Today we’re releasing Zoogle — a new web portal that leverages billions of years of evolutionary data to help predict which organisms are the most relevant models for different aspects of human biology. Although we’ve been building this technical framework for a while now, we finally put the parts together so that anyone can more easily tap into our workflows with the click of a button.

Zoogle is our first stab at a more user-friendly, data-driven way to search for organismal models. Use Zoogle to see how 60 of the more developed research organisms (all with some genetic tools) are ranked by our multi-dimensional analyses as models for Your Favorite Gene in humans. And vice versa — which human genes are predicted to be most strategically served by Your Favorite Organism.

You don’t have to study something expensive and low-throughput to approximate human disease in the lab. There are simpler models, and they’re not just simpler — they may sometimes actually be better.

We expand on this and why we think this approach matters below. But feel free to skip ahead and come back to that later. Start Zoogling here.

The success of biomedical efforts often depends on selecting the right research model organisms. You need model systems to investigate complex biology in vivo without causing harm to human subjects.

When this goes well, the upside is huge. Models can lead to awe-inspiring, serendipitous discoveries. They’re responsible for our basic understanding of many aspects of human biology — cell cycle, gene regulation, secretion pathways, developmental programs, brain circuitry, tissue regeneration, the cytoskeleton, and much, much more. Without these fundamentals in place, we wouldn’t have cures for nearly as many diseases as we have today.

Getting models wrong can also have big effects in the opposite direction. About 90% of drugs that enter clinical trials ultimately fail. The research informing trials leans heavily on non-human models, which don’t always deliver on predicting safety and efficacy. Why? Human disease biology is complex, and regulatory hurdles make it hard to innovate beyond best practices. 

Relying on traditionally accepted organisms like mice can even, in some cases, be clinically dangerous due to key species differences. Patients are subject to disastrous outcomes, such as catastrophic systemic organ failure or irreversible brain damage. Yet, we soldier on to the drumbeat of an estimated 50 billion dollars per year in the U.S. alone. We really need to figure out how to do better.

The fact that we’ve made as much progress as we have based on early intuitive choices around organisms is a pretty compelling testament to how much this approach has to offer. Comparative biology is a superpower. Full stop.

But even superpowers can be improved. To do this, it’s first helpful to think about how we ended up here. Historically, model selection has been a largely intuitive practice. This makes sense, given that most modern biomedical research organisms were initially chosen and developed in the first half of the 20th century. Tools, funding trends, and community efforts helped some of these become even more prevalent, creating a positive feedback loop that reinforced “supermodels” like mice and flies.

Our intuition has also led us to a widely-held belief that there’s an intrinsic trade-off between better models and experimental throughput. We tend to assume that the species that look or behave more like us would be better models for human biology. Simple sequence-based comparisons reinforce this bias, given that more recently diverging organisms have a naturally higher baseline sequence similarity to us. If we don’t account for that baseline, the data mirrors what we intuitively expect.

Because of this, we tell ourselves we have to compromise by working with “lesser than” models to make faster and cheaper headway in the lab. Rather than work on a monkey, we work on mice, flies, or yeast to speed up understanding. It feels too practically difficult (and ethically murky) to do otherwise. 

As a result, we as a scientific community have specialized in a relatively small number of organisms. Over 90% of research funded by the NIH can be claimed by one of six canonical research organisms (calculated based on these informative posts), < 0.0001% of what biology has to offer. We can do better, but part of why we haven’t is that we haven’t articulated to ourselves a sufficiently strong, data-driven argument for how else to choose the organisms we should work on.

We now have more data, understanding, and tools to conduct the kinds of unbiased, comparative analyses that move us beyond a narrow cannon. It’s long overdue for us to take a bird’s eye view of the tree of life in the lab.

The best research models aren't just easy to work with — they have proteins that function like ours do. While our closest evolutionary relatives naturally share more of our DNA, we've found some surprising functional similarities in unexpected places. Our analytical methods, detailed here and here, look at both gene-level evolutionary distance and predicted protein structure to spot these hidden similarities. 

A key concept that allows room for improvement is that most analyses don’t take into account the fact that different forces cause an organism’s individual genes to each evolve differently. Therefore, you lose a lot of information by aggregating and comparing whole genomes. By instead zooming in on individual genes, we're finding research models that mimic human biology in ways traditional approaches might miss.

Our results have disabused us of the simple trade-off narrative (that cheaper/easier models are biologically less useful) — check out example results for the gene that causes Duchenne muscular dystrophy below to see what we mean.

Unexpected conservation of the dystrophin gene (DMD) in invertebrate species suggests their potential as alternative models to mice for Duchenne muscular dystrophy research.

Dashed line is a linear regression, essentially showing how conserved we’d predict a given organism’s DMD gene to be based on how evolutionarily distant it is from humans. Organisms below the line are more conserved than we might expect and those above, like mice, are less so.

As you can see, depending on the specific human gene under investigation, our data suggests that many experimentally simpler organisms are predicted to punch above their weight as biological models. Our analyses have surfaced many examples of human genes that are well-modeled, or even better-modeled, by species that are also experimentally much higher-throughput than more standard animal models like mice. The results can sometimes be so surprising that they force you to reexamine your assumptions about the relevant biology. This could possibly lead to different or better hypotheses.

In many ways, what we’re saying is already intuitive if you work in drug development. “Mouse is a terrible model” is one of the worst-kept secrets in pharma. We all sense that there’s a better way. And Zoogle is our first attempt at a rational, data-driven path forward.


Ultimately, this all gets at the core motivation behind Arcadia. We want to leverage the full diversity of biology to solve problems. We're testing the hypothesis that more data-driven approaches to organismal selection will improve scientific and real-world outcomes while moving us away from organismal specialization.

Figuring out the usefulness of Zoogle requires more of the community, as Zoogle predictions are merely that — predictions. Our guesses aren’t worth much without empirical testing. And we’re doing this in-house through our own research and startups.

But more users and testers are needed to make this maximally useful to everyone. This is why Zoogle, our lessons, and our workflows are all open. The practical value of our open science principles is embodied in this — utility, speed, and impact. We hope this portal will draw more scientists and non-scientists alike to tell us about the work they’d like to do with Zoogle predictions. We’re committed to troubleshooting with you. We'll be tinkering with our prediction algorithms and updating the platform over time as we learn more from our work and others.

We’re optimistic that being more open-minded and data-driven about organisms may lead us to the thing we're most unapologetically fanatic about — exploring more organisms. Ironically, embracing some agnosticism may be the key to fully unlocking our childlike obsession with the wonders of biology. We hope you’ll join us.

Dive into Zoogle and submit feedback through the forms at the bottom of the page, or comment directly on our pubs.

Introducing Zoogle:
An Organismal Search Tool

It’s been about three years since Arcadia opened its doors. We’ve sorted through many challenges, tried out a symphony of operational and scientific ideas, and published a lot of results and tools that we’re proud of. And, unsurprisingly, our operating principles have evolved.

We are at a new inflection point as a company. In short, we have more focus. We’ve moved past our initial stage of maximizing random walks to explore a greater space of possibilities. We are now pushing more intensely on fewer goals that we have much higher conviction about. 

More than ever, we are determined to get some hard things done. As a result, we are updating our original operating principles to the ones below, which better reflect what defines our team today. 

We are ambitious, self-driven individuals who take control of our fates. We bias towards immediate problem-solving rather than finger-pointing. There’s a high degree of trust and autonomy amongst Arcadians, and we expect exceptional results and accountability in return.

We are scientists who believe in taking an iterative, rational approach to all we do. We pride ourselves on being curious, experimental, nimble, and even admitting when we’re wrong. We often repeat the mantra: data, not drama.

We are working on what we believe is the most important effort in the world we could be contributing to. We move quickly, refusing to waste our privileged, time-boxed opportunity to make a meaningful, scalable impact. We won’t wait for others to do what we think is right.

Our actions today can shape the future. We lean towards envisioning new realities that we can help build anew, as opposed to iterating on existing problems or systems that need a reboot. We want to shift the Overton window (aka what people think is “normal” or acceptable).

If our operating principles resonate, check out our latest job opportunities and FAQ page. We are more excited than ever about where we’re headed. There’s some fun but tough work ahead, and we want the most talented, mission-oriented team to tackle this with. After all, science is worth it.

Updating our core tenets as we embark on a new phase

Last month, Arcadia turned two. It’s been a wild ride. It doesn’t feel so long ago that we were huddled in a small warehouse space putting together our first website, learning how to incorporate. 

As we’ve been iterating, the question we get most often is “what are you?” After much pivoting and even more Cheez-its, we now have a much clearer answer for you. Arcadia is a for-profit startup incubator with our own research platform that leverages evolutionary biology for discovery. 

Don’t worry, we still love weird organisms. In fact, our shared in-house platform is a suite of technologies built by our scientists who are fully devoted to figuring out how we find the right weird organisms to inspire new inventions. We believe that comparing species across the tree of life can speed up our ability to deploy impactful solutions in therapeutics. We’re making this process replicable, scalable, and useful by incubating our own homegrown startups. 

Our setup has similarities to existing venture creation studios, including some aspects of how to strategize and operationalize biotech startups. We don’t need to fully reinvent the wheel here, and we’re lucky to be able to learn from their successes.

But there are important differences that make us unique. 

Fundamentally, Arcadia is a science-first company. We were founded by and for scientists, so that we can figure out new ways to sustain the kind of research we’d like to see more of in the world. We are very process-oriented and see knowledge as one of our products, in addition to successful startups. You can gauge our priorities by how we share our work.

Although we have two key external investors, we are also scientist-owned. Arcadians have shares in our company, and the way we distribute them is informed by our understanding of the full research lifecycle. Whether someone is de-risking early ideas, pouring plates, engineering tools, editing publications, or incubating startups — it’s all needed for scientific innovation. This is reflected in our profit-sharing approach.

Arcadia’s translational portfolio is also driven by our scientific process. We are trying to maximize the return-on-investment for our research by applying it to important, real-world challenges. The odds of success increase if scientists have enough context to think concretely about where their science is useful. It breeds a different kind of creativity. We therefore teach all of our scientists about market opportunities and bottlenecks. They are investors too, after all.

In short, we want to create a scientist’s vision of entrepreneurship at Arcadia. We are training and ramping up hiring to enable all of this. We are building an invention factory with scientists, engineers, builders, and entrepreneurs who can execute this very ambitious vision. Check out our new wave of job ads to see if this might be you too.

We know we have our work cut out for us, but we are intent on giving it our all to make the most of this opportunity. Our goal is to reach financial self-sustainment within the next 10 years. Having said that, we hope to one day report that our science-centric mindset yielded a strategy that was even more profitable than we could have imagined and also shaped how others approach their science. We are scientists and optimists, and this is our bet.

An update on our vision at the two-year mark of our experiment.

We have science news to share. But it’s not the type of update you might expect. It’s kind of an anti-announcement, if you will. We are excited to tell you about all the latest research we don’t plan to work on — and why. 

Because we are focused on exploration, Arcadia’s research is biased towards risk and breadth. For us, failing fast and often is a feature, not a bug. We see our “misses” as knowledge wins. With every roadblock, there is a lesson. If we’re doing it right, we should be churning out lots of lessons.

We know it’s not the norm to put time into sharing work we don’t plan to pursue. But we’ve decided that these pieces of work are more useful in the public domain than stored away in our freezer. Not only could this approach benefit other researchers, but there are also advantages to Arcadia as a company. Sharing science accelerates collective progress to move past mutual bottlenecks, brings in users for future tools we may build, gives potential job candidates clearer insights into what we do, and fosters a strong scientific community around us. We hope other companies consider adopting open science as a win-win strategy.

So today we’re announcing a new way we’d like to highlight our library of lessons, which we have coined our “Icebox.” These are literature searches, discussions, and research pilots that we worked on for a few months and decided to pause. 

It’s easy to assume that when projects don’t move forward in science, it’s because of bad ideas. But as most scientists know, there are typically lots of practical bottlenecks to navigate before you have the privilege to exercise taste in choosing whether an idea is “worth” pursuing. We will describe these bottlenecks in our iced pubs.

The usefulness of this effort depends on other scientists, which could be you. We hope our Icebox pubs help de-risk your ideas. Maybe you are building a technology perfectly suited to address a critical hurdle. Or maybe it helps you replicate our work, avoid our mistakes, or bring renewed attention to a problem as fields evolve.

You will also note that sometimes we release efforts that did, in fact, technically pan out. These are opportunities that we believe other scientists would be better positioned to move on. It’s not a signal for you to back off — it’s an explicit invitation. We are stating our intent to not pursue it for various reasons, such as technical expertise, mission alignment, commercial viability, or some combination of these.

To ease interpretation, we’ve come up with eight common reasons that we may ice a project at Arcadia. We’ve listed and defined them below. Look out for our tags and explainers in actual pubs in case you hold the key to unlocking their progress. We are excited to see if this experiment accelerates our work or yours.

If you want to revisit one of our ideas and take it off ice, go for it. Comment publicly if there’s anything we can do to assist. We’ve tried to flesh out our pubs so that they’re actionable to the next set of researchers. Give us feedback. Let us know how we can help you heat something up.

To make an impactful advancement, we’d need further technological or conceptual development.

We've decided not to pursue this area of science at the moment because it doesn't play to our unique strengths as a company.

We learned what we needed to learn from the project and there is no reason to continue.

What we discovered during the project lands us in an area where we lack internal expertise, or taking the project to the next step would require expertise we don’t have (yet).

We tried to derisk a project, but could not overcome barriers that would enable us to move forward, or our hypothesis was wrong in a way that the remaining possibilities are not of interest for us to pursue.

The market is not appropriate, big enough, well-scoped enough, or is in an area where we are not well positioned as a company to have a competitive advantage. The time horizon to make this translationally actionable is too long or the challenges are too layered to align with translational goals.

The project can’t be done without massive resources, it’s inefficient, or we don’t have a high-throughput approach to scan enough search space and have confidence we’ll be successful.

We lack the facilities, equipment, or tangible resources to act on this project.

Curating a menu of our stopping points that others can carry forward

Announcing an Anti-announcement: The “Icebox”

We founded Arcadia with an optimism for a different way of doing science. We wanted the Arcadian way to be rooted in our values and unique goals.

Our values can’t just be ideals though; they must be embodied in principled actions. This is, of course, easier said than done. Building a new organization involves a seemingly endless onslaught of decision points and pivoting. Abiding by principles in the face of this uncertainty and discomfort requires commitment.

To keep ourselves accountable, we’ve developed a set of operating principles to guide us. Our principles are both emergent and aspirational — reflective of the collective qualities of current Arcadians and also representative of what we want to strive toward. And that work is never done. By putting these out into the world, we hope you will help hold our feet to the fire too.

Below, we’ve shared the operating principles we think are most unique to Arcadia and our mission. As a team, we discussed concrete places where these ideals show up in our day-to-day, and we provide examples below.

Honesty and integrity are critical for our success. Principles shouldn’t be context-dependent, so the “right time” is always right now. We don’t wait for situations to be less risky or more convenient.

One of the ways this principle shows up is how we share our work with the scientific community. Most research findings stay hidden for months or even years before they’re published in scientific journals, adding drag to the scientific process. In our first major blog post, we stated, “...no work produced or funded by Arcadia will be published in journals.” Rather than wait for better platforms, more aligned career incentives, and greater certainty of the path forward, we decided to just start publishing our work on our own terms. We’ve since released 17 pubs — read more about our ongoing publishing experiment here.

This certainly made the decision to join Arcadia harder for some. This is a feature, not a bug. We understand the immense privilege we have to take this risk, but every Arcadian believes waiting for a more convenient time is a greater risk to scientific progress as a whole.

2. We maximize the utility of our science.

Ultimately, we want our science at Arcadia to be useful. We define our success by how much our science gets reused by others. This has to be our constant North Star.

We want all aspects of our science to have utility, which extends beyond commercial applications. We want our science to be reused in all sectors. We often discuss a very practical consideration — that our research equipment and reagents have to be cost-effective enough for most scientists to be able to adopt our approaches. The return-on-investment ceiling for our science depends on it. For this reason, we value frugal, scalable science. This guides our decision-making in a variety of areas, like which microscopes to buy, which software packages to build upon, and what general strategies to take. 

We also try to write about our work in a way that is simple and accessible. Learn more about our work, and check out a specific example of an imaging tool that we’ve designed to be useful to others.

We believe the whole is greater than the sum of its parts. Our strength lies in coordinating diverse, complementary strengths across the team to tackle science that can’t be achieved otherwise. Arcadians are low ego + high ambition individuals, and we work hard to align personal goals with collective mission.

A few months into starting Arcadia, we had our first company-wide scientific brainstorming session. It was kind of magical. With low-ego, mission-aligned people in the room representing an exceptionally diverse array of scientific disciplines, our ideas moved in directions that wouldn’t have been possible otherwise. And then Arcadians experimentally went after the ideas together at warp speed without being territorial. We’ve continued with these brainstorming sessions, now called “Sandbox.” And because you can't engineer people prioritizing collective outcomes, we aggressively recruit for people who already have this mindset.

We are trying to unlock a new way of doing science, which is critical for human progress. Our degree of impact is directly related to how many people outside Arcadia can adopt our model, so we must make it scalable and replicable by others.

We’ve been generously funded to try out a different approach to science over the next decade. We hope our lessons inform new organizational models that others pursue, sparking ecosystem-level change. But if there isn’t sufficient philanthropic funding for new efforts, the potential impact will be limited. That’s why we are testing the hypothesis that fundamental science can yield sufficient revenue to sustain operations, if approached strategically and over a long enough period of time. If we show that basic research is indeed a smart financial investment, our experiment could expand the pool of investors and resources for similar efforts.

To best position ourselves for this experiment, we made the early call to switch to a for-profit tax structure. Read more about our thinking.

When in doubt, do the experiment. We believe in the scientific process — an empirical and iterative approach. In the absence of an established blueprint for how Arcadia should work, we will be pragmatic and evidence-driven in lieu of inaction or forceful opinions.

Diving into the unknown means accepting partial information about what next steps are best. Ironically, the less we know, the more opinionated we become. However, in moments of uncertainty, we should strive to be more objective and scientific. This means running informative experiments. Our Software team has taken this approach many times. There are often differing opinions around tool selection, team structure, and computational training at Arcadia. Luckily, there are usually small test runs we can do to collect key data. For example, this is how we ultimately chose to use Nextflow as our workflow-building tool.

Arcadians should strive for self-awareness and learn to notice when we’re slipping into drama without data. Read more about how our Software team has navigated some of these moments.

We are doing something hard. We need to learn from every experiment. Poor execution is a bad experiment. We expect excellence across the organization, from science to operations.

This one’s pretty self-explanatory. If you’re excited to join a team of people who hold each other to a very high bar, check out our latest job opportunities!

How we’re aligning our values with action, especially when the going gets rough

Arcadia is an experiment. Over the past year, we’ve shared some of what we are building and testing. As we start the new year, we’d like to tell you more about the translational arm of our experiment. 

Arcadia is a crew of entrepreneurial scientists who want to solve real-world problems. We look to nature as the ultimate problem-solver — after all, every living organism is the result of millions or billions of years of evolutionary optimization. We study all sorts of creatures to discover biological innovations, providing a rich playground of solutions that we can co-opt to address human challenges. 

We think this focus creates a wealth of commercial opportunity that we can capture for good. We hypothesize that we can use our own revenue to financially sustain curiosity-driven, exploratory science at Arcadia. Strategically recycling the value of our discoveries in this way could enable longer-term and more ambitious scientific goals. 

This is a critical test for us. We want to build a financially sustainable, basic science-driven company that accelerates human progress and attracts mission-minded investors. We hope our model will be replicable by others in the future, drawing more resources and talent into the broader scientific ecosystem.

We’re still iterating on our translational strategy, but there are some things we know. Below are answers to the top five questions we get asked about the business side of Arcadia. 

We are conducting exploratory and early-stage research in-house that bridges discovery and translation. Right now, our primary focus is to spin out companies at roughly Series A stage, although we are open to a range of other assets. While we’re considering many impact areas, our current research best matches key opportunity spaces in therapeutics, climate, and synbio. 

At the end of the day, science is our north star. We are scientists, after all. The opportunities we see in our research will guide our asset portfolio.

2. Why structure Arcadia as a for-profit company? 

We get this question a lot. Given our emphasis on biology across the tree of life, our for-profit status often catches people off guard. Setting up Arcadia this way was a deliberate design choice. To make Arcadia financially sustainable, a for-profit structure was the most straightforward structure for our goals. And we’d like to normalize for-profit companies investing in basic science, which could attract funding for more scientific enterprises in the future.

It’s true that our financial goals are technically achievable as a non-profit. But in non-profit organizations, employees don’t share directly in financial wins. We think it’s important to be co-incentivized with our scientists and operators. We want Arcadians to feel a deep sense of ownership and agency. And we hope this spurs the next generation of science investors. 

Yes, we are lucky to have two unicorn investors — Jed McCaleb and Sam Altman — who are fully aligned with us in all our goals. In fact, the original motivator for each was to leverage this opportunity to re-think how science is done. They have both pushed us to think outside the box and take risks.

They have committed funding that could sustain a team of 150 people (with competitive biotech salaries) for about a decade. However, we will tailor our growth to our evolving scientific and financial goals, which may result in fewer people and a longer time horizon than originally projected. We also hope to become financially sustainable before using all of our seed funding.

4. How do you balance commercial efforts with open science publishing goals? 

This is a central part of our ongoing experiment (see here and here) – one that we find both exciting and challenging. We want our open science publishing to not just coexist, but synergize with our translational goals. Open discussion about our work will accelerate progress beyond what’s possible in isolation. We are testing this prediction by iterating on publishing and patenting strategies throughout the entire scientific and translational process. We recognize that the interplay between publishing and patenting is a unique challenge, and we are fully prepared to make mistakes and share lessons learned along the way.

If successful, this experiment could unlock a lot of science outside of traditional academia, much of which is never shared. We are joining many others to iterate on useful ways to publish outside journals, while remaining discoverable and interoperable within the broader scientific infrastructure. We’d like to see research-sharing become the norm for science-focused companies.

5. Do you fund external efforts or collaborate with universities? 

No. We focus on internal research and spin-outs. Also, to carry out our organization-scale experiment, it’s important to remain untethered from systems with different goals and incentive structures.

This is a broad overview of our plans, but we have already started many experiments to our translational approach. We will share more details over time through this blog and our publishing platform. In the meantime, keep asking us questions.

This post is an update to our Entrepreneur-in-Residence program description. You can read the original description and apply here.

It was wonderful to see such interest and excitement from the community for our recently posted Entrepreneur-In-Residence (EIR) position. However, as we began to review applications and talk to candidates, we realized that our initial imagining of how this position might bring in talent to help translate science was too broadly defined. In exploring the three different models we put forth (Idea-Ready, Idea-Agnostic, and Meta-Idea), we realized that we are currently best set-up to enable only one of these models.

After discussing with candidates, Arcadians, and advisors, we have decided to streamline our EIR search by focusing only on the Idea-Agnostic prototype. This focus reflects our greatest current need, as we have a lot of exciting research happening already at Arcadia with translational potential. We have an exceptional team of scientists who are interested in founding and leading companies one day, but they haven’t done it before. They have ideas. They need partners to build with.

Our interest in Idea-Agnostic candidates also reflects the broader problem we are interested in solving. Arcadia wants to experiment with how to best support translational ideas at a very amorphous, primordial stage that can often be challenging to get off the ground. We want to figure out how to promote ideation before there is preliminary data or a clear business plan. At this stage, there’s a real chance that key experiments won’t work or will yield surprising results. Actually, we think these surprises are what will lead to some of the most interesting ventures at Arcadia. These are the 0→1 opportunities. 

This early stage is extremely challenging for most life science entrepreneurs if they don’t have resources and flexibility to explore. In biology, this stage requires a significant amount of time, infrastructure, and people to experimentally de-risk opportunities. Biological innovation is an empirical sport. 

It’s also an unpredictable sport. To maximize the utility of all of our efforts, we and our investors want all Arcadians to embrace all outcomes. In our view, this includes openly publishing results that we realize are not commercially actionable for us. We don’t want ideas or IP to sit on the shelf. We need people who are aligned with us on this.

We are looking for open-minded operators who are ready to ideate and pivot. We need EIRs to come in with a tabula rasa, leveraging past entrepreneurial successes to brainstorm and chart out concrete strategies in collaboration with our scientists. They need to understand the venture creation process and startup progression to lead the way forward on commercializing biological tools or assets arising from our current discovery efforts. They should have a knack for how to constantly diligence ideas and transform the best ones into a concrete plan. They are our future co-founders. 

We are designing this role to be an adventurous sprint to enable EIRs to identify one company they could help spin out of Arcadia. The hope is that the EIRs will emerge as co-founders of newcos alongside one or more scientists at Arcadia. Along the way, Arcadia will provide feedback, resources (legal, scientific, operational, strategic), and a playground of scientific ideas emerging from our own internal discovery programs. At the end of the residency, EIRs will have the chance to pitch their idea to the Arcadia team for future funding and support. While this is the desired outcome of a residency, we believe that success should be defined in terms of what we have learned from the experiment, thus EIRs are encouraged to continuously evaluate the aspects of their experience that could be implemented more broadly at Arcadia to catalyze innovation and entrepreneurship.

With this in mind, we are ideally seeking serial entrepreneurs who are hungry for a chance to have an impact in biotech and the life sciences. They should already have had some lived experience in the early stages of company formation. They should be intensely action-oriented and enjoy rolling up their sleeves to get things done. Strong candidates will be flexible, enjoy connecting with other people, and love learning about science. They need to be able to begin at ground zero and consider market potential, IP white space, and team composition to help us build the next company that spins out of Arcadia. And they should be excited to leverage their EIR experience to help us iterate on our broader translational strategy at Arcadia in coming years.

No. At this time, we are interested in candidates who can join us in-person to fully immerse themselves within the creative, scientific community at Arcadia. 

Will Arcadia own IP emerging from EIR work?

Yes. And to allow maximal flexibility for this experiment, we are not currently looking at opportunities that involve outside partnerships or pre-existing IP. Also, it’s important for EIRs to embrace our broader open science experiment, which means that we will be openly publishing informative lessons (both positive and negative) from this work.

What percent of the spin-out will the EIR own?

This will be negotiated case-by-case. Our overall intention is to advocate strongly for scientists and founders while being financially sustainable as an organization. We believe we have a unique ability to do this given our shared goal with investors of optimizing scientist-led venture creation.

We are not tied to one specific market, since we hope to let our scientific discoveries drive the opportunities. We are open to a wide range, such as therapeutics, platforms, tools, climate, synbio, and agriculture. We love surprises…

We've refined our search space for this open opportunity

Arcadia’s First Virtual Open House!

1:47 - What do we want to change about science? 

4:28 - Is Arcadia bridging interactions between academia and industry? 

09:16 - Why is studying microbes important? 

11:38 - How does Arcadia research get funded? 

13:39 - How is our research self-sustaining? 

20:30 - How are we pushing open science? 

24:18 - How will we collaborate with academics? 

26:48 - How will Arcadia avoid territorialism?  

29:23 - What is our scientific mission? How do we choose organisms to study? 

33:38 - What does success look like at Arcadia? 

40:27 - How will we promote inclusivity? 

50:09 - Closing Remarks - Join our mission!

Co-founders Seemay Chou and Prachee Avasthi join Lauren Richardson to discuss Arcadia’s mission, structure, culture, and more

Back in January, we posted the below article on our hiring strategy. Since then, we’ve launched twelve searches and hired seven candidates, and in that time, we’ve learned quite a bit about our hiring needs and processes. 

The values laid out in the blog post still hold: we are dedicated to building a diverse (along multiple axes) team based on skillset and culture fit — not on inequitable metrics of success. 

But as we’ve grown, we’ve evolved our approach to better suit our needs. In the spirit of open science, here are our top four lessons and how we’ve adapted.

Hackathons are hard. The benefits of a (fully compensated) hackathon, as laid out below, are many, but so are the challenges. We were dedicating an immense amount of bandwidth to planning and coordination, and a global pandemic didn’t make this any easier. We decided to shift our approach to a skills-based assessment within the framework of a standard interview process for most roles. This still allows us to test candidates on their abilities and to unearth unexpected candidates, but without the complex superstructure of a hackathon. In particular, we’ve seen value in including screening questions at the initial application stage. The most effective questions are specific and practical, allowing the candidate to demonstrate their skills. This approach has given several qualified, mission-aligned candidates a significant boost. 


Passive recruitment doesn't cut it. To realize our workforce ambitions, we need to put in the work. We found that posting our job ads on our website and LinkedIn didn’t ensure a sufficiently deep and diverse candidate pool. We’re now incorporating an active scouting step in our recruitment, reaching out directly to the types of people we are hoping to attract. This active step involves searching LinkedIn, utilizing our advisors and their networks, scouring preprints, and more. While energy-intensive, this effort has paid off, bringing in excellent candidates and extending our own network.


One type of search doesn’t fit all. As we launched more job searches, we realized that they tend to fall within two distinct frameworks that require different workflows. To improve efficiency and to better align expectations with candidates, we now define these search types and their goals. This allows us to move quickly and precisely when needed, but also to be more open-ended and exploratory when appropriate. Both searches are important for building our dream team. 

An “Active Search” is where we have a clearly-defined and specific need and are actively looking to fill a position in the next two to three months. For these roles, we intend to move relatively quickly once we find the right candidate. 

An “Open Search,” on the other hand, is more flexible in terms of timing and skill set. We want to explore with a wide range of candidates the opportunity and scope of potential solutions. As such, this process is intentionally open-ended, and promising candidates should expect a dialogue about creative possibilities and mutual fit. Importantly, the job description may evolve as a result of these conversations and the timeline of these searches is highly variable. We do appreciate that this type of search was not the expectation of those who initially applied to the roles we have recategorized as “Open Searches” and may not be ideal for all job seekers, but we aim to be as transparent as possible on our shifting needs and associated timing.

This bifurcation allows us to fill the key roles we know we need efficiently, as well as leave the door open to serendipity and surprising results.


Scarcity is a mindset, and we should break it. It is certainly true that there is a hiring surge in biotech — particularly in the Bay Area — right now. Candidates are getting multiple offers and companies have to be competitive. Even still, many candidates, particularly those coming straight out of academia, don’t truly appreciate their value. We encourage job seekers to explore, apply, and interview at other companies, in addition to Arcadia. We believe that an empowered candidate, who understands their full range of possibilities, will likely be a better fit than one who thinks this is their only option. 

Our hiring strategy is an ongoing experiment and we will continue to refine our processes as Arcadia grows and our needs change. Hiring remains hard, but we will continue to iterate as our team is — by far — our biggest asset. 

Hiring is hard. Hiring at a new company that is trying to innovate in multiple dimensions and break free of traditional constraints is extra hard. But building our team is also the most essential and important activity for a company at our stage. We’ve got to find the people who can see the vision, who have the skills, who share our values, and who are ready to make big — sometimes scary big — steps forward. If we get hiring right, then everything else we try to do will be infinitely easier.

At Arcadia, we look at problems from first principles and question whether the inherited wisdom holds true when divorced from the confines of traditional institutions, like academia and biotech. In science, we’ve been so locked in to inequitable metrics of success (publication in specific journals, appointments at particular institutions, associations with certain labs, big name fellowships and awards, etc). However, these are not the right proxies for the type of scientist we need at Arcadia. We are actively challenging ourselves to ensure that we don’t rely on old metrics in our new system. 

We also recognize the incredible, untapped talent of people who never found their fit in traditional scientific institutions. We have to find new ways to unearth unexpected candidates, to work with candidates to determine if there’s a skills and culture fit, and to capture the wealth of ideas and solutions contained in our candidate pool.

With these goals in mind, we are setting up a number of experiments in hiring based on demonstrable abilities through hackathons. Hackathons have long been used as a hiring and talent discovery mechanism in the tech industry, but are far rarer in the biological sciences. Our hypothesis is that by creating shared projects that candidates work on alongside Arcadia scientists, we will get a far more realistic view of a person’s skills and workplace demeanor than the traditional interview process. Hackathons will allow us to explore challenging questions — like how to use bioinformatics to identify the right organisms to answer specific biological questions — with large groups of scientists with varied backgrounds and interests, giving us a better shot at finding a truly useful solution. Lastly, we want our hackathons to benefit all the participants, and not just the person eventually hired, so we are working to structure them so that everyone gains skills, connections, and resources, and are fairly compensated for their work.

We’ve already completed one successful hackathon — the results of which you can check out here — to find a cohort of science communicators to help us articulate and share the awesome science at Arcadia and beyond. We deem this hackathon a success, not only because we are absolutely delighted by the finalists and their final products, but because through this process we found communicators with a ton of potential and skills, but that don’t necessarily have a long list of previous professional writing credits at big name media outlets. Thus, this process allowed us to look beyond what would be assumed to be the most important metric for hiring and to find great candidates that fit the criteria that is actually the most important to us: the ability to do the work well. 

Of course, hackathons aren’t the answer to every recruitment need, and there are positions that aren’t amenable to this type of evaluation. We also acknowledge that devoting so much time to an interview process can be challenging for candidates, and to that end, we set up a survey to find out how we can make the hiring and hackathon process better for all parties.  

Hiring is still hard, and this is only the first iteration on a possible solution, but what’s important is that we are running the experiment, testing our assumptions, and looking for new answers to old problems.

We question whether the inherited wisdom holds true when divorced from the confines of traditional institutions, like academia and biotech.

One of the most frequent questions we get asked when talking about Arcadia is: “So if you aren’t studying model organisms, which organisms will you be studying?” 

The expectation is that we will give a list of species, like Chlamydomonas reinhardtii (a favorite of Prachee’s) or ticks (a focus of Seemay’s previous research). But this question and its expected answer misses a key point of what we are trying to achieve with our science program at Arcadia.

Our work is not to develop and anoint the next major model organisms. Our work is to better leverage an alternative to the model organism framework. Let us explain.

The first thing to acknowledge is that the model organisms (mice, yeast, roundworms, A. thaliana, fruit flies, etc) are incredibly powerful. The biological insights and technologies that have — and continue to be — derived from studying them as useful proxies of shared biological principles are absolutely essential to our current understanding of biology. Their tractability allows us to ask and answer a wide range of important questions about these shared biological principles.

But outliers are important too. The standard approach (in general) focuses heavily on evolutionary conservation, particularly that with humans, and overlooks biological exceptions. This perspective is not designed to take advantage of the awesome power of evolution, which has created an enormous catalog of biological innovation. By restricting research to our most common use of current model organisms — as models of human biology — we limit our ability to fully understand and tap into the vast richness and ingenuity of biology. 

This anthropocentric view implicitly assumes that if it isn’t like a human, then it is less useful to humans. But this of course isn’t true. Case in point: CRISPR. This incredibly versatile gene editing system — a biological technology — is now the foundation of a wide-range of novel therapeutics and companies. But CRISPR wasn’t found by studying lab-adapted E. coli strains on an agar plate, it was discovered by exploring the metagenomes of diverse bacterial species from environmental samples. The same is true for many other well-known enabling biological technologies, such as GFP (green fluorescent protein, found in jellyfish) and PCR (polymerase chain reaction, which uses an enzyme from an extremophile bacteria). 

Thus, the foundation of the Arcadia science program is to catalyze the shift from “model organisms” to “organisms as technology.” 

If we want to truly understand the technology that is biology, its design principles, and how best to leverage it, we need to look across the tree of life in a deep, intentional, and open-minded way.

If we want to truly understand the technology that is biology, its design principles, and how best to leverage it, we need to look across the tree of life in a deep, intentional, and open-minded way. We need to use the powerful tools developed in the model organisms, as well as create new tools for this purpose. 

Now, of course, we are not the first people to think up this approach to science or to argue for the benefits of comparative biology. We are standing on the shoulders of giants, and the viewpoint expressed here reflects hundreds of conversations with scientists and other builders. 

What is unique to Arcadia is the concerted effort, focus, and resources dedicated to the express purpose of harnessing organisms as technology. Critically, we are equally committed to both the discovery of these technologies and their translation into real-life tools. The ability to couple discovery and translation activities under the same roof supercharges these efforts, demonstrating that basic biology can form the basis of a viable business model. Lastly, these efforts are not just for the benefit of the scientists working at Arcadia, but for all researchers and for society.

We are at an incredible moment in scientific history when an array of distinct but complementary technologies for accessing and exploring diverse species are rapidly maturing, including genome sequencing, gene editing, microscopy, mass spectrometry, phylogenomics, machine learning, and many, many more. At Arcadia, our central aim is to develop research technologies that allow scientists to move nimbly between organisms to make the most of unique features to answer their question of interest, rather than providing a few more options on the model organism menu. And, of course, we will still be working with some of the classical model organisms at Arcadia. Our intention is not to cast aside or disparage these powerful systems, but instead to use them to their strengths to reveal new biological insights.

We believe that by combining our human-derived technologies with evolutionary-derived technologies, we can empower a new way to explore and utilize the wonders of biology.

The foundation of the Arcadia science program is to catalyze the shift from “model organisms” to “organisms as technology.”

One of the foundational elements of Arcadia is our mission to push the boundaries of open science and to develop new ways of sharing our knowledge and engaging with the broader scientific community. This is a big goal and to achieve it, we need to collaborate with those that are equally committed to open science. 

Lots of people are excited by the principles of open science, but transitioning out of the traditional, closed, and restrictive publishing system also requires a ton of hard work. That’s why we are proud to be partnering with the team at Incentivizing Collaborative and Open Research (ICOR). They are actively engaged in an array of open science experiments and initiatives that are moving the needle and are providing the key demonstrations that open science is better science. 

With ICOR we will be working on distributed experiments for collective gain. Some of the key areas of alignment between Arcadia and ICOR are in:

Developing Open Science Best Practices: As we develop our open science program, we will contribute to ICOR’s library of guidelines, sharing our approach, our documentation, and our learnings. 

Creating an IP Toolbox: We believe that open science and commercialization do not have to be mutually exclusive. Establishing a strong and creative IP strategy is essential for proving that open science can support and speed our commercial pursuits. We have already learned from the resources provided by ICOR and are working on developing agreements and means of tracking our progress, which we will share back with the community.

Building Research Output Management Systems (ROMS) and Using Persistent Identifiers (PIDs): Scientists have traditionally relied on journals and journal articles to house and disseminate their data, but the journal system wasn’t built with today’s diverse and ever-expanding datasets in mind. New systems are needed to share and organize scientific research. Arcadia is committed to using PIDs to facilitate discoverability and to depositing data in repositories that meet FAIR (Findability, Accessibility, Interoperability, and Reuse) principles. Working towards shared data schemas for all research outputs will help facilitate discussion, review, and reuse. 

Facilitated Collaboration: Collaboration is central to Arcadia’s success, and we aim to collaborate widely while maintaining our commitment to open science. We are in the process of developing our Collaborator Agreement, and will work with ICOR to share it, to track its success and any necessary revisions.

Modular Data and Review: It is current standard practice to release data and solicit peer review at the end of a project. We believe that releasing data more frequently and gathering and integrating community feedback more often and earlier in a project’s lifespan will accelerate science and produce better results.

Tracking Nano-Contributions: Author lists on journal articles do not accurately reflect a scientist’s contribution to a project and can promote territorialism and competition, rather than collaboration. Arcadia will be developing new methods for mapping contributions. These methods will provide a richer, more substantive picture of a person’s contribution, and ICOR will aid in measuring and tracking the success of these methods.

Metrics of Utilization: Knowing if and how people are using the data you produce is key to providing a valuable resource for the community. As we develop our ROMS, we will incorporate meaningful metrics to track utilization and will learn how to improve our data products to increase accessibility and reuse. 

In many ways, Arcadia is a full scale implementation of the ICOR initiative, with the exception of their laudable work to develop new rewards and incentives to promote open science practices within academia. At Arcadia, we have the opportunity to focus our science and our open science efforts more on maximizing utility, and less on personal career advancement as a metric of scientific contribution. We are excited to partner with ICOR to organize our experiments in open science to make them broadly useful outside of Arcadia.

With ICOR, we will be working on distributed experiments in open science for collective gain.

Arcadia and ICOR: Experiments in Open Science

If you talk to Prachee and Seemay about their vision for Arcadia, they’ll always emphasize that what we’re building and doing is not meant to be solely for the benefit of Arcadia itself, but for the greater scientific endeavor and community. So how do we ensure that the experiments we’re running — both in and out of the lab — maximally contribute to scientific progress? The answer is, of course, through communication. 

We need to share our scientific thinking, results, data, methods, and technologies in new ways that provide a richer, more dynamic resource than the established mode of scientific communication: journal articles. But even beyond our scientist-to-scientist communications, we need to share what we are learning and generating to a much wider audience — including scientists in (currently) unrelated fields, students of all ages, the scientifically curious, funders, philanthropists, public servants, and more — to truly make the most of the opportunity we have at Arcadia.

This is where our first hackathon comes in. We needed to find a team of people to help tell these science stories and to put them in an accessible context. In this post, we lay out how we’re using hackathons as a way to unearth talent and experiment with different modes of determining whether a candidate is the right fit for our company.

Our “sci comm” hackathon had three steps: first, candidates submitted their portfolios for assessment. Second, the top candidates were asked to create a short blurb (in their favorite medium) based on the Arcadia mission, to test their ability to describe biological phenomena engagingly and succinctly. Third, the final five candidates were asked to create a longer-form piece (again in their choice of medium) based on a recorded interview between Prachee and myself about Prachee’s research and model system, Chlamydomonas (you can watch the full interview here), to test their skills at identifying and developing compelling a narrative based on a conversation with an Arcadian scientist. 

We were so blown away by the creativity and skills demonstrated in each of the final submissions that we’ve decided to share them all, and more importantly, to continue working with all the finalists on a freelance basis to create more pieces on the amazing science being done both at Arcadia and in the larger non-model research community.

We did select one winner: Aaron Alcala. Aaron is a fifth year PhD candidate in Genetics at the University of Georgia where he studies how genes are regulated to build appendages (like limbs and external genitalia) during development. Aaron definitely understands the challenges — and vast benefits — of working with non-model organisms, as he’s worked with embryos of mice, lizards, snakes, turtles, and even alligators in the lab. In his spare time, Aaron produces educational content for use both inside and outside of the classroom, and enjoys photography, cooking, hiking, tennis, and playing with his cat, Tofu. Check out his awesome video submission “One Cell to Rule Them All: Chlamydomonas” on our YouTube page!

And check out all the final projects, presented here in alphabetical order. A huge thanks to all of the finalists, to everyone who applied and participated, to Arcadia staff for voting, and to Seemay and Prachee for, well, EVERYTHING! 

Kelly Montgomery is a Detroit native and currently a chemical biology PhD candidate at UCSF. She studies neurodegeneration, specifically how and why tau — a protein linked to neurodegenerative disorders — misfolds. When she’s not researching the fundamentals of tau protein aggregation, you’ll likely find her doodling in a coffee shop, or behind a camera capturing memories with friends. Check out her illustrated blog post “Keepin’ up with Chlamy: A single cell with infinite value”!

Justine Pinksey earned her PhD in Cell and Developmental Biology at the University of Michigan. She’s currently a postdoctoral researcher at the University of Texas Southwestern Medical Center, where she uses fancy microscopes to study tiny machines inside various weird and wonderful cells. Originally from Michigan, Justine enjoys being outdoors, makin’ stuff, and spending time with her fabulous family and friends. Check out her video “Actin up at Arcadia”!

Fayth Tan is a PhD candidate in biology at Caltech studying the evolution of animal regeneration. When not attempting to replicate 19th century experiments in the lab, they like to dabble in weird science history and literature. Check out their blog post “Actin, algae and answers in search of a question: a perspective on emerging systems in biology”!

Ana Wang grew up in Cocoa Beach, Florida and attended the University of Virginia in Charlottesville, where she majored in biochemistry. Her PhD is from Scripps Research and she’s developed proteomics methods for studying the human distal gut microbiome. Ana works full-time as a scientist and writer at a biotech in San Diego and part-time as a yoga instructor. Check out her blog post “Algal Architecture”!

How do we ensure that the experiments we’re running — both in and out of the lab — maximally contribute to scientific progress?

Arcadia’s First Hackathon: Science Communication!

The overarching goal of Arcadia is to develop tools and make discoveries in emerging research organisms. Particularly for scientists who explore new organisms, how they describe what they’ve done and share their results has a tremendous impact on their progress. They need to not only communicate their findings but also establish community and troubleshoot hurdles with a wide range of scientists. We are taking the opportunity of launching a new institute to rethink every part of the communication system Arcadia researchers use. Below are the core principles that are guiding this work.

The singular goal of Arcadia’s communication system must be to maximize the utility of our work. Current traditional “open” publishing, including rapid dissemination and barrier free access to work, is necessary but not sufficient to achieve this. We must also:

Diversify the ways that work is shared. We have ways of communicating and absorbing information today that are more diverse and powerful than those available when traditional journals were first established. Arcadia will develop technical and creative infrastructure to support new ways for scientists to communicate their ideas, methods, results and discoveries. We will recruit people whose job it is to help our scientists share their findings in the most effective way possible, and we will mentor our scientists to develop their own communication skills.

Diversify the types of information that we share. The fully digested stories found in traditional papers are an important part of scientific communication. But they are only part of it. Arcadia will therefore emphasize the sharing of observations, protocols, datasets, and experiments and analyses that didn’t work.

Share work quickly. Currently, the pace at which we communicate findings is at odds with the pace at which that information would be useful. We will make it easier for our scientists to share observations quickly, embracing the uncertainty this entails. 

Solicit as much feedback as possible. Utility is a high bar for rigor, and the fewer roadblocks there are to open feedback, the better. To catalyze lots of critical discussion from the broader community, we will promote transparent and more conversational feedback on all of our work, and ensure that this feedback is, like all of our communications, also public and barrier-free.

We believe that the best way to achieve these goals is to build new from these principles, rather than reform existing practices. Ensuring that Arcadia scientists have the space and incentives to fully innovate how they communicate requires a complete break from traditional academic publishing. Therefore, no work produced or funded by Arcadia will be published in journals. This is an ironclad, non-negotiable principle. Arcadia scientists will be evaluated on their ability to make their work useful to others. While we are still working out precisely what this will look like, we are committed to developing new communication tools to amplify utility, which will be available to all members of the global research community. We welcome everyone to join us in this endeavor and are excited to partner with others who are willing to experiment boldly with us.

Updated June 2, 2022: Our publishing platform and research can be found here.

We welcome everyone to join us in this endeavor and are excited to partner with others who are willing to experiment boldly with us.

We will pilot radical experiments in science communication to promote rigor, timeliness, accessibility, and reach

Publishing at Arcadia:
An Exercise in Maximizing Utility

As we embark on the exciting journey of setting up Arcadia Science, we feel incredibly lucky to have a chance to rethink how our structures serve our science, rather than the other way around. Like many of you, we see that we are sitting at a unique moment in history when some of us have the time and resources (accelerated in part by a re-awakening during a pandemic) to step back and re-evaluate how things are done. There is no better time than now to partner with more corners of our society to do this. We are taking this opportunity seriously. In coming weeks, we will be posting blog entries here that provide insight into some of our thinking. We will be trying some new things, which as all scientists know, will involve mistakes and course-correction. This is how we get better. 

We invite respectful dialogue as we challenge our own assumptions and biases.

Arcadia Science: An Emerging Research Organization

We are having the time of our lives over here dreaming big and recruiting awesome scientists to go on this adventure with. Read about our thought process and our next two hires here.

Can this tick biologist and her team of ‘weirdo science’ enthusiasts change the future of scientific research?

Can this tick biologist and her team of ‘weirdo science’ enthusiasts change the future of scientific research?.pdf

The 20(+1) under 40: Your guide to the next generation of biotech leaders

Latest blog posts

All blog posts

Press

We are Arcadia Science.