The holy grail Before going synthetic, dyeing clothes was like alchemy: it involved heating a cauldron of water, dye, fabric, and fixatives (often vinegar or urine) to bind pigments to the cloth.
“We’ve developed microorganisms that produce, deposit, and fixe dyes directly onto textiles, changing not just how dyes are made, but how they're applied,” he explains. “Doing things with biology means we can replace a lot of chemistry, reduce water and electricity inputs, and ultimately lower carbon emissions for this highly impactful industry.”
Using bacteria as small chemical factories begins with a comprehensive literature review and data analysis of pigments that exist in nature. For example, because fruit flies are one of the most studied model organisms, scientists have already characterized which genes map to different colors, allowing companies like Colorifix to use this data to source this pigment at scale.
However, it’s also critical to understand the underlying chemistry itself. Parameters that may not have been included in the training data, such as the water source or growth media, can dramatically change the resulting colors. In addition, each fabric has a unique chemical composition; therefore, the same process can create different colors on different textiles. For example, one bacterial strain can result in pink nylon and gray cotton.
Mihails Delmans, the head of informatics at Colorifix, describes the company as one that sells data that “represents the real physical world in the most clear way.” Artificial intelligence has the potential to grasp properties that humans cannot understand, which is particularly helpful in predicting the outcome. As more data becomes available, the process scales and improves naturally, representing information that is increasingly relevant to consumers.
These partnerships are paying off. Last year, Colorifix raised £18 million after a Series B funding round led by H&M’s group investment arm, CO:LAB. In the synbio textile dyeing space, Colorifix is joined by other startups such as
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