![]() The fungal drug discovery field is particularly hot with competitors differentiating on how quickly and accurately their algorithms can spot potentially useful sections of DNA. In the last 18 months they’ve raised $8 Million from private investors. These can be used to redesign a yeast with the press of a button. It’s also using a technology making it much faster to synthesize DNA by essentially downloading and printing copies of gene clusters. In addition to their proprietary algorithms Hexagon has moved to utilize the most efficient tools of the trade like DNA sequencing and automated workstations. They currently have roughly 22 compounds that show clinical promise. They then fit their test microorganisms with custom-printed DNA parts to produce likely compounds that might, for example, attack cancer cells. Hexagon mines the fungal genome of over 2,000 species of mushrooms and molds to predict which gene clusters are most likely to produce useful compounds. But discovery of new compounds has been largely haphazard and based on researcher’s intuition. Hexagon Bio: Some three-quarters of antibiotics and half of anticancer compounds, including penicillin and statins came from naturally occurring fungi (you know, mushrooms and molds). Here are a few snapshots of what’s underway. Needless to say, in materials published so far the innovators in this field have been shy about saying much about their proprietary algorithms other than that are based on deep learning. This feels like the age of deep learning in about 2010, still three years out from having image classification or speech recognition hit the 95% accuracy rate that ushered in 10,000 new AI startups and applications. ![]() Only 222 are identified as bioinformatics and only a portion of these are pursuing CSB. A little over 5,000 are targeting ‘Big Data’ and another 5,000 are categorized as ‘Analytics’. To give you a sense of how new and wide open this field is, the website which tracks the formation and investment in startups lists a little over 4 Million startups, the great majority of which are related to tech. Starting with the explosion in deep learning capabilities just two or three years ago, the first visionary biologist/data scientist teams began to explore how to exploit these new synergies in seemingly unrelated disciplines. For example how to assemble a full genome model or mark specific areas of DNA using SNPs (single nucleotide polymorphism) of which there are about 10 million in the human genome.ĬSB is Not Bioinformatics Business as Usual. Just as deep learning has had to wait for MPP and the use of GPUs to sufficiently accelerate compute, CSB remained mostly a concept through the decoding of the human genome in 2003 followed by the explosion of genomic data in the ensuing 15 years.Įarly bioinformatics attempted to solve problems appropriate for the beginning stages of our understanding of genomics. The discovery and use of restriction enzymes in 1978 is sometimes cited as the first use of engineering concepts in biology. Like most important innovations CSB wasn’t born yesterday. Perhaps the better way to frame this is which would you rather be working on, facial recognition to label your friends faces in Facebook, creating chatbots for that travel platform, or working to cure cancer and extend quality human lifetimes. To the data scientist and particularly the start-up world CSB is a newly emerging field that will capitalize on advances in deep learning.ĭepending on your personal sense of priority, CSB will remarkably accelerate cures to some of mankind’s most intractable diseases or be the foundation for the next generation of unicorns in the time frame of 5 to 7 years. Data scientists with deep learning skills will want to check this out.Īnd the next big thing in data science is (wait for it) – biology! Actually Computational Synthetic Biology (CSB) sometimes referred to as ‘computational systems biology’ or simply ‘synthetic biology’.įrom the biological researcher’s perspective CSB broadly refers to the design and fabrication of biological components and systems that don’t already exist in the natural world or to the redesign and fabrication of existing biological system. Big advancements and big investments are already starting to occur here. As the name implies, this lies at the intersection of data science and biological research. Summary: Computational Synthetic Biology (CSB) is likely to be both the next big thing and perhaps most important field to exploit data science.
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