Programming Cures with Synthetic Biology

Tim Lu uses his extensive background in computer programming, electrical engineering and micrcobiology to engineer cells to act as living therapeutics. At TEDMED 2018, Tim Lu shared how his work in bioengineered medicine is enabling dynamic responses to disease in previously unseen ways. Watch his Talk, “Biological engineering—the nexus between computer programming and medicine” and read his post below to learn more about his pioneering work.


Ever since the human genome was decoded, we’ve gained considerable insights into the origins of disease. The biological programs encoded by the DNA inside our cells are highly interconnected, allowing them to orchestrate the complex activities of life. When these fine-tuned interconnections within cells and between cells go awry, disease results.

With an increasing understanding of these dysfunctional biological programs and their role in human illness, scientists are trying to develop new ways to cure disease, not simply keep it at bay. However, our current armamentarium of medicines is dominated by small-molecule drugs and biologics, such as antibodies and enzymes. Although these medicines have resulted in tremendous advancements in human health, they are fundamentally limited in their activity and are nowhere as sophisticated as the disease networks they are trying to address.

For example, these drugs distribute systemically throughout the body and are not easily activated (if more activity is needed) or suppressed (if side effects are encountered). In addition, these medicines often only target a single mechanism of action, which may be insufficient to cure diseases. The basic problem is that we are using static, simple, non-living medicines to treat indications that are inherently dynamic, multi-factorial and living.

Fortunately, while we’ve been decoding our DNA and the biological programs we’re born with, we’ve also been learning how to design DNA to create new programs in living cells. The engineering discipline of synthetic biology has the potential to create powerful new medicines that can match the complexity of disease with even more sophisticated therapeutic programs. These medicines are called (1) cell therapies, where living cells are reprogrammed with artificial DNA programs and delivered into patients, and (2) gene therapies, where the artificial DNA programs are administered directly into patients, typically using a virus or a chemical carrier.

We’re already seeing these living cell and gene therapies have an impact on certain diseases, such as acute lymphoblastic leukemia (ALL). For example, a new class of medicines called CAR-T cells are made by extracting T cells from ALL patients, engineering them to kill any cells expressing a protein called CD19, and then reinfusing the living drug into the body, wherein the CAR-T cells eliminate CD19-positive leukemia cells, as well as normal B cells. These CD19-targeting CAR-T cells have achieved tremendous success in the clinic, with more than 80 percent complete response rates in some studies.

However, we’re only scratching the surface of what is possible with current cell and gene therapies. For example, CAR-T cells don’t work particularly well against solid tumors, such as ovarian, lung and liver cancers, or difficult-to-treat liquid tumors, such as acute myeloid leukemia (AML). Solid tumors have evolved multiple ways to block T cells from being active within the tumors, so that CAR-T cells can’t exert maximal killing activity against cancer cells.

More sophisticated genetic programming through synthetic biology can help overcome this challenge. For example, CAR-T cells can be engineered not only to kill cancer cells, but also to secrete multiple additional drugs that counteract solid tumor defenses in a multi-factorial fashion. Combination therapy encoded within a living cell therapy can address the complexity of cancer disease networks and significantly improve treatment effectiveness.

Moreover, diseases, such as AML, are highly heterogeneous, so that it’s difficult to find a single antigen target that can discriminate between cancer and healthy cells. Antibodies and CAR-T cells that only go after a single target can generate significant side effects by also killing healthy cells. This isn’t a major problem with CD19-targeting therapies in ALL, because people can survive ablation of all their healthy B cells (which make antibodies) by being supplemented with antibody infusions. However, when the healthy tissues that are inadvertently killed are irreplaceable — such as stem cells, cardiac tissue or lung cells — these side effects can cause substantial toxicity.

Fortunately, we no longer have to be satisfied with drugs that only rely on a single protein to distinguish between diseased and healthy cells. Leveraging synthetic biology, we can design cell therapies to sense multiple disease biomarkers and to respond only when a specific combination of biomarkers is encountered. For example, CAR-T cells can be outfitted with a “NOT gate” program to kill tumors when they express biomarker A but NOT biomarker B, and to prevent killing of healthy tissues that express both biomarker A and B. By doing so, we can significantly increase the safety margin of these drugs and enable enhanced potency against cancer cells.

These biological programs are just a few examples of how programming sophisticated living drugs can improve therapeutic outcomes. The emerging synthetic biology toolbox also enables living medicines that can be turned on or off by administering orally dosed FDA-approved small molecule drugs. Such medicines can be narrowly targeted against specific cell types or tissues, and that can even adapt their activity to dynamic and evolving diseases. A new era of programmable drugs is coming, and has the promise to deliver cures that match the complexity of human diseases.

Massive Science on Yaniv Erlich

Massive Science is a digital science media publication that brings together scientists and the science-curious public. The team at Massive joined us at TEDMED 2018 and covered talks by various speakers including Yaniv Erlich. Check out their coverage of Yaniv’s TEDMED 2018 talk below.


Humans have an inherent social drive, and in this age of social media, we are more connected than ever. However, by constructing the world’s largest family tree comprising 125 million people, computational geneticist Yaniv Erlich, has shown that some of these connections run deeper — down into our genes. Erlich, who is a professor and researcher at Columbia University and CSO of MyHeritage.com, is revolutionizing the field of genomics by linking genealogical data provided online by volunteers to DNA with striking accuracy. Earlier this year, Erlich and his colleagues sent a shock wave through the field of genetics by showing that it is possible to uncover the identities of males who have taken part in “anonymous” genetic research without ever matching their data to a sample of their DNA. All you really need is the internet.

“Smoking…determines ten years of our life expectancy, which is twice as much as what our genetics determines.”

Genomic data is incredibly powerful. It can reveal migration patterns, or uncover interesting details like the distance people move from their place of birth to procreate. But more importantly, genomic data allows us to ask questions about human health, like how much genetic variations account for differences in individual life-spans. Large family trees allow us to analyze both close relatives and distant relatives, teasing apart the difference between genetic variations and environmental factors. Erlich, for example, found that genes account for only 15 percent of the differences in individual life-spans, on average about five years. Speaking about these surprising findings, Erlich says, “I think there is this notion that there is some fountain of youth in our genome, and we just have to find the gene to unlock it. But it doesn’t seem this is the case.” Erlich explains that since 1960, lifespans have increased linearly by about two months every year, despite two World Wars. Despite the many catastrophes of the 20th century, lifespans continued to steadily grow. Erlich says these findings mean that our actions might matter more than our genes. “Smoking for example, determines ten years of our life expectancy, which is twice as much as what our genetics determines.”

While genes seem to have relatively little impact on our life span, genomic data has allowed us to identify risk factors for a numbers of diseases. Using genome-wide association studies (GWAS), it’s possible to link genetic variants in different individuals to particular traits. The more statistically significant the link is, the more the data looks like the skyline of Manhattan. Ten years ago, Erlich says, these Manhattan plots actually looked more like the skyline of Los Angeles. But bigger sample sizes have become easier for researchers to access, thanks to initiatives like the UK Biobank, where an increasing number of genetic risk factors are being identified. Using data from more than 100,000 donors, obtained through the website DNA.land, Erlich has himself been able to discover the genetic bases for several traits in Israeli families.

With the help of civilian genealogy enthusiasts, genomic data is changing not only the landscape of health care, but forensics too. In April, thanks to the website GEDmatch, the FBI was able to link DNA from the unidentified Golden State Killer to a third cousin of the suspect who had voluntarily provided their own DNA to the free online genealogy database. By building a large family tree, and scanning the different branches of the tree until they found a profile that exactly matched what they knew about the serial killer, they were able to track down the suspect, test his DNA, and charge him.

A Manhattan plot. The bars that rise higher than the rest are the ones of interest.
Ikram et al 2010 PLoS Genetics

Erlich is impressed by the power of genomics to improve demography, healthcare, and forensics. But he agrees there are many issues that still need to be addressed. For example, since these databases primarily contain people of European descent, non-European populations with certain genetic risk factors are missed, while risk factors identified in these European populations may not have the same implications for other groups. The most obvious reason for this disparity is economics. But many genealogy websites are free, and the price of DNA tests has dropped to as little as $49. Another reason may be access to family records. As Erlich says, “My family died in the Holocaust, so I have no means to go beyond a certain number of generations. It’s all lost.” A lack of record-keeping is also a problem for many populations. There’s also the question of social influence. “If I know someone who is doing genealogy, I’m now more willing to also do it. When you start with one community, it spreads from that community unequally.” Erlich does not have the answers for how to remedy the issue of diversity in databases, but believes that governments, at least in countries equipped with the resources, should take greater responsibility for driving genomic medicine.

Which of these people are represented in biobanks?
Serge Melki via Wikimedia Commons

Another complex issue is the issue of privacy. When it comes to genetic information, many of us are concerned that employers and insurance companies may use this information unethically. According to the Genetic Information Nondiscrimination Act of 2008 (GINA), employers and insurance companies cannot use our genetic information without our consent. But there are some major loopholes; for example, GINA doesn’t apply to life insurance. There’s also the question of how law enforcement should be allowed to use genetic information. The Golden State Killer case in particular raises many questions about privacy. Interestingly, 60 percent of Americans of European heritage (because they are over-represented in databases) have relinquished genetic information that could be used by law enforcement, and within three years, this number is expected to rise to 99 percent. Erlich says he’s not scared of these techniques being abused. He’s more worried about national security. “I’m more concerned about foreign governments using the same techniques to identify U.S. individuals. Think about CIA operation in some countries. The whole point is that it’s covert—you don’t know the identities of these people. It’s very easy to disguise your face and get a fake passport, but you can’t change your DNA.” At the end of the day, there are no easy answers. “It’s a tricky question of justice, and how to define that,” he says, pointing toward the need to make genetic information part of a public good, rather than be used for monetary gain. But the limits may be hard to find. He says, “I don’t know what’s the right answer.”


About the author: Yewande Pearse was born and bred in North London. She is now a Research Fellow based at LA Biomed, in affiliation with the University of California, Los Angeles (UCLA). She completed her PhD in Neuroscience at the Institute of Psychiatry in 2016, which focused on the potential use of gene therapy for the treatment of Batten disease, a fatal neurological pediatric disease. She is now working on stem cell gene therapy using CRISPR-Cas9 to treat Sanfilippo Syndrome. Before completing her PhD, she worked in the areas of Stroke and Huntington’s disease research and also worked in a care capacity, with people living with Autism, suicidal ideation, dementia and HIV Associated Neurocognitive Disorder.

Q & A with Yaniv Erlich

TEDMED: In your TEDMED 2018 talk, you describe “Uncle Bernie,” the family genealogist who corners family members to get more information. Are you the genealogist in your family? When did your interest in genealogy begin?

Yaniv Erlich: [laughing] I liked genealogy quite a lot, especially as a child. Like many Israeli teenagers, I conducted my own genealogy project while I was in seventh grade. It was so enjoyable that I asked my mother to take me to the Museum of the Jewish People at Beit Hatfutsot; it had one of the only sources for genealogical information available at the time. I loved how history intersects with family stories, and the process of finding ancestors felt like detective work. I did such a good job on this project that it won the title of best genealogy project of the year at my middle school. Now, since genealogy is my work, it is no longer a hobby of mine and the family genealogist is my aunt. 

The last time I spent time on the genealogy of my family was after my father passed away 2 years ago. In some way, I felt that tracing my ancestors connected me to my father and his childhood–and reviewing the lifecycles of my family relatives gave me some serenity and comfort that the sorrow that I was experiencing was simply part of the endless rivers of generations.

Photo Credit: Yaniv Erlich

TM:  What was the catalyst for you to begin professional research on genetics and family trees?

YE: I was invited to join a commercial genealogy and social networking website by my third cousin, who was able to trace me and send an invitation email. At that time, I was about to finish my PhD studies and become more interested in human genetics. When I started documenting my family tree on the website, I was shocked to discover that many of my relatives already existed there! This got me thinking — family trees are one of the most valuable assets in human genetics. Yet, large family trees are very hard to collect. 

A few months later, I started my own independent research group at the Whitehead Institute of MIT. I decided to try to collect all the data from that website as one of the first projects of the lab, so I sent a cold email to the CTO of the website at that time, Amos Elliston. He immediately agreed and instructed me on how to collect the data. Eventually we downloaded 86 million public profiles from the website.

But over time it became a very long project. We actually spent 8 years from inception to publication. 

TM: Did you have any hurdles during the project?

YE: First, we had to substantially enhance and validate the dataset. The central question was whether we can trust datasets that were produced by amateur genealogists the same way that we trust family trees built by scientists. So we subjected the data to a massive number of tests, such as measuring the error rates of family trees, whether the individuals in these datasets represented the general population at the time, and the accuracy of the demographic details inserted by the genealogists. Second, we had to find the correct questions. In some ways, this dataset was a blessing and a curse because so many things can be evaluated using such datasets, and we had to think carefully about the focus of our study. Finally, we had to develop the computational infrastructure to answer those questions. Most genetic algorithms were developed to work with family trees with up to several thousand individuals. We had to develop and improve these algorithms to work on a scale of tens of millions of people.

TM: A lot of your research focuses on the role of genetics in longevity. What was the main thing you wanted to understand about longevity when you began your research? 

YE: Longevity is probably the most important trait because the question: “When am I going to die?” is imminent to us as individuals and as a society. Surprisingly, not a lot is known about the genetics of longevity. Some studies in the past suggested that 25% of the variance in longevity is attributed to genetic differences. However, these differences were never spotted by any study! 

In addition, there is a long-lasting debate in human genetics regarding the manner in which genetic variations affect traits. One camp argues that each genetic variant contributes independently to a trait regardless of the status of other variants. Another camp claims that the contribution of each variant is a complex function that is affected by the status of other variants. It is possible to find which camp is right by inspecting the correlation of the trait in various types of relatives, from, say, fourth cousins to full siblings. However, until our study, nobody was able to collect large family trees with enough relatives to robustly differentiate between the two camps.

Using our data, we inspected the longevity readout of millions of pairs of relatives. Our analysis shows that longevity is much less heritable than we thought before and only ~15% of the variance in the population can be attributed to genetic differences. Moreover, we showed that at least in the case of longevity, the first camp is the correct one. The value of each genetic variant is independent of the other variants. This is actually great news for precision medicine, because if each variant works independently, it means that it should be easier to find those longevity variants in the future.

TM: In your TEDMED talk, you spoke about the immense potential of biomedical research and the many insights we can gain from genealogy research. What’s the future of genealogy research?

YE: DNA! We currently see an ongoing revolution in the field. DNA tests enable genealogists to find relatives beyond the information permitted in genealogical records and as a tool to validate these records. In addition, DNA helps to solve cases when records are missing such in the case of adoptees, holocaust survivors, and even child trafficking. Thanks to the genomics revolution DNA tests are now highly affordable, democratizing access by growing segments of the population. A recent Technology Review article estimated that more than 26 million people took such tests and the uptake shows an exponential increase. Some estimate that in a decade most people in Western societies will have access to their DNA information, which means that we may be able to create the world’s family tree based also on DNA matches and not just genealogical information and family stories.