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.

Unlocking the technological potential of biology through discovery and translation.

Applying an evidence-based approach to iteratively build a new type of science organization.

Confronting big challenges together — with purpose and relentless optimism.

Embracing open principles to advance knowledge faster and more rigorously.

We apply an evolutionary lens to accelerate purposeful discovery of nature's greatest innovations.

Arcadia is a science company.

We apply an evolutionary lens to accelerate purposeful discovery of nature's greatest innovations.

We are Arcadia Science.

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Arcadia Science is based in Emeryville, CA.