Innovation Spotlight: KBioBox Is Engineering the Future of Gene Sequencing

This is the fourth article for the new Innovation Spotlight series for the DCD Blog, where we interview some of the newest and most creative companies popping up in the Boston area. If you would like to be featured in our series, please email us at info@deepcoredata.com.

Spotlight Profile

Company: KBioBox
Interviewee:  Kryngle Daly
Website: https://www.kbiobox.com/
Facebook: https://www.facebook.com/kbiobox
Twitter: https://twitter.com/kbiobox
LinkedIn: https://www.linkedin.com/company/kbiobox

Disclaimer: Mad Science is my absolute favorite fantasy/sci-fi trope, so I may be completely biased when it comes to KBioBox.

Kryngle Daly, CEO and founder of KBioBox

Kryngle Daly, CEO and founder of KBioBox, had the sort of upbringing you’d expect from a scientific genius. He grew up learning how to program on Apple 2Cs, and he spent most of high school going to coding competitions. By the time he reached his senior year, he knew fifteen coding languages, and also, that the life of a programmer was not really something he was all that interested in.

So he went into physics instead. He did his undergrad research on String Theory Relativity, and when his grad school professors realized he could do high level math and programming, they chucked him head-first into biophysics. After deciding he didn’t like the “ivory tower” that was university life, he started his own contract research organization, and was introduced to the data-rich world of gene sequencing.

There he found that one of the biggest challenges in life science is that people aren’t interested in innovation. There are still so many problems to fix, so while scientists have been seeking solutions to new problems, many of the methods for old ones haven’t been updated since the 60’s. Daly wanted to find ways to improve on therapeutics because, “I like making new solutions that are robust and elegant rather than just working on things that are together with bubblegum and duct tape.”

One process specifically that cried out for modernization was DNA sequencing. According to Daly, the current problem with sequencers is that they’re getting better at producing more results, but there is currently no technology that can efficiently auto-analyze the vast swath of data, and doing it by hand takes forever.

Currently, when genomic sequencers run, they produce short strings of 125 nucleotides (that’s the ATP, GTP, CTP, and UTP molecules that make up DNA), and while you would think they’d come out all nice and neat in the same order they were fed into the sequencer…they don’t. So instead of translatable DNA sequences, you just get little genomic fragments that need to be pieced together and interpreted, and there’s no good way to handle it. Daly considers it a bottleneck across the board, regardless of whether scientists are sequencing bacteria or humans or hamsters.

Another big issue with these fragments is that they have a tendency to come out haphazardly; sometimes strands of nucleotides overlap, and sometimes the quality of the strand isn’t quite up to par. It’s not very good for gene sequencing, as you can imagine, since if a genome is not constructed in precisely the right way, things tend to come out a liiiiiiiitle out of whack.

Or you get a completely wrong diagnosis. Either way, these genomes need to be handled in the right order.

Unfortunately, current search algorithms just aren’t robust enough to handle the terabytes of data that make up a gene sequence. Even worse, they’re not precise enough, either. Daly used the example of searching for a “green Hello Kitty sword” on Google. You get a lot of Hello Kitty, you get some swords, but you can’t really find a green Hello Kitty sword (nevermind the fact that green Hello Kitty swords don’t actually exist).

…Except, apparently, as custom Attack on Titan skins.

However, when it comes to genomes, that “green” qualifier is just as important as the rest of the search phrase. If “green” is missing in the search, it could mean that you have some form of cancer, so the search algorithms can’t afford to overlook a single qualifier. Daly’s response to this was to create an algorithm that can not only search for all the qualifiers in a phrase, but can do so in less than a minute.

What does Daly want to do with this technology? Thankfully, he’s not actually interested in the mad science side of things, like designer babies and the ever adorable Owlcat.

No, he just wants to help.

“Y’know, things like cancer suck,” Daly says. “Growing up in the 90s, taking high school biology, you’re presented with DNA, and it’s like hey, it’s basically the programming language of the cell. Once we understood DNA, we would be able to do things! We should be able to fix things! Make people bigger, stronger, faster, whatever. And then you grow up, hit the real world, and you realize that we’re nowhere near that.

Who are adorable creations of genetic splicing? Owlcats are!

“So then, in my head was this idea. This should have been solved decades ago, why hasn’t it? …I’ll take a stab at it.”

His stab at solving the sequencer problem is pretty much set to benefit all of humanity, once it starts getting into the right hands. With how fast and cost-effective Daly’s sequencer (known as The BioEngine) is, it will be easy for doctors and pathologists to sequence everything in a patient’s blood, then take the file off the sequencer and compare the pathogenic genomes to the library of viruses and bacteria that could be making the person sick. Once they’ve identified the cause of a malaise, they can then use the patient’s DNA to determine the best and most effective treatment for them. He really hopes to use his algorithm to help reduce therapeutic development costs for pharmaceutical companies, which can make health care a lot more accessible and affordable to everyone.

Because his algorithm can be run on the cloud or on anyone’s local system, it can easily run the gamut from start-up to a global scale. But to be honest, he doesn’t even think of it that way; he’s much more concerned with improving just how much the research, biotech, and pharma groups can do. On the research side, KBioBox’s goal is to help scientists deal with the data that they currently can’t. If they can do better research with more data, they’ll be a step ahead when it comes time for technology development. This can help biologists perform good analyses without needing to love computers.

Before a lot of biotech startups are absorbed by pharma, they’ve got a limited budget for innovation. Daly wants to provide something similar for them where everything is faster, better, and more affordable. When it comes to the pharmacology industry, what they need are robust tools that let them build even bigger tools to streamline processes and give them a more affordable and efficient method for product development.

Ultimately, he’s hoping to see a fundamental shift. Right now, big data/informatics in medicine is being collected from the patients and then spitting sloppy statistics out. Daly’s sure that once the BioEngine starts getting put to use, we’ll start to see more data crunching and simulations, and can use bigger sets of data to understand the interactions between DNA, RNA, and nucleoid proteins. These will be aggregated with patient input to figure out more accurate and effective results, for more dimensions and vectors.

KBioBox and the BioEngine are on track to create a completely new standard for effective gene sequencing. Hopefully, it will be used to fight cancer rather than creating a wave of designer babies (because I’ve seen GATTACA and I’m no Ethan Hawke). But time will tell, and Kryngle Daly is ready to take on the challenge.

2017-02-21T09:40:27+00:00

About the Author:

Andrew is a technical writer for Deep Core Data. He has been writing creatively for 10 years, and has a strong background in graphic design. He enjoys reading blogs about the quirks and foibles of technology, gadgetry, and writing tips.

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