In a strange, neon green lit corner at MIT, a group of engineers, artists, biologists and self proclaimed mad scientists are gathering together to build things that sound like science fiction.
They are engineering glowing bacteria with robots, building DNA circuits and neural networks inside of cells. They are tinkering with genetic codes and building experimental biomachines and living materials.
No this is not a secret government lab. It is a class at the MIT Media Lab called How to Grow Almost Anything.
To understand how we got to growing almost anything, we have to look back at an epic lineage of ideas that emerged in the 20th century, starting right here at MIT.
In 1937 Claude Shannon’s MIT master’s thesis, regarded by some as the most important master’s thesis ever written, showed that logic could be encoded in binary systems. Around the same time, Alan Turing formalized the meaning of computation, showing that a machine could follow rules to perform any calculable operation. Shannon then gave this abstraction a physical form, showing how logic could be built into electric circuits.
Fast forward World War II, information was no longer just something spoken or written; it could be encoded, moved through wires, and stored in machines. Allied code breaking at Bletchley Park showed that machines could alter the fate of nations, processing information at unprecedented speed to break German ciphers and help the Allies win the war.
As the war drew to a close, John von Neumann defined the architecture of the modern computer: a central processor, memory and instructions stored alongside data. Two years later, in 1947, John Tukey gave this new unit of reality its name: the bit. That same year, deep inside Bell Labs, William Shockley, John Bardeen, and Walter Brattain invented the transistor: the tiny switch that made digital logic scalable. Together, these breakthroughs laid the foundation for the Information Age.
But these same ideas were also beginning to transform how scientists understood life. In his 1944 book What Is Life? Erwin Schrödinger argued that life must depend on some physical way of storing and passing on hereditary instructions from one generation to the next. A few years later, Von Neumann introduced the idea of self-reproducing automata in his 1948 lecture at Caltech, imagining machines that could contain instructions to build copies of themselves.
Around the same time in 1953, Watson and Crick’s discovery of DNA’s double-helix structure revealed the physical form of this biological code. Life was no longer understood only as chemistry, but as an encoded system: information written into molecules, copied across generations, and translated into living form. Crick later sharpened this idea through the central dogma of molecular biology, describing how information flows from DNA to RNA to protein. By the mid 1950’s, John McCarthy coined the term artificial intelligence.
It is no coincidence that the worlds of computation and biology began to converge. Once computation was understood as the manipulation of information, it became natural to ask whether life itself could be understood in similar terms. Could cells compute? Could DNA be programmed? Could biology become a design platform?
Over time, researchers began to see the sciences less as isolated disciplines and more as interconnected fields moving toward the same frontier: artificial life, self-replication, programmable matter, and the possibility that computation could move beyond screens and silicon into the physical world.
In 1959, Richard Feynman pushed this vision even further in his famous lecture, There’s Plenty of Room at the Bottom. He imagined a future in which humans could arrange atoms with precision. Biology had already proven that molecules could store instructions and build living systems. Feynman suggested that humans might one day learn to work at that same scale, turning matter itself into something programmable, from bits to atoms.
Decades later, this vision began to take material form through people like Neil Gershenfeld at MIT’s Center for Bits and Atoms. Gershenfeld gave the idea its modern twist through digital fabrication, where information becomes physical objects and ordinary people learn to build almost anything. His iconic course, How to Make Almost Anything, grew out of this world.
George Church carried the same logic into biology, building on the central dogma of molecular biology by treating DNA not only as something to read, but as something to write, edit, and program. His work helped transform DNA from a code scientists could decode into a medium they could program, contributing to the rise of modern synthetic biology. Joseph Jacobson pushed the idea into programmable matter, inventing E Ink and exploring how computation could be embedded directly into materials themselves.
Together, they created another strange and ambitious course at MIT: How to Grow Almost Anything.
Today, HTGAA is one of the most unique classes at MIT: a living continuation of ideas that trace back to the foundations of information theory, computer science, and molecular biology. It is a melting pot of people from radically different backgrounds all coming together to build weird, wonderful, ambitious, and sometimes slightly absurd projects that challenge how we think about life, matter and computation.
Hi, I’m Sami, a graduate student at MIT working on plasma physics. I started working at Goldman Sachs out of high school before coming to MIT, where my work has taken me from research on magnetic confinement simulations for fusion reactors to nano-fabrication, robotics and AI research. At the core, my interests sit at the intersection of computation, hardware, and the physical sciences: how information becomes matter, how code becomes machinery, and how we can use engineering to better understand and redesign the physical world.
This semester, I took How to Grow Almost Anything, and it quickly became one of the most ambitious and deeply hands-on educational experiences I have ever had.
Each week introduced us to a different frontier of modern biology and bioengineering. We were not just reading about synthetic biology, we were designing DNA, thinking about how to write and edit biological systems, working with lab automation, exploring protein design, building genetic circuits, learning about cell-free systems, imaging technologies, mass spectrometry, bioproduction, and automated cloud labs. The format was intense and inspiring: a lecture from leading experts, recitation, and then a lab session where the ideas became real through experiments, protocols, failures, discussions, and late-night documentation.
A major part of the class was the final project, where we had to take the tools and ideas we had learned throughout the semester and push them toward an original biological device or system. This was one of the coolest parts of HTGAA: it was not just a survey of modern bioengineering, but an invitation to actually imagine and build with it. MIT generously supported us with access to equipment, lab space, reagents, and even custom-designed DNA plasmids that could be ordered and used in our experiments. In a very real sense, the class gave us the tools to move from what if to let’s try to grow it.
For my final project, I wanted to bring together my interests in magnetic confinement systems used in fusion and synthetic biology. I ended up working on magnetically controlled neuromorphic computation: a project exploring whether magnetic fields could be used to spatially control biological signalling and computation inside engineered mammalian cells. In simple terms, I was asking whether we could use magnets as a remote control for living cells. Check it out here! https://pages.htgaa.org/2026a/sami-syed/projects/individual-final-project/index.html
The project was very ambitious for a single semester, but that was exactly what made it so exciting. It pushed me to think across scales: from reading cutting-edge papers and finding my own angle, to editing DNA plasmids, designing synthetic receptors, working through protein conjugation chemistry with magnetic nanoparticles, building magnetic actuation devices, and using fluorescence, flow cytometry, and microscopy to understand what was actually happening. It felt like exactly the kind of project HTGAA was built for: slightly crazy, technically difficult, deeply interdisciplinary, and only possible in an environment where biology, hardware, computation, and imagination are allowed to collide.
What also makes HTGAA so special is that it is not just a class at MIT. It is a global classroom. Led by David Kong, the course brings together students at MIT and Harvard with a much larger international community of global learners, nodes, and community labs around the world. It felt like a living example of what MIT and the Media Lab do at their best: democratizing access to frontier knowledge, decentralizing expertise, and inviting people from radically different backgrounds to participate in the future of science.
One of the most meaningful parts of the class for me was how open and accessible it felt. In fact, I was so excited by the course that I got my sister involved as well. She took HTGAA from the London node through Lifefabs, while I took it at MIT. Catching up with her each week, comparing what we had learned, and talking through the lectures and lab material virtually became one of my favorite parts of the semester. It was a surprisingly personal way to bring my family into the MIT network and to share in the strange, wonderful world of synthetic biology together.
The course was also made special by the people around it. Learning from figures like George Church and Joseph Jacobson, alongside David Kong’s energy and vision, gave the class a sense of history and ambition. But just as important were the TAs and course staff. Ronan’s incredible bio artwork experiments and a living gallery of MIT creations was awesome, https://opentrons-art.rcdonovan.com/gallery.
I spent many days working with Evan on my final project at the Weiss Lab and learnt so much. Alex, Suvin, Ice, Ren, and the rest of the team were there each week in the labs, answering questions, debugging protocols, and turning confusing ideas into chalkboard sessions, conversations, and actual experiments.
HTGAA is one of a kind because it is patient with people from any background while still being intellectually fearless. It invites you into biology not as something fixed and distant, but as something you can learn to read, write, design, automate, measure, and grow. For someone like me, it opened up an entirely new way of thinking about engineering. It made biology feel less like a separate discipline and more like the next great medium for building.
