Catch our latest podcast episode!

FVF Ep. 4: Unlocking Influenza Pandemic Prevention

Listen to the podcast here

Building a Convergence Science Agenda for Next-Generation, Universal Influenza Vaccines

A Framework for Accelerating Innovation


Every year, influenza costs hundreds of thousands of lives and millions of dollars in lost productivity.  When—not if—the next influenza pandemic occurs, it could cause tens of millions of deaths and disrupt global economic and social stability to a degree unmatched by any previous natural or man-made disaster. A broadly cross-protective universal influenza vaccine (UIV) is widely recognized as the surest way to protect the world from both the persistent burden of seasonal influenza and the looming pandemic threat; however, attempts to marshal human, technical, and financial assets to develop a UIV have so far proved vastly inadequate to the challenge. The urgency of the threat posed by influenza must be met by focusing the full power of science and technology on providing the world with a UIV.

Recognizing this imperative, the Sabin Vaccine Institute, with support from Flu Lab, began in 2018 to examine the current influenza vaccine ecosystem, and to consider how to spur pursuit of a UIV by creating opportunities for novel and innovative research.

In 2019, the Sabin-Aspen Vaccine Science & Policy Group engaged in deep assessment of the influenza ecosystem and the current state of next-generation influenza vaccine discovery and translational science.   They examined how the funding and financing of vaccine research and development underwrite existing vaccines, and how such investment promotes or stymies the development of new vaccines. Based on their findings, the Group identified the following critical barriers to the development of a UIV:

  • A lack of sufficient and necessary scientific understanding to advance discovery to development ;
  • An ecosystem that lacks sufficient opportunities for novel approaches to drive both discovery and development (for example, as compared with the HIV/AIDS ecosystem, which has readily incorporated advanced discoveries in immuno-oncology, platform technologies, and artificial intelligence); and,
  • A lack of catalytic funding sufficient to enable and support higher-risk, higher-reward opportunities, including promising approaches that lack early data or testing required to secure more traditional funding.

The Group’s recommendations, detailed in their report,[1] call for action to “develop and implement a UIV innovation agenda”—one that Sabin will endeavor to advance by introducing new science, new technology, and novel tools to multiple daunting challenges facing UIV development. Foundational to this effort is the concept of convergence: an approach to solving complex, vexing research problems that meet pressing societal needs by integrating different disciplines to create novel frameworks that catalyze scientific discovery and innovation.[2],[3]  To address the critical public health problem posed by influenza, Sabin envisions a convergence science agenda for a UIV that merges expertise from life sciences with physical, mathematical, and computational sciences, and with engineering—a blueprint for innovation that both builds on fundamental knowledge and stimulates novel, cross-cutting discoveries.

In 2020, Sabin will convene a series of working group meetings of forward-leaning researchers, innovators, and thought-leaders from across the science, technology, and engineering landscape.  Collectively and in collaboration, these individuals will shape novel frameworks of inquiry designed to inform, inspire, and catalyze fresh discoveries and insights: fuel for the pursuit of a UIV.

This Convergence Science Agenda for UIV will integrate knowledge of influenza and vaccine development with expertise across a diverse set of emerging scientific tools and methods.  Through working group presentations and discussions, representative experts will detail, discuss, and debate novel pathways and approaches to long-standing challenges in UIV development such as expanding the breadth and durability of vaccine protection; streamlining testing and clinical trials of candidates to replace existing vaccines; transforming production methods and timelines.

To drive ideas for collaborative convergence around these issues, leading innovators in the influenza research community will engage insights and expertise from those in the following promising areas of research and development:

  • Synthetic Biology
  • Bioengineering
  • Systems Biology
  • Biophysics
  • Artificial Intelligence/Machine      Learning



  • Bioimaging
  • Bioinformatics/Computational      Modeling
  • Chemical Engineering
  • Immuno-Oncology
  • Other areas of viral vaccine          development


This exchange will include the presentation of current convergent work involving these disciplines and collaborative interrogation of these examples for their potential application to UIV development. In addition to probing and detailing specific opportunity areas of science and technology, the working groups will consider HOW a ‘convergence’ of distinct skills, knowledge, methods, and technological capacity can be promoted and organized in order to bring new and diverse expertise and talent to bear on the compelling problem of developing a UIV.

Key questions to be considered include:

  • What forms of support and what types of incentives can effectively foster and sustain partnerships that unite divergent scientific or technological disciplines in pursuit of a UIV? What specific financial/funding, institutional, professional, or other needs must be met to achieve this goal?
  • How can we stimulate convergence of diverse disciplines around the specific challenges and opportunities associated with UIV? Can we learn from other programs or approaches that have created ‘on-ramps’ or ‘ladders’ for inter-disciplinary problem-solving?
  • Recognizing the breadth of constraints and opportunities faced by institutions and organizations around the globe, what approaches could attract new minds and focus novel talent and expertise on the scientific challenges, roadblocks, and opportunities that UIV development represents?
  • What characteristics of virtual or physical environments would promote convergent research and development efforts toward a UIV?
  • What existing or novel communications platforms are required to expand and enhance scientific and technological information-sharing across diverse disciplines and sectors to drive advances toward a UIV?
  • Do convergent approaches to high-reward R&D tend to attract or deter private investment in high-risk, novel ventures? Are there instructive examples of private investment in other areas of innovative, convergent R&D?
  • If the tension between fiscal stewardship and high-risk/high-reward investment limits private investment in convergent science, what role can public funding play? What forms of publicly supported R&D could inform efforts to facilitate convergent R&D for a UIV?

In promoting cross-disciplinary dialogue and disseminating the concept of a Convergence Science Agenda for UIV across the broad science and technology landscape, Sabin seeks to stimulate, inspire, and provide foundational guideposts for investing the knowledge, technical, and financial resources required to realize the transformative goal of next-generation, universal influenza vaccines.

The following brief sketches introduce some of the specific research areas and technologies that can contribute to the discovery of novel solutions to vexing problems in influenza vaccine research.  Advances in these fields already support vaccine discovery and development, including for influenza vaccines; however, there is potential for much deeper collaboration and integration focused on the pursuit of a UIV.  The degree to which these disciplines, approaches, and technologies exhibit overlap, interdependence and—in some cases—convergence serves as a foundation upon which to build the Convergence Science Agenda for UIV.

Vaccines for other viruses: HIV

HIV, like the influenza virus, has a constantly mutating surface structure—a feature that helps the virus evade the immune system and also poses a challenge to developing an enduring vaccine. Recent significant advancements toward an HIV vaccine could illuminate the path toward a UIV as well. For example, a promising HIV vaccine candidate gets a boost from cytomegalovirus (CMV), a virus that triggers such a severe onslaught of T cells that an infection rarely has a chance to cause symptoms.[4]  A vaccine raised to a genetic amalgam of CMV and HIV is entering human trials[5]—and now, supported by a Gates Foundation Grand Challenge grant, researchers are attempting to make similar progress by hitching core influenza genes to a CMV “Trojan Horse.”[6]

Another HIV vaccine strategy that has shown promise—and could do the same for influenza—hinges identifying vulnerable sites on the viral surface that, when bound by antibody, lead to an effective neutralization of many viral strains.  Starting with this most promising part of the immune response, researchers work to develop a vaccine that will induce it. This method was used to design an experimental HIV vaccine that neutralized dozens of HIV strains from around the world.[7] These researchers, who are also Grand Challenge grantees, will now attempt the same feat with influenza.[8]


Also known as cancer immunotherapy, immuno-oncology seeks to harness the immune system to prevent, treat, control, or eliminate cancer.[9]  Promising modes of cancer immunotherapy potentially translatable to UIV development focus on stimulating the activity of T cells and enabling them to find and attack tumors.[10]  This perspective informs a novel route to developing influenza vaccines, which currently rely on antibody defense against circulating viruses.[11] For example, researchers hope to make proteins in the viral interior—where antibodies can’t reach—recognizable by T cells that can attack influenza-infected cells.

The field of immuno-oncology has also pioneered innovative designs for clinical trials that could be adapted to efficiently test multiple vaccine candidates, speeding the development process.[12] Such master protocols enable efficient study of multiple targeted therapies for one disease, either simultaneously or according to an algorithm-defined platform through which candidates enter and leave testing.

Information hubs and other coordinating efforts within immuno-oncology also serve as examples for driving innovation and preventing redundancy in research. [13] These include a comprehensive list tracking the current immuno-oncology agents and the status of their clinical trials[14] and Nature’s editable, interactive cancer immunity website.[15]

Systems Biology

Although variously defined, systems biology aims to describe biological entities of all scales based on interactions among their underlying components.[16] This research approach overlaps with and depends upon additional emerging research areas and technologies, including synthetic biology and bioinformatics (discussed in subsequent sections).  Systems biology has also been described as a route to innovation: “an interdisciplinary approach that systematically describes the complex interactions between all the parts in a biological system, with a view to elucidating new biological rules capable of predicting the behavior of the biological system.”[17]

Describing inherently complex biological systems is beyond the capability of intuitive thinking; it requires mathematical and computational models that are typically constructed through machine learning (see below).[18] Refinement produces models that can predict system behavior under circumstances that would be too difficult, or costly, or both to probe experimentally, such the simultaneous analysis of many different conditions or perturbations.

Rapid, global characterization of host and pathogen responses at the genetic, transcriptomic, and proteomic level, complemented by novel bioinformatics-based analytics, can reveal new insights on the factors that shape an individual’s response to infection and vaccination.[19] Such inquiries could lead to the identification of critical immune epitopes, correlates of protection, and biomarkers of vaccine efficiency or adverse reactions, among other critical leads for innovative vaccine design. [20],[21] Applying systems biology and bioinformatics to  yellow fever vaccine both enhanced understanding of innate immunity and provided predictive models T cell responses.[22],[23]

Computer and Information Sciences: Bioinformatics, Machine Learning, and Artificial Intelligence

Bioinformatics provides tools that systems biology applies, including data repositories, software (for simulation, analysis, and visualization), and high-throughput molecular technologies, along with their attendant data-processing and analytical capabilities. [24]   The field of bioinformatics—which has been defined may different ways[25]–conceptualizes biology in terms of macromolecules, and applies mathematical, statistical and computer science techniques to understand and organize the massive and complex information associated with these molecules.[26]

The science of artificial intelligence (AI) designs machines to perform tasks that would otherwise require human involvement. AI machines acquire their “intelligence” through the technology of machine learning, which enables computers to learn from experience and acquire skills without human direction.[27]  This occurs by training an algorithm on massive amounts of data, a process of adjustment that improves the prediction-based performance of the algorithm.[28]

Machine-learning strategies can detect patterns in datasets too large and/or complex for human scrutiny, including those comprised of a range of variables of interest to drug discovery and development: gene sequences, metabolic measurements, epidemiological parameters, and clinical trial data, among others.  Recently, the first vaccine developed through AI—for seasonal influenza—entered Phase II clinical trials in the United States.[29],[30] Its developers reported that using AI had accelerated the vaccine’s discovery, cut development costs significantly, and led to a more effective candidate vaccine.

Not surprisingly, AI is also being applied to UIV development. For example:

  • A Gates Foundation Grand Challenge grant supports researchers using machine learning to improve design of both antigen and vaccination protocol in their efforts to increase breadth of protection against influenza strains.[31],[32]
  • Berg, a Boston-based pharmaceutical startup, obtains patient samples from partner Sanofi’s influenza vaccine clinical trials, characterized across multiple variables (e.g., mRNA variations and concentrations of metabolites and proteins). The resulting data are processed by AI with the goal of identifying biomarkers of protective immunity[33]—knowledge that could inform UIV design and accelerate testing and clinical trials of vaccines.
  • AI-based mathematical modeling of influenza strain diversity has identified areas known as highly immunogenic epitopes—structures that have been demonstrated to elicit immunity against certain influenza strains to which the host is naïve.[34] These epitopes are now being pursued to design a UIV.[35]
  • AI analysis of a vast database of molecular-level responses of white blood cells to vaccination with licensed, experimental vaccines will allow researchers to build a master list of candidate antibodies for a UIV.[36]

More broadly, AI applications across virology and infectious disease research serve as potential examples and/or supporting technologies to advance UIV development.  For example:

  • A technique known as structured illuminated microscopy (SIM) produces high resolution images of influenza virus structures for classification and identification using machine algorithms, advancing understanding structure-function relationships underlying immunogenicity.[37]
  • The AI-enabled identification of sensor-detected virions is being developed as a platform for point-of-care diagnostics for influenza and other viral diseases.[38]
  • Using machine learning technologies, researchers characterized complex host-microbe interactions and translated that knowledge into models that test and predict the efficacy of existing and novel treatments for the frequently drug-resistant pathogen Clostridium difficile.[39]
  • Models of viral adaptation and transmission created using machine-learning methods offer the possibility of predicting the behavior of emerging infectious diseases for which epidemiological data is lacking or inadequate.[40]

Biochemistry and Structural biology

Specific interactions between viral and host proteins may be exploited to interrupt infection or disease through rational drug design—but first these interfaces must be characterized in detail.  For example, a crystal structure of an influenza A non-structural protein docked to an antigen-binding antibody revealed a small number of viral amino acids critical to complex formation. [41] These residues are common to most currently-circulating seasonal influenza A strains, and therefore promising targets that could be modified to strengthen that interaction in order to increase the antibody’s inhibitory potency. Structural information can be used to reconfigure and engineer conserved epitopes to expose areas that exhibit high immunogenicity, or to insert multiple immunodominant epitopes within a single engineered antigen (e.g., a virus-like protein; see below).[42]

Protein folding—or mis-folding—can also be exploited to undermine viral infection.  Influenza viruses have been shown to hijack the protein-folding machinery in host cells to enable viral proteins to fold and function.[43] When access to this machinery is blocked, the viruses evolve more slowly. Protein folding is also critical to the ability of engineered antigens to induce immunoprotection. [44] To avoid such problems, researchers created a chemical glue, based on a bacterial protein, that firmly sticks epitopes to a protein platform and prevents misfolding.[45],[46]  This more reliable way of assembling vaccines could reduce their cost and speed development of new vaccines against emerging viruses.

Synthetic Biology

Synthetic biology is the (re-)design and fabrication of biological components and systems that do not naturally exist. Often this involves taking parts of natural biological systems, characterizing and simplifying them, and using them as components to build artificial biological systems. While genetic engineering usually involves the transfer of individual genes from one microbe or cell to another, synthetic biology envisions the assembly of novel microbial genomes from a set of standardized genetic parts that are then inserted into a microbe or cell.[47]

Synthetic biology products could potentially be designed to monitor and regulate a wide variety of phenomena in living cells; for example, initiating or shutting off production of a protein in order to fight infection.  Synthetic biological systems have already shown promise as the basis for low-cost diagnostic tools that could be rapidly deployed in an epidemic. [48] The first synthetic biology-derived animal vaccine—now in development—has been described as a “reprogrammed microbe” that attaches to respiratory epithelial cells without damaging them or raising an inflammatory response, portending greater safety and effectiveness.[49]  Synthetic biologists are also involved in designing and testing vaccines based on virus-like proteins (VLPs; see subsequent discussion), an alternative to antibody-based vaccines.[50]


Bioengineering is the application of engineering principles of design and analysis to biological systems and biomedical technologies.[51] This discipline plays a significant role in the pursuit of innovative vaccine delivery methods, such as VLPs: recombinantly produced viral structures that stimulate immunoprotection, but are noninfectious. [52] Several VLP matches to natural viruses have been developed and licensed as vaccines; now researchers are attempting to present heterologous antigens from a variety of pathogens, such as multiple influenza strains. Meanwhile, a similar but distinct novel platform (a double-layered protein nanoparticle), bearing a pair of influenza proteins, was found to protect mice against six different influenza strains.[53],[54]

Additional innovative vaccine delivery systems include microneedle patches, which recently proved safe and effective in a human clinical trial for influenza vaccination.[55],[56] This technology simply and painlessly delivers vaccine to immune cells just under the skin’s surface—no medical visit required. Microneedle patches do not require refrigeration, and they produce far less medical waste than syringes. These and other bioengineered materials may also deliver adjuvants, or even nanoparticles that do double-duty as carrier and adjuvant, that optimize the body’s responses to antigens or immunotherapy.[57]


Biophysics probes the mechanisms that underly how biomolecules are synthesized, how different parts of a cell move and function, and how complex multicellular systems—including the immune system—operate.[58] This field frequently overlaps and is interdependent with computer modeling, structural biology, nanotechnology, materials science, and bioimaging (discussed below).

Biophysicists studying the immune system have demonstrated the influence of biophysical features of molecular signals known as pathogen associated molecular patterns (PAMPs) in the immunogenic response. PAMPs are key molecules that dictate how hosts respond to infection and are being investigated as vaccine adjuvants.[59],[60] The nuances of these biophysical relationships may inform methods to control cellular responses to engineered antigens.

Biophysics also contributes to innovation in vaccine processing and drug development.  As a recent review noted, “rational design of antigen purification and formulation with adjuvants requires a thorough knowledge of intermolecular forces that affect their structural and colloidal stability. The biophysical techniques used to assess these forces and interactions constantly evolve, and often require custom modifications to achieve practical goals while conforming to the theoretical rigor.”[61]

Chemical Engineering

High-throughput screening, a triumph of chemical engineering, revolutionized drug discovery.  Combining robotics, data processing/control software, liquid handling devices, and sensitive detectors, high-throughput screens rapidly conduct millions of chemical, genetic, or pharmacological tests.  They enable researchers quickly to identify active compounds, antibodies, or genes that modulate a particular biomolecular pathway, in inquiries that range from answering fundamental scientific questions to drug design.[62]

Fundamental chemical and bioprocess engineering principles and tools inform vaccine manufacturing, and they will be essential in expanding access to improved vaccines, including a UIV. New formulation and filling technologies and a better understanding of the degradation process are needed to improve the stability of vaccines, and thereby to improve vaccine access for people in impoverished and remote regions.  These populations may also be served by portable factories that can be easily shipped and installed for much less capital and on shorter timelines than traditional facilities. [63]

Alternatively, researchers are testing the possibility that cell-free protein synthesis systems could be broadly distributed in kits, each of which could produce vaccine when and where an epidemic strikes.[64],[65]  The stockpiled kits would contain the biological machinery for vaccine production in a freeze-dried state, ready to be activated with the addition of water and a target pathogen.


A rapidly expanding range of novel and powerful imaging tools assist researchers in probing the immune system and also in drug development and testing.  These techniques—including advanced optical, fluorescence, and electron microscopy, and mass spectrometry—themselves represent convergence science, bringing together precision engineering, innovative materials, chemical and biological insights, and increasingly, computational analytics.[66]

High-throughput imaging (a form of high-throughput screening, described above) affords both increased resolution and depth of visualization.  At the molecular level, high-throughput imaging using antibodies tagged with specific ions allows researchers to label and visualize specific proteins or genes; these results can be combined with those of genomic-scale RNA detection to examine gene activity on a cell-by-cell basis.[67]  Multiplexed ion beam imaging, which employs antibodies bearing dozens of distinct metal tags, can simultaneously locate as many as 100 different clinically important molecules in a cell or a tissue sample or reveal the spatial features of protein expression in individual cells.[68] High-throughput methods detect numerous RNA species within cells and can determine which genes are actively producing proteins. These techniques offer the potential of mapping all RNAs in the body at single cell resolution.[69]

Imaging technologies are also used to evaluate vaccines as part of the development process.  For example, the morphological characteristics of VLPs can be assessed through imaging technologies such as cryoelectron microscopy, atomic force microscopy, and dynamic light scattering.[70] These imaging methods provide essential data to solve the three‐dimensional structure of a VLP to inform optimal insertion and display of a given antigen.


[1] Accelerating Universal Influenza Vaccine Development, Sabin Vaccine Institute, 2019


[3] Convergence: The Future of Health (MIT, 2016) p. 17







[10] Tang J, Shalabi A, Hubbard-Lucey V. Comprehensive analysis of the clinical immuno-oncology landscape. Annals of Oncology 2018;29(1):84-91.

[11] Towards a universal flu vaccine

[12] Woodcock J, LaVang LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med 2017;377(1):62-70.

[13] Tang J, Shalabi A, Hubbard-Lucey V. Comprehensive analysis of the clinical immuno-oncology landscape. Annals of Oncology 2018;29(1):84-91.

[14] CRI. I-O Landscape. 2019; Available at:


[16] Systems Biology: The Next Frontier for Bioinformatics

[17] Systems Biology Approaches to New Vaccine Development

[18] Systems Biology: The Next Frontier for Bioinformatics

[19] Systems Biology Approaches to New Vaccine Development

[20] Systems Biology Approaches to New Vaccine Development


[22] Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A, Gernert K, Deng J, Marzolf B, Kennedy K, Wu H, Bennouna S, Oluoch H, Miller J, Vencio RZ, Mulligan M, Aderem A, Ahmed R, Pulendran B. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116–125.

[23] Pulendran B. Learning immunology from the yellow fever vaccine: innate immunity to systems vaccinology. Nat Rev Immunol. 2009;9:741–747.

[24] Systems Biology: The Next Frontier for Bioinformatics



[27] What Is Deep Learning AI? A Simple Guide With 8 Practical Examples

[28] Artificial Intelligence in Healthcare

[29] Scientists claim to have developed world’s first vaccine with artificial intelligence

[30] AI-created flu vaccine starts testing in US


[32] Researchers bring us one step closer to universal influenza vaccine

[33] How AI is changing the future of how we handle the flu

[34] Thompson CP, Lourenço J, Walters AA, Obolski U, Edmans M, Palmer DS, et al. A naturally protective epitope of limited variability as an influenza vaccine target. Nature Communications 2018;9(1):3859.

[35] A novel universal influenza vaccine targeting epitopes of limited variability

[36] Faught A. Flu Fighter: Dr. James Crowe is leading a global effort to take the guesswork out of the flu shot. Vanderbilt Magazine .

[37] Laine RF, Goodfellow G, Young LJ, Travers J, Carroll D, Dibben O, et al. Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure. eLife 2018;7.

[38] Nanopore Detection of Single Flu Viruses to Control Outbreaks. R & D 2018 Nov 21.

[39] Leber A, Hontecillas R, Abedi V, Tubau-Juni N, Zoccoli-Rodriguez V, Stewart C, et al. Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection. Artificial Intelligence in Medicine 2017;78:1-13.

[40] Walker JW, ⨯ Barbara AH, Ott IM, Drake JM. Transmissibility of emerging viral zoonoses. PLoS One 2018 11;13(11).

[41] Biochemical and structural characterization of the interface mediating interaction between the influenza A virus non-structural protein-1 and a monoclonal antibody

[42] Synthetic biology for bioengineering virus‐like particle vaccines

[43] Biochemists discover mechanism that helps flu viruses evolve

[44] Synthetic biology for bioengineering virus‐like particle vaccines

[45] Synthetic biologists use bacterial superglue for faster vaccine development

[46] Karl D. Brune et al. Plug-and-Display: decoration of Virus-Like Particles via isopeptide bonds for modular immunization, Scientific Reports (2016). DOI: 10.1038/srep19234

[47] Synthetic Biology Explained

[48] Artificial Intelligence in Healthcare

[49] FROM Synthetic Genomes to Designer Vaccines

[50] Synthetic biology for bioengineering virus‐like particle vaccines


[52] Synthetic biology for bioengineering virus‐like particle vaccines

[53] Universal flu vaccine with nanoparticles that protects against six different influenza viruses in mice

[54] Ye Wang et al. Double‐Layered M2e‐NA Protein Nanoparticle Immunization Induces Broad Cross‐Protection against Different Influenza Viruses in     Mice, Advanced Healthcare Materials (2019). DOI: 10.1002/adhm.201901176

[55] Bioengineers imagine the future of vaccines and immunotherapy

[56] Trends in Immunology, Bookstaver et al.: “Improving vaccine and immunotherapy 1 design using biomaterials”

[57] Bioengineers imagine the future of vaccines and immunotherapy


[59] Biophysics plays key role in immune system signaling and response

[60] Jardin A. Leleux, Pallab Pradhan, and Krishnendu Roy, “Biophysical Attributes of CpG Presentation Control TLR9 Signaling to Differentially Polarize Systemic Immune Responses, Cell Reports, 2017. DOI: 10.1016/j.celrep.2016.12.073

[61] Developing biophysical methods for optimization of vaccine process and drug product development


[63] Chemical engineering perspectives on vaccine production. Chemical Engineering Progress 107(11):37-47 · November 2011

[64] Chemical engineers have figured out how to make vaccines faster

[65] Amin S.M. Salehi, Mark Thomas Smith, Anthony M. Bennett, Jacob B. Williams, William G. Pitt, Bradley C. Bundy. Cell-free protein synthesis of a cytotoxic cancer therapeutic: Onconase production and a just-add-water cell-free system. Biotechnology Journal, 2015; DOI: 10.1002/biot.201500237

[66] Artificial Intelligence in Healthcare

[67] Artificial Intelligence in Healthcare

[68] M. Angelo et al., “Multiplexed ion beam imaging of human breast tumors,” Nature Medicine 20, 436-42 (2014).

[69] Artificial Intelligence in Healthcare

[70]Synthetic biology for bioengineering virus‐like particle vaccines