Sam Levin’s grandfather was a fifth-generation potato farmer in western Massachusetts who was struggling to compete with falling potato prices from industrial farms in the Midwest. After trying to grow many other crops, a set of melon seeds smuggled in from Puerto Rico seemed to do well and thrived in the sandy loam soils of the area. It was ripe just in time for harvest, until the crop died.
Levin is currently CEO and co-founder of Melonfrost, a Brooklyn-based evolution startup that combines proprietary software and hardware to guide evolution in an automated, closed-loop manner. The technology aims to provide new ways to design and produce new microbes at scale for everything from food and energy to therapeutics and synthetic materials. Growing that. “For us, metaphorically speaking, it means that frost-tolerant melons can be tailored to their intended use, rather than being completely subject to the whims of disasters such as frost,” he said. I will explain.
Various tools for propagating microorganisms with specific traits for desired uses have historically been constrained by their ability to scale — in transitioning from edited strains to commercialized strains. A bottleneck arises — many methods for doing so rely on relatively expensive and somewhat brute force guess-and-check approaches, usually based on mutations to the gene sequence. Instead, Melonfrost’s recent $7 million seed round, co-led by Refactor Capital and Alexandria Venture Investments, has, as Levin puts it, “evolution has long been the premier designer of biology. I support the proposition to continue.
Central to this focus on phenotypic selection are Melonfrost’s Evolution Reactor hardware and Maia, a proprietary software platform. Maia learns how an organism evolves (in terms of various selective pressures and environmental conditions associated with the measured phenotype) and repeats a series of instructions in the form of additional selective pressures to continue the desired evolution. A suite of machine learning algorithms that return A set of traits regardless of yield or frost resistance. This input/output data connects Maia to Evolution Reactor. The Evolution Reactor is a device for individually controlling, measuring and applying these encoded selective pressures to cultivate thousands of independent microbial populations on parallel evolutionary trajectories.
Large-scale evolutionary steering is enabled by a series of hardware innovations encapsulated in the Evolution Reactor’s series of modular units, each holding approximately 250 individual microbial populations. His two platforms, virtual and machine, are woven together by cloud software that closes the loop on automated evolutionary steering platforms. This means that the data measured by the hardware is fed to the software and instructions are passed back to the hardware through updating the modeling software. Either the goal is achieved or the loop is broken. While the entire system now largely fits in his Melonfrost’s Brooklyn lab space, Levin articulates this vision of the hardware-software interface as a “biological data center” in the form of an Evolution Reactor warehouse. is expressed in
This seed round is the next step towards the full form of this evolutionary steering system. Build the Evolution Reactor hardware and fund the next phase of moving Melonfrost towards its first customers in the food sector’s edible fat space. “Feeding the world without destroying it in the process, especially in the field of synthetic biology, has many bottlenecks from initial construction to production,” Levin emphasizes. A focus on building a healthier world through food is nothing new for Levin and his co-founder, head of engineering and design, and childhood friend Lauren Amdahl-Kaleton. While in high school, the two started a farm for their cafeteria, increasing student investment in learning communities and promoting sustainability. Despite spending undergraduate and graduate years separated by oceans and entire countries, the two stayed in touch from Oxford to Stanford, studying evolutionary dynamics and reinforcement learning, respectively, and filling the gaps in evolutionary models with machines. I started to realize the possibilities. A learning tool given the analogy of the underlying mathematics. Melonfrost was born along with his two other childhood friends. Born from the drive to make a positive impact by integrating different disciplines, from cutting-edge machine learning and hardware engineering, to synthetic biology and precision custom his software tools.
“Any one of these efforts requires a lot of expertise, mistakes, and innovation, so it’s somewhat unusual to do all of these things at the same time,” admits Levin. Working in multiple disciplines simultaneously, integrating very different kinds of scientists and engineers to really grow the future. The goal is not just to bring new molecules and chemicals to market. Rather, we need to fundamentally change the way the world’s resources are and move. Melonfrost’s goal isn’t to eventually build a big factory and ship it in freight containers. Instead, the vision is to produce and optimize new strains quickly, cheaply and robustly. It extends to large-scale production, from learning the language of evolution to large-scale translation into reliable biological output, no matter when the metaphorical frost comes.
Thanks to Aishani Aatresh for providing additional research and reporting on this article. I am the founder of her SynBioBeta and some of the companies I write for are SynBioBeta Conference When weekly digest.