WIRED Futurist vs Geneticist Part 1
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Futurist vs. Geneticist: Hacking Genes Like Software

The future is coming at us very fast, but not everyone agrees on what it will look like when it gets here.

For some, the future involves hacking genes like software. While many dream about where the horizons of medicine and health technologies will lead, we decided to get a reality check on five predicted breakthroughs from both a well-known geneticist and a futurist in this two-part series.

While neither expects the imminent realization of a sci-fi future where people are genetically pre-programmed, both offered optimism, as well as cautionary words, about the dramatic changes we are about to see. We also want to know what you think, and invite you to jump in with your thoughts via the poll question following each scenario.

Futurist vs. Geneticist: Hacking Genes Like Software
Left: Professor of Medicine and Genetics at the Albert Einstein College of Medicine, Director of The Institute for Aging Research; Right: CEO of Six to Start, author of A History of the Future in 100 Objects, former neuroscientist.

Hacking Genes and DNA Like Software

SCENARIO: Human data mining is expected to become the ultimate coding project in the near future. That may play out in a few different ways inside the bodies of tomorrow: through the mapping of your genome, understanding your microbiome, or even analyzing your cardiovascular state. Either way, the possibility of the data contained in your genetic material becoming as modular and malleable as software—complete with features fit to be toggled on and off—will come with its share of advantages and concerns.

Geneticist Nir Barzilai perspectiveThis has great promise theoretically, but we still are a long way from finding what is going on and hacking our DNA like software. Currently, the DNA sequence of an individual can be accessed, but we still can’t explain why one gene is related to a particular disease. What’s missing is epigenetics, which is the mechanism by which the environment interacts with the gene. This scenario of “DNA hackable as software” doesn’t address that. To unfold the many mysteries of epigenetics is a huge task. We are making progress but we’re on the frontier and the study is relatively young. Our biology carries a lot of information and holds many mysteries, and we’re still trying to solve that puzzle.

Futurist Adrian Hon perspectiveOur DNA is certainly more hackable than it used to be. In the old days, gene therapy could be pretty dangerous but with things like CRISPR, it’s more do-able. There are trials going on using CRISPR to alter DNA to cure very specific single gene-related diseases and that’s exciting. Still, it’ll take a while before it’s hackable as software. Some things are going to be easier to change, and some will be harder.  But we humans are influenced by a large number of our genes and we don’t fully understand all of that. So this seems more far off. I do wonder, however, that when it’s easier to do, whether people will want to hack themselves. Some people will want to change DNA so they can run faster or have greater lung capacity. That could be useful.

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When we hack human DNA there may be unforeseen downsides and consequences. Do the potential advantages outweigh the risk?

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Full-Blown Genetic Medical Histories

SCENARIO: Examining your genetics as part of the history process has the potential to reveal various qualities and unique aspects of your biological makeup – especially heart health. Current big data research is leading to a future where unlocking the data in a full genome sequencing will alert tomorrow’s cardiologists to the hidden risk factors facing their patients. That’s only a small slice of what the data going into your giant genetic permanent record will be capable of, including hacking genes.

Geneticist Nir Barzilai perspectiveWe have so much biological data that we’re pulling in from so many places. Now we have to figure out how to weave all the data together, so we can learn how one bit of data works with the other. Then we can learn and decide what is important and what is not. What bit of information leads to insights and which aren’t helpful. This is the great challenge for data analysts.  When the data is spread apart and siloed, it may not make much sense. But once we dig into one layer and another and connect the dots — determining what interactions cause cancer or heart disease or diabetes and weigh what is important and what isn’t, — great things can happen.

Futurist Adrian Hon perspectiveThis is interesting, but you have to take into account a lot of other factors too, like diet, exercise, local pollution. Still, we know there’s a big hereditary connection between your genes and illness. So there’s a lot of potential here. We just have to be careful not to make spurious lines. The cost of getting your genome sequenced is getting lower and it’s become easier. But when you have all this data, what does it mean? Maybe you can draw a line between a single gene or a pattern of genes and a disease. Then what do you do with it? That’s where machine learning comes in. We can tease out the relationship between genomes and what they’ll accomplish in the real world.  There’s a lot of potential there.

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If you could see your entire genomic health map, would it change the way you live your life?

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Bespoke Medicines Suited To Your Genes

SCENARIO: The more we know about how our genes impact health, the better we get at designing highly-targeted treatments. Personalized healthcare involves tailoring medicine to a patient’s unique genetic makeup. Today, it works by integrating a person’s genetic blueprint and data on their lifestyle, and then comparing it alongside thousands of others to predict illness and determine the best treatment. Different approaches to this new way of using data for hacking genes and to target medicine are expected to bring innovations in many areas.

Geneticist Nir Barzilai perspectiveThe complexities of this are daunting. Here’s why: there are more than 7 billion people in the world, and each one has a unique genetic fingerprint. Add to that the fact that a disease may impact each of those 7 billion in a different way, creating even more mutations. So, first off, you’re dealing with an incredibly complex genetic puzzle. Can we unravel all the genetic signatures to create a “highly targeted treatment” for an individual patient? And can we do it at an efficient cost? That’s unlikely. Instead of creating medicine suitable to an individual’s genes, I think the wiser course would be to use big data-type analytic techniques to gather and study a whole range of genetic variants and find out what mutations produce and protect which diseases. This kind of study will have much greater impact on a larger group of people than trying to craft individualized medicine designed for one person.

Futurist Adrian Hon perspectiveThis is one of those things that always feels like it’s 10 years off, and I’m not sure why it’s never worked out.  Frankly, I wonder if it makes sense from a business point of view. It’s expensive to develop drugs, which is why drug companies typically work on making drugs that can be sold to as many people as possible and as frequently as possible. There are certain diseases that affect too few people for drug companies to devote much time and effort to researching. A company probably wouldn’t make money if you had to make 1,000 different drugs for 1,000 different subgroups of people and geno-types. If that changes, if we can figure faster or more efficient ways of researching and developing/testing drugs then it should become economical. We could flip it around and say which of the existing medicines should you use considering your genotype.

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Should medical researchers target the most pervasive diseases, or focus on individualized care?

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