Centuries-old genetic material can solve historical mysteries, from lost species to what killed Napoleon's army. A glowing, digital double helix represents the billions of base pairs scientists analyze when sequencing ancient DNA. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1976, workers excavating a tunnel for the Toronto subway system came across some very old bones. Using radiocarbon dating, researchers determined the partial cranium and fragments of antlers were roughly 12,000 years old.

Can AI create a life form? These "generative" genomes are a start Artificial intelligence can draw cat pictures and write emails. A research team in California says it used AI to propose new genetic codes for viruses--and managed to get several of these viruses to replicate and kill bacteria. The scientists, based at Stanford University and the nonprofit Arc Institute, both in Palo Alto, say the germs with AI-written DNA represent the "the first generative design of complete genomes." The work, described in a preprint paper, has the potential to create new treatments and accelerate research into artificially engineered cells. It is also an "impressive first step" toward AI-designed life forms, says Jef Boeke, a biologist at NYU Langone Health, who was provided an advance copy of the paper by .

Synchronization errors, such as insertions and deletions, present a fundamental challenge in DNA-based data storage systems, arising from both synthesis and sequencing noise. These channels are often modeled as insertion-deletion-substitution (IDS) channels, for which designing maximum-likelihood decoders is computationally expensive. In this work, we propose a data-driven approach based on neural polar decoders (NPDs) to design low-complexity decoders for channels with synchronization errors. The proposed architecture enables decoding over IDS channels with reduced complexity $O(AN log N )$, where $A$ is a tunable parameter independent of the channel. NPDs require only sample access to the channel and can be trained without an explicit channel model. Additionally, NPDs provide mutual information (MI) estimates that can be used to optimize input distributions and code design. We demonstrate the effectiveness of NPDs on both synthetic deletion and IDS channels. For deletion channels, we show that NPDs achieve near-optimal decoding performance and accurate MI estimation, with significantly lower complexity than trellis-based decoders. We also provide numerical estimates of the channel capacity for the deletion channel. We extend our evaluation to realistic DNA storage settings, including channels with multiple noisy reads and real-world Nanopore sequencing data. Our results show that NPDs match or surpass the performance of existing methods while using significantly fewer parameters than the state-of-the-art. These findings highlight the promise of NPDs for robust and efficient decoding in DNA data storage systems.

Recent years have seen an increasing amount of work (some of which is also covered in this book) on implementing machine learning methods in physical systems, and the concept of intelligent matter [18] is closely related to this idea. While many approaches of this type employ electronic, magnetic, or photonic systems, it is in principle a relatively natural idea to use soft and biological matter as a basis for physical neural networks. After all, artificial neural networks are inspired by the brain, and the brain is a soft matter system. DNA, the carrier of genetic information, naturally suggests itself for such approaches. It is a soft matter system that has evolved specifically for the purpose1 of storing and processing information.

The new method, published in Nature last week, is more efficient, storing 350 bits at a time by encoding strands in parallel. Peking University's Long Qian and team got the idea for such templates from the way cells share the same basic set of genes but behave differently in response to chemical changes in DNA strands. "Every cell in our bodies has the same genome sequence, but genetic programming comes from modifications to DNA. If life can do this, we can do this," she says. Once the bricks are locked into their assigned spots on the strand, researchers select which bricks to methylate, with the presence or absence of the modification standing in for binary values of 0 or 1.

The rapid growth of digital data, projected to reach 180 zettabytes by 2025, is causing a data storage crisis that cannot be addressed by existing storage technologies [Rydning, 2022]. In response, deoxyribonucleic acid (DNA) is emerging as a promising alternative storage medium due to its incredible density and durability. The DNA storage process includes four stages illustrated in Figure 1: (1) an "encoding" stage in which binary data files are encoded into DNA strands (design files) using error-correcting code (ECC) [Koblitz et al., 2000] schemes that may also overcome errors, (2) a "synthesis" stage, which produces artificial DNA strands of each design strand and are then stored in a storage container [LeProust et al., 2010], (3) a "sequencing" stage [Anavy et al., 2019, Erlich and Zielinski, 2017, Organick et al., 2018, Yazdi et al., 2017] which translates a DNA strand into a digital sequence known as a "read", and (4) a "retrieval" stage where reads are decoded back to binary data files while correcting any errors using the chosen coding methods. Despite the vast potential of DNA storage, current DNA sequencers are yet to overcome challenges such as low throughput and high costs compared to the traditional alternatives [Alliance, 2021, Shomorony et al., 2022, Yazdi et al., 2015]. The emerging Nanopore technology offers real-time sequencing of DNA strands with drastically lower costs and portability compared to traditional Illumina sequencing machines [Jain et al., 2016, Kono and Arakawa, 2019]. Despite having higher error rates compared to other sequencing technologies such as Illumina, Nanopore sequencing is gaining significant attention due to its lower cost, portability, and capability to sequence longer strands of DNA.

A liquid computer can use strands of DNA to run over 100 billion different simple programs. It could eventually be used for diagnosing diseases within living cells. Fei Wang at Shanghai Jiao Tong University in China and his colleagues set out to make circuits similar to those on a computer chip, except with DNA molecules acting as wires and instructing the wires to configure in certain ways. When you enter a command on a conventional computer, it instructs electrons to flow through a specific path on a silicon chip. These circuit configurations each correspond to different mathematical operations – adding functions to chips means adding such paths.

The concept of DNA storage was first suggested in 1959 by Richard Feynman who shared his vision regarding nanotechnology in the talk "There is plenty of room at the bottom". Later, towards the end of the 20-th century, the interest in storage solutions based on DNA molecules was increased as a result of the human genome project which in turn led to a significant progress in sequencing and assembly methods. DNA storage enjoys major advantages over the well-established magnetic and optical storage solutions. As opposed to magnetic solutions, DNA storage does not require electrical supply to maintain data integrity and is superior to other storage solutions in both density and durability. Given the trends in cost decreases of DNA synthesis and sequencing, it is now acknowledged that within the next 10-15 years DNA storage may become a highly competitive archiving technology and probably later the main such technology. With that said, the current implementations of DNA based storage systems are very limited and are not fully optimized to address the unique pattern of errors which characterize the synthesis and sequencing processes. In this work, we propose a robust, efficient and scalable solution to implement DNA-based storage systems. Our method deploys Deep Neural Networks (DNN) which reconstruct a sequence of letters based on imperfect cluster of copies generated by the synthesis and sequencing processes. A tailor-made Error-Correcting Code (ECC) is utilized to combat patterns of errors which occur during this process. Since our reconstruction method is adapted to imperfect clusters, our method overcomes the time bottleneck of the noisy DNA copies clustering process by allowing the use of a rapid and scalable pseudo-clustering instead. Our architecture combines between convolutions and transformers blocks and is trained using synthetic data modelled after real data statistics.

Caltech scientists have recently developed an AI made out of DNA that can tackle a classic machine learning problem by precisely recognizing written by hand numbers. The work is a critical advance in showing the ability to program AI into engineered biomolecular circuits. Lulu Qian, assistant professor of bioengineering at Caltech said, "Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable. Similar to how electronic computers and smartphones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come." Scientists' goal behind this study is to program intelligent behaviors (the ability to compute, make choices, and more) with artificial neural networks made out of DNA.

Scientists have developed a neural network using DNA, which can correctly identify numbers encoded in molecules using machine learning, an advance that may pave the way for biological machines with artificial intelligence. Artificial neural networks are mathematical models inspired by the human brain. Despite being much simplified compared to their biological counterparts, artificial neural networks function like networks of neurons and are capable of processing complex information. The ultimate goal for this work is to programme intelligent behaviours (the ability to compute, make choices, and more) with artificial neural networks made out of DNA. "In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible," said Lulu Qian, assistant professor at California Institute of Technology in the US.