The ingenuity of evolution
Life has found a way to, well, live. While scientists and philosophers alike have long debated what it means to be alive, the idea that life is a self-sustained system capable of undergoing Darwinian evolution is remarkably powerful. Simply put, to be alive is to be part of an engine that selects traits with survival advantages over many, many generations. Over time, living systems have become extraordinarily complex. In our last post, we highlighted this complexity in the context of plant biology and the chemical diversity that evolution has naturally generated1.
Finding solutions to major engineering biology problems today — developing new medicines, growing foods in novel ways, developing climate-resilient crops — is really challenging. Engineers design solutions through a rigorous optimization process. In the context of biology, however, designing is not always possible because we don’t know all the rules. As a result, we mostly discover. But, as we’ve seen over the last 3.7 billion years, evolutionary processes are uniquely powerful tools. In the context of building biological systems, what can we learn and utilize?
One of the earliest cited examples of co-opting functionality from microbes (excluding ancient practices for alcohol and cheese) is the discovery of a fungus during World War II. While stationed on the Solomon Islands in the South Pacific, the US Army was baffled by the troublesome deterioration of their tents, clothing, and other textiles. After taking samples and then screening over 14,000 molds recovered from the site, researchers at Natick Army Research Laboratories found the culprit: a strain of fungus, now known as Trichoderma reesei, which produced extracellular enzymes (i.e., cellulases) that degraded their textiles. This turned out to be a massively important discovery in the quest to convert biomass to biofuels: the enzymes that T. reesei produced degraded not only textiles and recalcitrant biomass (lignocellulose), in turn releasing sugars that could be further converted into fuel. However, performance was still not good enough for industrial production, so in the 1970s, researchers began work to further increase the efficiency of T. reesei. What followed were some of the original directed evolution methods whereby researchers introduced random mutagenesis by blasting the bugs with radiation and then screened the mutants in a functional assay. Using these experiments, they were able to ~3x the performance of the bug, creating the foundation for the gold standard strains we have today.
Today, directed evolution experiments are ingrained in the scientific canon: notably, Frances Arnold shared the Nobel Prize in 2018 for her ingenious approach of directed evolution to engineer enzymes. And yet, history has only scratched the surface of what directed evolution experiments can do. New tools are beginning to revolutionize what is possible. For example:
So what? If early directed evolution experiments were like a simple iterative algorithm, tomorrow’s directed evolution experiments can be much more like a black-box ML approach. We are quickly moving towards a world where we can program complex objective functions into biological systems, and build methods that let those systems solve the problems for us. Today, we are exploring this topic of directed evolution and how we might use new methods to solve problems across two applications: industrial synthetic biology and drug discovery.
Industrial synthetic biology: Engineering for stability and scale
Historical context: In industrial synthetic biology, we design microbes as factories for producing molecules of interest: everything from heme in Impossible Foods’ burgers to proteases for fabric detergent to cellulases as in Natick Army Research Laboratories. Microbes make great factories because they self-replicate, scale relatively quickly, and can be tuned to replace many existing, dirty manufacturing processes. The idea is straightforward: we should just program bugs to produce whatever we need in large industrial fermenters.
Companies have been working on this (seemingly simple) problem for decades. Genentech started the recombinant protein trend in the late 70s with somatostatin followed by insulin, and the rigorous engineering discipline picked up a lot of momentum at the turn of the century with the emergence of genomics and systems biology. More recently, companies like Cargill, IFF, Amyris, Ginkgo, and Zymergen2 play important roles in the ecosystem. Nevertheless, economic targets, especially in existing commodity markets, demand massive scale and leave little margin for error. Unfortunately, evolution poses a big problem in industrial biology. Microbial populations move quickly to do what life does best: rapidly evolve for survival, often in unpredictable ways that are highly sensitive to changing conditions (e.g., a scale-up from benchtop to >100k L bioreactors) and to the detriment of the economic targets (e.g., titer, doubling time) we set.
Unfortunately, bottom-up rational design tools (i.e., leveraging known parts and traits) 3have been unable to manage the complex tradeoffs between fitness under varying conditions and the production of the things we care about. Andrew Horwitz of Sestina Bio has a great blog post that discusses this tradeoff in greater detail.
Optimizing with evolution in mind (a brief review of helpful literature): Excitingly, new approaches are emerging that steer evolution in ways that better account for these tradeoffs (at both individual and population levels). To frame the environment and design space we’re operating in when we engineer living systems, Castle et al. introduced several concepts.
Most saliently, they describe the evotype - the evolutionary disposition of the system — which is a critical design component. Per the figure shown above, the evotype is a function of the “fitneity” of the system (i.e., reproductive fitness x desired utility), the phenotype (measured by desired utility), and the variation probability distribution (i.e., how likely is this “fitneity” phenotype). This landscape needs to be designed and traversed in biological systems design to ensure a stable and desired phenotype is reached. In a more tactical review, Sánchez et al. provides an overview of how tools of directed evolution might be applied to engineer microbial ecosystems4. Most of our directed evolution experiments today work in the context of a single bug or enzyme, but engineering ecosystems is incredibly relevant for industrial synthetic biology where we are working on the scale of ecosystems and need robust population dynamics. Similar to the conceptual framework described in Castle et al., this work imagines how to traverse the ecological landscapes to arrive at robust, stable, and desired phenotypes. Importantly, they describe the characteristics required for engineering these systems as:
The benefit of using evolutionary processes to solve these problems is that they can deliver traits to bugs that are fitness maxima and therefore stabilized and maintained by selection. This is in contrast to designed traits, which are often eroded by selection. This is an important characteristic of these populations because of the requirements to scale up production. Ultimately, the larger the fermenter, the more generations of selection, and the more potential for the populations of strains to veer off course. Processes that design for evolutionary dynamics can be more robust to the pressures of scale.
Drug discovery: Programming evolution to solve for function
Historical context: We use the term “drug discovery” rather than “drug design” for understandable reasons. Historically biomedical researchers have discovered small molecule drugs through a serendipitous game of brute force. Once we identify a target, we usually throw a bunch (read: millions) of molecules at it to see what sticks. This whole process is referred to as high-throughput screening. We have had the most success working on the canonical drug targets with well-defined and distinct small molecule binding pockets in their active sites. These canonical targets are sometimes referred to as the druggable proteome, though specific definitions of druggable aren’t specific and are constantly changing (e.g., as in kinases).
On the more in silico side, we’ve seen great work building generative chemistry approaches from investigators like Connor Coley, Regina Barzilay, and Gisbert Schneider, that have built hybrid computational-experimental methods. These show strong promise in generating novel chemical matter more efficiently than traditional approaches. That said, bottoms-up, rational design approaches are still early in generating results that translate to the clinic, especially in challenging, undruggable targets. There are a variety of reasons for this:
Evolution-driven approaches as an alternative: As in the prior example, where our understanding of biology falls short, evolution provides a potential solution. Evolution-driven approaches to solve these problems are in some ways analogous to a biological black-box ML algorithm: set an objective function for the microbe, for example, inhibition of a target, parameterize the search space, and let the microbe evolve to solve for a given function in ways that are often novel or not intuitive.
In an interesting example, Sarkar et al. built a system that does just this. They programmed an objective function into a microbe by requiring that the microbe inhibit an undruggable target, protein tyrosine phosphatase 1B (PTP1B), to survive6. They also gave the bug the tools for the job: in this case, adding pathways to enable the biosynthesis of terpenoids, a large class of natural products7.
The project was a great proof of concept of this approach. The investigators:
What we’re excited to see: Leveraging evolutionary processes in these ways represents an exciting future direction of drug discovery, combining trends of natural product chemistry, functional assays, allostery, and undruggable targets — equipping bugs with the tools of plants to augment medicinal chemists. We’re a long way off from fully automating medicinal chemistry, but bugs and biosynthesis may represent an interesting way to get there.
