TechBio: the convergence of the most advanced technologies on Earth
A bit of history
For billions of years, nature was left to its own devices, engineering itself through the process of evolution. As products of this evolution, humanity has struggled to view biology through any lens other than our own past evolution.
The history of life sciences goes all the way back to Aristotle’s creation of taxonomy, a method to classify animals and plants. Later in the XVII century, Robert Hook saw cells for the first time through a microscope and coined the term “cell”. 200 years later, Charles Darwin introduced the theory of evolution through natural selection and Mendel stated the laws of inheritance.
So far, progress in biology has been linear, and high level. It wasn’t until James Watson, and Francis Crick created an accurate model of DNA that the pace of change began to accelerate. 2 decades later, Herbert Boyer and Stanley Cohen would invent DNA recombinant technology. In 2003 the Human Genome Project was completed and scientists were already experimenting with CRISPR.
These centuries of research have helped us gain breadth of knowledge by discovering more species and have helped us achieve depth of understanding by reaching the molecular level. The latter has opened the door to the paradigm shift we are experiencing in the 21st century.
Biology is technology, and it’s perhaps the most advanced of them all. This is the essence of the biotechnology revolution. We are moving from basic to applied science, from only discovering to tinkering too. We are becoming bioengineers.
As a new engineering discipline, bioengineering (a combination of synthetic biology, computational biology, and modeling) now iterates through the design-build-test-learn (DBTL) cycle, a framework that accelerates the iteration, standardized production, and broadens the flexibility of biological technologies.
Design can happen at any level of the different modularities of biology (i.e. genes, cells, tissues, organs, microorganisms, and beyond). Biodesigners are currently working on better design platforms and even biological programming languages that reach a higher level of abstraction allowing scientists to model experiments mathematically prior to working at the wetlab.
Imagine what it would be like to program a mobile app using only 0s and 1s. Well, that’s been the status quo in the life sciences for a long time: designing biological circuits with As, Cs, Gs, and Ts. Advancements in the design phase aim to make the wetware as programmable as hardware.
Platforms like Benchling are making it easier to find any DNA sequence through integrations with Addgene, and their UI/UX is accessible even to beginners in the field. However, many scientists are still in search of yet better bio-programming languages.
“While there already exist many computational representations of biological entities, these are almost all designed for the annotation of natural systems and therefore struggle to describe the specifics of engineered designs” — creators of SBOL.
Dr. Guy-Bart Stan, head of the Control Engineering Synthetic Biology Group at Imperial College London, hopes the development and wider use of Bio-CAD can make bioscience more systematic and rigorous. “Any other industry that works with very complex systems uses this approach and biotechnology should be no different”.
Furthermore, Artificial Intelligence (including Machine Learning and Deep Learning) are proving to be game-changers at this step. Consider Google’s spin out company DeepMind and their Alpha Fold algorithm which can predict the way proteins fold with unprecedented accuracy and whose open-sourced code has been publicly released.
Next up in the DBTL cycle is the build phase, i.e., experiments done in the wetlab. The most common example of this is inserting a plasmid into microorganisms like yeast or bacteria in order to express a protein of interest. Indeed, an increasing number of companies are engineering biology in this way to produce animal-free products more efficiently and sustainably.
As in several other industries, automation will be a key driver to the bioeconomy. The insight behind companies like Opentrons is that a great part of manual activities done in the lab are repetitive. Though pipetting was the lowest hanging fruit to automate through robotics, even more complex procedures such as library prep for NGS are being automated too. Apart from helping to gain economies of scale, innovations like this are also standardizing biology.
Another example are smart bioreactors, where some companies have implemented Machine Learning to adjust reactor conditions automatically. In this space, Culture Biosciences’ vision is to make running bioreactors as easy as running code on a server. They’re creating cloud-based labs of bioreactor “farms” that are accessible to scientists all over the globe with the click of a button.
Next, we come to the Test phase.Testing is about measuring the results of the previously done experiments. This is truly the interface between biology and technology. This is when molecules, base pairs, and proteins become digital bits that we can read through software in a computer.
These days, there are a variety of tools scientists can use , including PCR, DNA sequencing, blot assays, flow cytometry, and much more. The best example of how technology comes into play here is miniON (by Nanopore), the DNA/RNA sequencer that costs the same as an iPhone. This pocket-sized device can be connected directly to a laptop and that way, transform up to 4 Mb of biological data into a computer language in real time. This device is probably more targeted towards individual researchers rather than organizations, compared to Illumina, for example.
As shown by Nanopore, the common denominator in “test” companies is intellectual property.
It is worth mentioning that traditional tech giants are not missing out on the biotech revolution. Apart from Google’s DeepMind, Microsoft Research has a synthetic biology team called Station B, who are partnering with Synthace, a company that automates everything from DNA assembly to ELISA assays. Amazon too, has recently joined AION Labs, an alliance of global pharma and technology leaders and investors with the goal of creating and adopting gateway AI and computational technologies for applications like drug discovery.
Finally, we come to the final phase of the DBTL cycle, the “learn” phase. This is when the biological data is analyzed to gather insights from the build phase. Software technologies like AI/ML and quantum computing are common features in this phase. Researchers in academia and industry have already adopted these tools to discover correlations between certain phenotypes and the expression of different genes, gut microbiome diversity, and many more applications.
Personally, my favorite example of this is Ginkgo Bioworks’ Codebase software. With this software, programmers can now easily download a digital biological portfolio allowing researchers to avoid coding everything from scratch. By creating a database of characterized organisms, DNA circuits, proteins, and more, Ginkgo Bioworks has provided the building blocks for the bioengineers of tomorrow.
It should be said that a company doesn’t need to cover all four stages of this biotech cycle. Some companies may specialize in only one stage like DeepMind and Culture Biosciences do, others like 23&Me may cover the “build and test” phases, while Ginkgo Bioworks and Microsoft Station B are iterating throughout the whole bioengineering cycle.
Contrasting techBio and biotech
As in many fields, terminology is important in Biology. In this case, we will consider a biotech company to be one that applies engineering (e.g. tissue engineering, molecular engineering, ect..) to create products in industries like healthcare, materials, agriculture, and others. They use wetware and their end-products are physical.
TechBio companies, on the other hand, are those that build either hardware or software for biological purposes. That means that in most cases, their target market consists of biotech companies or researchers that are in search for more efficient ways to engineer biology.
Indeed, these two types of companies contrast in various aspects which are worth analyzing more deeply.
While age is not necessarily correlated with a higher education degree, the common denominator for biotech startups is for the founder(s) to hold a PhD in a life sciences field. In fact, a recent trend in biotech startups is the rise of the founder scientists, who are typified by PhD graduates who spin out their research into a startup. Normally, people don’t pursue a PhD for the express purpose of starting a biotech company, PhD students become founders if they discover or invent something that they can imagine having an impact on patients and the market.
There are several reasons for this. The first, goes back to the initial idea that biology is not an invention of humankind. To engineer biology, one must first have a robust understanding of the systems at hand.
Interestingly, a 2010 study with a sample of 512 biotech companies found that bioinformatics was the field with most PhD founders.
As hardware and software have increasingly penetrated more markets and the workforce in the tech industry has expanded, many potential founders in the techBio space nowadays benefit from the fact that software and hardware skills are more easily acquired, in comparison to wetware skills, suggesting that the numbers from that previous study may be different these days.
In terms of capital investment, biotech is clearly more demanding. Biotech companies produce physical goods, which requires infrastructure of size commensurate with the company’s commercial stage. In fact, most products in the biotech industry will take over ten years in their go-to-market journey. These companies must figure out how their biology will behave at scale, navigate regulators, and conduct clinical trials, all of which requires a significant amount of capital.
In the midst of such hurdles, patents are one of the most valuable types of assets a biotech company can have. A company’s patent portfolio allows investors to quickly know if a company is doing something differentiated from potential competitors. In the long-run, patents protect the unique innovations made while doing R&D. An interesting counter-argument however, is that this will only be useful if the company also has enough capital to sue whoever tries to infringe their IP.
In any case, software is not usually patentable, which means that there’s also a lower barrier of entry for competitors in the techBio space. Some exceptions may apply to companies working with hardware.
techBio for biotech
As Ray Kurzweil suggests in his book “The Singularity is Near”, we are moving from an era of linear progress to one of exponential progress. Those who assume that the 21st century will follow past trends, aren’t taking into consideration the convergence of multiple sciences and technologies.
While biology has previously relied only on the whims of evolution to iterate on itself, biotech is now standing on the shoulders of giants: technologies like Artificial Intelligence, Quantum Computing, and robotics are making biology easier, cheaper, and faster to engineer.
These fields have impacted nearly every industry over the past five decades, and now they have enabled the application of the DBTL cycle to biology, empowering bioengineers to grow a healthier and more sustainable future.
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