BIOSYNTH – Artificial DNA for Mass Data Storage

The exponential growth of data: A look into the future

As we navigate through the digital age, the volume of data being generated is expanding at an unprecedented rate. Researchers predict a staggering rise to 284 zettabytes by 2027, highlighting the urgent need for innovative storage solutions to accommodate this data deluge.

Traditional storage systems are struggling to keep up with the massive amounts of data being produced, fueled by the widespread adoption of artificial intelligence technologies. In response to this challenge, three Fraunhofer Institutes are spearheading the development of a groundbreaking solution: microchip-based artificial DNA storage.

When considering the vast amount of information encoded in our own DNA, it becomes clear why researchers are turning to artificial DNA strands for binary storage. The fundamental building blocks of DNA – guanine (G), thymine (T), cytosine (C), and adenine (A) – can be leveraged to store data in a highly compact and durable format. By translating binary code into sequences of A, C, G, and T, data can be written onto the microchip with the added benefit of error-checking algorithms.

The Biosynth project represents a pioneering initiative aimed at developing a Modular High-Throughput Microplatform for Future Mass Data Storage Based on Synthetic Biology. With a focus on long-term objectives, the project seeks to revolutionize data storage capabilities through the convergence of biology and technology.

According to the researchers involved in the project, the ultimate goal is to create a portable, energy-efficient platform that can replace the bulky synthesis systems currently in use. By harnessing the principles of high integration and series production processes from the field of microelectronics, the team aims to enable commercial biologically based data storage. This innovative microchip platform is designed to write software-defined nucleotide sequences (DNA, RNA, or peptides) with a strong emphasis on high throughput, energy efficiency, and cost-effectiveness.

Source: Fraunhofer