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Bina Is Working to Make Personalized Medicine a Reality


Posted in Medical Data by Brian Buntz on January 25, 2013

Genomic analysis startup Bina Technologies (Redwood City, CA) has emerged from stealth mode and is planning its first official commercial launch in February. “Now, we have a proof of concept and a pilot product on the market,” says Bina’s CEO, Narges Bani Asadi. “We have been testing the product, validating it, and now we are getting ready for our official commercial launch.”

Bina BoxThe firm grew out of a multidisciplinary research project spanning three departments at Stanford and another at UC Berkeley, which project brought together computer science, electrical engineering, statistics, and medical researchers. While working towards a PhD in electrical engineering at Stanford, Asadi began working on developing a computing and data analysis platform for systems biology. “I got fascinated by learning about biology and how it is related to Big Data analysis,” she recounts.

While at Stanford, Asadi won a multi-million dollar grant from the National Cancer Institute for her research. “The goal was to build a graphical model that encodes causal relationships between proteins and how the interactions get disrupted with cancer,” she says. “The goal was to understand cancer at the molecular level.” Asadi’s research team went on to win several awards for their research and eventually started thinking about forming a startup to support the burgeoning field of genomic medicine.

A little over a decade ago, generating a genomic sequence remained a huge challenge, taking years to accomplish and hundreds of millions of dollars. “Now the challenge is not generating the data,” Asadi says. “The bigger challenge is how do you interpret the data. What does the genome mean? That is a Big Data problem,” she adds. “You need to look at the genome and put it in the context of many genomes correlated with diseases, treatments, outcomes, and so on. We look at it as both a statistical problem as well as a computational problem that needs the brightest minds in computer science and statistics.”

With that conclusion in mind, the Stanford researchers began working on a project that would evolve into its Bina Box product. Asadi describes it as a “self-contained plug-and-play solution for genomic analysis.” The platform takes the lowest form of information that the sequencing machines output assembles them, determining how they compare to a reference genome.

The platform is being piloted at several institutions. “One of the customers is the Stanford University’s genetics department. “Without our solution, it used to take them weeks to do the analysis. Now it takes a matter of hours. It has been a one-hundred-fold improvement.”

“What Bina has elected to do is to tackle some of the biggest and hardest problems first, which is what do you do with these billions of short reads that are coming off the sequencers today and the goal there is to assemble those into high quality finished genomes,” explains Mark Sutherland, the company’s senior vice president, business development. “That is a problem that we have largely solved,” he says. “The next challenge we want to do is tertiary analysis where you assemble many genomes and put them in the context of each other through a database environment where the key traits of each of those genomes will be compared with each other and correlated with diseases, patient groups, symptoms, etc.,” he adds. “

“There has been a lot of discussion beyond the outset of personalized medicine; really it has been waiting for two things: costs to drop, and that is largely happening; the other is for the information to be manageable, portable, and mineable,” Sutherland says. “And that largely hasn’t happened yet. That is the Bina opportunity.”

Brian Buntz is the editor-at-large at UBM Canon's medical group. Follow him on Twitter at @brian_buntz.

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