NEW SUSTAINABLE MODELS OF OPEN INNOVATION TO ACCELERATE TECHNOLOGY DEVELOPMENT IN CELLULAR

By MASSACHUSETTS INSTITUTE KEVIN KA-CHUN YUEN OF TECHNOLOGY B.S. Medical Science, Western University, 2012 JUN 2 7 2017 B.A. Honors Business Administration, Western University, 2012 LIBRARIES Submitted to the Integrated Design and Management Program in partial fulfillment of the requirements for the degree of ARCHIVES

MASTERS OF SCIENCE IN ENGINEERING AND MANAGEMENT at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2017 2017 Kevin Yuen. All rights reserved.

The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Signature of Author: Kevin Yuen Integrated Design & Management Program May 12, 2017 Signature redacted Certified by: / 'Peter Gloor Research Scientist, Center for CollectivyIntelligence, MIT Signature redacted Certified by: Isha Datar Director's Fellow (2017), MIT Media Lab; Executive Director, New Harvest Signature redacted Accepted by: Matt Kressy Executive Director, Integrated Design & Management Program, MIT This page is intentionally left blank.

2 NEW SUSTAINABLE MODELS OF OPEN INNOVATION TO ACCELERATE TECHNOLOGY DEVELOPMENT IN

By

Kevin Ka-Chun Yuen Submitted to the Integrated Design and Management Program in partial fulfillment of the requirements for the Degree of Masters of Science in Engineering & Management at the Massachusetts Institute of Technology

Abstract

Cellular agriculture is an emerging field to develop in-vitro agricultural products. Despite overwhelming public attention towards the field's trajectory, there are significant research hurdles to overcome in order to validate scalable applications. These challenges, referring to the translational development of cell lines, serum-free media, cell-scaffolds, and bioreactor designs with regulatory and market assessment efforts, require new models for industry collaboration.

The Open-Innovation Network Map was used to prioritize key collaboration networks to address the translational challenges of cellular agriculture, and three in-depth case studies from open- source models, big-science collaborations, and pre-competitive consortia were evaluated. Nine best practices to support open innovation across translational development were surfaced:

Open-Source Models I OpenCompute Foundation, a community for open-source data center hardware designs, highlights the focus on: (1) the modularization of biological parts, equipment and protocols to encourage reproducibility, (2) the scalability of proof-of-concepts through industry participation, and (3) the self-assembly of industry clusters to initiate standardization.

Big-Science Collaborations I The Human Genome Project, a large-scale collaboration to complete the sequencing of the human genome, exhibits attributes of successful research- intensive organizations, such as: (4) the centralization of leadership in distributed networks, and (5) policies to increase data-sharing frequency.

Pre-competitive Consortia I SEMATECH, a semi-conductor manufacturing consortium established to address bottlenecks in the product development process, reveals that: (6) a crisis is critical for industry cohesion, (7) investment in innovation hubs increases translatability across stakeholders, (8) 'honest brokers' should be created to promote trust, and (9) feedback loops with end-users are critical to test market applications for new scientific advancements.

The building of cellular agriculture's communities, channels, and technologies with appropriate open innovation models can enable stakeholders to collaborate and maintain a competitive edge. The conclusions of the thesis represent a convergence point among industry, academia and policy to discuss how to best shape and execute open innovation efforts in the future.

Thesis Advisor: Peter Gloor Title: Research Scientist, Center for Collective Intelligence, MIT

Thesis Advisor: Isha Datar Title: Director's Fellow (2017), MIT Media Lab; Executive Director, New Harvest

3 4 Acknowledgements

I would like to express my sincerest gratitude to both of my thesis advisors:

Dr. Peter Gloor, for his guidance in open innovation and his expertise in collective intelligence. I appreciate his time and patience as Dr. Gloor was available to provide advice in-person and on skype, to help me brainstorm ideas, and to provide feedback on potential trajectories for the thesis. This process has been a humbling experience for me, and will always remember his wise words: "the more you know, the more you know what you don't know (yet)?"

Isha Datar, for her expertise and insights into the cellular agriculture space. It's exciting to learn about the opportunities and challenges of the industry - either over the phone, on Skype or over tacos with New Harvest's Communications Director, Erin.

I also feel lucky to have had the support from the rest of the New Harvest (NH) community, and I appreciate the time the NH fellows took to contribute their views about 'openness' over the Slack channel.

I wish to thank my IDM Executive Director, Matt Kressy for giving me the opportunity to be part of the program. It was a unique experience to learn from the most exceptional and the most humble teachers and students in a world-class environment.

And, of course, to my incredible inaugural cohort of Integrated Design Management classmates for embarking on this amazing 2-yearjourney with me.

Finally, I wish to thank my family and friends for their love and support.

5 6 Table of Contents

A b s tra c t ...... 3

Ac k n o w le d g e m e n ts ...... 3

Research Objectives & Methodologies ...... 9

Part 1 1 Primary Challenges Of Cellular Agriculture ...... 11

i. Cellular Agriculture in Brief...... 11

ii. Past & Current Collaborations in Cellular Agriculture ...... 16

iii. Overview of Industry Challenges in Cellular Agriculture ...... 20

vi. Imperative for a Collaborative Approach in Translational Research ...... 33

Part 2 | Spectrum Of Open Innovation Models...... 34

i. Overview of Open Innovation...... 34

ii. Open Innovation Network Map...... 36

iii. Descriptions of Open Innovation Networks...... 41

Part 3 1 Models And Best Practices For Open Innovation For Cellular Agriculture...... 45

i. O p e n -S o u rce M o d e ls ...... 4 8

Overview: Open Compute Project (OCP)...... 48

Best Practice 1 I Modularity Encourages Reproducibility ...... 52

Best Practice 2 I Industry Participation Ensures Scalability ...... 58

Best Practice 3 | Clusters Initiate Standardization ...... 62

ii. Big Science Collaborations ...... 66

Overview: Human Genome Project (HGP) ...... 66

Best Practice 4 I Centralized Leadership Fosters Coordination ...... 68

Best Practice 5 I Data-Sharing Frequency Increases Transparency...... 72

iii. Pre-Competitive Consortia ...... 74

Overview: Semiconductor Manufacturer Technology (SEMATECH)...... 74

7 Best Practice 6 I Crisis Creates Cohesion ...... 76

Best Practice 7 I Innovation Hubs Nurture Translatability ...... 78

Best Practice 8 I 'Honest Brokers' Promote Trust...... 82

Best Practice 9 I User Engagement Validates Applicability...... 85

Discussion: Putting Best Practices Together ...... 89

C o n c lu s io n ...... 9 2

A p p e n d ix ...... 9 3

Appendix A. List of Research Interviews & Discussions...... 93

Appendix B. List of Open Innovation Networks ...... 94

Re fe re n c e s ...... 9 6

8 Research Objectives & Methodologies This thesis aims to identify and prioritize open innovation models that could help accelerate technology development in cellular agriculture, and is divided into three distinct parts (see Figure 1):

PART 1 PART 2 CHALLENGES OF SPECTRUM OF CELLULAR OPEN INNOVATION AGRICULTURE MODELS

PART 3 PRORITIZED MODELS & BEST PRACTICES FOR OPEN INNOVATION FOR CELLULAR AGRICULTURE

Figure 1: Research Structure

PART 1 | PRIMARY CHALLENGES IN CELLULAR AGRICULTURE What are the primary challenges for the acceleration of the technology development in the cellular agriculture sector?

Research methodology & Outcome: 20+ hours of primary interviews & discussions1 were conducted with cellular agriculture scientists and industry experts to outline industry challenges that spanned technological, regulatory, economic, and market barriers.

I See Appendix A for a List of Conducted Primary Interviews & Discussions 9 PART 2 | SPECTRUM OF OPEN INNOVATION MODELS What is the spectrum of open innovation models for collaboration across the foundational, translational & commercialization continuum?

Research methodology & Outcome: 42 open innovation activities 2 were identified and assessed regarding collaboration objectives. An Open Innovation Network Map was developed that identified open innovation models across three phases of discipline development: (1) Foundational Research, (2) Translational Development, and (3) Commercialization and Scaling.

PART 3 | PRIORITIZED MODELS & BEST PRACTICES FOR OPEN INNOVATION FOR CELLULAR AGRICULTURE What are the prioritized open innovation models & best practices that could support technology translation in cellular agriculture?

Research methodology & Outcome: An overview of the opportunities for cellular agriculture is presented via the analysis of three prioritized open innovation models across translational research: (1) Open-Source Models (OpenCompute Foundation), (2) Big Science Collaboration (Human Genome Project), (3) Pre-competitive Consortia (SEMATECH)

A discussion at the end of the thesis highlights nine best practices for open translational development across three main categories: Community-Building, Channel-Building, and Technology-Building. Achieving success across these three aspects is critical for the growth of an open research-intensive discipline.

2See Appendix B for a List of Open Innovation Activities

10 M

PART 1 | PRIMARY CHALLENGES OF CELLULAR AGRICULTURE What are the primary challenges in the development of the cellular agriculture sector?

PART 1 CHALLENGES OF CELLULAR AGRICULTURE

i. Cellular Agriculture in Brief Cellular agriculture is defined as 'the production of agricultural products using '. As an emerging discipline that intersects bioengineering, , material sciences and tissue-engineering, cellular agriculture has been regarded as the toolkit for modern farming to advance the bio-fabrication economy with in-vitro animal products, including meat, eggs, and gelatin, produced without animals [1].

Traditional animal products have significant impact on the environment, , antibiotic resistance, and food safety. The vision of cellular agriculture is to apply biomedical science research to food and animal products development to bypass negative effects from animal welfare, sustainability, and public health perspective [1], [2].

11 There are two primary categories of agricultural products within the cellular agriculture sector: acellular and cellular products.

Acellular Products Acellular products are made of organic molecules like proteins and fats and contain no cellular or living material. Examples of ventures producing acellular products include Perfect Day Foods, a San Francisco-based cellular agriculture company, founded in 2014, that makes milk from cell culture using a yeast fermentation process [3]. Modern Meadow, a Brooklyn-based company, raised $40M in June 2016 to bio-fabricate leather in a lab, and Bolt Threads launched its first product made with synthetic spider silk in March 2017.

The history of the field dates back to the mid-1 900s with the synthetic production of insulin. Originally used by Frederick Banting, Charles Best, and James Collip, insulin was extracted from ground-up pancreas cells of pigs or cows. Although bovine & porcine insulin are similar to human insulin, their compositions are slightly different - this caused a number of patients' immune systems to produce antibodies against it, neutralizing its action and resulting in inflammatory responses at injection sites.

DNA

Humad pancreas ceal reonhin DNA Recombinant Human insulin Human in ito a badlerid oeU Bactem IIEtrction 8 0 e nnl Recombinsi e oidIlft

Plavrij DNA Cut with Badsall NA Tank

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Figure 2: Production of Insulin (Source: Hong Kong Institute of Education, 2002)

These unwanted side effects led researchers to consider producing human insulin by inserting the product gene into a suitable vector, Escherichia Coli (E. Coli), to produce an insulin that is

12 chemically identical to its naturally produced counterpart in humans. In 1978, Stanley Cohen and Herbert Boyer successfully obtained small amounts of insulin after 'splicing' the insulin gene into a plasmid of the bacterium. This process is outlined in Figure 2. The bacterium-engineered 'insulin' was identical to the insulin made by humans, and today, a majority of the insulin used worldwide is recombinant 'human' insulin engineered by yeast or bacteria.

The acellular product development industry has been shaped by two vastly different, but converging industries: the biomedical and biofuel sectors. Research from small volume, high value product development from the pharmaceutical industry (i.e. biologics and enzymes) are being translated for adjacent applications suitable for the consumer market. Advancements in biofuels are also pushing the boundaries of high volume and low-cost bio-production techniques to optimize the yields of higher value goods for mass market. The main novelty of cellular agriculture over the last decade has been the confluence of these two trajectories to scale the bio-production of agricultural products like leather, milk and eggs.

Cellular Products Cellular products are made of living or once-living cells. Winston Churchill in 1931 suggested, "We shall escape the absurdity of growing a whole chicken in order to eat the breast or wing, by growing these parts separate." The theoretical possibility of growing animal protein in an industrial setting has sparked interest in the research and commercialization of culturing animal protein or developing . NASA began funding in-vitro meat experiments in 2001 and proposed two methodologies of cultured meat: (i) developing thin sheets of meat on a reusable polymer scaffold, and (ii) growing meat on small edible beads [5].

13 -U-

Figure 3: Mark Post with in-vitro meat burger (Source: David Parry / PA Wire - Cultured Beef Media Resources, Maastricht University [6])

Mark Post, a vascular biologist at the University of Maastricht in the Netherlands, produced and cooked the first cell-cultured hamburger in 2013. Its cost: $330,000 (See Figure 3) [7]. In 2016, Memphis Meats announced that the company produced the first in-vitro meatball prototype using stem cells, and showcased a proof-of-concept for chicken tenders and duck a l'orange in March 2017, claiming the first cultured poultry-based foods shown in public [8].

Tissue engineering and regenerative medicine research are the main technological disciplines that provide the scientific foundation for cellular products. Research in these medical fields have made significant technological leaps over the last decade by integrating cell development, scaffolding and vascularization, but have yet to reach mass-commercialization. Scientists are optimistic that the research in the cellular agriculture sector, beyond the boundaries of medical applications, will advance far enough to help optimize complementary research and commercialization methodologies in the biomedical sectors.

14 Status of Cellular Agriculture The increased interest in cellular agriculture is reflected by the growth of academic research, the rising influence of non-profits, and increased media attention for new ventures. In 2015, cellular agriculture entrepreneurs and startups such as Memphis Meats, Perfect Day, and Gelzen attracted over $20 million of investment funding [9], and New Harvest, a prominent non-profit focused on the scientific advancement of the sector, has seen tremendous growth in donations from $38,995 in 2013 to $891,565 in 2016 [10], [11].

However, researchers and industry experts acknowledge that although cellular agriculture is both possible and inevitable, the cellular agriculture industry and research are still in their infancy. In a recent article by ProAg, Paul Mozdiak, a professor in animal cell culture techniques at North Carolina State University stated that the industry is still at 'ground zero' [12]. With research funding challenges, "we have some proof of concepts, but there are really no production systems in place as it stands right now." Pandya, the CEO of Perfect Day, also acknowledges that we are "not here yet and maybe no way near here yet, but it is coming" [12].

15 ii. Past & Current Collaborations in Cellular Agriculture Cellular agriculture has struggled to develop a collaborative ecosystem given the industry's infancy and high potential payoff (i.e. recognition or financial reward) for the successful commercialization of product. The situation in this field reflects the general landscape prevalent in scientific research, where researchers compete, above all, to be the first. This competition is even stronger in the traditional biotech sectors because the investment needed to produce blockbuster products is high, and thus is the pressure to recuperate the costs via market dominance. Many of the researchers in the cellular agriculture field have left academic careers to pursue new ventures to commercialize radical cellular agricultural products, and due to pressures from investors, they must be the 'first' to do so, which means they have little incentive to share knowledge and data.

Not surprisingly, there have been minimal efforts for collaboration across different academic and industry stakeholders. The few collaborative efforts that have shaped the industry so far are presented below.

Academic Partnerships In the academic community, an In-Vitro Meat Consortium was established in 2008 to promote scientific excellence and coordinate research across the academic field in order to share research for alternative methodologies to conventional meat production, but the consortium quickly folded due to the lack of funding.

Non-Profit Organizations Many non-profit organizations, such as New Harvest (NH), and (GFI) have aligned themselves with the movement to transform current agriculture systems and to promote environmental sustainability and . Many of their initiatives encourage networking and partnerships between scientists, policy-makers, activists, and entrepreneurs with the goal to promote cellular agriculture. But given the technical challenges within the industry, the role of non-profit organizations (Non-governmental organizations, or NGOs) in cellular agriculture has shifted drastically over the years from being public educators to innovation catalysts in the industry. New Harvest, for example, strategically funds and conducts open, public, collaborative research by giving academic fellowships in critical areas of cellular agriculture. GFI has built a team to assess the readiness of cultured meat technologies, build an

16 innovation division that would identify food opportunities within cellular agriculture, and provide strategic guidance for emerging startups.

Conferences Conferences have become the primary medium for information exchange. The first academic In- Vitro Meat Consortium held its first international conference in 2008. Since then, there has been only a handful of academic conferences focused on the development of cultured meat, such as the International Symposium on Cultured Meat (2015, 2016). These academic forums include poster sessions and panels where scientific accomplishments are shared to push the boundaries of technical solutions for cultured meat. In 2016, New Harvest held its first cellular agriculture conference that counted over 309 attendees. Focused solely on cellular agriculture, New Harvest brought together a holistic exhibitor and speaker list, from a chef specialized in fermentation processes, industry leaders who understood regulatory processes (i.e. FDA), and innovation scouts from large corporations (i.e. ). For design- and material-focused ventures, BIOFABRICATE is a summit for the emerging world of grown materials. During the 2016 conference, Adidas launched its BioSteel synthetic silk shoe together with cellular agriculture venture AMSilk.

These conference speak to the interdisciplinary nature of the industry, and yet, there are many silos across the scientists, business leaders and designers who are solving challenges from different perspectives in the industry. It is critical for its success, that a diverse set of experts in the field, from industry, government and academia, are included, and conference organizers are experimenting with a variety of formats to increase the integration between these functions.

Competitions Competitions have also played a role in stimulating the cellular agriculture industry. In 2008, the People for Ethical Treatment of Animals (PETA) announced a $1 million prize for the first laboratory to use chicken cells to bring to market viable in vitro meat, as an innovative way to combat animal suffering in the meat industry. Their primary interest was to replace chicken factories, and the transport and slaughter processes. The contestants were expected to: " Produce an in vitro chicken-meat product with a taste and texture indistinguishable from real chicken flesh to non-meat-eaters and meat-eaters alike. " Manufacture the approved product in large enough quantities to be sold commercially, and successfully sell it, at a competitive price, in at least 10 states in the US.

17 In March 3, 2014, PETA released a statement to abandon the competition since the deadline expired without any successful projects. In a press release, PETA stated:

Although the contest had no winners, PETA is happy that its offer sparked debate, created a fellowship, spurred interest and investment from the food industry and 'dot.com millionaires,' and has seen patents pending for breakthroughs in developing the process, from tissue scaffolding to muscle development [13].

This competition raised significant consumer awareness about the cultured meat sector, but many scientists believe that the competition was created prematurely with an overly optimistic goal. The ability to produce a product that has similar taste and texture to meat is ambiguous and difficult to judge. Furthermore, the competition was solely focused on the commercialization and scalability of the product and did not take into account the technical and regulatory hurdles that needed to be overcome. In hindsight, this speaks to the limited knowledge that reaches the public regarding the challenges of cellular agriculture to meet this audacious goal.

Incubators, Accelerators and VCs

In 2016, at the 30th anniversary of the MIT Media Lab, Joi Ito said that "biology is the new digital," explaining that the cost of biotech has become more accessible to creative people outside closed institutions, paralleling the democratization of computers [14]. This is reflected in the wave of emerging startups, ranging from organism design and automation to product development, that help build infrastructure and product concepts in the cellular agriculture space.

Incubators and accelerators have a wide range of criteria in the companies that they invest in. Mark Altman, President of Y-Combinator (YC), voiced the accelerator's investment preferences for biotechnology platforms that can scale multiple products, such as Ginkgo Bioworks, a cellular agriculture venture that was part of YC's first biotech cohort in 2014 [15]. IndieBio, one of the largest life sciences accelerators to date, has also shaped the commercialization of reputable cellular agriculture startups such as Perfect Day, Memphis Meats, Geltor, Clara Foods, and most recently in 2017, Finless Foods. The accelerator selects startups and provides them with $250,000, lab and co-working space, mentorship and a network of entrepreneurs to aggressively push the business pitches forward.

18 Venture capital funds are also gaining interest in cellular agriculture investments. New Crop Capital has financed multiple ventures focused on alternative sources to traditional protein production, such as Memphis Meats and Geltor. Other venture capital funds do not have a particular specialty; for example, Horizon Ventures in 2016 led a $40M Series B for Modern Meadow, a venture bio-fabricating leather, and invested a $3.76M seed round in Algama, a microalgae food venture. Given the technological uncertainty and risk/return profiles of cellular product development, limited industry investments have been made in cultured meat - "the holy grail" of cellular agriculture.

Strategic Partnerships Strategic partnerships between corporations and ventures are frequent across the industry. In the synthetic textile space, Japan-based Spiber collaborated with North Face to create a prototype of its first product, a $1000 silk-spun "Moon Parka" jacket, in 2016, and Bolt Threads launched a partnership with Patagonia. AMSilk, that is developing commercial-grade spider silk, launched its 'Bio-Steel' shoe with Adidas. Many of these partnerships involve the prototyping and testing of technical proof-of-concepts in the acellular product development space.

It is unclear how strategic partnerships will shape up over the next decade - currently, the partnerships are focused on the vertical integration of new technologies that are ready to be scaled via the industry incumbents, but as the industry grows, the platforming of new biotech technologies may enable partnerships across different verticals in cellular agriculture.

From the current landscape of initiatives in the cellular agriculture industry, there is potential for more partnerships and alliances, but it is critical to first understand the key industry challenges and gaps that cellular agriculture faces before identifying potential collaborative opportunities.

19 iii. Overview of Industry Challenges in Cellular Agriculture Despite the media's enthusiasm for proof-of-concepts, there are significant obstacles which must be overcome through further research if products are ever to reach the market. An overview of the primary challenges of the industry was developed via 22 interviews across the cellular agriculture space that included entrepreneurs, research scientists, and innovation community experts with a simple question: What are the biggest gaps in cellular agriculture? These findings were later clustered and validated by extensive secondary research. A framework outlining these challenges is presented in Figure 4.

Distinct Challenges Faced Between Acellular and Cellular Product Development Acellular and cellular product development pathways are quite different due to the technological methods involved. For example, because different cells (e.g., bacteria & simple eukaryotic cells vs. mammalian cells) are used in acellular or cellular production, the technical hurdles to develop starter cell cultures for cellular products are more complex. This greatly impacts the differences between genetic complexity, scaffolding, and growth rates for different products.

These distinct challenges between acellular and cellular production vary greatly in upstream production processes, but start to converge in the latter stages of product development (i.e., scaling, regulatory, economic and market adoption challenges). This has strong implications on how collaborations for the industry should not be homologous across the entire product development process, but should be structured and tailored meaningfully across dissimilar phases and segments of the sector.

20 CELLULAR AGRICULTURE - INDUSTRY CHALLENGES

CATEGORIES CHALLENGES ACELLULAR (PROKARYOTIC& SIMPLE EUKARYOTIC) CELLULAR (COMPLEX EUKARYOTIC) Cell characte ustics not transferrable across cell types resulting in Select the optimal cell t ype along development stages: differentiated or ORGANISM SELECTION inconsistent cell behavior and expression yKwok 2010) undifferentiated to develop starter culture (Datare. al., 2009) CELL BIOLOGY " Modification of cells across multiple agricultural animal types to proliferate - Model prokaryotic and simple eukaryotic have used for production given indefinitely (GFI, 2016) STARTER CULTURE knowledge regarding biological characterization " Immortalization of cells in vitro a challenge free of tumor-genesis and abnormal characteristics (Maqsood et. at. 2O13) GENOME SCALE DESIGN - Vision that the biological parts work like Lego but parts may not be well SYNTHETIC characterized with variables that affect expression of sequence (Kwok, 2010) BIOLOGY & - Biological parts designed (i.e. switches, gates, pulse generators, circuits, Genome definition, pathway design &biological part characterization for compatibility with biological chassis (Fu, 2013) eukaryotic cells in preliminary stages (i.e. potential technological leap with METABOLIC PATHWAY DESIGNMETAOLICsensrs,& sensors & reulatrs)genomeregulators) for sequencing & CRISPR.Cas-9), but not apath dependent step ENGINEERING BIOLOGICAL PART CHARACTERIZATION - Defining the biological parts in context and how circuitry affects the cell's natural gene expression (Kwok, 2010) - Unsustainable to use fetal bovine serum (FBS). development of serum-free -' CELL MEDIA DEVELOPMENT media; both proliferation and differentiation media (Gstraunthaler, 2003; TISSUE & Luining, 2016) BIOMATERIALS & biomaterials engineering not critical inprotein For cells to form tissue, it is crucial to find a non-toxic, edible material ENGINEERING SCAFFOLDING production and extraction scaffold to provide nutrients & structure (Luining 2016) - Development of 3D tissue formation to create large (>100pm in diameter) 3D TISSUE FORMATION pieces of tissue with vascular network (Luining, 2016) INDUSTRIAL Development of suspension culture methods inbioreactors for (1) scalable and controlled expansion of cells and (2) guiding stem cell differentiation, while BIOREACTOR DEVELOPMENT& SCALING promoting cell survival and retaining cell functional properties with dynamic mixing of culture medium (Massai et. al., 2016) BIOTECHNOLOGY - Scalabilityand standardization in cellular manufacturing processes are still major challenges affecting cost economics of bioreactor design (Massai et. a., 2016)

OPEN-RELEASE & CONTAINED PRODUCTS - Regulations developed by USDA or FDA, uncertainty based on characterization and complexity of end-product (NAS, 2017) REGULATORY PLATFORMS - Regulatory of democratized wet lab platforms (i.e., cells, library prep kits) and 'dry lab platforms (i.e. vector drawing or CAD software) (NAS, 2017)

CAPITAL INVESTMENT& FORMATION - Development of risk mitigation strategies to invest in cellular agriculture with technical and regulatory uncertainty, and extensively integration capabilities ECONOMICS COST & ECONOMIES OF SCALE I Challenges to match costs with economies of scale and demand of product

MARKET CONSUMER ADOPTION - Significant efforts needed in integrating technology *push' withmarket *pull' - withdesign &development process (Urich-Eppinger. 2012)

Figure 4: Primary Challenge in Cellular Agriculture The key categories of the challenges found in cellular agriculture include:

Cell Biology In order to obtain the animal cells from farmed species, primary cells must be isolated to provide a sustainable source for continuous cell replication [16], [17]. Traditionally, stem cell research has been conducted for applications in regenerative medicine, and the translation from this research to traditional agricultural farm animal products has been difficult. The requirements for cells used for regenerative medicine are different from those used for agriculture products. Since the starter cell type selected is human in the case of regenerative medicine versus animal for agricultural products, there are tremendous variances between the cells' characteristics: level of immortality, high proliferation ability, surface independence, serum independence, and tissue-forming abilities [18].

There is a niche community of researchers working on stem cell research in animals, particularly for the development of new in-vitro animal-testing protocols [19]-[21]. For example, researchers in 2011 isolated cell lines from Atlantic sturgeon for different applications, including viral diagnostics (i.e. ecotoxicology, carcinogenesis, genetic regulation and expression), production of dietary supplements for the food industry (i.e. omega-3-fatty-acids), and biotechnological production of fish meal [22]. A majority of stem cell research funding has been allocated to regenerative medicine, and the advanced knowledge regarding media compatibility and scaffolding technologies for the studied stem cells are limited to human applications [23]. Therefore, future collaborations are necessary to partner the veterinary expertise of researchers that cultivate avian, aquatic, or mammalian starter cells with regenerative medicine researchers who have a rich understanding in the advancements of stem cells.

The wide discrepancy of funding available between human and animal stem cell research makes it difficult to make significant advancements in animal starter cell cultures. A scientist mentioned that in order to apply for grants outside of New Harvest funding, the grant applications need to be tailored for human biomedical sciences, even when scientist's primary focus is on the target animal models, like avian or mammalian cells. "A major problem is that cellular agriculture is quite niche, and most of the applications I write must relate to human ailments, like muscular dystrophy, in order to get funding." This ultimately detracts from the academic focus of cellular agriculture research and creates both administrative and financial barriers for animal cell biologists working in this field.

22 Synthetic Biology and Metabolic Engineering The objectives of synthetic biology and metabolic engineering in acellular product development are to manipulate biochemical reactions inside the cells for the optimization of the target product production, while minimizing the formation of byproducts [24], [25]. There are two primary methodologies: 1. To select a microbial host that makes the target molecule naturally and modifications are made for enhanced production in that host 2. To select microbial host that does not make the target molecule naturally but has other advantages (e.g., genetic malleability, extensive background information and tools, favorable regulatory status, fermentation robustness), and genetically introduce the chosen metabolic pathway.

There are different types of microbes that can function as platform organisms in industrial biotechnology, including bacteria, yeasts, and microalgae. Microbes, such as yeast (i.e. Saccharomyces cerevisiae) and E. coli, are selected most often due to their fast generation time, the vast experience researchers have working with them in laboratories [26], and adaptability to different industrial conditions.

Metabolic engineering has also contributed to the domestication of new production organisms by offering design tools to work with flexible microbial strains in industrial conditions. The biggest challenges working with these organism is to maintain their genomic expressions and characteristics in large-scale fermentation environments over long periods of time [27].

23 Tissue and Biomaterials Engineering The tissue and biomaterials engineering discipline primarily applies to cellular products, such as cultured meat. One of its major technical and cost related challenges is the development of serum-free media [18], [28]. The status quo for growing animal tissue involves the use of fetal bovine serum (FBS), which is a blood product extracted from fetal calves that supply cells with nutrient and growth factors. Given that the goal of cultured meat is to be an alternative meat product source that does not harm animals, it would be unreasonable to use FBS in the production of cultured protein. The consistency of the serum is also an issue given the batch-to- batch variation, so customized and standardized media will be required for the growth of selected cell lines.

The development of customized scaffolds to the cells in order to form tissue larger than 100 pm is also a limiting technology. For this technique to work it should be cost efficient to scale, non- toxic for the cells, edible for humans, and allow a flow of nutrients and oxygen. Although developments in scaffolding have been made in tissue engineering and regenerative medicine, there is limited need for such a matrix with the same level of engineering requirements [18].

Some researchers are producing natural and synthetic 3D scaffolds that support the proliferation of mammalian cells. Among them, Andrew Pelling, who experimented with an apple-derived cellulose scaffold, claims that plant scaffolds can be easily produced, inexpensive, and originate from a renewable source. However, these concepts of protein-plant hybrids are far from commercialization, given the complex processes to de-cellularize and re- cellularize scaffolds with desired pluripotent stem cells [29], [30].

In the advanced stages of cultured meat development, 3D vascularization and tissue engineering would be needed to create scalable structures for muscle tissue. In order for these cells to have comparable taste, structure and nutritional value to conventional meat, the growth of adipose (fat) cells would also be necessary [31].

This stage requires extensive integration between the selection of the starter culture and the customization of biomaterials and tissue engineering (i.e. media, scaffolding, and vascularization processes) for that particular cell. Since tissue engineering techniques are not transferable across different specifies, the starter culture must be selected carefully to take into account constraints of downstream processes.

24 Industrial Biotechnology and Scaling There are many significant challenges to scale-up the cellular and acellular product development. Bioreactors have come to be accepted as an indispensable tool to advance cell and tissue culture to manage (i) larger areas to be covered, leading to larger substrate and oxygen depletion zones, (ii) larger volumes of culture broth to be stirred and longer mixing times, and (iii) stronger hydraulic pressure gradients [32].

The bio-production scale-up process has traditionally been a focus of cell culturists specializing in the reproductive development of cells [33]. Over the last decade, a wide array of engineering tools that advance fermentation process-related challenges, such as statistical experimentation design tools and computational modeling algorithms, have been developed to enable higher productivity, but these tools have not been exploited to its full potential in the scale-up process. [34] Therefore, this stage must involve the interplay between these highly dependent sets of expertise: the algorithm-driven mechanical device (the bioreactor) and the biological component (cells in the dynamic culture) [35].

Mass transfer, morphology and rheology are the crucial elements to be studied as the bio- production scales up. Oxygen and nutrient levels must be distributed optimally while toxic compounds (such as C02) are removed. Although the primary bioreactor infrastructure may be similar between acellular and cellular product production, the distinction between the two lies in the resistance to mechanical stresses and shear. The morphology of bacteria and yeast is not as affected by mechanical mixing as the morphology of mammalian cells, which translates in an increase in the viability of acellular product production during scale up phases [34].

Taking these elements into account, scaling up industrial cell growth and metabolic processes from laboratory reactors to the production-scale bioreactors includes multiple steps in process optimization. Screening and selection of the strain, strain improvements, manipulation of media composition, and various process parameter optimizations have been previously considered variables in the proof-of-concept stage that must be carefully optimized for each increased level of scale [34]. Computational processes that simulate the optimization of scale-up are critical for the industry, but mathematical models can only help to a certain extent [32]. Scaling-up in practice must therefore be done iteratively, continuously comparing results between bench- scale and pilot-scale experiments with key data obtained from industrial-scale processes.

25 A primary challenge is that most industrial-scale data are proprietary. As a consequence, data from process scale-up studies cannot be found in scientific literature, and creates a gap between academic research and the research done by industrial biotechnology companies that have practical scale-up expertise [34].

Regulatory Regulation in cellular agriculture is considered "uncharted territory," a vital consideration along the developmental process that affects the downstream market processes, and upstream academic research funding. However, many entrepreneurs have voiced that the current regulations are outdated given the scientific advances on cellular agriculture.

In 2016, the U.S. Government launched an initiative to review and modify how U.S. agencies regulate agricultural biotechnology, but new challenges arise when new foods don't fit neatly into current regulatory definitions [27]. One tactic that companies are pursuing is to use microbes and enzymes that are already recognized as safe, such as previously approved yeast strains. 'Generally Recognized as Safe' (GRAS) is a safety determination by the U.S. Food & Drug Administration (FDA) for scientific procedures and substance specifications deemed safe under the conditions of its intended use [36], [37]. Therefore, many researchers and ventures start by selecting microbial strains that are known to be non-toxigenic and non-pathogenic, and use those strains to make their products [8]. These evolving regulations have tremendous influence on the initial selection of cells and their research applications.

A key example is the scenario for Perfect Day, the startup that makes milk proteins with yeast, and then add other ingredients to create a cow-free "milk." Despite alternate production processes, the caseins and whey produced are already recognized as safe given their structural congruency to the traditional milk proteins from cows. However, there are remaining legal implications regarding the naming of the products. The FDA has standards of identity that define milk as lacteal secretions from a cow, which "leaves out any kind of beverage produced by fermentation or other tools of molecular biology" [38].

The level of complexity increases when considering the regulation of cultured meat and cellular production. Whether in-vitro meat should be regulated under the USDA, FDA or both is a

26 question of constant debate. Meat from slaughterhouses is traditionally regulated under the USDA, while products produced from cell culture are assessed via the FDA.

The lack of a regulatory framework creates a risk-adverse culture in the cellular agriculture industry. As researchers and entrepreneurs consider how potential lab-based products might be regulated, they are likely to develop imitative products that are similar to existing products than develop something that is radically new. As such, the engagement of regulatory bodies is critical to shape the applications that the scientific research will be based on, affecting every step of the product development process.

27 _RW

Economics To successfully commercialize cellular agriculture products, the end-to-end R&D and scaling processes must be economically viable. The growth in biofuels was expected to support the rest of the bio-economy allowing the development of scalable infrastructure in biotechnology - however, due to the stalled trajectory of biofuels in the market3, the commercialization of industrial biotechnology has not accelerated as quickly as expected, which has increased the risk of initial capital for research and new ventures. Several studies have looked at cost models for the production process as a guide throughout the development program (see Figure 5).

The cellular agriculture sector is currently focused on "low volume and high price" applications such as biomedical products (for example insulin) and high-end textiles (for example leather, spider silk) to incrementally scale and survive the competitive environmental for industrial biotechnology. "Imitation" products developed from industrial biotechnology are in direct competition with those with traditional processes (i.e. agricultural and chemical). In order to become cost-competitive to consumers, the industry must gain economies of scale to achieve "high volume and low price" strategies. [39]

COST*

PROTOTYPE BENCH-SCALE LAB-SCALE PILOT-SCALE COMMERCIAL-SCALE

SCALE*

*Nore Vasual 1iustralive and not ctawn to scale

Figure 5: Illustration of Economies of Scale for Cellular Agriculture Research & Ventures

3 Due to rising cost of feedstock and decreased cost of ordinary oil. [116]

28 For example, in the textile space, Spiber has publicly stated that its commercial-scale silk will cost $20-$30 per kg. Experts forecast that synthetic spider silk manufacturers must meet a target cost of less than $10 per kg in the long-term to be competitive with conventional textiles on mass market, such as basic nylon or polyester. Randy Lewis, a biology professor at Utah State University and spider silk technology pioneer, is skeptical that the economics of bio- production processes can compete: "We are never going to make spider silk for $10 to $15 per kg, even with perfect fermentation and purification process. It just can't be done" [40]. That is why Spiber and other synthetic silk companies have looked to other niche & premium-priced applications. AMSilk initially sold their silk protein to shampoo and cosmetic care producers and introduced wound-healing coatings for silicone breast implants and medical devices (i.e. stents, catheters) in 2014. These products were tested and scaled before AMSilk produced silk textiles, partnering with Adidas in 2016 to develop a performance, bio-degradable shoe [41]. As ventures gradually aggregate a robust portfolio with multiple high-price, low-volume product niches, economies of scale for production can emerge and provide a buffer for scaling pressures.

As such, the R&D for cellular agriculture products must run parallel with the subsequent rounds of manufacturing. The cost implications of decisions for bench-scale prototypes compound and gets amplified as production scales. For example, while building and testing the initial strains that make the product of interest, questions like "whether or not the product is toxic to the host cells?" or "will the product be transported across the cell membrane or need to be extracted from the cells?" may be insignificant at first, but will drastically affect the product yield and economics. Stoichiometries of all metabolic reactions in the organism in the specific conditions should be modeled and become the determining factor on whether or not commercial viability can be achieved [24].

Riding the cost curve of economies is capital-intensive and risky, and a lot of proof-of-concept research conducted at bench-scale undervalues the constraints of scalability. Analogies can be made to the value of Design-for-Manufacturing concepts in the cellular agriculture sector, where the material type, form, and tolerance considerations of hardware production scaling are taken into account at the initial phases of prototyping [42]. Similarly, experts in the industrial biotechnology should be consulted at the beginning of research phases to integrate cost implications of decisions, such as cell-selection, media production and scaffolding.

29 Market Evaluation & Adoption One of the biggest challenges that faces cellular agriculture is the market's reception and adoption of in-vitro products, and the level of uncertainty fluctuates across different product applications and verticals. For example, a current assumption in the market is that consumers would be more comfortable wearing synthetic textile, than eating in-vitro food. For the development of new ventures, the forecasting of product adoption affects market size projections and cost economics for the chosen applications. Modern Meadow in its early days had a dual-focus on in-vitro meat and leather, but due to perspectives on a combination of (a) the market adoption rates of cultured materials vs. food, and (b) technological constraints, Modern Meadow's leadership in 2016 ultimately decided to focus on synthetic material production as its most economically viable foothold [43].

Consumer Awareness and Education When developing a new product, it is critical to listen to the unmet needs and perceptions of potential end-users [44]. Key features of textiles, fragrances, or foods should be outlined, drawing the connection between what the users need and what is feasible from a technological perspective. The public is also currently unfamiliar with biotechnological methods, such as genetic modification and editing, and thus, these terms may have negative implications when considering the adoption of the product. It is important that market research studies run parallel scientific translational research to help shape product conversations to prepare and educate the public before product launches. To sustain market adoption, the cellular agriculture industry should learn from Monsanto and avoid its missteps in public communication regarding Genetically Modified Organisms (GMOs) during the 1990s, which led to the company's unpopularity and public fear of biotechnology [45].

But educating consumers regarding hypothetical cellular agriculture concepts can only provide so much value without launching and testing actual products. For example, one of the most controversial products on the menu for cellular agriculture products is cultured meat. Some potential consumers say that they would eat cultured or 'clean' meat as a sustainable and ethical product, while others are more skeptical. But until the consumer actual tastes the product and evaluates all the parameters holistically (e.g. texture, color, and consistency), market surveys without a real product miss a significant factor of the consumer's decision-making: what the actual product will be. This rationale can be applied to all application verticals, like lab-grown leather or fermented rose fragrances, which increases the importance of small market tests with

30 real products. These experiments must be performed as the "sacrificial lamb" for new ventures to learn about how consumers will perceive new cellular agriculture products. Even when initial products fail, these market tests will allow the rest of the cellular agriculture community to react appropriately and shape future products. Creating safe environments and platforms for the cellular agriculture community to conduct these educational tests will be an important step for the sector moving forward.

Need for User Research and Prototyping Consumer education goes hand-in-hands with user research and prototyping efforts. Many conversations are still needed to identify potential value propositions for new products, make projection of market sizes and adoption rates, and to compare these figures with the production's viability at specific economies of scale. To decrease the risk profile of new ventures, niche, high-priced and low volume products will be launched first to help companies incrementally scale their prototypes in the market. This is reflected in the proposed first products of a spectrum of companies: * Bolt Threads: $314 tie made from synthetic spider silk (with only an initial batch of 50) * Spiber: $1000 synthetic spider silk-spun "Moon Parka" jacket * Impossible Foods: $13 Impossible [plant protein-based] burger with yeast-fermented 'heme' launched in 2016 with limited quantities at Chef David Chang's Momofuku Nishi

Nascent markets are difficult to analyze. These launches represent a new way to pilot and scale prototypes, while conducting market research among initial adopters. The earlier the industry get consumer feedback on how it will respond to specific products, the more responsive translational research and commercialization processes can be in meeting market requirements.

An industry example combining world-class science with robust consumer research is Procter & Gamble (P&G). P&G conducts Transaction Learning Experiments (TLEs) that are designed so that the team "makes a little and sells a little," to test science-based products with the market and let consumers vote with their wallets. The company also conducts ethnographic research to understand how consumers interact with novel products. By setting up a variety of small-format stores (i.e. online, mall kiosks, and pop-up stores), P&G developed a venture capital approach to test new products like Align, a probiotic supplement, providing seed capital for a controlled pilot [46]. This philosophy combining consumer education and testing can be applied to cellular

31 agriculture, where systematic experiments are conducted to surface key factors to shape the value proposition.

When the commercial value of the cellular agriculture industry can be appropriately triangulated with data points of success, potential investors and the public will gain enough confidence to fund more upstream and translational research.

32 vi. Imperative for a Collaborative Approach in Translational Research The outlined challenges that span from cell biology to market constraints do not work in isolation, but in an integrated fashion. Daan Luining, a researcher who worked in Dr. Mark Post's lab and a board member of the Cultured Meat Foundation, mentioned that the technical challenges alone present a significant barrier to pursue:

Building a new product in cellular agriculture is similar to creating the I-phone. Even if you have the memory chip, you need the rest of the infrastructure such as the screen, the microphone, batteries, in order for the integration of a new product to work. Similarly, you might have the cell- lines, but if you don't have the compatible scaffolding or the media, it's worthless. Add in the regulatory and marketing challenges - it's becomes a problem that not one person alone can solve.

It is evident that given the magnitude of challenges the cellular agriculture faces, meaningful collaboration across the ecosystem is necessary from the translational phase of the research to the commercialization spectrum. It may seem counter intuitive that academic researchers and companies would want to share data and give away their competitive advantage. An entrepreneur in the cellular agriculture stated:

The industry for cellular agriculture is still in its infancy - it isn't set up for collaboration. When the investors fund a new company, they want to ensure that there is a deep level of secrecy in research and development, and as a researcher, the incentives aren't clear what the value would be in sharing data amongst each other.

Many assume two things: First, that the possession of data and information does indeed give a competitive advantage and, second, that the closed operating model is financially sustainable. Given the long term future of cellular agriculture and the significant technological barriers it faces, the current success rates of individual researchers are not sufficient to sustain the long- term goals of the industry. Therefore, it is an imperative that the industry seek new, constructive methods of open innovation and collaboration.

33 PART 2 | SPECTRUM OF OPEN INNOVATION MODELS What is the spectrum of open innovation models for collaboration across the foundational, translational and commercialization continuum?

PART 2 SPECTRUM OF OPEN INNOVATION MODELS

i. Overview of Open Innovation Open Innovation is a term introduced by Chesbrough in 2003 to help organizations realize that they are unable to hold in-house all the competencies they require. It created a model to help organizations open up their research and development activities through collaborative initiatives, information and technology IP trading. (Gassmann, 2006).

The open innovation process is often contrasted to the traditional vertical integration and proprietary model where internal R&D activities lead to products that are developed and distributed by the firm. However, although many corporations were able to develop new concepts, ideas and technologies, the proprietary model frequently broke down when dealing with the exploitation of those innovations. Many of the IP that couldn't be internally commercialized was licensed to others, "sat on a shelf" for internal development, or "spilled over" to other firms.

Economics literature identifies three key benefits from R&D open innovation across an ecosystem. It allows firms to: (1) Capture "knowledge spillovers" that otherwise are lost to the firm, (2) Reduce duplication among the ecosystem's collective R&D investment, and (3) Support the exploitation of scale economies in R&D 34 Open Innovation in Ecosystem Management Speaking at the opening of a newly expanded innovation facility in 2013, President Obama stated, "We are seeing the pooling of research, of risk, and the potential for breakthroughs in manufacturing technology that only happen when we bring everyone together. No company alone would have the incentive to [make this investment] on its own, but together companies are willing to move forward" [47].

Open innovation has traditionally been applied to innovation strategy development from a firm perspective, but there is a research gap on its applications in the development of a discipline as a whole. Open innovation strategies across a mature discipline are easy to implement when a plethora of infrastructure, stakeholders and connections can be identified. When a technology- enabled discipline is just emerging, implementing an open innovation strategy is challenging. The limited research and funding becomes a bottleneck, and industry reacts by closing innovation efforts to the rest of the ecosystem. Innovation efforts become duplicative, and "knowledge spillover" is not captured, which creates silos of expertise and slows down the development of the sector.

Leadership is critical in guiding the growth of an ecosystem. An ecosystem typically contains a high rate of interdependency among member firms, and the member firms normally benefit from any value-creating member of the ecosystem. Working on the field of organizational behavior, Orr studied the efficacy of a collective pooling of responsibility and resources among stakeholders, of enhancing the improvisation and sharing new insights. [48] Knowledge transfer between community members becomes a key prerequisite to organizational learning, particularly for communities subsumed within an organization. Similarly, collective problem solving and joint artifact creation form the basis of connection and community amongst the individuals and institutions engaged in high energy physics research.

West and Lakhani (2008) addressed the role of communities in innovation. They developed the construct of innovation communities based on the community definition of Glaser (2001), which is a voluntary association of actors who have a shared innovation goal. One characteristic of an innovation community is the dependency between the value creation and capture processes, a relatively unexplored area of research (Chesbrough et al., 2006).

35 Risks Associated with Open Innovation In general, literature shows a positive relationship between openness and innovation, but there are studies that show that there is a "dark side" and risks associated with being "too open", which may affect the network's efficacy and growth in a particular industry [49], [50]: " There are 'transaction' costs caused by coordination, management, and control with too much openness - these requirements force companies to either invest more resources to filter out the noise, or stop engaging with the community altogether [49]. * The shift from closed to open ecosystem may be unsettling for companies because this paradigm redefines the boundaries between the firm and the environment. Companies engaged in open innovation networks must also setup separate policies and processes regulating proprietary vs. shareable properties. In line with game theory dynamics, the consequences of accidental knowledge spillover and unintended disclosure to partners of core competencies result in the hesitation and rejection of firms to engage in the open innovation community [51]. * Studies show that ecosystems that allow too many partners slow decision-making and product development projects with greater costs than usual [49]. There is strong evidence to support that there is an inverted U-shape correlation between the number of innovation sources that the firm draws from and the firm's innovative performance. At a certain point, there are diminishing returns in the increasing number of alliances on the productivity of an open innovation network [52].

ii. Open Innovation Network Map The Open Innovation Network Map was developed to illustrate the spectrum of the open innovation initiatives that can be pursued across the innovation process, enabling policymakers and industry stakeholders to choose the right collaboration model for the challenges of a particular industry. There is no 'one size fits all', since there are numerous variables (i.e. size of community, degree of competition) that affect the impact that each type of network can have on the growth of an industry. For example, crowdsourcing solution platforms may be an effective platform to exchange commercial ideas between partners, but may not be useful in creating industry standards.

To develop the Open Innovation Network Map, 42 successful open innovation networks were assessed and categorized based on their effectiveness in achieving primary collaboration

36 objectives. This framework (see Figure 6) builds on studies that analyze the range of collaboration models 4, and maps open-innovation archetypes across the three key phases of the Innovation Value Chain Continuum: Basic Research, Translational Development, and Commercialization.

OPEN INNOVATION NETWORK MAP Innovation Value -Chain Continuum FOUNGATIONALFESEARCH TRANSLATIONALDEVELOPMENT COMMERCIALU/ALION &SCALINGChiCotnu

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Figure 6: Breakdown of the Open Innovation Network Map

Understanding the Innovation Value Chain Continuum There are three key aspects that contribute to the sustainable growth of an emerging industry:

Basic and Foundational Research Basic and Foundational research is driven by a scientist's curiosity or interest in a scientific question. The main motivation is to expand knowledge, not to create or invent something, with little commercial value to the discoveries that result from basic research alone. For cellular agriculture, foundational science exists across the cell and developmental biology, synthetic biology and metabolic engineering, and tissue and biomaterials engineering. However, the knowledge acquired in basic research lacks integration between the narrowly defined fields and has untargeted applications. The research is often exemplified by the work of Niels Bohr who looked at the structure of atoms. Since the applications of basic research are often undefined, significant "payoffs" are limited and many scientists do not realize how their discoveries could be

4 Includes Opening Up to Precompetitive Collaboration [117], a study that examines intent of pre-competitive collaborations

37 applied to commercial applications. The primary objectives that basic and foundational research serve are: (1) Language Platform Building, (2) Data Aggregation and Storage, and (3) Hypothesis Testing.

Translational Development Translational Development is designed to solve practical problems, rather than to acquire knowledge for knowledge's sake. The research is often compared with the work conducted by Edison, who produced practical and scalable inventions, or Robert Langer, who is widely known for his application-based research in medicine and biotechnology. Similar to the research goals of pharmaceuticals, translation becomes the research necessary to transform basic biomedical research discoveries from "bench to bedside". The primary objectives for translational development phase are: (1) Standards and Infrastructure Development, (2) Application Testing, and (3) IP / Technology Sharing.

Commercialization Commercialization is the last phase of scaling and launching new innovations. The objective of ventures is to incubate the idea, and prototype, and commercialize the technology in the shortest time and as cost-effectively as possible. The primary objectives for the commercialization phase are: (1) Idea and Concept Exchange, (2) Solutions Development, and (3) Scaling Support.

Applications of the Open Innovation Network Map The framework can be applied to multiple industries since many technology-enabled disciplines grow through these three phases of research, development, and commercialization. The Open- Innovation Network Map enables policy-makers and industry leaders to choose where and how to spend limited resources to shape collaborations in the industry by: * Defining open-innovation initiatives according to their collaboration goals, " Map where current collaborative initiatives in the industry exist to identify deficient and saturated collaboration opportunities across the industry, and " Prioritize the implementation of open-innovation networks based on the needs of an emerging industry

38 Other Notes and Limitations The framework is meant to be a discussion starter regarding the different types of collaboration that are possible along the chain process of the innovation value. A limitation of the framework is that only the primary collaboration goals are represented under each phase, and the framework does not include an exhaustive list of all the intentions for collaboration. The open innovation networks are also represented as distinct archetypes, but there are common instances, where two or more network types can overlap. For example, open-source is an open innovation tool that can be created and applied to an open or closed community, and a citizen science community can be integrated with open-source platforms.

39 OPEN INNOVATION NETWORK MAP

O OPEN SOURCE

* CITIZEN SCIENCE 01 * 'BIG SCIENCE' COLLABORATIONS

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Figure 7: Open Innovation Network Map iii. Descriptions of Open Innovation Networks As shown in Figure 7, seven open innovation networks were identified based on the categorization of 42 cases of collaboration. Below is a brief description of each archetype that highlights the spectrum of intentions that each open innovation network meets:

1. Open Source Open Source models refer to a program in which the source code, designs, or information is available to the general public for use and/or modification from its original design, free of charge. Open source sprouted in the technological community as a response to proprietary software owned by corporations. Open source primarily made an impact in the software space with Linux and has expanded to other industries, mainly Open Hardware, the Internet of Things, and 3D printing. Primary collaboration objectives for open source models can span across all aspects of R&D and commercialization.

2. Citizen Science Citizen science is the involvement of the public in scientific research. 'Fold-It' is one of the prime examples of citizen science, and has become a poster child for public engagement and education in science experiments. However, a significant upfront investment is necessary in the development of the research study to ensure that that the public can properly engage and contribute valuable information to the community without the need for extensive expertise. This creates a constraint on citizen science, restricting public scientific participation to activities that are simple - this is reflected in most successful initiatives ranging from water quality surveys to conservation observation and literature annotations. Therefore, research disciplines with advanced terminologies and protocols, like highly academic biomedical and physical sciences, will have limited access of public engagement. The primary collaboration objectives for citizen science include data aggregation and storage and hypothesis testing.

3. 'Big Science' Collaborations 'Big Science' Collaborations, new models for large-scale scientific collaborative research, are emerging partly out of necessity to keep pace with the increase in complexity and interdisciplinary nature of science. The term was first coined by Alvin Weinerg to describe the large scale approach for modern nuclear technology development, such as the Manhattan Project. Many scientific endeavors - such as the colliding particles at CERN or sequencing genomes in the Human Genome Project - have become more complicated, requiring funding

41 and resources. However, the growth of communication and automation technologies that enabled collaborations have also emerged because of heightened possibilities: the Internet has allowed the self-assembly of experts, and a significant decreases in communications costs, facilitating multi-nodal interactions.

Across many of the Big Science collaborations, a spectrum of formats and goals can be identified. The Human Genome Project was an "execution-oriented" project where the mapping and sequencing tasks for researchers were strictly-defined, while the Manhattan Project was hypothesis and application-driven in experimenting methodologies to harness the fission power and develop an atomic bomb. Thus, primary collaboration objectives for Big Science Collaborations span across foundational research and translational development.

4. Crowdsourcing Solution Platforms and Contests Crowdsourcing Solution Platforms and Contests have become a popular model in the open innovation landscape. In this case, individuals and organization "outsource" the contributions of a community of users to obtain needed solutions and ideas. Innocentive, a pioneer in crowdsourcing, enables organizations to solve key problems by connecting them to participants of prize competitions. Studies show that Innocentive's success rate is 3 to 6 times the average industrial R&D, and the platform has inspired a range of enterprise "problem-solving" communities [53]. Many of the effective ideas are point-solutions to specific commercialization and ideation initiatives; therefore, the primary collaboration objectives span across exchanges of ideas/concepts and solution development.

5. Incubators and Accelerators Incubators and accelerators have become the 'launch pad' for new venture development. Another emerging trend is 'sharing' wet labs, which enables early-stage companies to share physical space and operational costs (utilities and routine lab and office supplies), lab infrastructure (i.e. freezers, hoods, purified water and gas), and equipment (i.e. centrifuges). IndieBio, Y Combinator, and Illumina are well-known accelerators that allow small ventures to share the high initial investment cost to prototype commercial biotechnology concepts. Community programs are developed to expand professional networks and increase chances of obtaining financing. Many corporations are also launching accelerators of their own to help identify future strategic opportunities and acquisitions. For example, Johnson and Johnson

42 allows entrepreneurs to incubate new clinical products in Janssen Labs. The primary collaborative objectives include solution development and limited scaling capabilities.

6. Strategic Industry Partnerships A strategic alliance is a product- or platform-based relationship between ecosystem stakeholders. These industry partnership can be product-based, enabling a vertical business model and creating a fully integrated alliance, or platform-based, sharing a set of tools, integrated technologies and services. Strategic partnerships enable stakeholders to specialize in their core competencies while leveraging the capabilities of others to gain a stronger foothold in the market.

In the rapidly growing industrial biotechnology ecosystem, the alliances made in 2016 by Ginkgo Bioworks, an organism engineering foundry, are examples of strategic partnerships shaping the specialization of commercial ventures. Bioworks recognizes its core competency in organism design and engineering, but must leverage the scaling capabilities of industry partners in order to be successful. In 2016, Bioworks formed a partnerships with (i) Cargill, a leader in bio- industrial fermentation, (ii) Amyris, a large scale bio-manufacturer of culture ingredients in flavor and fragrance, cosmetics, and nutrition, as well as (iii) Genomatica, a leader in bioengineering processes that have licensing expertise in process technology to scale Ginkgo's efforts. Jason Kelly, the CEO and co-founder of Gingko Bioworks stated: "Rather than individual companies trying to keep all aspects of the bio-industrial value chain in-house, [these] partnerships... show that by collaborating, companies can achieve more together" [54].

The primary collaboration objectives for strategic partnerships include idea and concept exchange, solution development, and scaling capabilities.

7. Precompetitive Consortia Precompetitive arrangements across ecosystem stakeholders share resources such as data, tools and analytics, that can help advance the collective sector. SEMATECH (Semiconductor Manufacturing Technology) has been used as a historical example of a successful US non-profit consortium that was initially stimulated by external competitive threats - the high performance standards of the Japanese semi-conductor industry - but later evolved into an industry leader in testing tools and standards and creating industry-wide technology roadmaps [55].

43 The role of SEMATECH in the semi-conductor sector has been examined as a case study to develop precompetitive consortia in other highly competitive and capital intensive industries like the biomedical research and pharmaceutical sector. Specific examples of successful biomedical consortia include the Biomarkers Consortium, the Predictive Safety and Toxicology Consortium, and the TransCelerate BioPharma. The latter was formed to allow industry participants to collaborate efficiently with one another, but also to promote interaction with the US Food and Drug Administration (FDA), academic researchers, and patient advocates. Projects included defining clinical data standards, creating a common language for investigator training and certification, developing a common portal for industry sponsored investigators, and streamlining mechanisms to allow more efficient commercial drugs comparisons for clinical trials. Not only does this enable better precompetitive interaction across the ecosystem, but also provides an explicit effort to better align the industry.

Primary collaboration objectives span a wide range of goals, from data aggregation and hypothesis testing in basic research, to standards and infrastructure development, application testing, and IP / technology sharing in translational development.

44 PART 3 | MODELS AND BEST PRACTICES FOR OPEN INNOVATION FOR CELLULAR AGRICULTURE

What are the prioritized open innovation models and best practices that could support technology translation in cellular agriculture?

PART 1 PART 2 CHALLENGES OF SPECTRUM OF CELLULAR OPEN INNOVATION AGRICULTURE MODELS

PART 3 PRORITIZED MODELS & BEST PRACTICES FOR OPEN INNOVATION FOR CELLULAR AGRICULTURE

In Part 1 of this thesis, a case was built for the need of more collaboration to solve application- based technical challenges in cellular agriculture. The integration of foundational research (i.e. in cell biology, synthetic biology and metabolic engineering, and tissue engineering) must be guided by the constraints of industrial biotechnology, regulatory, economic and market pressures. The intersection of these dimensions constitutes the basis of translational development (see Figure 8).

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Figure 8: Matching Cellular Agriculture Challenges with Open Innovation Models

Why is translational development so important? Translational research activities also called the "golden spike", the "missing link" or the "crossroads", bring the knowledge acquired in the lab into practice [56], [57]. Translational research is critical to bridge the gap existing between basic science and commercialization, often referred to as the "valley of death". In 2003, the National Institutes of Health identified 'translational research' as a missing link in the biomedical research system, and announced a long-term roadmap to improve application-based research infrastructure with a consortium of 60 Clinical and Translation Science Centers [58], [59]. For biomedical research, translational research has been defined as the bidirectional movement from basic research to patient-oriented and population-based research. It involves the collaboration among scientist from multiple disciplines [60]. Parallels can be drawn to cellular agriculture where translational research represents the integration of foundational research across different disciplines focused on product-specific applications.

In general, the public has undervalued the time it takes translational research to bear fruit. A study mapping the life cycle of translational research for medical interventions concluded:

46 "As scientists, we should convey to our funders and the public the immense difficulty of the scientific discovery process. Successful translation is demanding and takes a lot of effort and time even under the best circumstances; making unrealistic promises for quick discoveries and cures may damage the credibility of science in the eyes of the public" [61].

When robust translational research across an emerging industry is skipped because it is deemed "disruptive" in the commercialization of a new product, there is a danger of eroding the integrity of the science necessary to support these "disruptive" claims. As an example, the failure of the company Theranos in 2016 highlights the importance of collaborative translational research. Although the private sector can help to jump-start promising research, the checks put in place by the scientific community are critical for success. Holmes, the CEO of Theranos, kept the company's blood-testing technology proprietary, meaning that they did not reveal how their technology differed from other assays in the market, nor did they pressure test the technology by publishing their results in peer reviews and academic publications. Holmes believed that sharing would make Theranos lose their competitive advantage and prevent it from being the first. This level of secrecy created a bubble of unrealistic expectations for investors and the public, and ultimately, led to the demise of the venture [62], [63]. Scientists argue that no matter how competitive the field is, a technology-enabled company should leverage community resources without necessarily giving away its trade secrets. Therefore, not only does strong multi-disciplinary translational research bridge the gap between basic science and commercialization, but also encourages the community to collectively pressure test solutions that claim to solve the critical challenges of the industry.

Science is ultimately a collaborative effort, and is strongly reinforced in Part 2 of the research by the multitude of possible open-innovation models across the innovation value chain. These models were prioritized and matched to reflect the translational research challenges in cellular agriculture. As shown in Figure 8, three networks stand out: (1) Open Source Models, (2) 'Big Science' Collaborations, and (3) Pre-competitive Consortia. In Part 3 of the research, these three open innovation networks are profiled and 9 best practices are identified to build the community, the channel and the technologies needed for a collaborative translational development process.

47 i. Open-Source Models

An open-source model is a decentralized development model that encourages open collaboration. The open-source movement became popular with the emergence of Linux and Apache, which enabled anyone to copy, modify and redistribute the source code without paying royalties or fees, allowing the platform to evolve to suit the needs of a broader population. The open-source has also inspired increased transparency in a spectrum of industries, specifically in the hardware and the biotechnology space, via the creation of hacking and building communities.

OPEN SOURCE

'BIG SCIENCE' COLLABORATIONS

PRE-COMPETITIVE CONSORTIA

Overview: Open Compute Project (OCP) A case study to examine the open-source movement is the Open Compute Project, an open- source foundation that was created by Facebook to openly share its latest server and data center designs. Before this initiative, the competitiveness of the enterprise data center hardware industry was compared to 'Fight Club'. The giants of the internet (including Google, Amazon and Microsoft) were developing secret data center empires that relied on proprietary and premium products from vendors like Dell, HP and IBM, to support the growing demand of network and data processing bandwidth.

As they grew larger, these giants, also known as 'hyperscales', could no longer rely on typical closed hardware. They needed parts that were cheaper, more streamlined and adjustable. Given their size, these players have the purchasing power to request custom and semi-custom hardware, and they do so when the up-front engineering and research investment will justify the

48 operational cost savings and performance benefits that compound with more efficient infrastructure and scale.

But Heiliger, the VP of technical operations at Facebook proposed another approach because as he put it: "It's time to stop treating data center design [industry] like Fight Club and demystify the way these things are built." And Open Compute Project was created to share Facebook's initial designs with the public. Soon it was joined by fellow partners Intel, Rackspace, and Goldman Sachs. The Foundation states its project mission as follows: "The Open Compute Project Foundation is a rapidly growing, global community whose mission is to design, use, and enable mainstream delivery of the most efficient designs for scalable computing."

Through the OpenCompute Project, 'hyperscales' seek out open hardware solutions to identify new data-center designs, and build more efficient data centers. The non-profit foundation has been extremely successful in building an open-hardware community within a highly competitive sector. As shown in Figure 9, the community developed Incubation Project teams across different elements of the data center ecosystem (i.e. Networking, Server, and Storage). Within 5 years, the OCP community grew globally with industry leadership support. Over 600 companies were engaged in the 2016 OCP Summit (the community's annual global event).

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49 Growth and Key Participants of Open Compute Currently, Facebook, the original OCP content contributor, is the leading participant. Rackspace, another founding OCP member, publicly stated that half the company's systems used for cloud services are based on OCP designs, with a move toward 80-85% in the coming years. [65]

In 2016, Microsoft released a version 2.0 of Project Olympus, its Open CloudServer datacenter design, which is regarded as the next generation of hyperscale cloud hardware design (see Figure 10). Kushagra Vaid, General Manager of Azure Hardware Infrastructure stated:

"We're taking a very different approach by contributing our next generation cloud hardware designs when they are approximately 50% complete - much earlier in the cycle than any previous OCP project. By sharing designs that are actively in development, Project Olympus will allow the community to contribute to the ecosystem by downloading, modifying, and forking the hardware design just like open source software" [66].

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Figure X: Illustration of Project Olympus - Microsoft's Contribution to OpenCompute Project (Source: Microsoft Azure [66])

50 These designs are promoted as modular blocks that can be used independently and modified to fit customized user specifications. With flexibility for modification, participants are allowed to shape and build upon Microsoft's contribution. At this scale, this network of contributions enable stakeholders in the ecosystem, and competing internet giants, to accelerate the advancements of their data center infrastructure. As such, Apple joined OCP in 2015 and Google, who historically had been secretive about its hardware infrastructure IP, also joined in 2016. Google's participation and contribution to the community speak to the power of collaboration of open-source platforms. [67]

Facebook has publicized that these optimization efforts have helped save more than $1.2 billion in infrastructure costs over the last three years [68], and new IT supply chain business models have emerged over the last decade to provide "openness" and democratize hardware solutions.

By breaking down proprietary technology into modular components, the OpenCompute Project was able to align the ecosystem on new industry standards and develop a greater level of user customization. This case study highlights the potential impact for a centralized open-source platform in a highly competitive industry like cellular agriculture and can enable greater collaboration across the industry with open biological components, protocols and equipment. However, one must question how feasible is it to open-source everything in cellular agriculture? Given the current stage of the industry, are there some components in the industry that are worth modularizing more than others and what are the cost associated? The best practices surfaced in this section dive deeper into the properties of the hardware industry and how its successes and challenges can be translated to cellular agriculture.

51 Best Practice 1 | Modularity Encourages Reproducibility

CELL AGRICULTURE INDUSTRY CHALLENGE As an industry matures, technological components or processes (i.e. designs, protocols, and components) can be modularized to gain market efficiency. Many industries aspire to build an open-source platform such as the OpenCompute Foundation in order to design common, standard language that can be shared across stakeholders. However, developing such a system takes a considerable amount of effort and resources. The evaluation of potential open- source opportunities will highlight the cost-benefit trade-offs of modularization in the context of biotechnology and cellular agriculture.

Cost of Technology Modularization Developing a modular system for an entire industry is costly. There is an initial cost of communicating standards to participants and securing agreements with them. Also, modular systems may not able to be "tuned" as tightly as proprietary, integrated systems.

Clayton Christensen and his colleagues have described the arch of technology-driven industries that outline the natural progression from proprietary to modular product formats. Initially, users take advantage of systemic fine-tuning and prefer higher performance proprietary designs. The fine-tuned system continues to improve in performance, and eventually overshoots typical user needs. At this point, users prefer lower production costs over the unnecessary tune-ability of the industry, and the integrated system will give way to distributed networks of producers and open systems (and platforms) taking advantage of the modularity [69].

Industries such as software, aerospace, and automotive manufacturing have already evolved in a way such that with modularity, lowers the cost and increases the benefits. Other sectors, such as biotechnology have not matured enough to warrant such modularization. If modularization efforts are forced and conducted pre-maturely, inefficiencies can be created because the developed modules may not be recognized as proper 'units' fit for assembly.

Current State of Open-Source Biotechnology The biology community shares an ultimate vision to create the open-source equivalent of hardware and software in the biotechnology space. Drew Endy's article Foundations for

52 Engineering Biology promotes foundational technologies inspired by computer engineering, deriving three methodological rules from engineering:

(1) Standardization presupposes the full description and characterization of biological parts. (2) Decoupling is a strategy for simplifying a task by dividing it into manageable independent operations. (3) Abstraction consists in dealing with each level of complexity separately, regardless of their interactions.

This approach, emphasizing modularity, interoperability, transparency and reliability can be viewed as a continuation of the 'engineering ideal' for biology [70]. Many initiatives have branched out from these philosophy to understand the 'building blocks of life." For example, the compilation of DNA genome sequences (i.e. from parts, devices, and systems) has been inspired and accelerated by efforts such as the Biobricks Foundation [71], [72]. The development of standardized biological parts allows for the rapid assembly of sequences and enhances the ability to test individual parts and devices independently.

Other government-supported initiatives are paving the path for this vision. DARPA has developed two Living Foundries programs to create biologically based manufacturing platforms for new materials and manufacturing capabilities. These programs aim to accelerate the biological design-build-test-learn cycle in both time and cost via the development of next- generation tools, creating scalable and integrated infrastructure [73].

BEST PRACTICE DETAILS Open-source initiatives in cellular agriculture are still in its early days focused on the characterization and sharing of biological parts and protocols. The landscape of early developments of open-source and open-access in cellular agriculture are outlined in Figure 11.

Open-source refers to the free distribution of the biological part or equipment source-code that can be distributed, modified, and assembled freely by other users. However, when biological components cannot be modularized to enable the open-modification of parts, such as mammalian cell-lines, open-source is not viable option. Open access refers to the unrestricted public access and can be applied to the sharing of all resources (i.e. biological components, equipment infrastructure, and protocols) [74], [75].

53 Examples Open-Source &Open-Access Initiatives in Cellular Agriculture I

S iGEM (International Genetically Engineered Machine) Registry of Standard Biological Parts - Core resource with over 20,000 documented biological parts (iGEM, 2017) BiOS (Biological Open Source) License & Material Transfer Agreements (MTA)- Legally enforceable frameworks to enable 2f F kC f i - ) C C k fhti Y~ C sharing of patented and non-patented technologies (BiOS, bNock contributors (c2 . 2017) 2017) C om on ins aenlrr ewith buildina BioBricks Foundation Open MTA -Simple, standardized legal tools that enable individuals and organizations to share their materials on an open basis (BioBricks, 2017)

- Open-Source Selective Laser Sintering of Nylon and Biocompatible Polycaprolactone (Kinstlinger, Bastian, Paulsen, et al., 2016) *61 1f DIY C02 Incubator (Modulevsky, Cuerrier, & Pelling, 2015) DDIY 3D-printed Spectrophotometer (PublicLab.org. 2017) Equipment DDIY CD-ROM Agitator (dannewoo - Instructables, 2016) Infrastructure . ,Open Source Software to Control Bioflo Bioreactors (Burdge & Libourel, 2014) BioRec - Bioreactor Controller, Graphical control for data logging and process monitoring (Oitvision, 2017) - System Biology Research Tool (SBRT) - Facilitates computational aspects of systems biology (Wright &Wagner. 2008)

- OpenWetWare.org - Open laboratory protocol wiki for Detailed protocols and journal articles: bio l esarc2 discussion and publication o Plant-based scaffolding (Modulevsky, Cuerrier. & Pelling. Protocols OpenWetWare, 2017) 2015) - Open Insulin Project - Development of the first open-source o Formation of Microvascular Networks (Morgan, Delnero, protocol to create insulin (Franco & Bethencourt, 2015) Zheng, et a1., 2013)

Figure 11. Examples of Open-Source & Open-Access Initiatives in Cellular Agriculture

Open-Sourcing Acellular Products: Cell-Line Biological Parts Drew Endy's engineering philosophy has been applied to acellular product development for established cell types to: (1) characterize the genome sequence, and (2) provide a toolbox of reusable biological parts to be plugged into circuits like Lego bricks. The current library is growing, but our understanding of the biological system hasn't matured enough to make this a reality. Many of the parts collected have been deemed undefined and incompatible, and the circuitry is highly unpredictable with an unwieldly level of complexity [76]. Many synthetic biologists have compared existing catalogues of genetic parts to pieces of Lego in different sizes that don't fit together. Another technical challenge, transposing genomic parts from one species to another or one cell type to another, also limits the application of biological parts.

For popular cell types, such as yeast Saccharomyces cerevisiae, initiatives are underway to decode and synthesize the entire genome. Sc2.0 is an international project with a team of more than 200 contributors across academia and industry, including John Hopkins, Tsinghua

54 University, New York University (NYU) Langone, BGI (i.e. a leading Chinese genomics company), and Genscript (i.e. a leader in molecular cloning) [77].

The first synthetic yeast chromosome (synthetic chromosome 3 or synill) comprising of 272,871 base pairs, the chemical units that make up the DNA code, was assembled in 2014. This accomplishment represented 2.4% of the entire yeast genome sequence, and since then, tremendous progress has been made. By March 2017, the Sc2.0 team announced that 30% of the S. cerevisae's genetic material had been swapped out for engineered replacements [78]. Genetically modified yeast are one of the most well-known cell factories to produce products ranging from painkillers, spider silk, insulin, biofuels, and alcohol; completion of this genome sequencing and synthetic assembly would enable a transformative platform in yeast-developed acellular products.

The Sc2.0 project would also aid in understanding the genomic structure of other less studied and more complex starter cells. Initiatives such as DARPA's Living Foundries would continue to complement these efforts in developing the infrastructure and technologies to accelerate the biological design-build-test-learn cycle for new cell types.

Caveats of Democratizing Acellular Product Development How open should cellular agriculture be is still an open question. There has been a movement to democratize biotechnology into a do-it-yourself (DIY) process that would allow people to tinker with biology in the open-science community. Home-brewed insulin and the open insulin project are some of the latest concepts to emerge from the bio-hacking movement. Ryan Bethencourt leads a project to biohack insulin. In his words: "Insulin is the first medicine we're trying this with. It is probably the largest need of any biologic drug I know of." Ryan's team is developing a cloud-based automated laboratory platform that takes the DNA plasmid expressing the insulin gene, insert it into the bacteria and make insulin at a far lower cost than the commercial product [79], [80].

However, these projects pose concerns regarding ethics, regulatory and scalability. Dr. Marcus Hompesch, president, CEO and a founder of the Profile Institute for Clinical Research, Inc. in San Diego, says the home-brewing idea is irresponsible and foolhardy. "Manufacturing insulin or any peptide or any biologic for that matter is a very complex affair. If you don't understand what it all entails, you could end up manufacturing something that is downright dangerous for

55 patients." The integration of regulation and quality control with the open-source data and protocols should be monitored closely to ensure that the product development process is appropriately managed [80].

Open-Sourcing Cellular Production: Protocols and Best Practices In cellular product development, the technical challenges faced in tissue engineering and material science, from the media formulation to scaffolding and vascularization, are overwhelming. The knowledge accumulated in the industry regarding the characterization of each of these elements isn't enough for an open-source library of these elements to be valuable. "We are still like the Wright Brothers, putting pieces of wood and paper together. You fly one thing and it crashes. You try another thing and maybe it flies a bit better" [76]. At this stage of the industry, the modularization would only be possible after a test-and-learn process that would allow a full understanding of the cell interactions in more complex cell lines.

While advancing the translational research for cellular products, other elements of the biological research, such as protocol development, should be considered as the 'unit platform of modularity' for open-source. One area of such focus would be the modularization and standardization of experimental protocols, which is a common point of frustration across the biological research sector.

Natalie Rubio, a PhD candidate from Tufts and a research fellow scientist funded by New Harvest, stated: "This is a common problem throughout biological research. Even when you publish a protocol, and details of the protocol are not standardized, there are inefficiencies when trying to replicate the procedure."

Jess Krieger, a PhD candidate at Kent State University also funded by New Harvest, stated that keeping up with up-to-date protocols is difficult. "Sometimes when I examine published protocols, I find that the researchers have already optimized their methodologies and the protocol has evolved. Or an incomplete version of the protocol is published. Because for self- preservation reasons, scientists want others to be informed about their work, but they share just enough to stay one step ahead of the community's understanding."

Protocol standardization is also challenging due to the tremendous variability in environmental conditions and in the cells themselves, which makes troubleshooting difficult for researchers.

56 This can be contrasted to the easily-replicable lab equipment hardware, like DIY bioreactors and incubators [81]-[84]. When all the parts are accessible with complete schematics on how to build hardware, a researcher can 'hack' the hardware equipment together quite easily with little variation.

Examples such as OpenWetWare and descriptive protocols for biomaterials scaffolding (from the Pelling [84] and Stroock Labs [85]) provide inspiration for the sector to share, collaborate, and build on each other's procedures, but parallel investments in biological part characterization, protocol standardization, and equipment calibration will be necessary for open- source platforms in cellular agriculture to truly accessible.

57 Best Practice 2 1 Industry Participation Ensures Scalability

CELL AGRICULTURE INDUSTRY CHALLENGE One of the biggest challenges for translational research in the cellular agriculture is to ensure scalability. Despite what the hype and media attention to proof-of-concepts may suggest, the scaling from a bench-scale prototype to a commercial-scale product is risky and resource- intensive.

Although scaling open software may be simple, there are many challenges to scaling hardware and biotechnology proof-of-concepts. The factors that affect the differences between these industries, evident across the OpenCompute community, are:

Design Costs: Creating a specific implementation or change in software based on open source code is dependent on developer time as a primary cost. However, implementing any hardware design modification is likely to cost significantly more, as each change impacts the entire hardware value chain. Modifications in the unit biotechnology, whether the genome, cell type, or apparatus, would not only affect intra-cell interactions, and could yield different expressions across the lab-scale to commercial scale phases.

Economies of Scale: Hardware is different from software with respect to open economies of scale. Manufacturing, inventory, shipment and installation are all physical processes, and their efficiency models do not scale the same way that software does. In biotechnology, the economies of scale are amplified because the capex investment necessary to move from prototype-scale to commercial-scale are significantly higher than the corresponding ones in hardware.

Manufacturing and Distribution Costs and Efforts: Software product investment is a developer's time investment. Unlike hardware, software has low manufacturing and distribution costs; lead time for new software distribution and delivery is relatively straightforward and low- cost. On the other hand, bringing new hardware to market requires procurement of inventory, manufacturing, logistics, and distribution management. In biotechnology, these costs and resource extend further. The production and distribution of intermediary and end-products from cellular agriculture require a higher level of sterilization, regulatory standards and downstream processing.

58 Given the comparisons in product scalability between hardware, software and biotechnology, how can cellular agriculture proof-of-concepts be scaled efficiently to increase the relevance of translational research?

BEST PRACTICE DETAILS Challenges scaling cellular agriculture proof-of-concepts are important to address and collaborations across the industry should focus to improve the viability of translational research.

As the OpenCompute community openly shares hardware designs, there is significant engagement from certified contract manufacturers, such as Hyve Solutions and Quanta Computer, to manufacture the designs developed by the community. These players pool demand from OpenCompute specifications to achieve the economies of scale necessary to justify the high CapEx for each batch.

As the cellular agriculture sector explores open communities, building flexible supporting infrastructure to scale new concepts is critical. Recently, the biotechnology community has conceptualized new 'uber' like platforms to scale proof-of-concepts to reduce costs, capex demands, and spread efficiency. These models build a network of traditional contract research organizations (CROs), and allows new biotech ventures to run scaling tests on the idle infrastructure of industrial biotechnology companies. This resource allocation platform across the industry enables translational research to be tested more frequently with lower costs, while industrial biotechnology companies can utilize unused capacity more efficiently.

Companies, like REG Okeenchobee, with fermentation expertise and demonstration-scale capacity, have received requests from the biotechnology science community to "borrow" the facility when it is not being used. This trend represents a big shift in 'scaling services' for cellular agriculture. Utilizing such a platform would provide a gain in critical expertise in the scaling up metabolic processes, in optimizing the microbe production and running it at scale [86].

Most of the data and expertise in scaling is currently proprietary, and industry collaborations should be built to link experts in biotech scaling with application-based researchers. The Advanced Biofuels Process Demonstration Unit (ABPDU) at the Lawrence Berkeley National Labs is a best in-class example of industry-academia collaborations focused on the optimization

59 and scaling of technologies for bio-based chemicals, materials and fuels. With an acronym ABPDU, the institution has a nickname that depicts its integral role in the biotech industry: "All Bio-Economy Processes Developed Upstairs." Operational since 2012, ABPDU team focuses on translating bench scale data and processes into recommendations for commercial bioprocess systems design across three main phases:

(1) Pilot - Integrating unit operations and validating techno-economic assessments (2) Demonstration - Verifying industrial-scale performance specifications for a pioneer plant (3) Pioneer Commercial - Proving economic production at commercial volumes

These three phases allow cellular agriculture ventures to create 'stage-gate milestones' towards full-scale commercialization, synthesize critical learnings at each step, and de-risk critical scaling uncertainties for researchers to attract additional capital investment (see Figure 12).

I SCAUNG DEVELOPMENT PATHWAY i I __ Investment Pilot Demonstration Pioneer Commercial Full Scale commercial Attractiveriess

Figure 12. Development Pathway at the Advanced Biofuels Process Demonstration Unit (Source: Advanced Biofuels Process Demonstration Unit Capabilities [87])

ABPDU has partnered with multiple cellular agriculture ventures including Clara Foods, Gelzen Inc., and Gingko Bioworks. The lab also collaborated with Muufri (now Perfect Day) in 2016 to optimize the yield of milk proteins through Pichia, a bio-engineered yeast species. Highlights of

60 the collaboration were published to feature the scaling process from 2L to 300L, with improvements in the recovery yield and protein purification processes [88].

Institutions such as ABPDU represent the missing link between academic research and industrial scale-up processes in the biotech industry. As cellular agriculture researchers continue to validate proof-of-concepts, it is critical that industry-supported mechanisms are in place to test the scalability of ongoing translational research.

61 Best Practice 3 | Clusters Initiate Standardization

CELL AG INDUSTRY INDUSTRY CHALLENGE Cellular agriculture is a cross-cutting sector with technical challenges and a vast number of dependent specifications. It is challenging to manage a growing community that has an intensive value chain as well as a broad portfolio of potential applications. How do you organize communities of scientific researchers with diverse perspectives, skillsets and resources to work towards application-based translational research and create standards to push the industry forward?

BEST PRACTICE DETAILS Standards are published requirements, guidelines or characteristics that can be used consistently to ensure that materials, product, processes and services are fit for their purposes. When approved as standards, community-wide specifications allow the market to be responsive to the needs of its stakeholders and achieve interoperability across the industry [89].

Because the OpenCompute Project has a wide range of needs from contributors and vendors in the community, it is a challenge for all players to make a rapid shift from proprietary hardware approaches to the open standards set by OpenCompute. Migrating to an open platform takes time and adjustment since proprietary technologies may provide critical features that modular components may not. Another challenge is that user needs are not homogenous throughout the data center hardware industry; depending on factors like geographic location, data processing speeds and level of security, different hardware specifications are necessary.

For example, many US-centric businesses have different needs and requirements from those in other regions. Enterprise data center hardware consumers in Japan found the OCP rack specifications too large with different power supply requirements. OCP recommended that the Japanese businesses with unique data center hardware requirements created a community of their own to cluster open design specifications among themselves to solve critical issues like power, rack size, and cost. The self-assembled communities or 'clusters' were extremely successful; other solutions such as new earthquake-resistant elements emerged for their own version of the OCP rack.

62 What can be observed is a set of user specifications slowly maturing into community-approved standards. OpenCompute became a flexible platform that allowed users with similar specifications and requirements (e.g., rack size and power supply requirements) to co-develop customized hardware for their communities.

Standardization allows (and rewards) interoperability. A classic example of influential standards is the story of 'VHS versus Betamax'. Sony launched Betamax as a proprietary technology in 1975 and with a superior product, they believed that they could do it alone. There were high incentives to make the most out of their first-to-market advantage and recoup R&D investments through high margins. With significant introductory costs, Betamax was unfortunately unable to achieve significant market adoption.

On the other hand, VHS was developed by JVC (launched in 1976, later than Betamax), and was licensed and shared across industry competitors and suppliers. Through the availability and compatibility of the technology, critical mass for VHS was established and the technology specifications evolved into an open standard. VHS became the dominant infrastructure used for video hardware technology. As such, it is important to note that the best technologies do not always win in the market; the impact of industry interoperability, compatibility and open-ness often prevail over closed systems with the creation of industry standards [90].

Infrastructure and organization of communities should be built in cellular agriculture to allow for the self-assembly of innovators with similar specifications. The ecosystem can be broken down into communities where cellular agriculture researchers can share needs and specifications; when these needs are aggregated and gain critical mass, they can be approved by industry leaders as standards necessary to align the industry.

Figure 13 is an illustration of how cellular agriculture can be structured into three primary communities to help shape translational research.

63 Geographic-focused Communities

Challenges to Explore

e Regulation - Capital Investment & Formation Cell Type- Consumer Preferences Communities

Challenges to Explore - Genome Scale Design - Pathway Design - Biological Part Characterization - Cell Media Development - Scaffolding Application-Specific Communities

Challenges to Explore - 3D Tissue Formation - Bioreactor Development & Scaling - Cost & Economies of Scale - Consumer Adoption

Figure 13: Proposed Primary Communities for Cellular Agriculture

The three proposed groups include:

" Cell Type Communities - In the early stages of the value chain, the limiting factor to the science is the design and development of cell-lines. Since the characteristics of each cell species are so different and rarely transferable, each cell line should have its own community where scientific best practices, protocols, and technologies can be shared. Functional expertise across other technical challenges, such as scaffolding, media, and vascularization, would be segmented based on these cell lines given the limited transferability across species.

* Geographic Communities - These communities would help define the regulatory and market characteristics of the geography, shaping aspects of the value proposition and capital formation for translational research.

64 * Application-Specific Communities - These communities are in the latter stages of the value chain and can help gather data on user needs, market adoption, and regulatory standards for specific cellular agriculture applications.

These communities or clusters can help shape and define user specifications across translational research for particular applications. Cellular agriculture is a field with high levels of uncertainty across research efficiency, regulatory hurdles, and market access; therefore, appropriate structures must be developed to allow stakeholders to band together and de-risk critical assumptions. Standardization can emerge from critical mass, and the industry can more easily overcome market and regulatory uncertainties with collective influence. The more efficiently specifications across the industry can be clustered and outlined, the more responsive the industry can be in addressing the bottlenecks that hinder industry growth.

65 ii. Big Science Collaborations 'Big Science' Collaborations are large-scale scientific collaborative research for complex science and technology development, such as the Manhattan Project and the Human Genome Project. The Institute of Medicine and National Research Council addressed the purpose of Large-Scale Biomedical Science: "to produce a public good - an end project that is valuable for society and is useful to many or all investigators in the field" [91]. The report goes on to point out that "large-scale collaborative projects may also complement smaller projects by achieving an important, complex goal that could not be accomplished through the traditional model of single- investigator, small-scale research." There are several criteria that characterize projects that are best carried out on a large scale, including: (i) external coordination and management, (ii) a required budget larger than the budget that could be met under traditional funding mechanisms, (iii) a time frame longer than the corresponding to smaller projects, and (iv) strategic planning with intermediate goals and endpoints, as well as (v) a phase-out strategy [91].

OPEN SOURCE

'BIG SCIENCE' COLLABORATIONS

PRE-COMPETITIVE CONSORTIA

Overview: Human Genome Project (HGP) The Human Genome Project (HGP) is the largest scientific and technological enterprise in the history of biology. Costing in excess of $3 billion and stretching over a decade and a half, HGP involved two agencies in Washington, a major funding organization in England, and scientists in six countries. The Project was coordinated by the National Institutes of Health and the U.S. Department of Energy. Additional contributors included universities across the United States and international partners in the United Kingdom, France, Germany, Japan, and China. The Human Genome Project formally began in 1990 and was completed in 2003, 2 years ahead of its original schedule. The international Human Genome Sequencing Consortium published the

66 first draft of the human genome in the journal Nature in February 2001 with the sequence's three billion base pairs 90% complete.

Our aim is to look at the Human Genome Project as a result not only as a scientific achievement, but also as a success of scientific organization and management that could be translated to adjacent industries like cellular agriculture.

67 Best Practice 4 1 Centralized Leadership Fosters Coordination

CELL AGRICULTURE INDUSTRY CHALLENGE Within the cellular agriculture sector, small research collaborations are starting to emerge: " At New Harvest, avian cell-lines developed by Marie Gibbons from the North Carolina State University were handed-off to Natalie Rubio at Tufts University so that she would experiment with possible scaffolding opportunities. Marie also tested new scaffolding techniques, after visiting Jess Krieger at Andrew's Pelling Lab, to experiment with cultured meat and jackfruit scaffolding combinations. " Good Food Institute is creating roles such as "Innovation Director", "Business Innovation Specialist" and "Startup Idea Interns", with a goal to identify white-space business opportunities for alternative protein sources and support emerging food ventures. " The Cellular Agriculture Society (CAS) was developed in 2017 to engage university students who are interested in solving the technical and marketing challenges of cellular agriculture collaboratively. The CAS executive team realized the need for a "Collaboration Platform" while considering how cellular agriculture research can be coordinated across virtual students from different academic institutions.

As the cellular agriculture research grows, the main question is how industry-wide and collaborative public research should be structured to enable optimal scalability and efficiency.

BEST PRACTICE DETAILS The magnitude of the challenges in cellular agriculture can be compared to large-scale collaborations such as the Human Genome Project. For big science collaboration projects, there have been two distinct pathways to "manage" the vast pool of scientific talent taking part in mega-science projects. On one hand, the Manhattan Project, created to build an atomic bomb, had a top-down strategy. On the other, the HGP had a bottoms-up strategy, involving input from leading scientists at international laboratories funded through the peer-review process, and advisory councils of experts consulting with hundreds of scientists from different fields. This last approach enabled a "center" strategy, which placed the National Institute of Health at the hub of a vast network of 20 major academic research centers around the world'. Each of these

s Countries involved in HGP: United States, England, France, Israel, Germany, Canada, and Japan

68 research centers had a specific role and were assigned specific chromosomes to research and sequence. Five of these centers 6 formed the core of the human sequencing component of HGP. Known as the G-5, these centers performed 85% of the genome sequencing by the time the project ended in 2003. NIH had two principal resource and funding partners: the US Department of Energy, and the Wellcome Trust, which was reputed to be the wealthiest health-oriented foundation in the world. The large amounts of data of genome sequence obtained across the network from different disciplines and countries were ultimately funneled to the leadership of the collaboration where these data were processed and assembled.

John Sulston, director of the Sanger Centre (now the Sanger Institute) in the UK, from 1993 to 2000, recalls that "at first everyone did everything," following the tradition of manual sequencing groups. However, it soon became apparent to Sulston and others that, for the sake of efficiency and accuracy, it was best to recruit staff of varying skills-from sequencing technology to computer analysis-and to allocate the work accordingly. [92]

Coordination among the G5 was established in 1988 with a weekly conference call; Sulston recalled the precarious culture of collaboration across previously competitive laboratories:

Initially somewhat prickly, these calls served to share technical and experimental advances within this group whose members had, only a few months before, been competing for the same pool of funds. One useful innovation was to spend part of each call on a "lab meeting" format, in which each center in rotation would present some new advance in automation, experimental protocol, or computational analysis [92].

What made this "large-scale approach" to science different from other life-science research projects at NIH was that there was less emphasis on theory and hypothesis testing. Many scientists acknowledge that this project focused on technical capacity to gather huge data sets of a particular type, and assemble them in a meaningful pattern.

6 Institutions included: Whitehead Institute for Biomedical Research; Washington University in St. Louis; Baylor College of Medicine; the Joint Genome Institute (a cluster of three national laboratories under DOE); and the Sanger Centre (now Sanger Institute) in England

69 The distributed model of science adopted is one that can push cellular agriculture to overcome the current technical challenges and move it to the execution level. Michael Selden, the CEO and co-founder of Finless Foods, stated:

"We might not need everyone on the team or in the room to be the smartest tissue engineers, material engineers, etc. In fact, there is a lot of grunt lab work to be done, and we might just need a few experts, while the rest of the team could be executing protocols."

This has tremendous implications on how scientific research in cellular agriculture can be structured as the sector progress. For science collaborations to be successful, the research network must have 'active management'. According to Karim R. Lakhani, a professor at Harvard Business School who studies crowd-based innovation models and digital transformation of companies and industries:

"You need a coordinator or project manager who understands the science, and is good at linking people together. The coordination role that is needed is not a manager saying, 'tomorrow you better make this happen,' but rather a coordination of knowledge across the organization. That is critical. You can't rely on scientists to self-organize." [93].

In 2016, the Good Food Institute developed a Technology Readiness Assessment of cultured meat. This evolving report could become a good starting point to get alignment among academics, researchers and industry stakeholders on the challenges for the industry. Efforts should be made to translate these challenges into a comprehensive roadmap for cellular agriculture products to (a) define potential milestones for the industry and (b) distribute mini- tasks across the community. As the community grows, it is necessary for these tasks to be coordinated and integrated via a centralized committee to eliminate inefficiencies along the translational research process.

This roadmap for cellular agriculture research created by GFI can be highly complementary to the research leadership and structures piloted at New Harvest. From the experience funding four research fellows in cellular agriculture, the executive team at New Harvest has developed robust strategies that can be widely scaled across the cellular agriculture sector. For example, New Harvest began to disburse research funds in shorter intervals (i.e. 6 months intervals) to align funding with specific progress milestones. New Harvest also tackled coordination

70 challenges across the virtual researchers by experimenting with different communication platforms. An open Slack Channel was created to allow researchers to communicate with the other fellows and guests on the communication channel regarding: (1) immediate research challenges to solicit input, (2) best practices and failures among the team, and (3) updates across the cellular agriculture industry. For more in-depth discussions regarding logistics and research progress, a weekly Slack forum between the Research Fellows and New Harvest Staff was implemented to promote collaboration in a virtual lab-meeting format. A Research Director was also hired in 2017 to capture and coordinate knowledge acquired from the fellowship program, drawing connections across researchers and redirect the focus of research efforts if necessary [94].

As research contributions in advancing cellular agriculture grow, the network will become increasingly dispersed and distributed across geographies, laboratories, cultures, and expertise. Leadership will be necessary to play a key role in 'actively managing' a sector-wide roadmap with appropriate governance structures to align and coordinate research communities.

71 Best Practice 5 | Data-Sharing Frequency Increases Transparency

CELL AGRICULTURE INDUSTRY CHALLENGE Many studies have shown that the speed of data sharing affects the progress of science in large-scale collaborations. The dissemination of knowledge across cellular agriculture has been limited to emerging conferences, select journal articles, and media outlets. Policies on the frequency of data sharing are non-existent in the cellular agriculture, and should be considered in the future as an effective tool to speed up the pace of research.

BEST PRACTICE DETAILS In 1992, the National Institutes of Health (NIH) and the Department of Energy (DOE) established guidelines on sharing data and resources, which granted researchers a 12-month latency period between the generation of data and its required release.

But as a competitive race began between Celera and the Human Genome Project (HGP), one strategy that HGP sought out to speed up progress was to get information from the HGP out quicker, and to seek more communication across centers. In February 1996, the Wellcome Trust organized an International Strategy Meeting on the Human Genome Sequencing in Bermuda. In this meeting, the scientific leadership of the Human Genome Project determined that the rate of data sharing needed to be quicker. A "Bermuda Accord" was struck to release data within 24 hours, and to make the entire sequence freely available to the public for both research and development in order to maximize the benefits to society. [95] So every 24 hours, thousands of new bits of data were added to the several billion already collected on GenBank, the international Web site maintained by the National Center for Biotechnology. [96]

In order to make efficient advancements, the scientific community needs the latest data as soon as possible in order to drive further research, while allowing researchers time to prepare for publication and apply for patents. A study by Jorge Conteras in 2010 underscores the competing needs inherent to data-release decisions, and concludes that the regulators must weigh the rights of researchers, also called data producers, against those of data users. "While it would be preferable, from a pure scientific advancement standpoint, to have every piece of data released immediately to the public," Conteras states that, "you must have a compromise. Otherwise these commons, or bodies of data, aren't going to be created."

72 Cellular agriculture can learn from these policy developments. As more contributors get involved in the development of the sector, minimizing the duplication of research becomes a priority to increase efficiency of the overall network. Other data-sharing policy considerations are included in Table 14.

Table 14. Key Data Sharing Policy Considerations

Comprehensiveness Quality filtered for 'utility' across ecosystem stakeholders

Format Optimized templates to enhance standardization and inter-operability

Deposition Centralized depositories to integrate and store data sets

Metrics Aligned metrics across scientists and funding agencies

Accountability Appropriate incentives & governance to ensure adherence for standards

Timing Duration dependent on the nature of effort generating data

The ecosystem must also be mindful of the upper limits and transaction costs associated with data-sharing. As mentioned in Part 2 of the research, an open innovation ecosystem that is 'too open' may slow down the progress because there is excessive data for research to filter out and capture what is useful to them. Using statistical physics and network models, scientists at Rensselaer Polytechnic Institute found that in large-scale computer networks, overwhelming amounts of data creates stress on the nodes and can increase delays in communication, leading to performance deterioration and the eventual collapse of the entire system. "Understanding the impact of [node] delays can enable network operators to know when less communication efforts can actually be more efficient for overall performance. [97]"

To build a robust data-sharing channels within cellular agriculture, stakeholders must take into consideration the optimal characterization of data shared across the sector as well as the bandwidth the ecosystem to determine the right media and frequency for communication.

73 iii. Pre-Competitive Consortia

Pre-competitive research has been defined by Janet Woodcock as "science participated in collaboratively by those who ordinarily are commercial competitors" [98]. Industries, such as pharmaceuticals and semi-conductors that require high capital investment in research and face a decline in research productivity, have seen a substantial expansion in the number of pre- competitive, collaborative and multi-stakeholder consortia to promote collaboration and risk- sharing across the industry.

OPEN SOURCE

'BIG SCIENCE' COLLABORATIONS

PRE-COMPETITIVE CONSORTIA

Overview: Semiconductor Manufacturer Technology (SEMATECH)

One historic example of a pre-competitive consortium is SEMATECH (Semiconductor Manufacturer Technology), a collaboration across 14 American chip makers, including Intel and Texas Instruments. Started in 1988, with support from DARPA and the U.S. government, the consortium had an ambitious goal: to revitalize the U.S. semiconductor industry by finding ways to reduce manufacturing costs and product defects. SEMATECH has become a widely celebrated case study focused on pre-competitive R&D - cooperatively developing standards, building sector infrastructure (tools, materials and processes), and ensuring the all the players were aligned on improving product manufacturability and accelerating commercialization.

"Before SEMATECH, it took 30 percent more research and development dollars to bring about each new generation of chip miniaturization," says G. Dan Hutcheson, CEO of market researcher VLSI Research. That figure dropped to 12.5 percent shortly after the advent of

74 SEMATECH and has since fallen to the low single digits, with reductions to the miniaturization cycles from three years to two [99].

Given the consortium's contribution to the growth of the semiconductor industry, it has become a model for how new industries can be 'jumpstarted'. SEMATECH has inspired the development of other pre-competitive consortia to reduce the cost of collaboration and increase R&D productivity, such as the National Alliance for Advanced Transportation Battery Cell Manufacturer and the U.S. Department of Energy's SunShot Initiative [99].

75 Best Practice 6 | Crisis Creates Cohesion

CELL AGRICULTURE INDUSTRY CHALLENGE Creating cohesion in a competitive industry is tough, especially in an early stage sector such as cellular agriculture. Many academic researchers have voiced that industry players are extremely secretive about their progress, and as a result, the industry suffers from R&D inefficiency. An academic researcher stated: "I could be working on a problem that has already been solved by industry, but because the work isn't public, I wouldn't know." An evaluation of SEMATECH and other pre-competitive collaborations suggests that 'identifying and amplifying a crisis' is a critical factor in getting alignment across the industry.

BEST PRACTICE DETAILS Leaders of consortia, such as Sharon Terry, president and CEO of Genetic Alliance, have reflected on the fact that a crisis is needed in the industry to align the different parties on a common vision necessary for collaboration [100]. SEMATECH was born out of desperation: The severe threat from foreign Japanese competition motivated the formation of large-sale, horizontal consortia in a highly competitive semi-conductor industry. Others examples of collaborations aligned by an industry threat include USCAR7, formed to address the threat and opportunity of efficient and environmentally-friendly automobiles, and MARITECH, a government-industry consortium established to strengthen the competitiveness of the shipbuilding industry in the US [101].

Scientists in genomic research have tried to create a sense of urgency for cohesion to solve "the declining productivity of research". There is unanimous agreement among genome researchers that "we have spent a lot of money on research, and we don't see a lot of results. We might have hyped things in earlier years without meaning or wanting to, and this stuff takes a long time" [100]. But is this rationale compelling enough to rally an industry? Literature has pointed out the frustration among scientists suffering from 'consortium fatigue' of large industry- wide committees, spurring the development of smaller, strategically coordinated initiatives that can be more effective by focusing on more targeted and measurable crisis points [102].

7 United States Council for Automotive Research: Umbrella organization of DaimlerChrysler, Ford and General Motors, formed to conduct cooperative, pre-competitive research.

76 Defining and elevating a crisis across an ecosystem will take significant effort and time, and support from government has proved to be necessary to achieve consensus. McFadden, the secretary of the Board of Directors at SEMATECH, emphasized that, it was unlikely, that the participation in SEMATECH membership would have been so high without the involvement of DARPA and the government to trigger the creation of this consortium. What government participation provides is the sense of both urgency and commitment, and most of the companies feel as though they cannot afford to be left out. Besides contributing financial resources, there are other types of government intervention, such as IP policies, that link to the necessary stimulation of new industry growth, including IP negotiations with the Manufacturers Aircraft Association and the price capping of the antibiotics during the Anthrax attacks. [103]

In order to truly align all stakeholders across a competitive ecosystem, the cellular agriculture industry must carefully consider crafting and communicating its opportunity to solve an existing or emerging crisis that will resonate and be prioritized by the government. There are current initiatives to support sustainability and ethical upsides of cellular agriculture, but this is not enough to create solidarity across the ecosystem. These opportunities may include black swan events that disrupt current production infrastructure. Also, it is important to disseminate how cellular agriculture can be prepared to solve and alleviate these significant challenges. Governments should be involved in the process to help identify and prioritize these issues, which range from food security to long-term space travel'. By creating a sense of urgency and triggering the government to tackle crises with cellular agriculture solutions, support for translational research can effectively create cohesion across the competitive industry.

8 In 2002, NASA had funded a cultured goldfish meat project to investigate food production possibilities for astronauts on long-range space missions. As research into space tourism and long-term habitation becomes a reality, there will be a critical need for a continuous supply of edible animal muscle protein for passengers to alleviate re-supply and storage issues.

77 Best Practice 7 1 Innovation Hubs Nurture Translatability

CELL AGRICULTURE INDUSTRY CHALLENGE Communication and data dissemination across the cellular agriculture landscape is limited. For an industry to grow, nodes and connections must be built for community outreach to increase diffusion levels of new developments and promote greater knowledge 'spill-over'.

One of the limiting factors is the cost of communication. Transferring biological data is expensive. A study found that among geneticists, 45 percent withheld data because it cost them too much to send data to scientists who requested them:."When data is a physical thing such as a reagent, an antibody, a chemical, a mouse, or a reengineered organism, the cost and administrative difficulties are important obstacles [to consider]" [104].

This challenge is not uncommon within the cellular agriculture sector. The administrative, logistics and regulatory paperwork to ship biological wetware material are extensive and time- consuming, and the expenses shipping biological materials can add up due to a combination of preservation efforts (i.e. dry ice), the speed of delivery (i.e. overnight shipping costs), and other specialized delivery conditions.

Jess Kreiger, a PhD candidate from Kent State University and New Harvest Fellow, recalls an instance when a potential partnership was being explored that required the sharing of specialized cell culture. When she received the package, the cells were deemed D.O.A. or "Dead on Arrival" due to improper conditions or handling, and as a result the collaboration ultimately fell through. Even when potential partners agree to participate in collaborative initiatives, the cost of sharing information places financial risk on stakeholders and a burden on the sector.

A potential role of pre-competitive consortia to lower these transaction costs of communication through the development of new tools and distribution channels should be explored.

78 BEST PRACTICE DETAILS In the early stages developing a precompetitive consortium, it is critical to invest in infrastructure and communication channels to reduce R&D duplication [55].

SEMATECH recognized the need for cooperation between device makers and their equipment suppliers, and the consortium made the growth of this interaction a priority. Before SEMATECH's intervention, many of the equipment companies were one-product firms and were tied to a one-manufacturing process, which restricted the firm's flexibility to pursue new growth opportunities. Because the device companies were risk-adverse trying new manufacturing processes, their bottom lines were also tied to the life cycle of the machinery they were using. SEMATECH worked with both parties to create reliable tools, focus on quality control, and increased sophistication in the manufacturing process. SEMATECH became an innovation hub between the device makers and equipment suppliers in translating industry needs into opportunities, and SEMATECH's work began to be recognized as one that could be done centrally.

According to one study, the SEMATEC consortium eliminated duplicative projects across the industry, while maintaining the same the R&D production levels across the industry. This allowed the community to achieve greater levels of R&D efficiency [105], which is vital at early stages of product and process innovation [106]

Models created by pre-competitive consortia in the biomedical sector can also be applied to cellular agriculture to solve industry-limiting steps, such as the development of cell lines. Sage Bionetworks builds data sets, strengthens collaborative research toolkits, and creates probabilistic causal models, which are valuable assets for the biomarkers community to predict outcomes and responses to specific treatments. Incentives are created so that, when these critical tools are used, channels of communication across researchers are opened to encourage collaboration. Dr. Friends, Chairman and past president of Sage Bionetworks, emphasizes:

You can dream all you want about how beautiful it is to have public-private partnerships, but if you don't have leaders who are willing to build those tools and an infrastructure that allows sharing, it's very hard.

79 This calls for standardized communication methodologies that are aligned across the industry. "Think of a world where inter-lab communication is equal to intra-lab communication, where the ability to talk back and forth between labs is the same as it is within labs. To do that, we've got to have funders be able to agree that their investigators will be sharing data in certain ways" [100].

Key initiatives for pre-competitive consortiums include the development of innovation hubs or information 'utilities' infrastructure, to build data standards, tools, and communication platforms. Although often underappreciated, these assets to broadcast innovation and lower sharing transaction costs, when scaled, are vital for the development of the industry.

Studies about innovative ecosystems examine the effective role of 'attention workers' in the distribution of new knowledge. The study by Nordfors et al. outlined the pitfalls of ecosystems that lack sufficient communication infrastructure to translate information across a multi- disciplinary set of collaborators. According to them: "if an innovation is not discussed and communicated, or if there is no shared language to do so, it simply does not exist according to the different players in the innovation ecosystem" [107]. 'Attention workers' are networked stakeholders across an innovation ecosystem that aggregates information about industry developments, validates the quality of the innovation, and shapes its value for relevant stakeholder groups before broadcasting.

The role of innovation hubs in cellular agriculture can also help lower the transaction costs of sharing physical data and information. Having centralized locations where biological materials can be stored, preserved, and analyzed enables the sector to pool the demand for key information. For example, the International Stem Cell Banking Initiative (ISCBI) is a global interoperable network of stem cell banks, working together to identify best practices for banking, characterization and testing of pluripotent stem cell lines. The ISCBI, in 2008, created its first set of best practices: the "Consensus Guidance for Banking and Supply of Human Embryonic Stem Cell Lines for Research Purposes" to provide comprehensive guidance in managing bio- resources, and will help shape industry standards for clinical translation covering: procurement, characterization, testing, maintenance, and shipment [108]. For cellular agriculture, partnerships with cell banks and other centralized institutes should be explored to optimize methods to create cost-effective distribution processes for wetware materials.

80 Leaders in networking science have analyzed the startling resemblances between world-class systems, such as neuron networks, the US defense department's ARPANET (i.e. precursor to the internet), and social media communities, with naturally-occurring interaction networks built by fungi. For the network ecosystem to thrive, colonies create self-organized, adaptive networks that symbiotically facilitate the transportation of nutrients through its roots and channels [109]. Similarly in cellular agriculture, the distribution of biological information, tools and data becomes the backbone for collaboration, and development of innovation hubs can nurture the translatability of information across the community and lower transaction costs for sharing.

81 Best Practice 8 1 'Honest Brokers' Promote Trust

CELL AGRICULTURE INDUSTRY CHALLENGE Science and institution policies are frequently at odds: the incentives for scientists and industry stakeholders to keep their data proprietary in hopes to advance their careers stunt opportunities for scientific efforts to build upon each other.

A significant hurdle across the cellular agriculture industry is the development of an effective science-policy interface. For example, for New Harvest, a non-profit promoting open science in cultured meat development, one of the limiting factors of data and IP sharing is the IP ownership of universities that do the research. This limits the opportunities for collaboration across the fellowship, as well as the sharing of research to the rest of the cellular agriculture community. How can intermediaries gain trust across the cellular agriculture ecosystem to actively and safely share IP and technologies?

BEST PRACTICE DETAILS Non-profit organizations and agencies play a spectrum of unique roles in developing the collaborative growth of the industry vision. The collaboration success of SEMATECH is commonly attributed to its role as an 'honest broker' - an entity that collects sets of private information and distributes parts of these sets to other entities who should not have access to the entire set. SEMATECH collected key technical information from the members, but prevented the transfer of the information between suppliers. (Olson and Berger, 2011)

The 'honest broker' model has been replicated frequently in clinical research on biological specimens and biomedical intellectual property. For example, the Foundational National Institute of Health (FNIH) acts as trusted third party to manage the IP ownership and distribution among a select group of academics and pharmaceutical companies for specific clinical trials9.

Key highlights of FNIH's role as an honest broker include: * An agreement that recognizes that pre-existing IP being pooled together from each of the stakeholders stay with their originators.

9 I-SPY 2 Trial

82 * New IP that is discovered by the trial, such as new genome signatures or predictive analytics tools, will be shared fairly by the collaborators. " The inventing organizations will grant exclusive licenses to the FNIH for the new IP so that the patents can be prosecuted and managed fairly (i.e. based on equity or research contributions), licensed to interested parties, and return a fair share of royalties 0 to participating organizations.

Establishing a neutral middleman that could be trusted by the broad base of stakeholders is essential to make these IP coordination efforts a success, "creating a safe, legal harbor needed for collaborations to occur" [100].

Building the stakeholders' trust in the neutral entity is challenging. That is why setting up pre- competitive consortia takes longer than expected. " When SEMATECH was initially set, the member firms were unable to agree on an appropriate structure for sharing competitive technologies, and took four years, from 1986 to 1990, to agree on the appropriate 'precompetitive work' [101]. * Setting up the Human Blood Plasma Metabolome Consortium took a year and a half longer, and experts agreed that ensuring the public's trust was 'a really serious problem' [100].

Developing an 'honest broker' model within the cellular agriculture sector would be beneficial in opening lines of IP sharing across the industry (see Figure 15). Material Transfer Agreements (MTAs) can be explored to govern the transfer of tangible research materials between multiple organizations. Biological materials, such as reagents, cell lines, plasmids, and vectors are the most frequently transferred materials, but this portfolio can be expanded to other components, such as protocols and software. Specific niche and critical technologies would need to be explored first, if they are to gain quick adoption in the sector. The Biological Materials Transfer Project was developed by Science Commons to deploy standard, modular contracts for biological materials, but was terminated in 2009 when it failed to be adopted.

10 Minus expenses

83 Inventing organizations grant exclusive W licenses to new IP discoveries to Broker

INV[ N1 ING ORGANIZATIONS INTERESTED PARTIES

Broker prosecutes and manages resulting Research Lab A: Cell Line patents and Material Transfer Agreements Academic Researchers

CELL AG HONEST Third Party Researchers Lab B: Scaffolding i BROKER I

Start-up C: Bioreactor software Industry & Startups

!W Brokers markets, negotiates and licenses IP to interested parties 4 Broker returns fair share of royalties to IP originators

Figure 15: Sample Schematic of an 'Honest Broker Model' for Cellular Agriculture (Adapted from Source: Olson & Berger, 2011)

Given the challenges that exist to gain alignment across stakeholders in the cellular agriculture industry, the need for lawyers to be innovative is an essential requirement in the creation and discussion of pre-competitive initiatives. Lakhani, a professor at Harvard Business School who studies open innovation ecosystems, emphasized the need to create tailored innovative models for IP sharing at the start of the ecosystem collaborations: "Don't let the lawyers tell you it can't be done-force them to do some creative problem solving. You want to spend a lot of time to get this right."

As the development of open-source communities for cellular agriculture matures and becomes mainstream, open-access of biological materials is one of the only viable ways to share data, technology and IP across the sector (See Figure 11). Short-term goals of researchers to keep IP proprietary will constantly be at odds with long-term industry incentives, limiting the growth of the sector. Tools like MTAs are critical, but only a part of the puzzle in building vital connections within the industry. Developing a sense of trust across the community becomes a priority, and the leadership of honest-brokers can play an integral role in facilitating these interactions.

84 Best Practice 9 1 User Engagement Validates Applicability

CELL AGRICULTURE INDUSTRY CHALLENGE For emerging technology industries, the biggest pitfall is to lose touch with the consumer. It is critical to continuously ideate, prototype and test products to better understand the unmet needs of the market. Many cellular agriculture researchers have been focused on technological advancements to address the technical challenges, but as the technology becomes more accessible, shaping the value proposition of potential products will be equally, if not more important.

This tickles down to the importance of understanding the spectrum of applications. Whether a researcher is designing a new textile material or a cultured food product, the consumers' needs must be well understood. How can a pre-competitive consortium create a culture across the industry that interweaves the dependencies between market/user needs and the technical requirements that guide scientific development?

BEST PRACTICE DETAILS SEMATECH evolved across multiple stages from its inception in 1987 to the present. In the early stages of the consortium development, its focus was on horizontal firm-to-firm collaborations, until the organization realized the opportunity to strengthen vertical supplier-to- firm relationships. Relationships with other consortia were also established to develop new manufacturing systems for , like the 300-mm wafers. Specific manufacturing methodologies were shared among device makers and tested with customers, which created a nimble and agile platform that allowed for a quick response to market trends and user needs.

85 DEFICIT-LINEAR MODEL ROUND-TABLE MODEL

Data

Knowledge Data +Knowkedge Pollicr kmn r- Decisions

Decisions

Public and stakeholders

Figure 16. Models of Technology Development & Policy Decision-Making (Source: Soranno, Cheruvelil, Elliott, & Montgomery, 2015)

Traditionally, scientific information is fed into technological development and policy decisions in a linear manner. Outlined in the Deficit-Linear Model (see Figure 16), this approach creates isolation between the scientific community, the public and policymakers, and most social disputes were caused by a lack of scientific understanding. SEMATECH's structure, resembling the Round-Table Model, allowed for a more inclusive approach where information was disseminated to policymakers and the public to help shape constructive discussion on future product migration decisions [110].

When applying this concept to cellular agriculture, the web of stakeholders required in the conversation becomes highly complex. One may compare innovation in cellular agriculture as the combination of the pharma innovation process (i.e. high capital expenditures, tough regulations) with the product development processes of the specific consumer-facing application categories that cellular agriculture serves (i.e. textiles, fragrances, food). Studies have examined the differences in the duration of innovation cycles and resources necessary to develop new products across different industries [44], [111].

By establishing deep relationships with consumers and regulation, the cellular agriculture sector can increase transparency in traditionally closed industries:

86 " Milk industry players have voiced their concern over the lack of information and communication within their industry. Theo Hendrickse, CEO of Parmalat, stated: "With rising input costs on the farm and processing side, we must engage conversations from consumer to producer. [These] conversations have to be about more than just the milk price" [112]. * In the fragrance industry, top performers are called 'illusionists', referring to the lack of transparency with consumers and the rest of the industry. "Understanding perfumery is like looking for a needle in a haystack, complete with several fake needles and fake haystacks. Or like the Indian parable of blind men and an elephant" [113]. * Increasing consumer and clothing manufacturer concerns are voiced regarding the need for transparent, sustainable practices in global textile supply chains [114]

To elevate the transparency of these industries, there are opportunities (shown in Figure 17) for pre-competitive consortia to 'plug' into existing product development ecosystems across the different industries.

Technical Requirements Market Needs

Product CELLULAR Development AGRICULTURE Ecosystems COMMUNITY across Different Industries

Figure 17: Illustration of a "Plug-In" Platform to Translate User's Needs into Technical Needs for Cellular Ag Community

This proposed model illustrates the need to involve the right stakeholders early in the translational research, and to prioritize applications as 'north stars' to guide development. This includes:

87 Consumers - Engagement from users is critical to: (1) understand the needs of the market, and (2) test potential product ideas and prototypes with users. Engagement with industry-focused communities, such as textiles, food and drink, and fragrances will allow the cellular agriculture industry to focus its scientific efforts on specific engineering and design requirements. Lager, Ulrich and Eppinger have pointed out the importance of process and product innovation with a human-centered design perspective in the biopharmaceutical industry [115].

Regulators - Effective partnerships for precompetitive consortia include the industry regulatory bodies, such as the FDA, to co-develop necessary trials, protocols and achieve specific study goals. "If we had just designed the study protocol and handed it to FDA [without soliciting their prior input], they would have rejected it because it did not follow the role. You have to have the regulators at the table as advisors and participants in this process" [93].

As such, building a round-table model for cellular agriculture will allow translational researchers to help test potential applications with consumers and interact with regulatory bodies to increase adoption of new cellular agriculture processes and products.

88 Discussion: Putting Best Practices Together From what we have learned in the case studies across (1) Open Source Model, (2) Big Science Collaborations, and (3) Pre-Competitive Consortia, the best practices of open innovation for translational research can be categorized into three categories: Community Building, Channel Building and Technology Building.

Shown in Figure 18, these three components represent inter-dependent building blocks for Open Translational Development.

COMMUNITY- BUILDING

Open Translational Development

CHANNEL- TECHNOLOGY- BUILDING BUILDING

Figure 18: Components of Open Translational Development

Community Building is one of the core components of Open Translational Development. Urgency via a crisis is necessary to create cohesion across different stakeholders for a specific vision. As the community grows, leadership becomes increasingly vital to develop strong roadmaps for the industry and delegate roles to achieve research milestones. Self-assembled clusters across the industry enable innovators and researchers to start developing specifications for translational research processes, and with a big enough population, cross over the threshold to trigger the creation of standards.

Channel Building encompasses the development of essential pathways connecting the 'nodes' across the industry. The purpose of these channels is to enable sharing across researchers and

89 industry stakeholders; implemented data-sharing frequency policies and norms can increase transparency in the sector. Innovation hubs, with attention workers, can translate and direct new sector-wide innovations and developments across the industry, while honest brokers can promote trust across highly competitive dynamics. These developments are the 'utilities' of an industry and should be scalable as the sector grows.

Technology Building is the last piece to Open Translational Development: creating an ecosystem that ensures that the community's research is conducted purposefully. Modularity across an industry is inevitable, but stakeholders must evaluate whether the shareable "unit" increases reproducibility in translational research or becomes a burden on the system. Industry participation is key to provide guidance on the scalability of bench-scale research, and user engagement keeps the researcher targeted on the key design/technical specifications with constant prototyping and user-testing processes.

CRISIS CREATES COHESION COMMUNITY CENTRALIZED LEADERSHIP FOSTERS COORDINATION BUILDING CLUSTERS INITIATE STANDARDIZATION

DATA-SHARING FREQUENCY INCREASES TRANSPARENCY CHANNEL INNOVATION HUBS NURTURE TRANSLATABILITY BUILDING ' HONEST BROKERS PROMOTE TRUST

MODULARITY ENCOURAGES REPRODUCIBILITY

TECHNOLOGY INDUSTRY PARTICIPATION ENSURES SCALABILITY BUILDING USER ENGAGEMENT VALIDATES APPLICABILITY

Figure 19: Nine Best Practices for Open Translational Development

90 Achieving success across these three aspects is critical for the growth of a research-intensive discipline (see Figure 19). It is important to note that these best practices and categories in isolation will not push open translational development forward. For example, without a robust community, developing modular technologies and shaping data-sharing policies are meaningless activities. This has significant implications on resource allocation for an emerging industry with limited financial resources, and future industry roadmaps should take into account a balanced approach across community-, channel-, and technology-building when designing new collaborative strategies and infrastructure.

91 Conclusion The pursuit of cellular agriculture is a moonshot that underscores the potential for human scientific endeavor. The impact of this emerging industry will continue to drive fundamental changes in how we live - from what we wear to how our food is produced. Although significant scientific advancements have been made, navigating the "Valley of Death" of translational research ahead is difficult and costly. On one end, there are many commercial concepts that look attractive on paper, and on the other, basic scientific research that must be integrated for scalable user applications. The ability to connect the dots through translational research is the key to accelerate technology development and bridge this gap in cellular agriculture.

There has never been a more critical time for open innovation to engage critical stakeholders across the industry - academia, industry, and policymakers - to work together and make meaningful advancements. Collaboration will not only allow for the diversity of expertise to solve the critical challenges, but also provide collective accountability for the sector's scientific foundation and integrity.

Surfacing nine best practices from the open-innovation networks that meet the needs of translational development (i.e. open-source models, big science collaborations, and pre- competitive consortia) will help foster an industry-wide discussion regarding how collaboration can be initiated. Communities, channels and technologies within the cellular agriculture should be strengthened to develop a vibrant open innovation network where unmet market applications are integrated with technological know-how. Key efforts must focus on creating cohesion across stakeholders, lowering transaction costs of communication and eliminating duplicative research efforts in the sector. However, ensuring a balance is necessary: ecosystems that are too open with excessive data sharing and little coordination can overwhelm and hinder the industry's progress.

These best practices - whether it is the modularization of technology or the implementation of honest brokers to share IP - merely become a part of the sector's toolkit; a complementary puzzle piece will be how the incentives of the industry can be shaped and aligned to make cellular agriculture's collaborative culture a reality.

Together, let's keep building. And grow.

92 Appendix

Appendix A. List of Research Interviews & Discussions

1 Kim deMora Director of Business Development, iGEM Foundation 32 minutes

2 Julia Borden Cellular Engineer, Modern Meadow 50 minutes

3 Nicholas Roehner Executive, Synthetic Biology Open Language 45 minutes

4 Aproov Gupta Research Scientist, MIT Chemical Engineering 1 hour 25 minutes

5 Kristala Prather Associate Professor, MIT Chemical Engineering 35 minutes

6 Jacqueline Ohmura Research Scientist, MIT Koch Institute 35 minutes

7 Vincent Liu Automation Engineer, Gingko Bioworks 45 minutes

8 Bill O'Connor Director, AutoDesk Innovation Genome 25 minutes

9 Natalie Rubio Research Scientist, Tufts University; New Harvest Fellow 1 hour 40 minutes

10 Marie Gibbons Research Scientist, North Carolina State University, New Harvest Fellow 1 hour 20 minutes

11 Kristopher Gasteratos Co-Founder, Cellular Agriculture Society 1 hour 50 minutes

12 Saam Shahrokhi Co-Founder, Cellular Agriculture Society 1 hour 25 minutes

13 Christie Lagally Senior Scientist, Good Food Institute 1 hour 3 minutes

14 Liz Specht Senior Scientist, Good Food Institute 1 hour 3 minutes

15 Erin Kim Director of Communication, New Harvest 34 minutes

16 Mike Selden Founder, Finless Foods 45 minutes

17 Andras Forgacs CEO and Co-Founder, Modern Meadow 1 hour

18 Daan Luining Board Member, Culture Meat Foundation 70 minutes

Tucker Graduate Fellow, Integrated Management Program, MIT, Digital Innovation 40 minutes 19 Matt Platform Specialist

Santolini Postdoctoral Associate, Center for Interdisciplinary Research on Complex 1 hour 10 minutes 20 Marc Systems & Center for Complex Network Research, Northeastern University

Graduate Student, Department of Organismic and Evolutionary Biology, 40 minutes 21 Leo Blondel Harvard University

22 Jess Krieger Research Scientist, Kent State University, New Harvest Fellow 47 minutes

Estimated Total Duration of Primary Research 22 hours

93 Appendix B. List of Open Innovation Networks

Category Organization Description Industry

Linux Computer operating system under the model of free distribution & Software development

Redhat world's leading provider of open source solutions, using a Software community-powered approach

StackOverflow Q&A communities for software programmers Software

CERN Open Data Portal CRISTAL data-tracking software Hardware

Open-Source Model OpenCompute Specifications and design documents for the custom-built servers, Hardware racks, and other equipment used in Facebook's data centers.

MakerBot Global leader in the 3D printing industry that serves the wider Hardware needs of professionals and educators

Bio-Bricks Standard for interchangable parts, developed with a view to Biotechnology building biological systems in living cells.

Synberc Non-profit organization leading the field of engineering biology Biotechnology

SBOL Synthetic biology open language for computational biologists Biotechnology

Galazy Zoo - Interactive project that allows the user to participate in a large- Astronomy Zooniverse scale project of galaxy research

Solar Stormwatch A project to map eruptions from the surface of the Sun. Astronomy

MAPPER 'Morphology Analysis Project for Participatory Exploration and Astronomy Research"

Monarch Watch Trains nature enthusiastics to contribute to the Monarch Butterfly Conservation population assessment efforts

Citizen ScienceCitien SieneNatureWatch Naure~tcheffo rts Trains nature enthusiastics to contribute to biodiversity assessment Conservation Collects information from people who felt an earthquake and Did You Feel It creates maps that show what people experienced and the extent of Conservation damage.

Mark2Cure Citizen project to mark and annotate scientific literature for rare Biotechnology genetic diseases

Fold-It Foldit is an online puzzle video game about protein folding Biotechnology

EyeWire Maps 3D structure of neurons and contribute to revolutionary Biotechnology crowd-sourced scientific discovery from Seung Lab.

Manhattan Project Research and development undertaking during World War II that Physics produced the first nuclear weapons.

Large Hadron Collider World's largest and most powerful particle accelerator Physics

CERN European Organization for Nuclear Research Physics

ollab raions ITER World's largest fusion experiment with 35 nations collaborating Physics

Human Brain Project Ten-year research project that aims to build a collaborative Biotechnology based scientific research infrastructure ICT-

Human Genome Project International scientific research project with the goal of determining Biotechnology the sequence of nucleotide base pairs that make up human DNA

CancerMoonShot Comprehensive and collaborative effort that seeks to rapidly Biotechnology 2020 accelerate cancer research

Innocentive Crowdsourcing company that accepts solutions by framing Cross-Sector Crowdsourcing Solution challenge problems" Platforms & Contests Xprize Public competitions intended to Xprizedevelopment encourage technological Cross-Sector

94 OpenlDEO Platform connecting social challenges with human-centered Cross-Sector design-driven ideas

Foundry Platform for start-ups and innovators to engage, collaborate and Cross-Sector explore business ideas with Unilever

IdeaConnection Crowd-sourced challenge solution development for companies Cross-Sector

One Billion Minds Platform connecting Corporations and Non Profits looking for Social Challenges innovation and talent for social challenges

Startup accelerator for synthetic biology and biotechnology start- Biotechnology IndieBio ups

Y Combinator Model for funding early stage startups, including biotechnology Cross-Sector

Incubators & Accelerators Tech Stars Accelerator ecosystem that helps entrepreneurs build businesses Cross-Sector

P&G Connect & Program that helps initiate partnerships to meet today's needs Cross-Sector Develop across the P&G business Illumina Accelerator Startup incubator for new genomic technologies and applications Biotechnology Program-

Apple & ARM intoSupported iPADs ARM holding processor commercialization from phones Hardware

atnerships Facebook & Zynga Co-developed a platform to new business ventures (i.e. Farmville) Software

Amyris & Ginkgo Contract agreements and intellectual property transfer Biotechnology Bioworks

Biomarkers Consotium Major public-private biomedical research partnership managed by Biotechnology the Foundation for the National Institutes of Health (FNIH) Innovative Medicines Europe's largest public-private initiative aiming to speed up the Biotechnology Initiative development of medicines

European Lead Factory Open access to a drug discovery platform for any disease, any Biotechnology at a European SME or institute. Pre-Competitive Consortia _target, anyresearcher Structural Genomics Not-for-profit organization formed to determine the three- Biotechnology Consortium dimensional structures of proteins of medical relevance

Medicines for MMV is a nonprofit foundation created to discover, develop and Biotechnology Venture deliver new, affordable antimalarial drugs through effective public- private partnerships. 12-member global consortium of major computer chip SEMATECH manufacturers, coordinates and oversees next-generation Semiconductor I I_research, development & commercialization

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