At Dyno, we are empowering a diverse team of the best problem-solvers to drive cutting-edge science toward improving patient health.

This is easy to say but impossible to do without innovating on how biotech companies approach work. At Dyno, we believe that by nurturing a healthy work experience that promotes courage, openness, respect, focus, and commitment our employees, or AAViators, will improve patient health in a state of joy. Today, as a 50-employee company, we work in a Scrum-inspired Agile framework, which we will scale as we grow to 150 employees in the next few years.

We seek to improve patient health by optimizing gene vectors (e.g., capsids) so our partners can deliver safer and more efficacious gene therapies. We do this through a closed-loop process which combines machine learning with in vivo & in vitro experimentation: With each iteration we get closer to capsids that move the needle on what is possible in gene therapy.

While it would be nice to simply dream success into reality, in practice, it requires effective teamwork. The typical biotech approach is to use traditional project management, a sequential process of initiation, planning, execution, monitoring & controlling, and finally, project closure. This approach requires having done similar work in the past so one can anticipate most of what is needed to be successful, as well as having stable technology one can rely on. While this works really well in manufacturing and construction, it often falls short in high-tech industries.

Starting in the 1980s, the pitfalls of traditional project management began to be examined as described in The New New Product Development Game. What followed was the slow adoption of Agile work practices across a growing set of industries involved in complex work. Complex means the work you are undertaking likely hasn’t been done before, everything you need to do to be successful isn’t known, and you still need to develop technology which doesn’t currently exist. Agile, practiced through a light-weight framework called Scrum, overcomes complexity by guiding teams to approach work with an empirical, incremental, and lean mindset. As teams routinely apply what they learn, and prioritize what to do next, they are able to minimize wasted effort, quickly take advantage of new learnings, and complete projects which have never been done before.

Since our work at Dyno is highly complex, traditional project management, although more familiar, just doesn’t fit the nature of our work or our AAViators expectations. By the time a project manager finishes drafting one detailed plan, the team will have learned something which significantly alters the said plan. What’s worse is the psychological toll continuous change has on a team when using traditional project management—which aims to minimize the occurrence of change! The result is that change begins to feel like failure, so much that it’s tempting to try to prevent change, ultimately leading to poorer outcomes.

Next, I’ll highlight our approach with its key roles and responsibilities.

We plan, execute, and learn in two-week increments called Sprints, which begin with planning … But before we focus on planning the next two weeks, we take a step back and refine our roadmap together. This roadmap acts to visualize how we think we can achieve our company goals over the next 12-18 months. We find it important to understand where our efforts today will take us and to have confidence we are balancing our desire for success with a sustainable experience. After we commit to our revised roadmap, every team, across both R&D and corporate, does Sprint Planning together. We are open about the goals we are committing ourselves to accomplish over the next two weeks, while accounting for known constraints like taking time to vacation or develop ourselves. We make these commitments extremely clear by documenting  “Definitions of Done”, or brief statements of what it means for each deliverable to be officially completed.

We execute … Every workday during the Sprint, each team meets for 15 minutes to see how the Sprint is progressing and adapts it as needed; at the end of each day, all team leads meet for another 15 minutes and discuss any crossteam impacts which need to be raised and resolved so that Sprint goals can be achieved. Individuals and teams have significant flexibility over the course of the Sprint to accomplish what they committed to during planning.

We learn … At the end of the Sprint, every team conducts a Sprint Review to determine what was done, what was not done and why, and to decide what they should do next. This reinforces accountability in a healthy way. The Sprint Review is followed by a Sprint Retrospective where each team inspects how they are working as a team and commits to doing something to improve teamwork in the next Sprint. A Review and Retrospective is also conducted at the Company level to ensure broad transparency and commitment to evolving how Scrum is used at Dyno.

Of course, our approach would never work without the focused involvement of three key roles: The Team who collectively determines the work that needs to be done, the Product Owner who leads the team and prioritizes work, and the Agile Captain who facilitates the framework while promoting teamwork. The Agile Captain is a volunteer from the team, some stick with the role, other teams go through a role rotation, either way the role is vital for team success in our Agile framework.

Our approach requires even the most involved AAViators to spend less than 10% of their time within the framework events, which is an important metric we respect as we look for ways to improve how we work together. We constantly ask ourselves: How can we make our framework better while keeping the time required under 10%? Additionally, when teams have unsuccessful Sprints it’s psychologically nice for them to know that they can start afresh in the next Sprint, now with the added benefit of their learnings. Finally, each Sprint presents an opportunity to pause, take stock of the current situation, and refocus the teams’ and company’s efforts on the most important priorities. We get 24 opportunities a year to do this; that far exceeds what you could expect within a traditional project management framework.  

Since our first Sprint, our framework has continually evolved to achieve our desired outcomes. We do not expect the way we work today to enable success in a company with 150 employees; however, we have absolute confidence that we can evolve our framework and maintain a healthy work experience which promotes courage, openness, respect, focus, and commitment.

We’re excited about the progress we’ve made thus far, and enthusiastically welcome others to join us on our awesome journey! Come learn more at the upcoming 8 June 2021 webinar, Biotech and Scrum: Rethinking How Biotech Innovates in the 21st Century.

Please join us at the 2021 Dyno Therapeutics ASGCT After-Party, May 13th beginning at 7p!

The event is hosted in an ohyay virtual space that can be accessed using this link. You’ll be prompted to create a free account before popping into our curated venue. Come say hi, play a round of chess, setup in any number of cozy spots for a longer conversation, or help solve a jigsaw puzzle – we would love to see you!

Be sure to continue following our story on twitter @Dyno_Tx and on our blog.

Dyno’s Data Science team hosted its first class of interns amidst the socially distant heat of summer 2020. Five outstanding individuals joined us to help tackle challenges in computational biology, machine learning, software engineering, and, of course, remote company building.  

These individuals left lasting contributions to our science and our culture. This post is an opportunity to hear their stories.

Our 2020 class of summer interns consisted of Stephen Malina (Graduate student at Columbia University), Tiwalayo Aina (rising senior at MIT), Stewart Slocum (rising senior at Johns Hopkins University), Jeff Chen (rising junior at MIT), and Maxwell Kazman (rising junior at Georgia Tech). Since interning at Dyno, Stephen decided to join us full time, Tiwa and Stewy have decided to go to graduate school, and Jeff and Max are continuing their undergraduate studies. 

What brought you to Dyno? How did it compare to what you expected?

Stewy: Going into my senior year and looking towards the future, I wanted to spend my summer with a smaller company in a machine learning role. Dyno was exciting to me for two reasons: because the problem space is interesting and impactful, and because I liked the company’s attitude. This summer, I worked mainly on sequence proposal strategies for the AAV cap gene, but also got to do some supervised model development and build ML infrastructure. Dyno’s work is on the frontiers of machine learning and biology, which gave me the chance to think deeply about how to do things that haven’t been done before. I had an absolute blast! I learned a ton and had a great time collaborating with others, especially my mentor and the other interns. The environment was very open, and I had a lot of fun working with such capable people. 

Jeff: I’ve been interested in the application of CS in the biotech space, much more seriously the last year or so and so when I learned about the novelty of Dyno’s data and experimental process, I was excited to have a chance to be a part of Dyno, especially in its high growth cycle. 

Max: Before I started my internship, I was expecting an environment similar to an academic research lab, as many AAViators came from this background. However, the culture at Dyno reminds me much more of a lean tech startup and everyone is excited to play a part in bringing our technology to the market. Much of the work is very academically rigorous and exploratory, but I also got to work on engineering and infrastructure problems, something I wasn’t expecting as much. Working at Dyno has been super exciting and has exposed me to a variety of interesting projects and challenges.

Tiwa: Coming from a background in quantitative finance, I had some experience building models, and I knew I wanted to apply that experience to another field. After meeting some data scientists from Dyno, I was introduced to Dyno’s mission—in particular, I learned about the novelty and difficulty of the problems Dyno aims to solve, and I was immediately interested. The experience was just as challenging as I anticipated and enhanced by the opportunity to meet with mentors and collaborate with other interns.

 

What did you enjoy most about being here at Dyno? 

Stephen: Besides the people, one thing I really like about Dyno is that we’re working on a problem that I feel is both important and part of a larger societal project in which I’m personally very invested. Regarding the former, I’m, I think rationally, optimistic about the prospect for engineering better AAVs to help prevent and cure genetic disease. When I let myself indulge my fantastical side, I imagine a future in which gene therapy is so safe & cheap that it’s ubiquitous and widely applied towards improving human health. Regarding the societal project I mentioned, I believe that understanding and learning to engineer biology is one of the most exciting long-term goals of the 21st century and view Dyno as one of the companies building a toolkit for doing this more efficiently and safely. 

Stewy: I really enjoyed being part of a company like Dyno that pushes boundaries and tries to define what is possible instead of just accepting it. Dyno’s mission is incredibly ambitious – it’s risky and seriously hard. But it feels great to be part of something you believe in, and I am confident that ML-guided design will be key to the future of gene therapy.

Jeff: I definitely enjoyed the science and the people the most. Giving a data scientist direction and rich data, is like setting him or her loose in a playground. It was very exciting for me to be able to make my own novel discoveries here and integrate my work into the Dyno pipeline. For the people, it was great to be surrounded by so many smart people that felt driven in what they do. That environment is refreshing. 

Tiwa: The thing I enjoy most here at Dyno is the academic rigor that many events have. I find it really cool that the whole company will listen to a person’s presentation on research they did. There are also Journal Clubs where we review academic literature in machine learning. We even had a lecture series where wet lab biologists would teach the data scientists about interesting topics in biology!

 

What was the most difficult aspect of working at Dyno?

Stewy: There was a lot of paper reading and wikipedia scouring over the first couple of weeks as I tried to orient myself in the context of ML-guided sequence design. Afterwards, the challenge was to do things that weren’t in papers. Dyno’s business is to try to solve unsolved problems, which is usually pretty difficult, but I always felt supported enough to get help when I needed it. I also appreciate my mentor’s reminders to not bite off more than I could chew, and to focus on what I could.

Tiwa: The open-ended nature of the work is the most difficult aspect of working at Dyno! The problems AAViators work on haven’t been solved before, so you have to think really hard about what will/won’t work. Thinking creatively about how to approach problems with no answer key is really hard (though the work wouldn’t be nearly as fun if it weren’t so challenging!)

Jeff: The most difficult aspect of working at Dyno was the lack of social interaction due to the COVID restrictions that forced many people in the company to work from home. There are a lot of cool people at Dyno that I wish I could talk more in a more casual context. I missed having the coffee chat breaks with employees and relaxed lunches. These are interactions that, from past experience, not only stimulate friendship and a sense of belonging, but also creativity and work efficiency. So it was difficult being surrounded by all of these driven people and not being able to get as close as I’d like.

Stephen: At Dyno, the ground truth about how good our AAVs are is quite expensive to determine and can take on the order of months. Instead, we have to figure out ways to validate our methods using proxies that hopefully reflect the actual results of experiments.

Max: There were many steep learning curves I had to overcome when I started at Dyno, which made it challenging for me to progress with my project at the start. Catching up with the literature and learning about the tools and techniques Dyno uses were certainly not trivial. However, I enjoyed the learning process and I had a lot of help to overcome these learning curves. Reaching out for help is something I could have done more often, and would have lowered some of these barriers.

 

How did you grow professionally while you were here?

Max: This internship has been an incredibly valuable experience for me professionally. Being a small company, everyone at Dyno (including interns!) has the opportunity to observe or participate in many aspects of the business. This includes listening in on partnership presentations or interview presentations for potential hires, as well as discussions regarding the company culture and business goals. I learned a lot about how the company as a whole operates. On the technical side of things, I learned a lot about the infrastructure and design of our pipelines, as well as explored new ideas that could be useful long after I leave. The level of involvement I have had in bigger design questions was very unique and extremely valuable.

Stephen: I’ve worked as a traditional software engineer, but this was my first time working full-time on machine learning in an industry setting, especially a biotech one. As a result, I in general learned a lot about how to build, validate, & share ML models in an industry setting. In particular, I spent a lot of time working on, thinking about, and learning from other Dyno team members validating our models in lieu of being able to get ground truth validation of their new predictions. I have a longer term goal of becoming the type of person who can reliably and productively think independently, and I feel that the Dyno DS/ML team members and environment moved me closer to that goal by giving me the time and support to seek solutions on my own and explore outside my comfort zone.

Stewy: I came into this summer having taken courses and done some projects involving machine learning, but I hadn’t worked on any real-world ML systems. Having the opportunity to do that this summer was hugely valuable. Knowing how to deal with data imbalances and spot silent training bugs, seeing through the hype and getting a feel for what works and what doesn’t – you don’t get these kinds of things from reading papers, only from working on real systems. I still have a lot to learn, but after this summer, I feel much more confident in my ability as a machine learning practitioner.

Jeff: I grew substantially in my ability to do research and make experimental decisions. Because I was given a large degree of independence, I was able to really stretch my creativity and navigate my own decision tree. Learning how to fail and fail quickly, to collaborate toward directions outside your expertise, and to brainstorm experiment design, these are all skills that I felt like I lacked in my undergraduate research experience. 

Tiwa: In addition to improving my machine learning skills, I grew professionally by chatting with people all over the company, cultivating my network of both data scientists and biologists. Nobody minded taking the time to speak to me about their role, background, and goals. Additionally, I attended internal career panels which gave me a better sense of the important nuances underpinning pursuing careers in ML/AI.

 

What’s your advice for people who are interested in interning at Dyno? 

Max: Working at Dyno can be a super fulfilling experience. Come ready to learn a lot, and also struggle a lot. Being independent is important to facilitate the learning process, but that doesn’t mean you shouldn’t ask for help often. Be ready to tackle hard problems where no one knows what the answer should be, but also have a lot of fun doing it. Make sure to reach out for help, ask questions, read, collaborate often, and have fun!

Tiwa: One piece of advice I would give is to always be ready to learn. The importance of this is clear when you’re stuck and you’re looking for feedback/advice, but it really applies in general when interning here. I think that a hunger to learn more is a big part of the culture at Dyno; the experience will be even more valuable if you’re always willing to ask questions and expose yourself to learning opportunities.

Stewy: Dyno is a great place to be. Every person I met really cared about me, and I will always appreciate that. But every person I met also really cared about the work they were doing, and I think that it’s important to share that excitement. If you are considering working at Dyno for the summer, I suggest you read up and learn about what the company is doing, get excited about it, and then hit me up!

If reading this post makes you excited about interning at a startup that aims to help millions of people live better through enabling gene therapy using tools at the cutting edge of machine learning and synthetic biology, consider applying through our careers page.

The capsid of Adeno-associated Virus (AAV) is a naturally occurring, replication-deficient, virus that is widely considered the frontrunner for solving the delivery problem in gene therapy. These viruses are known to be harmless to humans, and are relatively simple to manipulate. One well-known drawback of natural capsids however, which are currently used for delivery, is that many patients with pre-existing immunity to the virus (due to previous natural exposure) may be ineligible for life-changing treatment.  

In previous work (published in Science), we validated the use of computational models in conjunction with high-throughput experiments to design better liver-targeting variants of naturally occurring AAV capsids. In that work we were primarily focused on single edits to the capsid, and hypothesis that the effect of a combination of single mutations, at least when the number of total edits is limited, can be approximated by the sum of the effect of each mutation. Through this approach we validated that a model-guided method can lead to more efficient design of better capsids for more effective liver targeting. 

The paradigm of measuring the effects of mutations independently and combining the best ones no longer works as we attempt to modify the capsid beyond a handful of mutations. Making capsids with many changes relative to natural variants increases our chances of being able to treat the thousands of potential recipients of gene therapy by evading pre-existing immunity. To achieve the ability to introduce a large number of changes to the capsid sequence without breaking its essential abilities, a wholly new approach was needed, which our latest study in Nature Biotechnology aims to address. Our goal was to design highly diverse AAV capsids, for which we used much more advanced machine learning models and trained on more complex datasets The work was a result of years of collaboration between teams at Dyno, Harvard’s Wyss Institute, and Google Research.

To test these methods, we focused on a representative region of the capsid (positions 560-588, seen in pink in the fully assembled virus, the hexamer assembly, and the individual subunit in the figure above) that had both surface-exposed and buried residues (Generally speaking, surface-exposed residues are known to be more mutation-tolerant) This region is also well known for the presence of immunogenic structures, as well as its role for tissue targeting. Our aim was to introduce as many mutations as we could in this 28 amino-region, including substitutions and insertions, the latter of which is a  less common type of mutation in nature. When we started this study, it was unknown if machine learning models would be reliable for predicting the effects of mutations for variants beyond 5-10 edits to the original sequence. We expected this was possible, however, based on analyzing the diversity of sequences that have been isolated from natural sources. In this region, the average difference between two AAV serotypes is 12 amino-acids (often with few or no insertions). Nonetheless, we pushed the models to propose sequences with up to 29 substitutions and insertions. 

Using the naturally observed level of diversity as a benchmark, we set our goal to generate diversity beyond that observed in nature, while maintaining the capsid’s viability. After screening billions of potential sequences in-silico using machine learning models, we settled on ~200,000 designed variants which we experimentally tested for their viability. Of those, approximately 110,000 produced viable viruses (many of our attempts were deep into the sequence space, where it is very hard to propose viable viruses). About 57,000 variants were farther than 12 mutations away from the AAV2 serotype. By generating more than two thousand sequences that were 25 or more mutations away, we decisively demonstrated the power of machine learning models to design diverse synthetic capsid sequences. 

In this study, largely conducted before Dyno’s official launch, we report one of the largest AI-driven protein design assays published to date and validated the utility of these techniques for capsid engineering. The success of this approach bolstered our confidence in  Dyno’s foundational science.  Building upon this foundation, we have established infrastructure and machine learning techniques at Dyno to expand and optimize the AAV repertoire for multiple traits (including in-vivo targeting of challenging tissues), multiple serotypes, and at a larger scale. This study is just the beginning of our endeavour.  

This work was a multi-year collaboration between Dyno co-founders Eric Kelsic, Sam Sinai, and George Church, colleagues at Harvard’s Wyss Institute including Nina Jain and Pierce Ogden and members of the Google Accelerated Science team including Patrick Riley, co-first authors Drew Bryant, Ali Bashir, and co-corresponding author Lucy Colwell. 

Delivering gene therapy’s promise
January 26, 2021

The human body consists of trillions of cells, each possessing their own copy of DNA that provides the blueprint for making a human. Despite sharing the same DNA, different cells activate a varied mix of genes within them to enable differentiation into various tissues and organs. For millions of people, unfortunately, some of these genes do not function properly, resulting in diseases and conditions that are sometimes severely debilitating. 

Currently, there are thousands of diseases of different organs that can be cured if the set of cells that rely on the defective gene are provided with a healthy copy of it. However, a significant obstacle in achieving such a cure is that it is challenging to deliver genes into the correct set of cells. The most promising route that is currently available uses AAV capsids, protein shells derived from the naturally benign human Adeno-associated Virus (AAV), as a vector to carry the healthy copy of the gene to the diseased organ or tissue. Unfortunately, natural AAV capsids have not evolved to be specialized in the way needed to deliver therapeutic genes. Most AAV capsids are not able to target a specific organ or tissue that a particular treatment requires. Moreover, the human immune system is familiar with these natural strains, and can quickly neutralize them when used for gene therapy, preventing them from reaching their destination.

To circumvent this problem, the traditional way of engineering natural viruses has been to evolve them in the lab, generating new synthetic viruses that are both novel to the immune system and have higher affinity to the target tissue. Apart from being time-consuming, this process scales poorly as the lessons learned from one experiment don’t generalize to others. There are multiple reasons for this. For instance, the process of generating novelty to select from cannot be precisely controlled during an experiment, but rather occurs by random genetic mutations. With this approach, the traits of interest might simply be too rare to find with random variation. Additionally, using experimental selection processes to improve a specific property often presents a risk of losing other desirable properties of a capsid that are not being selected. An evolutionary path taken in one experiment may not be repeatable for other traits, and the experimental heuristics learned could be irrelevant for other properties. Given these significant shortcomings, success in the field has been limited to those experiments which just happened to produce good results for a particular therapy.

Dyno has instead opted to build a platform that aims to solve the delivery problem for all therapies, thereby removing a major obstacle for realizing effective gene therapies across thousands of diseases. Dyno’s model is built on the recognition that designing capsids capable of delivering genes to different organs and tissues share commonality that if captured, enables the design of specialized vectors quickly and efficiently. To pursue this opportunity, our platform combines two cutting-edge technologies: 

  • First, it makes use of the most recent developments in artificial intelligence, to build a smart system capable of learning patterns within the virus’ coat that can help it hide from the immune system and more effectively target organs and tissues of interest. 
  • Second, it proposes millions of sequences, which can be directly and accurately synthesized using the recent advances in DNA technology. These viruses are then experimentally validated in primates.

Data gathered from these validation experiments help making Dyno’s AI smarter, which in turn can help produce better and more diverse viruses. Dyno contends that over time, its AI system will become smart enough to design viruses that are capable of targeting any organ or cell type in the body and predict whether it will work in humans, without the need for multiple experiments. The efficiency of our AI platform only grows as we engage with more partners to produce delivery vectors for different tissues and indications. 

This is why Dyno’s approach has generated interest from companies that are industry leaders in  gene therapy. Better delivery vectors are impactful and easy to adopt for current and future therapies. Our partners recognize the value of this technology and are helping to validate this approach – all the way into the clinic. Together with our partners, we hope to reach new heights in the gene therapy landscape and unlock its promise for the millions of patients whose life would be transformed by effective delivery of a new gene. 

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