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  • The Synthetic Biology Revolution

    For roughly four decades the Trends editors have been tracking the technology of ¡°manufacturing using the mechanisms of living organisms,¡± which is called synthetic biology. The whole thing started simply enough when Genentech found that inserting the human insulin gene into yeast cells enabled those cells to produce the critical protein needed for treating diabetes.

    What started in the healthcare industry is expanding into many sectors with the ultimate objective of profoundly impacting the way a vast array of products are manufactured, including lab-grown meat, cosmetics and biodegradable packaging.

    However, after over 40 years, we¡¯ve only scratched the surface when it comes to harnessing synthetic biology¡¯s potential. In fact, a May 2020 report from McKinsey & Company estimates that as much as 60% of the global economy¡¯s physical inputs could be made using synthetic biology, resulting in direct economic benefits of up to $3.4 trillion a year between 2030 and 2040.

    Why now? Exponential cost curves as well as the convergence of powerful supporting technologies is accelerating progress in synthetic biology and rapidly expanding its use into new applications.

    In the coming years, the growing impact of synthetic biology across multiple industries will create many attractive investment opportunities, while the global push for sustainability will create an additional catalyst for adoption. Like the internet revolution, companies that enable or effectively harness synthetic biology will thrive by disrupting the profit pools dominated by incumbents. That means managers and investors can no longer afford to ignore the broad, disruptive potential of synthetic biology.

    To appreciate how revolutionary this could be, it¡¯s important to realize that the last major advancement in material science occurred early in the 20th century with the invention of plastics as a byproduct of petroleum. Now, roughly 100 years later we¡¯re about to experience another huge leap. From using CO2 in the air as a production input to creating completely biodegradable products, synthetic biology will transform the environmental footprint of our daily lives, fueled by the drive for sustainability.

    In our book Ride the Wave, we discussed the potential for plummeting DNA sequencing costs to revolutionize the healthcare industry. And as we also suggested at the time, DNA is taking a great leap into many other industries as well. Now, it¡¯s increasingly clear that the ¡°genomics revolution¡± is starting to take-off.

    But despite big breakthroughs, investors inevitably underestimate the longer-term impact of exponentially developing and converging technologies turbocharging the pace of discovery and new product development. Conceptually what we see in synthetic biology is like what we experienced with computing, where processing power doubled every two years, opening vast markets at ever-lower prices for entirely new applications. Longer-term predictions about the ultimate sales volumes of computers or cellphones always fell far short of reality because new applications created exponential growth and that challenges our familiar linear thought processes.

    Now, exponential costs curves related to DNA sequencing, big-data analysis, DNA synthesis and gene editing are recreating this nonlinear future for synthetic biology.

    We can see this playing out as the price of DNA sequencing, has declined faster than Moore¡¯s Law would imply. In fact, the price of reading a human genome has declined by a factor of roughly one million since 2000. And this progress has greatly accelerated discoveries linking DNA to specific proteins and their functions, revealing new ways to leverage synthetic biology.

    A key result of this ever-cheaper DNA sequencing is a fast-growing trove of genomic data, as well as the ability to process that data faster and more effectively. A single human genome contains roughly 3 billion base pairs. Today, analyzing this genomic information is much less daunting because of advances in big data analysis enabled by cheap processing power and new data-science tools including machine learning. Armed with these new capabilities, scientists can now recognize patterns, linking DNA changes to bodily functions and health outcomes.

    In addition to plummeting DNA sequencing and big-data computing costs, the price of creating new DNA from scratch, called ¡°DNA synthesis,¡± is also rapidly declining. When Genentech inserted the insulin gene into a yeast cell, it needed the right human gene and DNA. Researchers can now order cheap segments of DNA online, after uploading the string of A, T, C and G that describes what they want. This advance enables entirely new experiments designed to answer questions such as ¡°What if a slightly different version of a gene was inserted into the host cell?¡± or ¡°What other proteins can a host cell produce?¡±

    This is critically important because, if DNA is the programming code for life, reading DNA is akin to passively consuming information on your computer. DNA synthesis, in contrast, is like using your keyboard to create new content. Until recently, DNA synthesis was prohibitively expensive. Imagine how different our lives with computers would be if pressing a single key on your keyboard cost $5. Then, imagine how empowering it would be if that price fell to 5 centsand ultimately to nearly free. That¡¯s an illustration of the power of cheap, easy DNA synthesis.

    But that¡¯s not all. Genomics and synthetic biology, in addition to enjoying exponential cost curves, is benefiting from recent advances in tools for cheaply and accurately altering an existing genome. Called gene editing, it¡¯s like the copy-and-paste function in Microsoft Word. And it¡¯s so easy to use that anyone can order a gene-editing kit online and experiment in their own home. Researchers can now take that 3-billion-character genetic instruction book and make precise changes to it, inserting new DNA they¡¯ve created and watching what happens.

    In genomics, one of the most challenging questions is how to link millions of genetic differences to their actual meaning in terms of bodily function. Until recently, researchers were ¡°stuck¡± using existing genomes, hoping to learn from the small number that featured rare mutations. They would experiment with single cells such as bacteria, bombarding the genome with radiation to induce random changes.

    Compared to the newest tools like CRISPR-Cas9, that process is like watching monkeys type randomly on keyboards until a coherent piece of writing emerges. Now, with the new gene editing methods, scientists can make a targeted change in a genome and then observe. Not surprisingly, research into gene editing technology is becoming pervasive, being mentioned in more than 6,200 scientific publications in 2020, compared with less than 100 just a decade earlier. Already, it has transformed the field from ¡°observing differences¡± to ¡°engineering alterations¡±; this is now turbocharging our ability to understand DNA and manipulate physical matter. And researchers aren¡¯t limited by nature¡¯s genomes or proteins anymore. The potential combinations of amino acids that can form proteins vastly exceeds the number of atoms in the universe.

    What¡¯s the bottom line?

    The converging technologies of DNA sequencing, DNA synthesis, gene editing and AI-based data analysis are combining to produce results that are larger than the sum of their parts. Without the power of big data, interpreting DNA data would be nearly impossible. And without gene editing and synthesis technologies, scientists would be limited in how far they could explore new genetic possibilities.

    The power of technology convergence that we¡¯re now seeing in synthetic biology is nothing new. It¡¯ has happened over and over again since the beginning of the industrial revolution nearly 250 years ago.

    - One of the most recent examples is the ridesharing industry dominated by Uber. That firm sits at the convergence of ubiquitous smartphone adoption and cheap GPS technology which enables you to track the car you hailed on your phone while you wait.

    - Netflix is another great example. Started as a mail-order service for DVDs, the company really took off when ubiquitous broadband adoption offered fast enough internet to stream videos right to your TV, with sophisticated software that suggests personalized content.

    - Likewise, the convergence of technologies in synthetic biology will surely create some of tomorrow¡¯s big new companies.

    Given this trend, we offer the following forecasts for your consideration.

    First, synthetic biology will disrupt many existing industries beginning in the 2020s, but its full impact will not be felt until around 2050. Expect cost declines and new discoveries in synthetic biology to shrink profits for incumbents, while creating opportunities for new leaders. Since both costs and technological capabilities are progressing rapidly, forecasts for the impact of synthetic biology can¡¯t be based on today¡¯s capabilities. This includes the pace at which synthetic biology penetrates products and industries outside healthcare. It will be important for managers and investors to monitor the advance of these developments to avoid missing big opportunities and threats.

    Second, in the coming decade personalized medicine is likely to be the primary beneficiary of synthetic biology.

    Drugs will increasingly target specific patients, with genetic tests guiding doctors to the most effective therapies. New biotech medicines had no presence in the drug industry¡¯s pipeline when they emerged in the 1970s; today, they account for more than 40% of that pipeline. In this flourishing ecosystem, smaller biotech companies are eagerly adopting new genomic technologies. And smaller pharmaceutical developers today account for roughly 90% of the drugs in research pipelines. Large pharma incumbents are already feeling the pressure of this disruption dynamic.

    Third, much of the short-term economic activity related to synthetic biology will be confined to startups and the suppliers which serve them.

    VC funding for synthetic biology rose sharply in 2020, reflecting enthusiasm for new applications of the technology, including sectors outside healthcare. As synthetic biology spreads to other industries and costs continue to decline, companies that enable this research and production will benefit. This includes those involved in DNA sequencing, DNA synthesis, gene editing and AI-based data analysis. The power of genomics will be leveraged to help discover new products, as well as to improve the manufacturing efficiency of production cells. In particular, this VC ¡°gold rush¡± will drive higher demand for DNA sequencing systems and related consumables. Other analytical technologies like chromatography and mass spectrometry will also play a role. And moving samples around a lab between different testing modalities requires robots referred to as liquid handling automation equipment. And,
     
    Fourth, over the next thirty years, synthetic biology will play an indispensable role in moving humans past the ¡°dematerialization frontier.¡±

    As explained in the January 2020 issue of Trends, advanced economies have already reached the point where absolute consumption of resources is shrinking even as GDP rises. The rest of the world will cross this frontier as their affluence increases. Over the next 30 years, synthetic biology will play a huge role in this transition. Cheap DNA sequencing, DNA synthesis, data analytics, and gene editing have unleashed a modern-day gold rush to discover the next blockbuster synthetic biology product. As these discoveries come to market, expect the economy to produce more and more, with less and less.

    Resource List
    1. Alliance Bernstein. November 2021. Edward Bryan. The Synthetic Biology Revolution.

    2. McKinsey Global Institute. May 2020. Lames Manyika, Sven Smit, & Jonathan Woetzel. The Bio Revolution.

    3. National Human Genome Research Institute. November 1, 2021. Kris A. Wetterstrand, M.S. DNA Sequencing Costs: Data.