James Philp
Directorate for Science, Technology and Innovation, OECD
The Digitalisation of Science, Technology and Innovation
Key Developments and Policies
6. Digitalisation in the bioeconomy: Convergence for the bio-based industries
Abstract
Chapter 6 focuses on digitalisation and the bio-based industries that are starting to make impacts in the chemicals and materials sectors. As a result of next-generation genome sequencing, biology and biotechnology have become data-rich. Developing bioprocesses has often been hampered at the biological stage – the efficiency of the production strain or biocatalyst. The new discipline of synthetic biology or engineering biology is ushering in an era of more precise control of construction of DNA parts, genes, and all the way to production strains. Engineering biology needs digitalisation and vice versa. The bioeconomy is wider than biotechnology, however. There are many other ways that converging technologies and digitalisation can be applicable to the bioeconomy.
Introduction
In essence, the bioeconomy is about using renewable feedstocks to produce everyday goods and services. The bioeconomy concept has expanded well beyond the boundaries set in the OECD (2009) publication The Bioeconomy to 2030: Designing a policy agenda. It now encompasses a wide range of sectors and activities including chemicals, food, agriculture, dairy, forestry, pulp and paper, waste management and others. The bioeconomy is not just concerned with biotechnology. It is now seen as a new means of production that will gradually replace fossil-based production and be consistent with the concept of a circular economy (Philp and Winickoff, 2018).
Synthetic biology is an interdisciplinary field that aims to design and make biological parts and systems. It has to become an engineering discipline to take its place in future advanced manufacturing. If synthetic biology goes beyond the domain of science, many of the potential impacts linked to successful manufacturing will be achieved.
There is optimism in the future of synthetic biology. Biology has gone from being a data-poor discipline to being data-rich, which makes biology amenable to much greater computational analysis. And where there are algorithms, there is the possibility for automation. Automation brings faster “design-build-test” cycles, which will go a long way to conquering two of the long-term challenges of biotechnology, namely the lack of reproducibility and reliability.
The whole bioeconomy business cycle is ripe for digitalisation. This includes extraction and procurement of materials, as well as logistics and distribution of intermediate goods. It also comprises the retail of final products to consumers, including, as envisioned in a circular economy, the reuse, repair and recycling of products and materials.
At the heart of the bioeconomy’s future is the need for a different kind of workforce with multi- and interdisciplinary skills. Among several other key attributes, professionals in future bio-based industries will need to be much more familiar with digital skills such as programming and data science. This chapter illustrates there is still much to do, even in educating a future “biomechatronics-ready” workforce to drive this far-reaching but still-to-be-achieved manufacturing sector.
The great convergence
This section is concerned with examples of how digitalisation and biotechnology can work together. Together, they can provide solutions to major bioeconomy policy goals that could not be tackled by either alone. This can be seen as a form of convergence, which OECD defines as the coming together of different technologies to solve problems that cannot be addressed by a single technology.
The combination of digital and biological transformation may greatly change the design and handling of production processes and their products. A workshop of the Global Bioeconomy Summit of 2018 in Berlin was entitled “The great convergence: Digitalisation, biologicalisation and the future of manufacturing”. It described how “bio-intelligent value adding” could be disruptive in future manufacturing.
While this form of convergence is usually considered a future potential, a form of convergence of special interest to the bioeconomy is already functioning. This is the mix of industrial biotechnology with green chemistry (Philp, Ritchie and Allan, 2013). This chapter addresses some aspects of this convergence and provides examples.
Why is convergence necessary?
This subsection explores the need for convergence. Box 6.1 summarises the challenges and policies needed to bring synthetic or engineering biology into advanced manufacturing. Convergence is becoming a necessity for business survival. Sean Ward, Chief Technology Officer of Synthace in the United Kingdom has said, “As working with the physical world is becoming increasingly digital, every company that is out there is discovering that they either are a technology business or they are dead. And that is what is happening with biology: it is becoming a technology business” (Quaglia, 20 February 2017).
Throughout the history of biology, experimentation has been difficult due to a scarcity of data. That situation has changed dramatically this century as technological improvements in experimental high-throughput (HT) measurement have made biology data-rich. This has created a need for tools to facilitate the analysis and interpretation of biological data (Fong, 2014). In the data-rich age, predictive design and rapid evaluation are at the core of any engineering (synthetic) biology approach. These accompany assembly of new materials through laboratory automation, HT characterisation and post-production processing.
In the earliest years of bio-based production, it took 50-300 person years and many millions of dollars to bring a metabolically engineered product to market (Hong and Nielsen, 2012; Carlson, 2018). Even recently, it took on average over seven years to launch a bio-based product (Il Bioeconomista, 10 June 2015). The earliest commercial successes of such products were achieved without the full advantages of rich data. If a deluge of metabolically engineered microorganisms producing bio-based chemicals was subsequently expected, then that deluge has not arrived. Follow-on commercial successes have been few (e.g. Van Dien, 2013). Some progress has been made, however. For example, commercial scale production of 1,4-BDO (an organic compound) was performed less than five years after the first detectable amount of BDO was produced in an engineered E. coli strain (Burgard et al., 2016).
Box 6.1. Concepts to unite manufacturing and materials-discovery communities to harness opportunities emerging from synthetic biology
The unifying concepts are:
platform technologies to support the delivery of synthetic biological materials
a highly trained interdisciplinary workforce
academic/industry/government co-development that can implement and innovate these technologies
standardisation and interoperability of biological parts for new materials
sustainable materials manufacturing and management
a common language and vision that places synthetic biology at the nexus of other disciplines, especially materials science, chemistry, computer science and engineering.
Source: Le Feuvre and Scrutton (2018), “A living foundry for synthetic biological materials: A synthetic biology roadmap to new advanced materials”.
Overarching view: Greater integration of biotechnology with the engineering design cycle
At a fundamental level, most biotechnology as yet fails to meet some of the specific criteria of engineering. Essential differences between the scientific method (test a hypothesis through experimentation) and engineering design (design a solution to a problem and test the outcome) must be addressed. Concepts such as interoperability, separation of design from manufacture, standardisation of parts and systems, all of which are central to engineering disciplines, have been largely absent from biotechnology (OECD, 2014). Therefore, weaknesses can be expected at the level of the engineering cycle, depicted in a generic way in Figure 6.1.
Many variants on the engineering cycle exist, but Figure 6.1 shows the basic elements through phases of initial design, building and testing of a part/system/device. No one expects an optimal design on the first attempt. Thereafter, the process is iterated as often as is necessary to meet the engineering specifications.
Figure 6.1. The engineering design cycle
Source: Kitney et al. (2019), “Enabling the advanced bioeconomy through public policy supporting biofoundries and engineering biology”, https://www.cell.com/trends/biotechnology/pdf/S0167-7799(19)30076-9.pdf.
The test phase is the current bottleneck
Within the engineering design cycle, the test phase is the primary bottleneck – a challenge that will only be solved through biology and automation of the iterative processes. Evaluation of an organism’s phenotype – its observable physical properties – is a major rate-limiting step in metabolic engineering (Wang, 2014). When constructing production strains for biofuels or bio-based chemicals, design success will be measured by the amount of product formed. This may require separation of individual strains and determination of the concentration of the chemical of interest produced by each. If so, the process of multiplexing (bringing many input streams into one) in design and build has been defeated. In effect, this results in demultiplexing (breaking one input stream into many) (Rogers and Church, 2016).
This is where an important bottleneck remains – orders of magnitude fewer constructs can be tested than can be designed and built. The throughput is limited to hundreds of thousands of design evaluations per day. Improving this throughput by mechanical or electronic automation will be limited as the orders of magnitude of improvement needed are so high. The needed advances must come from biology itself (e.g. Rogers et al., 2015; Xiao et al., 2016), but are ultimately linked to automation of the iterative processes.
An integrated technology platform could unlock the potential
Integrating engineering design with biotechnology could unlock commercial potential, especially when combined with digitalisation and automation.
Genomatica, an American company, is a leader in producing bio-based chemicals from metabolically engineered strains. In its view, the key to removing bottlenecks is: “an integrated technology platform encompassing metabolic modelling, HT pathway and strain construction, quantitative small-scale screening, and systems biology, all of which are intimately linked to fermentation and process engineering” (Burgard et al., 2016).
This agrees with the view of Lee and Kim (2015). They believe one reason the process is so challenging is “that researchers often fail to consider a fully integrated industrial bioprocess when developing microbial strains with new activities”. They refer to this as the “systems metabolic engineering framework”.
The integration of technologies, especially to enable multiple iterations of design and construction of strains, could typically benefit from digitalisation and automation. The incorporation of artificial learning and artificial intelligence (AI) would remove the need for laborious, time-consuming human intervention between iterations. For example, the large number of metabolic engineering studies could provide an invaluable database. This source could capture information on titre (the concentration), yield and productivity in response to genetic and fermentation conditions. These data, in turn, could be built into machine-learning models, which increasingly remove human involvement in the design-build-test cycle. The day should come when the results of one round of “test” iteration should inform the next round of “design” without human intervention.
Accelerated discovery of natural biological materials (see section 3.9 on the Earth BioGenome Project) is required to explore the diversity of materials and provide access to new materials properties that are lacking. The further development of next-generation deoxyribonucleic acid (DNA) sequencing and DNA synthesis is vital to such efforts. Research programmes could embrace these new technologies to give access to the potential power of vast libraries of biological materials (natural and synthetic) to create the materials and composites of the future.
Regarding these libraries of biological materials, Hadadi et al. (2016) used computational tools to construct a database of all theoretical biochemical reactions based on known biochemical principles and compounds. This database complements projects such as the Earth BioGenome, which would open up the many “unknowns”. The database includes more than 130 000 hypothetical enzymatic reactions that connect two or more metabolites through novel enzymatic reactions. These reactions have never been reported in living organisms. Through the database, users can search for all possible routes from any substrate compound to any product.
Reproducibility is a continuing problem
The essence of the reproducibility problem is that design tools for process-based research and development (R&D) are inadequate. Increasingly, life science, and chemical and food product development have become a global supply chain of people, instruments, organisations, knowledge and data. This supply chain must be orchestrated to deliver an increasingly complex portfolio of products, while meeting intensifying cost and regulatory pressures. Integrating software therefore needs to go far beyond integrating in the laboratory: integration across the entire business is the best way to reduce errors.
A recent survey identified reproducibility as an issue for design for a majority of respondents. The survey concerning scientific reproducibility achieved responses of 1 576 researchers, of which 703 were biologists. More than half pointed to insufficient replication in the lab, poor oversight or low statistical power. Physicists and chemists were the most confident of the reproducibility of their scientific literature. When respondents were asked how best to address the reproducibility issue, nearly 90% – more than 1 000 people – ticked “more robust experimental design”, “better statistics” and “better mentorship” (Baker, 2016).
Early in the history of synthetic biology, Kwok (2010) highlighted reproducibility as a challenge and it remains so (e.g. Hayden, 2015; Beal et al., 2016). This challenge has to be conquered for bio-based manufacturing to become a credible manufacturing platform of the future.
Many researchers have called for completely new computational languages for biotechnology. They argue that variants of natural languages such as English are too imprecise and ambiguous to tackle the highly complex systems of biology and biotechnology. Antha is perhaps the first bona fide attempt to create a programming language for general-purpose computation in biology (Sadowski, Grant and Fell, 2016). It is built on Google’s Go programming language, but incorporates domain-specific features, such as liquid handling planning. Antha is claimed to enable experiments of an entirely new level of complexity. It embraces the departure from experimenting by changing one-factor-at-a-time enshrined in the scientific method, by enabling detection of interactions between different experimental factors.
The creator of Antha, Synthace of London, exemplifies the challenge of reproducibility. Synthace worked with Merck to create a new microbial manufacturing platform for bio-therapeutics. They examined the interactions between 27 factors to integrate strain construction with process development. This is far too complex and time-consuming to address with a screening approach. Even screening a billion assays a second would result in impossible time periods to investigate every permutation of these 27 genetic and process factors. Using multifactorial methods, the system navigated this space, revealing key factor interactions in a small fraction of the time.
Reliability, predictability and reproducibility
Reliability and predictability are two other facets affecting reproducibility. The challenge of designing a fully predictable gene network is preventing engineering biology from realising its full economic impact. Many areas of engineering have confronted and solved similar challenges. Key to resolving this situation is the automation of the design-build-test bio-based engineering cycle.
Robust and predictable scale-up is also necessary for success in biological manufacturing. Scale-up presents new and different challenges compared to laboratory-scale design. For example, a microbial production strain needs to be robust to function in an industrial-scale fermentation process: what works in a laboratory has every chance of failing in a 10 000 litre fermenter.
Only a few examples have deliberately employed synthetic biology to increase robustness in bio-based production. To this end, the United States’ Defense Advanced Research Projects Agency (DARPA) has a research programme on Biological Robustness in Complex Settings (BRICS). BRICS is pursuing the fundamental understanding and component technologies needed to transition engineering biology systems from well-defined laboratory environments into more complex settings. In this new environment, they can achieve greater biomedical, industrial, and strategic potential.
Automation can help address test-phase obstacles
Automation in synthetic biology promises to clear bottlenecks in the test phase, but it needs engineering standards to facilitate data exchange. New HT evaluation and metrology methods are needed to overcome the test-phase bottleneck. These often involve bio-imaging methods and informatics workflows that are generally automated. They depend on sophisticated software for acquisition and management of both qualitative and quantitative data.
The pursuit of automation in synthetic biology has been termed bio-design automation (BDA) (Densmore, 2012). This approach is predicated upon solving small parts of a larger problem one piece at a time. After all the necessary pieces are defined and solved, solutions for each sub-problem can be automated, connected and reused to solve larger problems (Appleton et al., 2017). This process can arguably increase abstraction and reuse, and create greatly scaled systems, in size and complexity.
One of the greatest challenges to realising BDA is the lack of engineering standards and documentation needed for repeatedly engineering these systems. All stages of the design cycle have opportunities to store and exchange data on genetic designs. Standards facilitate these data exchanges. Two of the most common standards in synthetic biology for these purposes are the Synthetic Biology Open Language and the Systems Biology Markup Language, the latter supported by more than 250 different software tools. Other standards are reviewed by Appleton et al. (2017), who also describe future needs, several of which call for open-source approaches to software development.
Similarly, most research-based pharmaceutical companies use HT screening methods. This allows simultaneous tests of hundreds of thousands of compounds against a specific model of disease. Automation with robots has been necessary to achieve levels of throughput not feasible with humans. Now, a new generation of automation is tackling even more complex functions. Known as intelligent automation, it is based on robotic process automation systems that combine process automation software and AI (KPMG, 2018).
Manufacturing in the modern economy works because design and testing software can talk to manufacturing hardware via multiple layers of application programming interfaces. This points to the need for biotechnology to have its own high-level programming language(s) and software to transform the engineering design, testing and learning cycle.
Industrial biotechnology and green chemistry convergence
This subsection explores the convergence between industrial biotechnology and green chemistry. For Le Feuvre and Scrutton (2018),
“(t)he conflation of synthetic biology and (combinatorial) synthetic chemistry, and exploration of potential connections with contemporary manufacturing platforms such as Additive Manufacturing (3D printing), defines a new era in the exploration of new advanced materials…”
Digitalisation can hasten the convergence of green chemistry and industrial biotechnology. Green chemistry involves designing environmentally benign chemical processes. As such, it is one of the most important and practical tools to integrate principles of sustainable economic development into chemistry and the chemical industry (Makarova et al., 2017). Industrial biotechnology is largely about using biotechnology to produce chemicals of various types. The policy objectives of industrial biotechnology and green chemistry are, then, effectively the same. Both are “wet” sciences or technologies, and each discipline can serve the other. These shared qualities create a natural evolution towards convergence. To speed that evolution requires more than serendipity; there are clear ways in which digitalisation can hasten product development.
Chemistry can help overcome a key technical challenge that undermines production of bio-based equivalents of high-volume chemicals. Three key metrics of bioprocesses are often poorer than in petrochemistry: titre, yield and productivity. These metrics are often too low to be scalable because most natural microbial processes are incompatible with an industrial process (e.g. Harder, Bettenbrock and Klamt, 2016; Maiti et al., 2016). Chemistry can improve these metrics. In the case of ethanol, the titres and yields from fermentation are adequate. For many other chemicals this is not the case.
Some bio-based chemicals are best made from biomass using a purely chemical process. In the end, the desired result is the same. Unsustainable chemicals and materials are eventually replaced with bio-based equivalents that are sustainable and renewable. This is not simply about using chemical tools to aid biology or biology tools to aid chemistry. Rather, it is a genuine co‑operation to make a better result.
Industrial biotechnology converges with chemistry and with information technology/computing
There is plenty of scope for digitalisation to enhance the production advantages of combining industrial biotechnology and green chemistry. For example, Gerbaud et al. (2017) have proposed computer-aided molecular design (CAMD) for bio-based commodity molecules. They discussed coupling CAMD tools with computer-aided organic synthesis tools for two purposes. First, they could propose enhanced bio-sourced molecules, which could be synthesised using eco-friendly pathways. Second, they could analyse their sustainability.
Data analysis and storage as bottlenecks
Conquering the challenges of the test phase and convergence will push the bottleneck into data analysis and storage. This subsection looks at using DNA to avoid the storage problem, and how policy makers can support this process.
A fully multiplexed design-build-test cycle that links phenotype to DNA sequence will enable the evaluation of millions of designs per cycle. However, this will also create an unprecedented amount of data. This, in turn, may move the production bottleneck to data storage.
In the age of ML, data should ultimately inform the next iteration of design in the absence of humans (Rogers and Church, 2016). For example, AutoBioCAD promises to design genetic “circuits” for E. coli with virtually no human user input (Rodrigo and Jaramillo, 2013). Thus, algorithms are needed that incorporate ML to correlate data from different data sets. The aim is to link genes, proteins and pathways without a priori knowledge (Wurtzel and Kutchan, 2016).
Is DNA storage the answer?
A crisis in data storage is looming in the next two decades as silicon-based storage methods struggle to keep pace with demand. Long-term storage is perhaps the fastest growing segment of the data storage market. In 2015 and 2016 combined, more data were created than in all of preceding history (Service, 2017). By 2040, if all data were stored for instant access, the archive would consume 10 to 100 times the expected supply of microchip-grade silicon (Zhirnov et al., 2016). Without radical change, a data crunch may be unavoidable.
DNA as a storage medium may offer a way to prevent a storage crisis. It seems far-fetched to store digital data in DNA, but it is already possible to translate digital information into genetic information. In 2016, researchers at Microsoft and the University of Washington broke the record for storing digital data in DNA. They managed to store and retrieve 200 megabytes (MB) of information (including high-definition video, multiple books and articles as well as a database) using DNA provided by Twist Bioscience (Ogunnaike, 2016). In 2018, they doubled their record to 400 MB of data on DNA. Their breakthroughs could pave the way to exabyte storage (Tung, 2018).
As an example of the possibilities of DNA storage, Shipman et al. (2017) encoded real information (images) and optimised the method of delivery, nucleotide content of the sequences and reconstruction method. They used a population of bacteria.
The storage potential of DNA vastly exceeds that of all other media. One estimate suggests all the world’s data could be stored in 1 kilogramme of DNA (Extance, 2016). Another proposes that 215 petabytes (PB) (215 million gigabytes) – roughly all the information on the Internet – could be stored in a single gramme of DNA (Service, 2017).
DNA storage is much too expensive as a storage medium, as the technology is only in its formative stages. While the cost of DNA sequencing has become trivial, DNA synthesis (writing), despite reduced costs, is still too expensive for mass exploitation. It remains orders of magnitude higher than sequencing costs.1 What needs to be done, in general terms, to commercialise DNA storage, is the following:
Develop better algorithms to translate digital information into biological information and to enable fast, accurate and cost-efficient retrieval of information.
Invent and advance new chemistries to enable cheap DNA synthesis.
Incorporate more automation in production workflows to achieve cost reductions.
Public policy can help achieve all of these goals, especially in research subsidy, support for small and medium-sized enterprises and spin-outs, and policies to support technology transfer. In particular, support for automation through public foundries would be important. Reducing transaction costs by identifying fruitful public-private partnerships would also hasten progress: a leading partnership between Microsoft and the University of Washington could be a model. Research programmes that target industry-academia collaboration would be one way to build such partnerships.
Blockchain for benefit sharing and protecting sensitive information
Blockchain, which uses a highly secure, distributed database technology, holds a number of advantages for different types of life sciences projects and companies. It is “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way” (Iansiti and Lakhani, 2017). The technology, with its high level of encryption and security, is at the heart of Bitcoin and other virtual currencies.
The Earth BioGenome Project (EBP) aims to sequence all the plants, animals and single-celled organisms on Earth (the eukaryotic species) within ten years to help unlock the vast economic potential of biodiversity (EBP, n.d.). As one hurdle for such an ambitious project, data sharing must balance two goals. On the one hand, it must ensure a permanent, freely available resource for future scientific discovery. On the other, it must respect the access and benefit sharing guidelines of the Nagoya Protocol2 (Lewin et al., 2018).
The EBP aims to address the challenge of data storage. The completed project will generate around 200 PB of data. This will require new architectures, algorithms and software for improved quality, efficiency and cost-effectiveness, as well as data analysis, big data visualisation and sharing. The project is expected to promote these tools for equitable worldwide sharing of data, analytic tools and data mining resources.
Blockchain could also support traceability for benefits sharing and prevention of bio-piracy. By registering biological and biomimetic intellectual property (IP) assets on the blockchain, code banks could record the provenance, rights and obligations associated with nature’s assets. This could help track their provenance and use (World Economic Forum, 2018).
Blockchain may help tackle the quite different challenges applying to the health and pharmaceuticals industry, especially around sensitive patient data. This branch of the life services industry is generating an increasing amount of sensitive data and transactions. Some have proposed that blockchain will become essential in dealing with these growing data (KPMG, 2018). Blockchain is well suited for managing areas such as supply chain, privacy, transaction processing, contracts and licensing, and sensitive medical records.
Digital security
All life sciences, whether public or private, are vulnerable to cyber-attacks. Bio-based industries that help produce chemicals and materials have similar concerns for cybersecurity as the chemicals industry. Bio-production relies heavily on data, on IP and research, all of which need protection for firms to reap the financial benefits of their investments.
The health and pharmaceuticals sector of the life sciences face these and other more specific issues, such as patient privacy. A recent survey indicated that companies are elevating cybersecurity to a strategic imperative. However, the pace of protection lags behind their desire to adopt digital technologies to drive innovation (KPMG, 2018). There are many ways to launch a cyber-attack on a bio-production company.
Many different types of organisation are involved in bio-production security. They range from feedstock suppliers and customers to information technology (IT) professionals from law firms and IP offices. Cybersecurity is only as strong as the weakest link in the overall system of protection.
Cloud computing
Life sciences companies in health and pharmaceuticals are increasingly using cloud computing to optimise complex processes with a view to reducing business costs. For example, user-based pricing models are paving the way to lower capital investment and operational costs (KPMG, 2018). Cloud-based solutions can make data available for clinical trials while meeting security and regulatory requirements. Further, the cloud enables complex data analysis from Internet of Things and real-time devices. For such reasons, cloud technology is one of the top priorities in enhancing internal efficiency.
Frontiers in bio-production
This section examines three of many different and intersecting future bio-production strategies: biofoundries, bio-based three-dimensional (3D) printing and cell-free synthetic biology. The three are described in ascending order of their expected deployment. First, as “design, build, test” iteration facilities, biofoundries are expected to drastically reduce the time and effort needed to go from idea to product. Second, bio-based 3D printing can capture the complexity of a biological entity (e.g. cell, tissue or higher form of biological specialisation such as an organ). It requires an intimate marriage of genetic and digital code to guarantee the high levels of accuracy needed. Third, cell-free synthetic biology expresses much of the control at the digital level to create cell-free biomanufacturing processes.
Biofoundries
This subsection concentrates on the need for biofoundries to be created within public research organisations. Biofoundries can integrate tools, technologies and overall process analysis into a platform to enable more efficient biological engineering. Through reduced cycle times and increased capacity, biofoundries might help achieve sustainability goals.
A biofoundry develops and integrates industrially relevant production microbes; advanced tools for biological engineering and data analysis; and robust, scaled-up processes for integrated biomanufacturing. In a traditional biorefinery, fermentation science and engineering may dominate at a large industrial scale. Biorefineries, conversely, are seldom discussed in terms of production strain manufacture and biological engineering. The biofoundry might also be viewed as a much smaller facility for HT iterative processes. These processes are driven by robotics and automation prior to scale-up in a larger facility such as a biopharmaceutical production plant or an industrial biorefinery. Ultimately, the streamlining of both into a single industrial workflow could be possible.
Box 6.2. Examples of public biofoundries
The Edinburgh Genome Foundry (EGF). The EGF claims to be the only fully automated DNA design, assembly and test facility in the United Kingdom. The EGF hosts CUBA, a collection of free public apps to assist with various DNA design and manufacturing tasks. It also has graphical frameworks and computational libraries for DNA design and manufacturing. It is creating EMMA-DB, a new web platform to manage genetic parts for the EMMA assembly standard, and to design new constructs from these parts (EGF, n.d.).
National University of Singapore biofoundry. The aim is to drive foundational science towards translational clinical and industrial biotechnology applications. The foundry is equipped with a robotic system that interfaces with various HT analytical instruments. This enables the biofoundry to systematically (re)design, build, test and learn to make an efficient, automated manufacturing platform. The Singapore biofoundry aims to become a central hub for synthetic biology research in Asia.
The MIT-Broad Foundry. Faced with uncertainties about the technology, this biofoundry was tasked with building organisms to produce ten molecules in three months without the biofoundry staff knowing the molecules in advance. The foundry produced the desired molecule, or a closely related one, for six out of ten targets and advanced towards production of the others (Casini et al., 2018; MIT-Broad Foundary, n.d.).
Alternatively, the classic hallmark of engineering may be envisioned with design in a biofoundry and manufacture in a separate plant at a different, even international, location. Instead of biotechnology companies owning and running their own laboratories, biofoundries in the future could do this for them (The Economist, 2018). The earliest biofoundries have already arrived (Box 6.2).
McClymont and Freemont (2017) argue that existing or new automation technologies can enable reproducible research. For this to happen, the technologies must be present in both individual research groups and centralised DNA foundries that can be accessed using cloud-based applications. They envisage that individual laboratories with in-house, low-cost automation work cells can access biofoundries via the cloud to carry out more complex experimental workflows. Technology companies exist to start enabling this process. Individual researchers and organisations can send experimental designs to foundries and return output data to the researchers.
McClymont and Freemont (2017) contend this strategy of individual, decentralised researcher/organisation and centralised biofoundry linked to the cloud via technology companies has tremendous potential. They believe it should “shift a growing proportion of molecular, cellular and synthetic biology into a fully quantitative and reproducible era”.
3D bio-printing
In 3D bio-printing, layer-by-layer precise positioning of biological materials, biochemicals and living cells is used to fabricate 3D structures (Murphy and Atala, 2014). Much of the literature concentrates on printing tissues and organs. Work has already started on 3D printing of bacteria for various bio-production purposes, although the field is still in its infancy. Previous work on producing chassis strains for production has focused on making minimal cells that act as the chassis, to which other functionalities are subsequently added (e.g. Kim et al., 2016).
Alternatively, Kyle (2018) discussed 3D printing for applications as diverse as bioremediation, environmental biosensors, oil spill filters and wound dressings. A particularly enticing prospect is to use bacteria to couple materials production with 3D printing technology. There are many challenges. Apart from the sheer volume of technical work, the future of the field will have to reconcile many issues before 3D bio-printing of bacteria can become “the next frontier in biofabrication” (Kyle, 2018). These issues include reusability, scalability, faster printing times and the environmental impact of 3D bacterial printing systems.
Cell-free synthetic biology
For now, the most relevant application of cell-free synthetic biology relates to metabolic engineering for the production of fuels, chemicals and materials. Naturally, it also applies to other bio-production processes and products. Directly related to the presence of the cell itself, various problems arise when using microbes as living chemical factories. Even in a simple bacterium, cellular metabolism is complicated and hard to control. The desired product, if it accumulates within the cell, is often toxic to the cell.
Alternatively, cell-free systems present several critical advantages. These include fast synthesis rates, direct reaction control and tolerance to toxic substrates or products. Also, cell-free systems circumvent the oft-quoted problem of scale-up because they are inherently industrially scalable (Zawada et al., 2011). The “inefficiencies” of fermentation processes (yield, titre and productivity) can be overcome in the absence of the cell. Therefore, cell-free systems provide a better possibility to produce the substance of interest at maximal yield to improve the bio-production process (Lu, 2017).
For the policy maker and risk assessment community, cell-free synthetic biology in environmental applications generates certain benefits. In bioremediation, for example, it would allow deployment of gene networks and metabolic pathways without risk of unrestrained replication and spread of new microbial strains (Karig, 2017). This would therefore circumvent the need to assess the risk from genetically modified organisms (e.g. OECD, 2015). Nevertheless, any potential risks from cell-free synthetic biology would still require science-based risk assessment.
Predictably, many difficult technical challenges remain. To broaden the applications, cell-free synthetic biology needs to be integrated with other technologies, such as 3D printing and AI. Thus, the need for greater convergence with chemistry and information technologies is evident.
Skills and education for the bioeconomy workforce
This section looks at the need for greater inter- and multidisciplinary education that would equip graduates with sufficient depth and breadth to drive the bioeconomy workforce. Delebecque and Philp (2018) looked at skills and education gaps from the production workforce to R&D. They concluded that higher education is not ready for a revolution in manufacturing that includes bio-based production. Time is limited to address the challenges: the Netherlands alone will soon require an estimate 10 000 bioeconomy experts (Langeveld, Meesters and Breure, 2016).
At the nub of the issue is the need for much greater inter- and multidisciplinary education. This training must combine biology and engineering fields with sufficient depth so as not to trivialise them. At the same time, these graduates need sufficient breadth to be truly problem-solving pioneers of engineering biology.
Backcasting: Mechatronics revisited to shape the education of the future engineering biologist
Mechatronics, already central to the modern global economy, could yield lessons for educating future engineering biologists. A translation of the French standard NF E 01-010 (Norme Française, 2008) defines mechatronics as “an approach aiming at the synergistic integration of mechanics, electronics, control theory and computer science within product design and manufacturing, in order to improve and/or optimise its functionality”.
Historically, the rise of the mechatronics engineer depended on uniting the principles of mechanics, electronics and computing to generate simpler, more economical and reliable systems. Education was refined over decades to optimise the undergraduate curriculum. This helped create the mechatronics engineers that have revolutionised manufacturing. Such an education necessitated multi- and inter-disciplinarity in critical fields such as mechanical, electrical, electronic, computer and control engineering.
The experience of mechatronics studies could inform an approach to educating a workforce for the bioeconomy. The integration of various fields has resulted in mechatronics engineers who can both solve design problems and manufacture. This is exactly the mix required by engineering biology.
The transition from an orientation based on research to production will require a paradigm shift in biotechnology education (Delebecque and Philp, 2018). Universities will need to attract school-leavers with a more mathematical background into biotechnology. Students who graduate will need to be equally capable in DNA engineering and computation.
Digitalisation of the forestry bioeconomy
Digitalisation could offer forestry solutions that add value to the bioeconomy. Many countries with a significant forestry industry have large numbers of forest owners and few forests. Europe alone has some 16 million forest owners (Hetemäki, 2014). Compare this with the oil industry, where over 80% of the world’s proven crude oil reserves are located in the 13 OPEC countries.3 Forestry biorefineries, by comparison to oil refineries, are expected to be small to medium facilities with local production and perhaps local consumption, a classic example of distributed manufacturing (Srai et al., 2016).
A local forest bioeconomy ecosystem and value chain could include hundreds of thousands of forest owners, entrepreneurs and companies specialising in forest service, harvesting, transport and logistics, and the production of forest products or energy. Managing this complexity requires IT tools such as apps, websites, consumer platforms and databases. Consumers are using several IT tools to both steer demand and extend their influence throughout the value chain (MISTRA, 2017). With the circular forestry bioeconomy in mind, Figure 6.2 shows a concept of how digital solutions can add value to the bioeconomy.
Figure 6.2. Digitalisation and the circular forestry bioeconomy
Source: Adapted from MISTRA (2017), “Bioeconomy and digitalisation”.
Satellite technology in the forest bioeconomy
Satellite technology may be a critical tool for the forest bioeconomy, both to monitor biodiversity and to combat illegal logging. National forest monitoring systems need to deliver cost-effective and quality-controlled information across the three pillars of the bioeconomy (social, economic and environmental). Most recently, climate change has become a driving force for forest monitoring, especially concerning forest degradation and deforestation (Asner, 2009; Mitchell, Rosenqvist and Mora, 2017). The mitigation of climate change through forest management by storing carbon in the forest ecosystem is likely to become an economic and financial tool for forestry (Holmgren and Marklund, 2007). But without robust statistics, understanding the loss of biodiversity and reduction of carbon sequestration capacity from deforestation and forest degradation becomes much more difficult.
Forest monitoring is no easy task. In the past, foresters would use field and aerial surveys to collect forest cover data and aerial photography to analyse forest stocks. All of these methods were slow, laborious and expensive.
Satellite monitoring may be the only feasible future method of forest monitoring (Lynch et al., 2013). In an interesting development, a Finnish company combines machine-vision software and light detection and ranging technology (Arbonaut, n.d.). At an altitude of around 2 kilometres, laser beams can generate three-dimensional point data on an object as small as a single tree. Knowing the diameter of the crown of the tree can predict its volume (MEAE, 2017).
Making such forestry inventories supports sustainable forestry management (Crowther et al., 2015). The technology can also be used to assess carbon stocks in tropical forests. It can calculate the amount of carbon dioxide (CO2) removed from the atmosphere, entitling a country to payments for carbon capture via forests under the Paris Agreement.
Another major issue for a burgeoning forest bioeconomy is illegal logging. This practice already costs nations tens of billions of dollars annually, and contributes some 12% of total anthropogenic CO2 emissions globally (Lynch et al., 2013). Illegal logging is linked to warlordism, land grabbing and violent crime (Nuwer, 2016). It is also, of course, in violation of national regulations.
A satellite-based alert system could prove a potent weapon in the fight against deforestation through illegal logging. Less than eight hours after it detects that trees are being cut, a system can send e-mails to warn that an area is endangered. That rapid response could enable environmental managers to catch illegal loggers before they damage large swathes of forest (Popkin, 2016). The traditional methods of forest monitoring are far too slow to be useful against illegal logging, as speed is essential.
Examples of the potential for future bio-based materials
Many examples could illustrate the potential for bio-based materials. The three selected all have high economic and societal value, but differ in terms of engineering biology and IT or chemistry convergence (Box 6.3).
Box 6.3. Selected bio-based materials of high economic and social value
Angiotensin converting enzyme inhibitors
Captopril was the first marketed angiotensin converting enzyme (ACE) inhibitor. Its effects on blood pressure mechanisms mimicked those of a peptide discovered in the Brazilian pit viper Bothrops jararaca (Mladic et al., 2017). The viper uses an ACE molecule to make its prey faint from a rapid drop in blood pressure. The discovery heralded major changes in the approach to treatment of hypertension and heart failure.
ACE inhibitors have been credited with saving millions of lives. The ACE market, valued at USD 11.7 billion in 2015, was expected to reach USD 12.45 billion by 2024. The search continues for new ACE inhibitors due to the prevalence of hypertension as the human population ages.
It is unlikely that much benefit reverts back to the genetic origins of the initial discovery. A goal of the Nagoya Protocol is to distribute wealth created from genetic discoveries more evenly. The Access and Benefit-sharing Clearing-House (ABS Clearing-House) is a key tool for monitoring the use of genetic resources along the value chain, including through the internationally recognised certificate of compliance. Blockchain technology lends itself to this task. It can record transactions between two parties efficiently in a verifiable and permanent way, thus providing secure traceability. Blockchain could also provide enhanced security of data in clinical trials.
Graphene and green chemistry
Graphene is a key material of the future. It conducts electricity better than copper and will eventually find its way into consumer electronics. Electricity conductance and physical flexibility mean that graphene has many potential applications. These range from energy storage devices to lighting and displays, solar panels, tyres, bicycle frames and fashion items (Mertens, 2018). For example, deformable batteries with flexible, foldable and/or stretchable capabilities are ideal for wearable and portable electronics (Ye et al., 2018). Graphene may be the material of choice for 3D printable batteries. Estimating the market value of graphene is complicated as the range of uses cannot yet be fully explored. It is mainly limited to research applications due to high costs. The 2015 price was some USD 500 per gramme.
Researchers in Australia have created a new method of graphene synthesis. It involves heating soybean oil in air until it breaks down into carbon building units that are essential for the synthesis of graphene (Seo et al., 2017). Moreover, the researchers demonstrated the versatility of the method by using other renewable carbon-containing materials such as butter.
While soybean oil has other valuable uses, lignin is generated in large quantities. However, it is difficult to valorise in any value-added process due to its complexity. Lignin is produced in large quantities in the pulp and paper industry, and often burned for power generation. However, Liu, Chen and Gao (2017) described a method for converting lignin into graphene.
Spider silk
Spider silks are the toughest known biological materials. They are lightweight and virtually invisible to the human immune system, and thus have “revolutionary potential for medicine and industry” (Babb et al., 2017). Among newer applications of spider silk being considered are microphones in hearing aids and cell phones. Stronger than steel, tougher than Kevlar, the range of applications is large. For example, the US army has been testing protective garments for soldiers made from spider silk. An E. coli variant of spider silk could replace Kevlar in air bags as it is both strong and flexible. And in 2017, Adidas unveiled a spider silk shoe using Biosteel fibres from AMSilk. Called the Adidas Futurecraft Biofabric, the shoes are reported to be biodegradable in less than 36 hours in the presence of an enzyme.
Biologists are attracted to the study of spider silk because of the large diversity of silks and proteins involved in their synthesis. Even after decades of research on orb-weaver spider silks, knowledge of all the proteins within an orb-weaver species is incomplete – and there are tens of thousands of spider species. Moreover, nature can also inform a production process: there are genes that encode proteins that turn liquid silk into solid silk thread. Genomics is the newest tool to unravel this complexity.
Engineering biologists are interested in spider silk as there are many candidates of genes and proteins for transgenic studies. This implies the possibility of tailor-made spider silks for different materials and applications. However, working with spiders as factories is impracticable. The expression of spider silk genes in a microorganism with subsequent fermentation processes is much more attractive. Much remains to be discovered. The sheer diversity of spiders and their silks lends itself to use of digital tools for curating knowledge, as well as for the “pick and mix” analysis for new consumer applications. Expression in microorganisms is extremely complex. Digitally assisted design, screening and automation will be needed to drastically reduce the design, build and test time.
Policy implications
Engineering biology materials have implications for policy makers with respect to platform technologies to support delivery of the materials; standardisation, interoperability and IT; sustainability; and digital sector. This section unpacks each of these implications.
Platform technologies to support the delivery of engineering biology materials
Focus R&D subsidy on achieving reproducibility of bio-production processes: Precompetitive design of R&D programmes (for laboratory-scale considerations) and near-market collaborative programmes could ensure that research proposals are only successful if they concentrate on improving reproducibility. Less fashionable near-market research issues also need to be investigated. These include robustness-in-design (e.g. DARPA’s BRICS); titre, yield and productivity; bioprocess variables, such as the effects of media variability (e.g. different sources of molasses); internal gradients, such as oxygen and redox; and tolerance to shear stress that can cause cell breakage. Combining digital and biological tools is the best available way to reduce discovery time given the complexity of biology.
Platform technologies of various sorts: Governments need to support the platform technologies required (e.g. biofoundries, distributed R&D networks, digital platforms, data curation and digital/genetic data storage). This is the case because investment risks are too high for the private sector, and the imperatives for private action may be missing (e.g. a clear route to market). This goes beyond R&D subsidy. Innovative forms of public-private partnership are needed. These would enable both public and private actors to gain fair access to equipment, services and data (see suggestions below on IP and licences).
Academic/industry/government co-development that can implement and innovate these technologies: Implementation that involves both public and private actors could involve national action plans and roadmaps. In the United Kingdom, for example, a “leadership council” is constituted to ensure that deadlines and milestones for implementation are met. This council can easily report at ministerial level to maintain an appropriate political focus and vision.
A highly trained interdisciplinary workforce: For too long, the life sciences have been compartmentalised by discipline, such as microbiology, biochemistry and molecular biology. A greater focus on problem solving, using interdisciplinarity and including soft skills, is more appropriate to graduating biologists seeking careers in manufacturing (Delebecque and Philp, 2018). In a related issue, policy makers should prioritise identifying a common language and vision, both computing and spoken. It should place engineering biology at the nexus of other disciplines, especially materials science, automation engineering, chemistry, computer science and engineering. Both chemistry and biology benefit from greater levels of digitalisation, and the extremely important synergy of engineering biology with green chemistry should be a specific focus.
Standardisation, interoperability and intellectual property
Standardisation, interoperability and IP: Standardisation and interoperability policies can be seen throughout the history of the microprocessor industry and, more recently, in information and communication technology (ICT). The issues for engineering biology are similar, but the modern context highlights some differences. In particular, policy makers need to consider carefully the ongoing debate about open access versus IP protection to satisfy the desires of academia and the need for sufficient protection to motivate private investment.
Legal issues are inextricably linked with standards that enable product and process interoperability. Rules may be required that licences be either royalty-free or royalty-bearing on terms that are “fair, reasonable and non-discriminatory”, a system used extensively in the ICT sector (Contreras, Rai and Torrance, 2015). If patents on standards are obtained, what rules will govern the terms on which they will be made available to the community? Best outcomes for engineering biology will likely result from simultaneous consideration of technical standards and IP issues, with lessons to be learned from the ICT sector.
The use of materials transfer agreements (MTAs) provide an example of potential difficulties. MTAs underlie the legal frameworks within which biotechnology practitioners define the terms and conditions for sharing biomaterials. However, MTA legal arrangements pre-date the widespread adoption of the Internet, engineering biology, genome sequencing and gene synthesis. As such, they can place restrictions on the redistribution and commercial use of biomaterials. Moreover, they are not aligned with changes in the social objectives of science.
In response, Kahl et al. (2018) suggested a new model, the Open Materials Transfer Agreement (OMTA). This would relax restrictions and support widespread adoption within automated and semi-automated administration systems. Benefits of electronic platforms are various. Incorporation of the OMTA within electronic platforms could enable less restrictive options for sharing biomaterials as appropriate. Technology transfer offices could still review and approve such transfers, but electronic communications could replace paperwork and individual negotiations. Such electronic platforms could also offer provenance tracking, which may be a sustainability consideration.
Sustainability
Sustainable materials manufacturing and management: There are roles for digital technologies in judging sustainability. Sustainability standards should be an intense focus in the bioeconomy generally, and specifically in engineering biology and biomanufacturing. Issues such as the provenance of feedstocks could be explored using blockchain technology. Automated, digitalised protocols for sustainability assessment would decrease the financial burden on small companies tasked with proving the sustainability of their products and processes. For example, it could compare greenhouse gas emissions savings associated with products and primary fossil energy savings of the manufacturing processes with costs of fossil counterparts.
Digital security
Digital security: Individual facilities, whether publicly or privately held, could develop and validate methods and protocols for facility staff or external service providers to fortify the facility (Murch et al., 2018). This has special applicability to public-private partnerships as public research organisations are notoriously co‑operative and “leaky”.
Governments could encourage the sharing of timely cyberthreat information by providing protections related to lawsuits, public disclosure and antitrust concerns, as well as safeguarding privacy and civil liberties. Cybercrimes should be prosecuted vigorously. Perpetrators should be held responsible for harm to operating systems, for stealing IP and trade secrets, or for unlawfully obtaining personal information for financial gain.
Governments could encourage cybersecurity awareness-building and co‑operation. One example could be to encourage public sector actors to run cyber-attack simulations and to share the lessons learned. Efforts to enhance cybersecurity should be recognised through, for example, voluntary standards, regulations, industry programmes and information-sharing frameworks.
Conclusion
This chapter attempts to draw the needs associated with digitalisation to the attention of policy makers. This starts with education. In the near term, engineering biology needs successes. For the public policy maker, there is nothing better than success stories to show that taxpayers’ money is being wisely spent. But policy must also give the private sector confidence that governments realise that the era of bio-based production has arrived.
Due to foundations laid down in previous decades, biology, quite suddenly in this decade, finds itself in a data-rich era. This trend will undoubtedly continue and has implications for biotechnology and the emerging engineering biology. Literally hundreds of engineering (synthetic) biology start-ups are receiving investments. However, engineering biology needs a large increase in its quantitative precision to qualify as a manufacturing discipline. Some solutions can come from biology itself, but a greater alignment with automation, as in so much of modern manufacturing, is also needed. When married to the complexity of biology, there is an obvious need for a step-change in digitalisation.
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Notes
← 1. Estimates of trends in DNA sequencing and synthesis costs are available at the Bioeconomy Dashboard: www.bioeconomycapital.com/bioeconomy-dashboard/.
← 2. The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity is an international agreement that aims at sharing the benefits arising from the utilisation of genetic resources in a fair and equitable way.
← 3. See data provided by the Organization for Petroleum Exporting Countries, www.opec.org/opec_web/en/data_graphs/330.htm.