Automating Scientific Discovery: A Research Agenda for Advancing Self-Driving Labs

Despite significant advances in scientific tools and methods, the traditional, labor-intensive model of scientific research in materials discovery has seen little innovation. The reliance on highly skilled but underpaid graduate students as labor to run experiments hinders the labor productivity of our scientific ecosystem. An emerging technology platform known as Self-Driving Labs (SDLs), which use commoditized robotics and artificial intelligence for automated experimentation, presents a potential solution to these challenges.

SDLs are not just theoretical constructs but have already been implemented at small scales in a few labs. An ARPA-E-funded Grand Challenge could drive funding, innovation, and development of SDLs, accelerating their integration into the scientific process. A Focused Research Organization (FRO) can also help create more modular and open-source components for SDLs and can be funded by philanthropies or the Department of Energy’s (DOE) new foundation. With additional funding, DOE national labs can also establish user facilities for scientists across the country to gain more experience working with autonomous scientific discovery platforms. In an era of strategic competition, funding emerging technology platforms like SDLs is all the more important to help the United States maintain its lead in materials innovation.

Challenge and Opportunity

New scientific ideas are critical for technological progress. These ideas often form the seed insight to creating new technologies: lighter cars that are more energy efficient, stronger submarines to support national security, and more efficient clean energy like solar panels and offshore wind. While the past several centuries have seen incredible progress in scientific understanding, the fundamental labor structure of how we do science has not changed. Our microscopes have become far more sophisticated, yet the actual synthesizing and testing of new materials is still laboriously done in university laboratories by highly knowledgeable graduate students. The lack of innovation in how we historically use scientific labor pools may account for stagnation of research labor productivity, a primary cause of concerns about the slowing of scientific progress. Indeed, analysis of scientific literature suggests that scientific papers are becoming less disruptive over time and that new ideas are getting harder to find. The slowing rate of new scientific ideas, particularly in the discovery of new materials or advances in materials efficiency, poses a substantial risk, potentially costing billions of dollars in economic value and jeopardizing global competitiveness. However, incredible advances in artificial intelligence (AI) coupled with the rise of cheap but robust robot arms are leading to a promising new paradigm of material discovery and innovation: Self-Driving Labs. An SDL is a platform where material synthesis and characterization is done by robots, with AI models intelligently selecting new material designs to test based on previous experimental results. These platforms enable researchers to rapidly explore and optimize designs within otherwise unfeasibly large search spaces.

Today, most material science labs are organized around a faculty member or principal investigator (PI), who manages a team of graduate students. Each graduate student designs experiments and hypotheses in collaboration with a PI, and then executes the experiment, synthesizing the material and characterizing its property. Unfortunately, that last step is often laborious and the most time-consuming. This sequential method to material discovery, where highly knowledgeable graduate students spend large portions of their time doing manual wet lab work, rate limits the amount of experiments and potential discoveries by a given lab group. SDLs can significantly improve the labor productivity of our scientific enterprise, freeing highly skilled graduate students from menial experimental labor to craft new theories or distill novel insights from autonomously collected data. Additionally, they yield more reproducible outcomes as experiments are run by code-driven motors, rather than by humans who may forget to include certain experimental details or have natural variations between procedures.

Self-Driving Labs are not a pipe dream. The biotech industry has spent decades developing advanced high-throughput synthesis and automation. For instance, while in the 1970s statins (one of the most successful cholesterol-lowering drug families) were discovered in part by a researcher testing 3800 cultures manually over a year, today, companies like AstraZeneca invest millions of dollars in automation and high-throughput research equipment (see figure 1). While drug and material discovery share some characteristics (e.g., combinatorially large search spaces and high impact of discovery), materials R&D has historically seen fewer capital investments in automation, primarily because it sits further upstream from where private investments anticipate predictable returns. There are, however, a few notable examples of SDLs being developed today. For instance, researchers at Boston University used a robot arm to test 3D-printed designs for uniaxial compression energy adsorption, an important mechanical property for designing stronger structures in civil engineering and aerospace. A Bayesian optimizer was then used to iterate over 25,000 designs in a search space with trillions of possible candidates, which led to an optimized structure with the highest recorded mechanical energy adsorption to date. Researchers at North Carolina State University used a microfluidic platform to autonomously synthesize >100 quantum dots, discovering formulations that were better than the previous state of the art in that material family.

 AstraZeneca automation equipment

These first-of-a-kind SDLs have shown exciting initial results demonstrating their ability to discover new material designs in a haystack of thousands to trillions of possible designs, which would be too large for any human researcher to grasp. However, SDLs are still an emerging technology platform. In order to scale up and realize their full potential, the federal government will need to make significant and coordinated research investments to derisk this materials innovation platform and demonstrate the return on capital before the private sector is willing to invest it.

Other nations are beginning to recognize the importance of a structured approach to funding SDLs: University of Toronto’s Alan Aspuru-Guzik, a former Harvard professor who left the United States in 2018, has created an Acceleration Consortium to deploy these SDLs and recently received $200 million in research funding, Canada’s largest ever research grant. In an era of strategic competition and climate challenges, maintaining U.S. competitiveness in materials innovation is more important than ever. Building a strong research program to fund, build, and deploy SDLs in research labs should be a part of the U.S. innovation portfolio.

Plan of Action

While several labs in the United States are working on SDLs, they have all received small, ad-hoc grants that are not coordinated in any way. A federal government funding program dedicated to self-driving labs does not currently exist. As a result, the SDLs are constrained to low-hanging material systems (e.g., microfluidics), with the lack of patient capital hindering labs’ ability to scale these systems and realize their true potential. A coordinated U.S. research program for Self-Driving Labs should:

Initiate an ARPA-E SDL Grand Challenge: Drawing inspiration from DARPA’s previous grand challenges that have catalyzed advancements in self-driving vehicles, ARPA-E should establish a Grand Challenge to catalyze state-of-the-art advancements in SDLs for scientific research. This challenge would involve an open call for teams to submit proposals for SDL projects, with a transparent set of performance metrics and benchmarks. Successful applicants would then receive funding to develop SDLs that demonstrate breakthroughs in automated scientific research. A projected budget for this initiative is $30 million1, divided among six selected teams, each receiving $5 million over a four-year period to build and validate their SDL concepts. While ARPA-E is best positioned in terms of authority and funding flexibility, other institutions like National Science Foundation (NSF) or DARPA itself could also fund similar programs.

Establish a Focused Research Organization to open-source SDL components: This FRO would be responsible for developing modular, open-source hardware and software specifically designed for SDL applications. Creating common standards for both the hardware and software needed for SDLs will make such technology more accessible and encourage wider adoption. The FRO would also conduct research on how automation via SDLs is likely to reshape labor roles within scientific research and provide best practices on how to incorporate SDLs into scientific workflows. A proposed operational timeframe for this organization is five years, with an estimated budget of $18 million over that time period. The organization would work on prototyping SDL-specific hardware solutions and make them available on an open-source basis to foster wider community participation and iterative improvement. A FRO could be spun out of the DOE’s new Foundation for Energy Security (FESI), which would continue to establish the DOE’s role as an innovative science funder and be an exciting opportunity for FESI to work with nontraditional technical organizations. Using FESI would not require any new authorities and could leverage philanthropic funding, rather than requiring congressional appropriations.

Provide dedicated funding for the DOE national labs to build self-driving lab user facilities, so the United States can build institutional expertise in SDL operations and allow other U.S. scientists to familiarize themselves with these platforms. This funding can be specifically set aside by the DOE Office of Science or through line-item appropriations from Congress. Existing prototype SDLs, like the Argonne National Lab Rapid Prototyping Lab or Berkeley Lab’s A-Lab, that have emerged in the past several years lack sustained DOE funding but could be scaled up and supported with only $50 million in total funding over the next five years. SDLs are also one of the primary applications identified by the national labs in the “AI for Science, Energy, and Security” report, demonstrating willingness to build out this infrastructure and underscoring the recognized strategic importance of SDLs by the scientific research community.

Frequently Asked Questions
What factors determine whether an SDL is appropriate for materials innovation?

As with any new laboratory technique, SDLs are not necessarily an appropriate tool for everything. Given that their main benefit lies in automation and the ability to rapidly iterate through designs experimentally, SDLs are likely best suited for:



  • Material families with combinatorially large design spaces that lack clear design theories or numerical models (e.g., metal organic frameworks, perovskites)

  • Experiments where synthesis and characterization are either relatively quick or cheap and are amenable to automated handling (e.g., UV-vis spectroscopy is relatively simple, in-situ characterization technique)

  • Scientific fields where numerical models are not accurate enough to use for training surrogate models or where there is a lack of experimental data repositories (e.g., the challenges of using density functional theory in material science as a reliable surrogate model)


While these heuristics are suggested as guidelines, it will take a full-fledged program with actual results to determine what systems are most amenable to SDL disruption.

What aren’t SDLs?

When it comes to exciting new technologies, there can be incentives to misuse terms. Self-Driving Labs can be precisely defined as the automation of both material synthesis and characterization that includes some degree of intelligent, automated decision-making in-the-loop. Based on this definition, here are common classes of experiments that are not SDLs:



  • High-throughput synthesis, where synthesis automation allows for the rapid synthesis of many different material formulations in parallel (lacks characterization and AI-in-the-loop)

  • Using AI as a surrogate trained over numerical models, which is based on software-only results. Using an AI surrogate model to make material predictions and then synthesizing an optimal material is also not a SDL, though certainly still quite the accomplishment for AI in science (lacks discovery of synthesis procedures and requires numerical models or prior existing data, neither of which are always readily available in the material sciences).

Will SDLs “automate” away scientists? How will they change the labor structure of science?

SDLs, like every other technology that we have adopted over the years, eliminate routine tasks that scientists must currently spend their time on. They will allow scientists to spend more time understanding scientific data, validating theories, and developing models for further experiments. They can automate routine tasks but not the job of being a scientist.


However, because SDLs require more firmware and software, they may favor larger facilities that can maintain long-term technicians and engineers who maintain and customize SDL platforms for various applications. An FRO could help address this asymmetry by developing open-source and modular software that smaller labs can adopt more easily upfront.

Risk and Reward in Peer Review

This article was written as a part of the FRO Forecasting project, a partnership between the Federation of American Scientists and Metaculus. This project aims to conduct a pilot study of forecasting as an approach for assessing the scientific and societal value of proposals for Focused Research Organizations. To learn more about the project, see the press release here. To participate in the pilot, you can access the public forecasting tournament here.

The United States federal government is the single largest funder of scientific research in the world. Thus, the way that science agencies like the National Science Foundation and the National Institutes of Health distribute research funding has a significant impact on the trajectory of science as a whole. Peer review is considered the gold standard for evaluating the merit of scientific research proposals, and agencies rely on peer review committees to help determine which proposals to fund. However, peer review has its own challenges. It is a difficult task to balance science agencies’ dual mission of protecting government funding from being spent on overly risky investments while also being ambitious in funding proposals that will push the frontiers of science, and research suggests that peer review may be designed more for the former rather than the latter. We at FAS are exploring innovative approaches to peer review to help tackle this challenge.

Biases in Peer Review

A frequently echoed concern across the scientific and metascientific community is that funding agencies’ current approach to peer review of science proposals tends to be overly risk-averse, leading to bias against proposals that entail high risk or high uncertainty about the outcomes. Reasons for this conservativeness include reviewer preferences for feasibility over potential impact, contagious negativity, and problems with the way that peer review scores are averaged together.

This concern, alongside studies suggesting that scientific progress is slowing down, has led to a renewed effort to experiment with new ways of conducting peer review, such as golden tickets and lottery mechanisms. While golden tickets and lottery mechanisms aim to complement traditional peer review with alternate means of making funding decisions — namely individual discretion and randomness, respectively — they don’t fundamentally change the way that peer review itself is conducted. 

Traditional peer review asks reviewers to assess research proposals based on a rubric of several criteria, which typically include potential value, novelty, feasibility, expertise, and resources. These criteria are given a score based on a numerical scale; for example, the National Institutes of Health uses a scale from 1 (best) to 9 (worst). Reviewers then provide an overall score that need not be calculated in any specific way based on the criteria scores. Next, all of the reviewers convene to discuss the proposal and submit their final overall scores, which may be different from what they submitted prior to the discussion. The final overall scores are averaged across all of the reviewers for a specific proposal. Proposals are then ranked based on their average overall score and funding is prioritized for those ranked before a certain cutoff score, though depending on the agency, some discretion by program administrators is permitted.  

The way that this process is designed allows for the biases mentioned at the beginning—reviewer preferences for feasibility, contagious negativity, and averaging problems—to influence funding decisions. First, reviewer discretion in deciding overall scores allows them to weigh feasibility more heavily than potential impact and novelty in their final scores. Second, when evaluations are discussed reviewers tend to adjust their scores to better align with their peers. This adjustment tends to be greater when correcting in the negative direction than in the positive direction, resulting in a stronger negative bias. Lastly, since funding tends to be quite limited, cutoff scores tend to be quite close to the best score. This means that even if almost all of the reviewers rate a proposal positively, one very negative review can potentially bring the average below the cutoff.

Designing a New Approach to Peer Review

In 2021, the researchers Chiara Franzoni and Paula Stephan published a working paper arguing that risk in science results from three sources of uncertainty: uncertainty of research outcomes, uncertainty of the probability of success, and uncertainty of the value of the research outcomes. To comprehensively and consistently account for these sources of uncertainty, they proposed a new expected utility approach to peer review evaluations, in which reviewers are asked to

  1. Identify the primary expected outcome of a research proposal and, optionally, a potential secondary outcome;
  2. Assess the probability between 0 to 1 of achieving each expected outcome (P(j); and
  3. Assess the value of achieving each expected outcome (uj) on a numerical scale (e.g., 0 to 100).

From this, the total expected utility can be calculated for each proposal and used to rank them.1 This systematic approach addresses the first bias we discussed by limiting the extent to which reviewers’ preferences for more feasible proposals would impact the final score of each proposal.

We at FAS see a lot of potential in Franzoni and Stephan’s expected value approach to peer review, and it inspired us to design a pilot study using a similar approach that aims to chip away at the other biases in review.

To explore potential solutions for negativity bias, we are taking a cue from forecasting by complementing the peer review process with a resolution and scoring process. This means that at a set time in the future, reviewers’ assessments will be compared to a ground truth based on the actual events that have occurred (i.e., was the outcome actually achieved and, if so, what was its actual impact?). Our theory is that if implemented in peer review, resolution and scoring could incentivize reviewers to make better, more accurate predictions over time and provide empirical estimates of a committee’s tendency to provide overly negative (or positive) assessments, thus potentially countering the effects of contagion during review panels and helping more ambitious proposals secure support. 

Additionally, we sought to design a new numerical scale for assessing the value or impact of a research proposal, which we call an impact score. Typically, peer reviewers are free to interpret the numerical scale for each criteria as they wish; Franzoni and Stephan’s design also did not specify how the numerical scale for the value of the research outcome should work. We decided to use a scale ranging from 1 (low) to 10 (high) that was base 2 exponential, meaning that a proposal that receives a score of 5 has double the impact of a proposal that receives a score of 4, and quadruple the impact of a proposal that receives a score of 3.

Plot demonstrating the exponential nature of the impact score: a score of 1 shows an impact of zero, while a score of 10 shows an impact for 1000.
Figure 1. Plot demonstrating the exponential nature of the impact score.
Table 1. Example of how to interpret the impact score.
ScoreImpact
1None or negative
2Minimal
3Low or mixed
4Moderate
5High
6Very high
7Exceptional
8Transformative
9Revolutionary
10Paradigm-shifting

The choice of an exponential scale reflects the tendency in science for a small number of research projects to have an outsized impact (Figure 2), and provides more room at the top end of the scale for reviewers to increase the rating of the proposals that they believe will have an exceptional impact. We believe that this could help address the last bias we discussed, which is that currently, bad scores are more likely to pull a proposal’s average below the cutoff than good scores are likely to pull a proposal’s average above the cutoff.

Figure 2. Citation distribution of accepted and rejected journal articles

Source: Siler, Lee, and Bero (2014)

We are now piloting this approach on a series of proposals in the life sciences that we have collected for Focused Research Organizations, a new type of non-profit research organization designed to tackle challenges that neither academia or industry is incentivized to work on. The pilot study was developed in collaboration with Metaculus, a forecasting platform and aggregator, and will be hosted on their website. We welcome subject matter experts in the life sciences — or anyone interested! — to participate in making forecasts on these proposals here. Stay tuned for the results of this pilot, which we will publish in a report early next year.

A “Focused Research Organization” to Measure Complete Neuronal Input-Output Functions

Measuring how neurons integrate their inputs and respond to them is key to understanding the impressive and complex behavior of humans and animals. However, a complete measurement of neuronal Input-Output Functions (IOFs) has not been achieved in any animal. Undertaking the complete measurement of IOFs in the model system C. elegans could refine critical methods and discover principles that will generalize across neuroscience.

Problem Statement

Systems neuroscience aims to understand the complex interplay of neurons in the brain, which enables the impressive behaviors of animals. A critical component of this understanding is the Input-Output Function (IOF) of neurons, which characterizes how a neuron integrates its inputs and responds to them. However, despite its importance, a complete measurement of IOFs in any animal has not been achieved, creating a significant blind spot in our understanding of brain function. While parts of IOFs have been measured through various experiments, these efforts only control a narrow subset of the inputs to any given neuron, providing only a small slice of the true IOF. Furthermore, the output of neurons is a complex nonlinear function of their inputs, adding to the complexity of the problem. To truly understand the computation and function of the brain, we need a detailed functioning of the IOFs, which requires controlling all of the inputs and observing the factors that shape IOFs. Pursuing this in a single animal model would uncover new methods, tools, and scientific principles that could catalyze large-scale innovation across neuroscience.

Project Concept

The project aims to unlock a deeper understanding of how neurons in the brain process information through the complete mapping of neuronal IOFs using the model organism C. elegans. This comprehensive mapping is a vital step in understanding how the brain’s neurons receive, integrate, and respond to signals. The project will use a combination of advanced techniques including optogenetics, modern microscopy, and microfluidics to control and observe the nervous system of the worms, and will develop models to predict how neurons respond based on their inputs. The team will also explore how different factors, such as chemicals, drugs, and non-neural cells influence these responses. To ensure maximal benefit to the field and the scientific community, all data and findings will be shared openly and resources will be allocated to promoting collaboration with outside experts on experimental design, technology development, and computational and theoretical analysis.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because it requires a level of coordinated development and engineering that is too big for a single academic lab, too complex for a loose multi-lab collaboration, and not directly profitable enough for a venture-backed startup or industrial R&D project. The project also aims to create a suite of public goods through its commitment to open science, with plans to share data and code as they are developed. More broadly, the work lends itself well to the development of a new set of tools and methodologies rather than the products or papers incentivized by traditional research models.

How This Project Will Benefit Scientific Progress

This project aims to revolutionize neuroscience by providing the first complete measurement of neuronal Input-Output Functions (IOFs) in the model organism C. elegans. Identifying causal interactions between neurons will provide unprecedented insight into brain function, and the methods developed in the process pave the way for understanding more complex nervous systems.

Key Contacts

Authors

Referrers

A “Focused Research Organization” to Develop a Modular and Scalable Platform for Human Molecular Monitoring

Wearable health electronics are now ubiquitous, but continuous molecular monitoring is only widely available for glucose. Decades of research have expanded continuous monitoring to other molecules, but these techniques are restricted to research labs and remain disconnected from daily human use. We propose a platform to translate and distribute these emerging technologies, enabling the mapping of the time-varying human metabolome and the design of closed-loop devices for personalized health.

Problem Statement

Humans are the best model organisms for humans, yet we have few tools to study human biochemistry in situ and in real time. The Human Metabolome Database lists ~20,000 detected compounds, of which only ~3,000 have been quantified. Even fewer of these biomolecules have been studied with time resolution in longitudinal human studies. 

Cardiovascular, metabolic/endocrine and drug pharmacokinetic phenomena are driven by biomolecules varying on the time scale of seconds to minutes to hours, impacting behavior and well-being on similar time scales. Currently, the only way to measure these molecules is through laboratory testing, which is obtrusive to daily life. Moreover, laboratory testing cannot be conducted at the frequency necessary to capture all of these variations. 

In contrast, wearable monitors are user-friendly and enable continuous measurements at the correct time scale. These monitors require specially engineered biosensors, since there are a limited number of naturally-occurring enzymes that generate continuous, time-varying electrical signals like those used by continuous glucose monitors, which are currently the only commercially available device of this kind. However, most labs that pioneer biosensing strategies do not develop human-compatible devices and vice versa, creating a chasm between these two areas of research and development.

Project Concept

This project aims to achieve minimally invasive continuous monitoring of 100+ analytes in the human body and deliver devices to researchers. This project will develop a medical device testbed that can use the myriad biosensing strategies pursued by academic labs to develop devices for human experiments. Our technical approach combines synthetic ion channels and conformational switches coupled with a CMOS array to assess many analytes in parallel. We already have access to customized fabrication techniques, and the cost and barriers in designing and manufacturing proteins and silicon sensors are trending downwards.

The project will progress through four interdependent stages:

  1. Survey and prioritize metabolic, hormone, and immune targets that provide the greatest explanatory power for well-being. Select biosensor and transduction systems reported in the literature to interface with our platform.
  2. Develop an integrated circuit functionalized with biosensors for parallel multi-analyte sensing packaged in a wearable form factor. Test devices in humans to validate against conventional blood sample analyses.
  3. Specify and share validated devices in bulk to catalyze large-scale human field research.
  4. Curate a time-varying human metabolome.

The project will progress through four interdependent stages.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project suits a FRO-style model because the four research stages require tight feedback loops and standardization and the medical device industry is not incentivized to pursue this kind of research. Private companies typically focus on a handful of molecules most relevant to diabetes care, taking advantage of proven biosensors and predictable insurance reimbursement. A stand-alone, non-profit institute is best suited to standardize and derisk experiments on molecular monitoring to catalyze the formation of a consortium of experimenters. Just as the nonprofit AddGene has standardized and democratized access to genetic material, we seek to develop the analog institution for medical devices.

How This Project Will Benefit Scientific Progress 

Human physiology is currently a poorly explored, high-dimensional space, and scientific labs lack the tools to measure human biochemistry over time. The devices developed through this project will first help scientists study specific questions in domains such as disease etiology, human behavior, and drug discovery. Study validity will be enhanced by providing additional molecular time courses which will clarify the relationships between conditions, specific biomarkers, and interventions. Next, these studies will enable the development of a human molecular atlas, similar to the Human Metabolome Database but with time resolution. This database could act as a powerful tool for developing nuanced models of human physiology to uncover previously overlooked phenomena. Finally, similar devices will ultimately become accessible as consumer health products, enabling the next generation of personalized health.

Key Contacts

Authors

Referrers

Learn more about FROs, and see our full library of FRO project proposals here.

Frequently Asked Questions
What new technical advances will enable this approach? Why now?

The overarching strategy involves converting biochemical interactions into a time-varying electrical signal and isolating specific interactions into separate channels on a CMOS chip. We envision a universal platform that can accept the myriad of biosensors from labs worldwide. Advances in synthetic biology and protein engineering have unlocked various modes of biosensing. Oxidoreductases, like glucose oxidase used in CGMs, provide a direct conversion of the local ligand concentration to an electrical current. Recent advances in deep learning protein engineering have shown proof of concept for the de novo design of enzymes, but some classes of biomolecules such as nucleic acids and proteins would be out of reach for enzymatic detection. In turn, we can either mimic nature’s ion channels or create our own electronic transduction systems. Aptamers are amenable to high-throughput evolution to bind a variety of targets and provide a hairpin motion that can move redox probes to and away from an electrode surface (cf. example of a cortisol sensor). Recent advances in DNA origami have enabled synthetic, self-assembling nanopores whose current can be modulated by the binding of other biomolecules. Both biological and solid-state nanopores are increasingly used in commercial scientific equipment, providing a tailwind for medical devices.


All of these biosensor and transduction systems can be integrated into field-effect transistors (FETs). Even strategies involving membrane proteins can now be interfaced with transistors thanks to organic electrochemical transistors with supported lipid bilayers harboring ion channels and nanopores. Modern complementary metal oxide semiconductor (CMOS) processes allow us to parallelize many sensors onto a single device at a low per-unit cost. Each sensor consists of a FET sensing unit, where the presence of molecules of interest induces a surface potential in the FET gate. This modulates the current in the FET, which is then measured with read-out circuitry. Each sensor’s current is then digitized and used as an indicator of molecular concentrations. Since relatively few channels are required (~100s) compared to conventional CMOS devices, coarser fabrication processes could be used (e.g. 130 nm) which would reduce cost and time while still allowing for a compact active area (~mm2).


The CMOS device will be packaged in a wearable form factor miniaturized for on-body measurements using a battery and wireless data transmission to a smartphone (e.g. using Bluetooth low-energy). We will draw interstitial fluid via reverse iontophoresis to reach the CMOS chip sitting next to the skin surface. Since different analytes have different rates of variation and concentration ranges, we will model the appropriate temporal resolution and SNR we can achieve. This device form factor and reverse iontophoresis method has been validated for ~mM glucose measurements with ~min resolution. In contrast, hormones may be present at ~6 orders of magnitude lower concentrations but only vary over days-weeks. Using our CMOS configuration, different channels can be read out individually. To efficiently capture these changes, different sampling rates will be used to ensure we capture their peaks and troughs while minimizing energy consumption. For analytes with lower baseline concentrations, techniques like averaging (over multiple samples and CMOS channels) may be required to improve sensitivity. The mean absolute relative difference (MARD) will be computed against blood LC-MS ground truth.


We are in a position where our data science methods outpace quality datasets. For example, geometric deep learning is useful for mapping interactions in a metabolome, and long short-term memory is well-established for analyzing and predicting time series data. These data can become actionable in a closed-loop device where a biosensing event triggers an alert or pharmacological intervention as in an artificial pancreas. Here again, control theory approaches to physiology are sufficiently mature.

A “Focused Research Organization” to Systematically Study Bacteriophage Genes and their Functions

Systematically sequencing the genome and studying the function of genes from all viruses that infect a set of model bacteria with significant scientific, biotechnological, and human health relevance will enable the development of phage-gene libraries that can in turn enable the faster development of genetic tools for advancing molecular biology.

Problem Statement

Viruses have been evolving host-modifying factors for billions of years. This wealth of naturally engineered proteins holds the key to unlocking the full potential of the cell. Virus-derived genetic tools have driven most key advances in molecular biology, from recombinant DNA to CRISPR genetic engineering. Although most such transformative discoveries have resulted from the study of bacteriophages (viruses of bacteria), phage research has relied primarily on inferential work rather than systematic approaches to discern the functions of phage genes. With serendipity as the primary engine of discovery, experimental approaches have not kept pace with phage genome sequencing over the past decade. Consequently, the vast majority of phage genetic diversity is still entirely unexplored.

Project Concept

We have built a high throughput screening platform to characterize phage genes and completed a pilot of the entire pipeline, from gene selection through functional screening and mechanistic follow-up (manuscript in preparation and available upon request). Our FRO will scale this platform and use it to chemically synthesize and test all non-redundant phage genes from two clinically relevant families of bacteria (Enterobacteria and Mycobacteria) which collectively host ~40% of all isolated phages, allowing us to test a large swathe of phage genetic diversity in a set of model species. The phage-gene library generated from this process will enable us to pursue the following objectives: 1) discover new molecular tools with revolutionary potential (eg. broadly understanding the principles of protein detection in antiviral immunity could yield a generalizable protein-targeting framework without some of the pitfalls of antibodies), 2) develop therapeutic avenues for antimicrobial resistant infections inspired by natural antiviral defense and counter-defense strategies, 3) build an inventory of phage design principles and engineering methods for therapeutic, industrial, and microbiome-directed applications, and 4) gain a complete understanding of interactions between phage and their hosts.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because to achieve our scientific goals, we will need to scale our platform ~10,000-fold from 104-5 assays in the pilot to ~108-9 assays at the FRO. Massively parallelizing these assays will involve a highly systematic effort with a tightly coordinated and dedicated team, a substantial initial investment in gene-library synthesis and platform engineering, and long publishing timelines, which are qualities unsuitable for traditional grant funding. For these reasons, an FRO is the ideal (and probably the only viable) structure for this project.

How This Project Will Benefit Scientific Progress

Paradigm shifts in biology have often started with the humble bacteriophage. With 108-9 prospects across the oldest and most diverse host-pathogen interface in the biosphere, our FRO presents abundant opportunities for making impactful discoveries, and will pioneer a new field of functional metaviromics. Moreover, the phage-gene libraries we will create are analogous to small-molecule screening libraries, consisting of 104-105 phage-derived natural products that can be used to find potentiators or suppressors of any cellular stressor of interest. We expect these resources to enable discovery far beyond the scope and timeline of our FRO.

Key Contacts

Authors

Referrers

Learn more about FROs, and see our full library of FRO project proposals here.

A “Focused Research Organization” to Reduce Antibiotic Resistance In Aquaculture

Research and engineering to reverse antibiotic resistance in aquatic bacteria, through the application of a well-validated CRISPR-based genetic system, can help catalyze safer, more sustainable land-based aquaculture as a nutritious and affordable food source.

Problem Statement

The growing human population needs affordable, healthy sources of protein. With overfishing putting severe pressure on global fish stocks, aquafarming presents a potential alternative. The U.S. currently imports about 80% of its seafood, and most imports are produced by foreign aquaculture; expanding domestic aquaculture could help to close the $17 billion seafood trade deficit. But domestic aquafarming poses its own challenges, including the potential for environmental contamination near ocean-based operations. In such scenarios, high concentrations of fish within netted areas lead to bacterial and other waste contamination spreading beyond the arena of fish confinement. The alternative strategy of raising fish in isolated inland enclosures may pose less environmental risk, but also requires maintenance of water quality, frequent water filtration and, often, the use of high antibiotic concentrations mitigate bacterial fish pathogens that thrive in such overcrowded conditions. In practice, aquafarmers often try to reduce the level of antibiotics added to the water in the last few weeks of fish growth to drop their concentrations below mandated health standards for commercial fish, but these efforts are only partly effective and create significant logistical burdens. 

Project Concept

We proposed the development of genetic systems to reduce the prevalence of antibiotic resistance in land-based aquafarming enclosures. We will develop harmless strains of environmental bacteria capable of transferring self-copying genetic cassettes to pathogenic bacterial strains of concern in aquaculture. With these strains, we aim to reduce virulence of those bacterial pathogens in high-density fish enclosures and scrub their antibiotic resistance.

The heart of the project is to apply a well-validated self-amplifying genetic system, referred to as Prokaryotic-Active Genetics (Pro-AG), to the task of scrubbing virulence and antibiotic resistance factors from bacterial pathogens in aquaculture facilities. Since publication of the seminal study describing this CRISPR-based system for reversing antibiotic resistance (Valderrama et al., 2019, Nat. Comm. 10, 5726), we have further advanced the Pro-AG platform by combining it with means of spreading between bacteria through horizontal transfer systems such as conjugal transfer elements or bacteriophage. We have also incorporated new genetic features to the Pro-AG toolkit including a system to cleanly and efficiently delete genetic elements such as virulence factors responsible for antibiotic resistance. Building on these core achievements, we will transfer the Pro-AG framework and novel integrated phage-based systems to several bacterial strains of concern to aquaculture with the goal of diminishing their antibiotic resistance (AR) genes and virulence potential.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach for three reasons. First, it would be very difficult to attract VC or industry funding for this effort. The expected timeline is too long for most VCs who want to see a shorter horizon on return for their investments (on the order of 2-3 years). Second, the project has significant technical risk since we do not know how the Pro-AG systems will perform in the context of large enclosures densely packed with fish, which is a daunting environment for any anti-microbial intervention. Third, the scale of just the laboratory component of the project exceeds the level of funding normally available through standard channels of support for academic science, since Pro-AG delivery systems would need to be engineered in parallel for several different species of fish pathogens. This will also require more “applied” work than is typically supported by many academic research programs. For these reasons, the project fits perfectly in the sweet spot for a FRO. 

How This Project Will Benefit Scientific Progress

If successful, our systems would greatly reduce the necessary frequency and concentrations of antibiotics to control bacterial fish pathogens. Solving or attenuating this central challenge to land-based aquaculture should help foster safe, sustainable and affordable sources of nutritious, uncontaminated fresh fish and help catalyze a shift away from unsustainable overfishing practices in the open ocean and environmentally hazardous practices in ocean-based aquafarms. This project could also have broader knock-on effects by enabling similar technical advances to reduce antibiotic resistance prevalence in other environmental settings (e.g., livestock, sewage treatment), which are also substantial sources of worldwide antibiotic resistance.

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A “Focused Research Organization” to Develop RNA Sequencing Technologies

RNA therapeutics are gaining popularity since they are cheap and easy to make, but sequencing technologies today rely on converting RNA back to cDNA, which collapses information on the more than 150 different chemically modified bases for RNA into just four bases. To address this knowledge gap, this project aims to develop direct sequencing tools specifically for RNA including chemical modifications in order to enable the complete sequencing of RNA bases and improve RNA therapeutics.

Problem Statement

RNA encodes regulatory information that directs cellular functions. This dynamic information is written in four canonical bases, each of which can be chemically modified to create more than 150 different bases. Today, sequences of RNA are mostly obtained by converting RNA back to cDNA which is then sequenced; in that process of reverse transcription, all the modifications on RNA are lost. This knowledge gap significantly weakens our understanding and treatment of human diseases. 

Project Concept

As a Focused Research Organization, this project will recruit an interdisciplinary team to develop a low-cost tabletop device that can sequence RNA, including chemical modifications, as accurately as we sequence DNA, and make this product available to researchers at large. We propose to 1) improve high-resolution imaging technologies, 2) develop single-cell mass spectrometry for RNA sequencing, and 3) advance nanopore technology to enhance their accuracy and resolution. Ultimately, the imaging technology will be coupled with the mass spectrometry or nanopore technology to extract full-length RNA transcripts from subcellular locations and then sequence them. In parallel, we will develop algorithms to deconvolute data and build data displays to quantitatively reflect the locations of RNA transcripts and their sequences with all modifications.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because academia is too siloed to support the kind of large-scale, interdisciplinary research project that this would require, and companies that currently provide DNA-based RNA sequencing tools have no motivation to innovate beyond tweaks to the current methods. 

How This Project Will Benefit Scientific Progress

The development of direct RNA sequencing technologies with all chemical modifications will enable the execution of the Human RNome Project, which is anticipated to result from the the National Academies of Sciences, Engineering and Medicine Report (NASEM) on RNA Sequencing. A similar NASEM report resulted in the creation of the Human Genome Project. The Human RNome Project aims to identify all the possible chemical modifications of RNA and create a true sequence of RNA, i.e. the RNome. Direct RNA sequencing technologies will also enable scientists to sequence the complete genome of RNA viruses, such as SARS-CoV-2, the hepatitis C virus, and influenza viruses and accelerate the development of new RNA therapeutics for these viruses and other diseases.

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A “Focused Research Organization” to Design and Synthesize Spiroligomer Catalysts

This FRO will design and synthesize a library of spiroligomer enzyme-like catalysts to enable the development of new industrial processes for the production of green fuels and chemicals.

Problem Statement

Humanity needs catalysts to create fuel, feedstocks to make materials, and fertilizers to grow food. Catalysts allow us to arrange atoms into the molecules we need with extremely high selectivity, cleanliness, and low energy input.  Ever since Emil Fischer first conceived of the “lock and key” hypothesis of enzyme function, scientists have dreamed of rationally designing enzyme-like molecules. In 2021 the Nobel prize was awarded to Benjamin List and David MacMillan for developing organocatalysis – organic molecules that demonstrate basic catalytic function in enzymes. However, organocatalysts demonstrate only a small fraction (1/1,000,000,000) of the natural activity of the most capable enzymes because they are too small and do not display the deep, complex pockets of enzyme active sites needed to stabilize the transition states of reactions.

Project Concept

Spiroligomer nanostructures enable the creation of deep, complex, structured pockets that will allow us to design, assemble, and understand much more capable active sites. Using spiroligomer synthesis technology developed and scaled up over the last three years, this FRO will design, synthesize, and screen a library of spiroligomer catalysts. This will require the additional work of X-ray structure determination of active catalysts, measurement of activity using chemical kinetics, and computational modeling of active sites and reaction transition states. 

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because…Developing enzyme-like catalysts requires the engineering of highly structured molecules that are at least ten times larger than the kinds of molecules that synthetic chemists commonly create (5,000 Daltons for enzyme-like catalysts rather than 500 Daltons for typical small molecule therapeutics or organocatalysts). This will require a large engineering team with complex automation, instrumentation, and computation capabilities and professional synthetic chemists.

How This Project Will Benefit Scientific Progress

Unlike natural proteins that unfold and lose their activity when removed from their optimal temperature and solvent conditions, the spiroligomer-based catalysts we develop will be much more robust and valuable as industrial catalysis. Using synthetic chemistry, they can be produced at scale and made with much better quality control than natural proteins. It will also be easier to modify and tune their properties to create desired products. These catalysts display a much wider variety of chemically reactive groups than proteins do. This will open up the possibility of entirely new chemical production processes, such as artificial photosynthesis to create clean fuels and the production of other green chemicals and feedstocks.

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Learn more about FROs, and see our full library of FRO project proposals here.

A “Focused Research Organization” to Characterize Antibodies Through Open Science

Many antibodies that scientists purchase from commercial manufacturers to conduct their research do not work as advertised, because most have never been validated properly. This project brings together the public and private sectors to conduct independent, third-party testing of commercial antibody manufacturers’ catalogs and publish the results in the public domain, such that no scientist ever uses an ineffective antibody again.

Problem Statement

Thousands of scientists use antibodies – each of which targets one of the 20,000 human proteins – to develop fundamental theories of human biology, and to identify targets for new medicines. These antibodies are often purchased from commercial antibody manufacturers, whose combined catalog contains between 3.5 million and 4.8 million products. But for more than 30 years, the scientific community has been aware that many of these antibodies do not work as advertised, meaning that they do not recognize the intended protein target, or recognize the target but also recognize non-specific targets that confound their use. This occurs because many if not most antibodies have never been validated, or have been validated using inferior or outdated scientific methods, and because academics do not have resources or skill sets to test them themselves. When an antibody binds to a non-targeted protein, a researcher may believe that the target protein, perhaps a drug target, is present in a particular cell type or subcellular organelle when in reality it is not. These erroneous results lead to a vast waste of time, resources, and human capital.

Project Concept

The science on the optimal antibody testing methodology is largely settled: using an appropriately selected wild type human cell and a CRISPR knockout version of the same cell as the basis for testing yields the most rigorous and broadly applicable results.  However, the cost of testing for an individual target or antibody is often prohibitive for any individual academic lab or company.  Our organization, YCharOS (Antibody Characterization through Open Science), couples the settled science with a unique open science business model, in which a consortium of antibody manufacturers provide, in-kind, all their renewable antibodies  (i.e. monoclonal or recombinant, which once tested are of value in perpetuity) to any given target to YCharOS for use in direct, head-to-head comparisons. This centralized testing model creates massive economic efficiencies for the sector while also providing immense scientific benefit to the public.   Moreover, since all data will be released into the public domain using the principles of open science, the benefits accrue to all.  We envision a world where no scientist ever uses an antibody that has not been rigorously tested by an independent third party. We believe that renewable antibodies for all 20,000 human proteins can be knockout validated in many applications for a one-time total budget of approximately $100 million.  

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because antibody characterization is a time limited project that, once completed, will identify high-performing antibodies that can be produced and used in perpetuity. Antibody validation itself is unlikely to generate papers, but will create a public good that enables the production of new research results using properly validated antibodies.

How This Project Will Benefit Scientific Progress

Academic and pharmaceutical scientists laboring to advance our understanding and treatment of human disease will be able to save time and money and produce higher quality research using validated antibodies. Monetarily, scientists spend an estimated $1 billion per year on ineffective antibodies that could otherwise be spent on conducting further research. Furthermore, there is a not insignificant volume of faulty research publications that have resulted from scientists unknowingly using ineffective antibodies.

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A “Focused Research Organization” to Quantify Ocean Carbon

The [C]Worthy Project will create first-of-its-kind, open-source software infrastructure for ocean carbon measurement, reporting and verification (MRV) to help drive the nascent marine-based carbon dioxide removal market.

Problem Statement

There is scientific consensus that industrial-scale Carbon Dioxide Removal (CDR) will be necessary to meet the Paris Agreement’s goal of keeping the rise in global temperature to within 1.5°C or 2°C. By enhancing the ocean’s natural capacity for carbon sequestration and long-term storage, ocean-based CDR is one of the few strategies with the potential to remove carbon at the necessary scales. While investments are pouring into the design and early-stage deployment of ocean-based CDR technologies, the ocean is a complex ecosystem, a constantly moving fluid, posing challenges to quantifying ocean-based CDR projects. It is essential to establish scientifically credible methods for quantifying net carbon removal from CDR deployments and codify these as standards for Monitoring, Reporting, and Verification (MRV). [C]worthy is proposing transformative, open-source, technical solutions to confront these challenges.

Project Concept

[C]worthy aims at building the first foundational open-source computational infrastructure for MRV of ocean-based carbon dioxide removal technologies. The [C]worthy team will develop an innovative and first-of-its-kind MRV software infrastructure applicable across the range of ocean-based CDR technologies that are currently under study and testing in the US and internationally. This MRV platform will integrate ocean observations and advanced Earth system modeling tools at regional to global scales; it will incorporate ocean biogeochemistry and ecosystem models and data assimilation capabilities and it will advance techniques for efficient computation and analysis. This platform will be designed as open-source infrastructure with interoperable components—ensuring transparency and broad access to a growing CDR community.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because the [C]worthy development project requires a level of coordinated computational engineering that is too big for a single academic lab, too complex for a loose multi-lab collaboration, and not directly profitable enough for a venture-backed startup or industrial R&D project. It is aiming to develop an open-source IT infrastructure, which is a product of public benefit for science and technology. This requires tight-knit coordinated effort in a fast-paced start-up-like environment. It is therefore best fit for the FRO model. 

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A “Focused Research Organization” to Build the Foodome Project for the Future of Nutrition

Our current knowledge of the biochemical compounds in food is incredibly limited, but existing databases of MassSpec scans contain massive amounts of untapped, unannotated information about food ingredients. A project to leverage these databases with the tools of data mining, AI, and high-throughput measurement will systematically unveil the chemical composition of all food ingredients and revolutionize our understanding of food and health.

Problem Statement

Diet is the single biggest determinant of health over which we have direct control. An unhealthy diet poses more risk to morbidity than alcohol, tobacco, drug use, and unsafe sex combined. Indeed, our diet exposes us to thousands of food molecules, many of which are known to play an important role in multiple diseases including coronary heart disease, cancer, stroke, and diabetes. Despite the demonstrated and complex role of diet on health, nutrition science remains focused on molecules that serve as energy sources such as sugars, fats, and vitamins, leaving most disease-causing compounds uncatalogued and invisible to researchers and health care professionals. Further, our current understanding of the way food affects health is limited to nutritional guidelines that rely on a panel of 150 essential micro- and macro-nutrients in our diet. This is a tiny fraction of the more than 130,000 compounds known to be present in food, hence limiting our ability to unveil the health implications of our diet. 

Project Concept

The Foodome project aims to unveil this “dark matter of nutrition” by creating an open-access high-resolution compendium of food compounds through a strategy that combines Big Data, ML/AI, and experimental techniques, implemented by a focused cross-disciplinary team, motivated to bring transformative change and maximize public benefits.

In the past five years, BarabásiLab has curated the largest library of compounds in food, consisting of more than 135,000 biochemicals linked to 3,500 foods. While the number of biochemicals is exceptional, the coverage is highly uneven, sparse, and largely unquantified. Yet, information about the missing biochemicals is carried by the unannotated MassSpec peaks available for each MassSpec scan of food ingredients. Because chemicals are invisible to the one-chemical-one-peak tools employed today, we have designed a strategy that relies on data mining, AI, and high-throughput measurements to resolve them: we plan to collect the more than 3,000,000 MassSpec scans already available in databases, and mine the full scientific literature to collect knowledge on food composition. We also plan to take advantage of the increasing number of annotated genomes to infer their chemical makeup. These data will serve as input for a ML/AI platform designed to learn associations between biochemical structures and the ingredients’ phylogenetic position, helping us systematically unveil the chemical composition of all food ingredients.

What is a Focused Research Organization? 

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because the Foodome platform and knowledge base will address problems in health science beyond the competence of any single academic group or start-up. The project started in the academic environment involving groups at Northeastern University, Harvard Medical School, and Tufts Medical School, but typical academic researchers and institutions are motivated by short term publication strategies and unable to devote the years needed to develop a public resource. Federal nutrition research funding exists, but is fragmented, and normal funding channels are generally unable to offer sustained support for a project of this size. With VC funding, we were able to move the project to a startup environment to standardize the toolset and develop key technologies, but company management decided that the Foodome platform’s timeline is too far from the market. Based on these experiences, an FRO appears to be the best framework to accomplish the vision of Foodome. The project enters a field limited by technological stagnation, and will fundamentally change our understanding of health and disease, impacting multiple fields and industries. 

How This Project Will Benefit Scientific Progress

A high-resolution knowledgebase on the composition of food will revolutionize our ability to explore the role of each food-borne molecule in human health, impacting multiple fields: 1) It will be transformative for health care, changing our ability to prevent and control disease. 2) It will aid the development of healthier, more nutritious, and biochemically balanced foods. 3) It will facilitate the development of novel pharmaceuticals. 4) By improving MassSpec annotations, it will provide a more accurate biochemical descriptions of any sample, empowering diagnosis, and detection. 5) It will unlock innovations in personalized nutrition and precision medicine, allowing clinicians to offer precision advice to a patient on how to use diet to prevent and manage disease.

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A “Focused Research Organization” for Superconducting Optoelectronic Intelligence

Artificial intelligence places strenuous demands on current computing hardware, but the integration of semiconductors, superconductors, and optical hardware will create revolutionary new tools for AI.

Problem Statement

Digital computers are excellent for number crunching, but their operations and architecture contrast with the operations that support intelligence. When used for AI, vast amounts of energy, data, and time are required to train new models. Meanwhile, the field of computational neuroscience relies on these digital computers to simulate cognition. Because the underlying computational hardware is poorly matched to the operations of synapses, dendrites, and neurons, the same problems with time and energy arise. We can address both these needs with advances in computer hardware.

Project Concept

We can address both these needs with advances in computer hardware. Our approach builds upon the silicon transistors of digital computing, adding superconducting circuitry to accomplish neural computations, and optical components to realize extensive communication across human-brain-scale systems. We have already made substantial progress in demonstrating key components and are ready to scale to a multiyear effort to integrate into a chip-scale cortex (see slides).

Where are we now?

You can learn more about superconducting optoelectronic networks in this slide deck.

What is a Focused Research Organization?

Focused Research Organizations (FROs) are time-limited mission-focused research teams organized like a startup to tackle a specific mid-scale science or technology challenge. FRO projects seek to produce transformative new tools, technologies, processes, or datasets that serve as public goods, creating new capabilities for the research community with the goal of accelerating scientific and technological progress more broadly. Crucially, FRO projects are those that often fall between the cracks left by existing research funding sources due to conflicting incentives, processes, mission, or culture. There are likely a large range of possible project concepts for which agencies could leverage FRO-style entities to achieve their mission and advance scientific progress.

This project is suited for a FRO-style approach because the integration of semiconductors, superconductors, and optical hardware is beyond the scope of a single academic or government research group, and this endeavor will require appreciable investment in a well-orchestrated, focused team, more akin to a startup. However, given the complexity of the technology, five years will be required to bring a competitive product to market, which is still too early for venture capitalists. Because AI hardware is well-established and continuously improving, gaining market traction will require not only superior hardware, but also streamlined software and user interfaces. An FRO is the ideal context to pursue a complete system meeting the needs of a large and diverse pool of users.

How This Project Will Benefit Scientific Progress

By realizing superconducting optoelectronic networks, we will achieve cognitive AI with vastly more computational power than has been possible with the largest supercomputing clusters of today, while consuming only a fraction of their power. Continued scaling of our technology will not come at the cost of environmental harm. Scientists, engineers, and entrepreneurs across the country will have access to a revolutionary new tool to interpret and analyze complex, multi-modal datasets. This form of advanced AI will change how we provide health care, harness energy, model Earth’s climate, and more. Superconducting optoelectronic hardware will usher the largest transition in computation since silicon, enabling powerful tools for computing and an experimental testbed to elucidate the mechanisms of our own minds.

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