to any information submitted under this RFI. Responses to this notice are not offers and cannot be accepted by the Government to form a binding contract.

Respondents are solely responsible for all expenses associated with responding to this RFI.

IARPA will not provide reimbursement for costs incurred in responding to this RFI. It is the respondent's responsibility to ensure that the submitted material has been approved for public release by the information owner. The Government does not intend to award a contract on the basis of this RFI or to otherwise pay for the information solicited, nor is the Government obligated to issue a solicitation based on responses received. No proprietary and no classified concepts or information shall be included in the submittal. However, should a respondent wish to submit classified concepts or information, prior coordination must be made with the IARPA Chief of Security. Email the Primary Point of Contact with a request for coordination with the IARPA Chief of Security. Input on technical aspects of the responses may be solicited by IARPA from non-Government consultants/experts who are bound by appropriate non-disclosure requirements.

Contracting Office Address:

Office of the Director of National Intelligence, Intelligence Advanced Research Projects Activity Washington, District of Columbia 20511 United States

Primary Point of Contact:

Dr. Michael Patterson Program Manager Intelligence Advanced Research Projects Activity

References

[1] Y.-H. P. Zhang, J. Sun and Y. Ma, "Biomanufacturing: history and perspective," Journal of Industrial Microbiology and Biotechnology, 2017.

[2] M. Chui, M. Evers, J. Manyika, A. Zheng and T. Nisbet, "The Bio Revolution: Innovations transforming economies, societies, and our lives," McKinsey Global Institute, San Francisco, 2020.

[3] F. Candelon, M. Gombeaud, G. Stokol, V. Patel, A. Gourévitch and N. Goeldel, "Synthetic Biology Is About to Disrupt Your Industry," Boston Consulting Group, 2022. [Online]. Available:

https://www.bcg.com/en-us/publications/2022/synthetic-biology-is-about-to-disrupt-your-industry.

[4] Fast Track Action Subcommittee on Critical and Emerging Technologies, "Critical and Emerging Technologies List Update," National Science and Technology Council, 2022.

[5] Y. Cao, W. Niu, J. Guo, M. Xian and H. Liu, "Biotechnological production of 1,2,4-butanetriol: An efficient process to synthesize energetic material precursor from renewable biomass," Nature:

Scientific Reports, 2015.

[6] S. Doliente, A. Narayan, J. F. D. Tapia, N. J. Samsatli, Y. Zhao and S. Samsatli, "Bio-aviation Fuel: A Comprehensive Review and Analysis of the Supply Chain Components," Frontiers in Energy Research, 2020.

[7] S. Galanie, K. Thodey, I. Trenchard, M. F. Interrante and C. Smolke, "Complete biosynthesis of opioids in yeast," Science, 2015.

[8] Nature Biotechnology, "How biomanufacturing can save the world," Nature Biotechnology, 2021.

[9] C. Voigt, "Synthetic biology 2020–2030: six commercially-available products that are changing our world," Nature Communications, 2020.

[10] Department of Justice - Office of Public Affairs, "Harvard University Professor and Two Chinese

Nationals Charged in Three Separate China Related Cases," 2020. [Online].

[11] Department of Justice - Office of Public Affairs, "Hospital Researcher Sentenced to Prison for Conspiring to Steal Trade Secrets and Sell to China," 2021. [Online].

[12] S. Quadir, J. Chen, D. Fote, N. Asadizanjani, S. Shahbazmohamadi, L. Wang, J. Chandy and M.

Tehranipoor, "A Survey on Chip to System Reverse Engineering," ACM JOurnal on Emerging Technologies in Computing Systems, 2017.

[13] W. Wang, Technology of Reinvention, CRC Press: Taylor & Francis Group, 2014.

[14] A. Jumar, P. Jain and P. Pathak, Reverse Engineering in Product Manufacturing: An Overview, DAAAM International Scientific Book, 2013.

[15] A. B. Ramzi, S. N. Baharum, H. Bunawan and N. S. Scrutton, "Streamlining Natural Products

Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering," Frontiers in Bioengineering and Biotechnology, 2020.

[16] F. P. Breitwieser, J. Lu and S. L. Salzberg, "A review of methods and databases for metagenomic classification and assembly," Briefings in Bioinformatics, 2019.

[17] R. G. Brinson and E. al., "Enabling adoption of 2D-NMR for the higher order structure assessment of monoclonal antibody therapeutics," mAbs, 2018.

[18] E. Callaway, "Revolutionary cryo-EM is taking over structural biology," Nature, 2020. [Online].

Available: https://www.nature.com/articles/d41586-020-00341-9.

[19] M. Feuersteina, R. Kurulugamab, S. Hanna and T. Causon, "Novel acquisition strategies for metabolomics using drift tube ion mobility-quadrupole resolved all ions time-of-flight mass spectrometry (IM-QRAI-TOFMS)," Analytica Chemica Acta, 2021.

[20] J. Dong, M. Zhao, Y. Liu, Y. Su and X. Zeng, "Deep learning in retrosynthesis planning: datasets, models and tools," Briefings in Bioinformatics, 2021.

[21] M. Baek and e. al., "Accurate prediction of protein structures and interactions using a three-track neural network," Science, 2021.

[22] J. Jumper and e. Al., "Highly accurate protein structure prediction with AlphaFold," Nature, 2021.

[23] C. Lawson and e. Al., "Machine learning for metabolic engineering: A review," Metabolic

Engineering, 2021.

[24] L. Cao and e. Al., "Design of protein binding proteins from target structure alone," Nature, 2022.

[25] M. Bileschi and e. Al., "Using deep learning to annotate the protein universe," Nature

Biotechnology, 2022.