From fruit flies to silicon brains: the evolving idea of a model organism (part 1)
Research thoughts bite: 08.
What makes a model organism?
Biologists have long relied on so-called model organisms, species that stand in for the messy complexity of life. A good model organism is small, cheap to grow, easy to manipulate, and general enough to reveal rules that apply more broadly. During my time in a lab, Escherichia coli taught me how genes are switched on and off (and how to use it to express recombinant proteins). Drosophila melanogaster, the humble fruit fly, revealed the logic of inheritance. Caenorhabditis elegans gave us the first transparent look at how a multicellular organism develops, cell by cell.
The impact of these organisms has been so profound that many Nobel Prizes were awarded for discoveries made in them, such as the 1965 Prize to François Jacob, André Lwoff, and Jacques Monod for their work on genetic regulation in E. coli; the 1933 Prize to Thomas Hunt Morgan for his pioneering genetics work in Drosophila; and the 2002 Prize to Sydney Brenner, H. Robert Horvitz, and John Sulston for their groundbreaking studies of organ development and programmed cell death in C. elegans. Model organisms simplify biology but also open entirely new chapters of science.
Model organisms are, in short, stand-ins for complexity. And in the process, they’ve become more than species: they are intellectual tools. But what if the concept of the “model organism” is shifting? What if the newest models aren’t organisms at all?
The models of the 20th century
The great strength of the classical model organisms was that they were accessible: easy to grow, cheap to keep, and experimentally tractable. Escherichia coli and Bacillus subtilis grew in flasks by the billions, offering rapid ways to test hypotheses about genes, proteins, and replication. Saccharomyces cerevisiae and Schizosaccharomyces pombe could be coaxed into dividing or pausing, revealing the molecular clocks that govern cell cycles, insights later recognized with the 2001 Nobel Prize to Leland Hartwell, Tim Hunt, and Paul Nurse.
But these organisms were also representative, serving as windows into biological truths that extend far beyond themselves. In yeast, the cell-cycle machinery turned out to be conserved all the way to humans. Caenorhabditis elegans, transparent and barely a millimeter long, mapped out universal principles of development and programmed cell death, achievements honored with the 2002 Nobel Prize to Sydney Brenner, H. Robert Horvitz, and John Sulston. The fruit fly, Drosophila melanogaster, revealed the logic of developmental patterning through the discovery of Hox genes, a breakthrough awarded the 1995 Nobel Prize to Edward B. Lewis, Christiane Nüsslein-Volhard, and Eric Wieschaus.
Above all, these models were transformative. They did not merely answer existing questions: they opened entirely new fields. Arabidopsis thaliana made plant genetics predictive, turning a common weed into the Rosetta Stone of plant biology. And Mus musculus, the laboratory mouse, became the mammalian workhorse of genetics, physiology, and disease modeling, where gene targeting and knockout technologies (recognized with the 2007 Nobel Prize to Mario Capecchi, Martin Evans, and Oliver Smithies) transformed medicine by allowing scientists to recreate human diseases in a living system.
Together, these canonical organisms became the foundation of 20th-century biology, chosen not for their glamour but for their power as proxies: manageable windows into the complexity of life.
Broadening the spectrum: modern and emerging models
As biology advanced, the spectrum of model organisms expanded. The choices reflected not only curiosity but also new criteria: relevance to human health, novel traits, and the power of emerging technologies. Once again, the reasons these organisms were adopted can be understood as accessible, representative, and transformative.
They became more accessible thanks to new methods; transparent embryos, genome editing, and live imaging. Danio rerio (zebrafish) and Xenopus (African clawed frog) embody this perfectly: their embryos are large or transparent, easy to manipulate, and ideal for watching vertebrate development unfold in real time. Accessibility was also practical; zebrafish breed prolifically, making them cost-effective stand-ins for more complex vertebrates.
They were also representative, chosen because they illuminate processes we care about across biology. Planarians, with their remarkable regenerative powers, became symbols of stem cell biology and tissue renewal. Tardigrades, the tiny “water bears,” survive freezing, desiccation, and even space vacuum, representing the extreme limits of life and stress tolerance. Trichoplax adhaerens, one of the simplest multicellular animals, stripped down to just a few cell types, now represents a baseline for understanding how multicellularity works at all. Even the concept of the holobiont, treating host plus microbiome as a single system, emerged as a representative model for the complex symbioses that define life.
And they proved transformative. These new models did not simply add to the existing canon; they opened yet again new horizons of inquiry. Planarians challenged assumptions about aging and cellular limits. Tardigrades suggested new avenues for biotechnology and medicine, from radioprotection to stress resistance. Holobiont systems reframed organisms not as individuals but as ecosystems, transforming how we think about health, disease, and evolution.
Where the canonical models of the 20th century were about uncovering the shared fundamentals of biology, the modern and emerging models of today are about exploring its frontiers: resilience, regeneration, symbiosis, and complexity itself.
Beyond nature: synthetic biology as model systems
If the 20th century was defined by the discovery of natural model organisms, the 21st century has increasingly been shaped by the deliberate construction of new ones. Advances in genetic engineering, tissue culture, and synthetic biology have allowed researchers to adopt convenient species and to design model systems from the ground up.
One striking example is the creation of minimal synthetic cells, such as JCVI-syn3.0. By paring down the genome of Mycoplasma mycoides to 473 essential genes, scientists built the simplest possible cell capable of life. This stripped-down organism functions as a living testbed for understanding what is truly fundamental to cellular existence: essentially, a “designed bacterium” standing in for life’s core functions.
Another breakthrough came with organoids. By coaxing stem cells into forming miniature brain, gut, or retinal tissues, researchers created lab-grown models that mimic the architecture and behavior of whole organs. These organoids cannot replace entire organisms, but they allow scientists to study human development, disease, and drug responses in ways that classical model organisms never could.
Even more radical are cell-free systems, where transcription and translation are reconstituted outside of a cell (my last resort during lab days, when recombinant protein expression in E. coli just didn’t work). Here, life’s molecular machinery is uncoupled from the constraints of an organism altogether. These systems let scientists isolate and study gene expression in its purest form, free from the noise of cellular context.
Taken together, these synthetic models mark a turning point. Where fruit flies and worms once stood in for the complexity of multicellular life, today’s synthetic constructs represent a new philosophy: if nature’s organisms do not provide the model we need, we can build one ourselves.
The conceptual leap: could LLMs be model organisms?
From bacteria in flasks to synthetic genomes and organoids in culture, the history of model organisms shows a consistent theme: we choose proxies that make complexity visible. At first, these proxies were found in nature; more recently, they have been engineered in the lab. But what if the next step is neither natural nor biological?
Large language models (LLMs), such as GPT-based systems, offer a striking parallel. They are not alive, but they share the essential logic of a model organism: they are simplified, tractable systems that reveal something about a much larger, more complex domain. Just as E. coli stood in for the molecular logic of all bacteria, LLMs may stand in for the statistical patterns of human language and reasoning.
Like classic biological models, LLMs are accessible; open APIs and community-driven models make them easy to experiment with. They are representative, in that their emergent behaviours capture aspects of human knowledge and cognition, even if imperfectly. And they are transformative, opening new lines of inquiry in linguistics, neuroscience, and even philosophy, much as fruit flies or worms once transformed genetics.
In this sense, LLMs can be seen as the model organisms of cognition. They are not the real thing (just as no biologist mistakes a fly for a human) but they are revealing nonetheless. They allow us to probe questions about meaning, memory, and reasoning at scale, providing a “transparent worm” of sorts for studying intelligence itself.
The future of model organisms: biology meets AI
The story of model organisms is, at its core, the story of how science manages complexity. Each generation of models has reflected the tools and questions of its time. In the early 20th century, fruit flies and bacteria made genetics accessible. By the late 20th century, worms, mice, and plants stood in for development, physiology, and ecology. In the early 21st century, zebrafish, planarians, and tardigrades broadened the spectrum to resilience, regeneration, and symbiosis. Synthetic cells, organoids, and cell-free systems then marked a shift from discovery to design.
Now, with large language models and artificial systems, the concept itself is stretching. We may be entering an era where model organisms are not bound to biology at all, but instead serve as model systems for cognition, knowledge, and society. Just as mice were never perfect stand-ins for humans, LLMs are not perfect models of thought; but they may be good enough to illuminate hidden rules and generate new questions.
The future may bring hybrids: digital twins of biological systems, AI-designed synthetic cells, or organoid–machine interfaces where biology and computation feed back on each other. In such a landscape, the definition of a “model organism” expands beyond species, beyond cells, even beyond life.
From fruit flies to silicon brains, the thread is the same: we choose models not because they are complete, but because they let us see what was invisible before. Each new stand-in extends the frontier of what we can ask.. and of what we dare to imagine.