Data Science in Biotechnology

In the field of genomics, data science is being used to analyze variation in genetics and perform clinical and phenotype analyses. The National Human Genome Research Institute has a strategic plan for data science in order to modernize how data is used at the institute.

There are plenty of other biotechnology fields wrestling with big data.

In fact, when it comes to human microbes – the bacteria, fungi and viruses that live on or inside us – we’re talking about astronomical amounts of data.

Researchers at the Harvard Medical School are working to determine how many genes exist in the entire human microbiome. It is possible that the answer outnumbers stars in the observable human universe. This task of “microbial fingerprinting” could lead to breakthroughs such as more precise therapies.

Like genomics, there are also plenty of start-ups – Libra Biosciences, Vendanta Biosciences, Seres Therapeutics – looking to capitalize on new discoveries.

“Big Data is only going to be as good as the questions that are being asked of it. It’s the human element in the loop that’s able to interrogate that data.” 

– John Reynders, Big Data has arrived in biotech. Now what?

On the last day of February 1953, two men barreled into the Eagle Pub in Cambridge. “We have found the secret of life!” one of the men announced to startled patrons.

That, at any rate, is the myth surrounding James Watson and Francis Crick’s discovery of the structure of DNA. Though it ignores the contributions of Rosalind Franklin and others, it does hint at one undeniable fact – the incredible leap forward that biotechnology (and data analysis) took in the 20th century.

In 1919 Károly Ereky, a Hungarian agricultural engineer, first used the word in his book, Biotechnology of Meat, Fat and Milk Production in an Agricultural Large-Scale Farm. For Ereky, biotechnology was the means to upgrade raw materials biologically to reveal socially useful products.”

Large amounts of scientific data were, of course, integral to the development of the field. As the world of travel and telecommunications shrank, so too did the barriers to sharing information.

War accelerated the process. With a little help from Alexander Fleming and Clodomiro (Clorito) Picado Twight, a coordinated effort was mounted to mass-produce the wonder drug called penicillin. By 1943, scientists had discovered a moldy cantaloupe in Peoria contained the best strain for production. By 1944, 2.3 million doses were available for the invasion of Normandy.

Then along came genetics. In 1958, DNA was first made in a test tube. In 1981, scientists at Ohio University transferred genes from other animals into mice to create the first transgenic animals. A year later, the FDA approved the first biotech drug (human insulin) produced in genetically modified bacteria.

These discoveries were aided and abetted by advances in technology. In the mid-1970s, automated protein and DNA sequencing became a reality. A decade down the track, scientists could remotely access huge quantities of data stored in central computer repositories.

Many biotechnologists were eager to share their findings amongst colleagues. In 1977, Rodger Staden and his group at Cambridge developed the data-packed Staden Package for DNA sequences, initially available to academics, then eventually open source.

Over in the United States, the NIH was involved in sponsoring PROPHET, “a national computing resource tailored to meet the data management and analysis needs of life scientists.” PROPHET’s main attraction was “a broad spectrum of integrated, graphics-oriented information-handling tools.”

But it was in the years that Madonna reigned supreme that biotechnology and data analytics really hit their stride. Academic scientists, the NIH, the EMBL and large research funding centers poured their time – and their money – into new bioinformatic databases and software.

Highlights of this period include:

This explosion of data contributed to a slew of firsts for the biotechnology sector in 21st century. Industries seized upon the discoveries, pumping funds into the development of new drugs, bio-engineered farming and alternative energy.

Big-bang events in this period include:

In 2011, players of an online game called Foldit took three weeks to produce an accurate 3D model of the M-PMV retroviral protease enzyme. The structure of the enzyme – which plays an important role in the spread of an AIDS-like virus in rhesus monkeys – had eluded researchers for fifteen years.

In January 2012, gamers had another stunning success – the first crowdsourced redesign of a protein. By adding 13 amino acids to an enzyme that catalyzes Diels-Alders reactions, Foldit players increased its activity more than 18 times.

In a world of social networking sites, online communities and publicly funded projects, crowdsourcing has become an integral part of people’s lives. Forward-thinking scientists have begun to use this collective wisdom to advance their research and development goals.

They’re also partnering with private companies to access information. 23andMe made its name by offering a personal genome test kit. Customers provide a saliva sample, and the company supplies an online analysis of inherited traits, genealogy, and possible congenital risk factors.

Their ever-growing bank of digital patient data, including one of the largest databases on genes involved in Parkinson’s disease, has put them in a pivotal position of power. In past years, they have:

One data challenge for biotechnologists is synthesis. How can scientists integrate large quantities and diverse sets of data – genomic, proteomic, phenotypic, clinical, semantic, social etc. – into a coherent whole?

Many teams are busy providing answers:

With data sets multiplying by the minute, data scientists aren’t suffering for lack of raw materials.

What’s that phrase again? Every rose has its thorn? Well, in the field of biotechnology, every discovery has a caveat.

As AstraZeneca R&D Information Vice President John Reynders warns in Big Data Has Arrived in Biotech. Now What?, hypothesis-generation and predictive analytics are a little easier when you’re just trying to guess what books someone may prefer. Genomic data, on the other hand, is far more complex and extensive.

The volume, velocity and variety of data (3Vs) are creating similar headaches. When faced with an ever-growing mountain of information, it can take a great deal of human skill to understand what questions you need to ask and how best to find the answers.

In more prosaic terms, as Warp Drive Bio CEO Alexis Borisy notes on FierceBiotech: “Our clinical, phenomic data sucks.”

These aren’t insurmountable problems, but they’re big ones. As the 3Vs accelerate, biotech companies will likely have to be careful that they keep their minds open and their hubris in check.

Unlike Europe, the U.S. lacks an overarching data protection law. It does, however, have a great deal of federal and state legislation that affects companies who handle personal data. These laws and regulations can vary according to the industries involved.

Biotechnology companies who partner with health care providers, for example, may run into the Health Insurance Portability and Accountability Act (HIPAA). Enacted in 1996, the HIPAA Privacy Rule:

“…requires appropriate safeguards to protect the privacy of personal health information, and sets limits and conditions on the uses and disclosures that may be made of such information without patient authorization.”

Going one step further, the 2009 HITECH Act makes the HIPAA privacy provisions applicable to business associates.

Companies who intend to store personal data must also be aware of the rigid laws in place to protect U.S. consumers. The FTC has full power to bring enforcement actions to ensure that companies are living up to the promises in their privacy statements.

Last updated: June 2020