On today’s episode we are discussing the results and implications of a recent study that describes the creation of a new set of tools to turn off or on any region in the genome with high specificity. Host Lauren Richardson and a16z general partner Vijay Pande are joined by the senior author of the article, “Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing”, Jonathan Weissman, Professor of Biology at the Whitehead Institute at MIT. Jonathan talks about how they developed these tools using the CRISPR gene editor as a backbone, the advantages of modulating the epigenome as opposed to the genome, and the various applications — both in the lab and in the clinic — for these epigenome editors. 

On today’s episode, we are making the full arc from the theoretical and borderline philosophical to the applied. Let’s start with the theory: embodied intelligence posits that the body, or the physical form, plays an active and significant role in shaping an agent's mind and cognitive capacities. For example, human intelligence is not just the function of our brain, but a combination of our brain, our body, and the environment in which we exist. But when it comes to designing artificial intelligence (AI), a physical form and an environment are typically not part of the equation. It’s a disembodied cognition. Our guests, Li Fei-Fei and Surya Ganguli of the Stanford Institute for Human-Centered AI, set out to develop what they call an “evolutionary playground” to explore the development of embodied intelligence in AI and its connection with the environment and with learning using in silico experiments. They discuss with a16z general partner Vijay Pande and host Lauren Richardson how they created a suite of virtual environments in which agents evolve through a process that mimics aspects of Darwinian evolution. These agents, called the unimal, or universal animal, start off as a central node, and with each generation can add or subtract limbs and change various properties of their physical forms, like how flexible their joints are. Just like in real evolution, different forms arose based on the particularities of the environment, but what is really exciting is what Fei-Fei, Surya, and colleagues discovered about the intelligence encoded in some of these forms, such as an increased ability to learn a novel task. Which brings us to the applied section of our discussion. These results provide new insights for how we think about designing robots capable of performing unique tasks, and for understanding the possible limitations of disembodied AI models, like GTP-3. The results are described in the pre-print "Embodied Intelligence via Learning and Evolution" posted on arXiv.org. And watch the unimal evolve here!

Today’s episode is all about the history and future of infusing tech into healthcare with the goals of improving outcomes and lowering costs, and features one of the leading voices in this field, Jonathan Bush. Jonathan, aka JB, started his career in healthcare as an ambulance driver and army medic, and then met Todd Park, another Bio Eats World guest, while at Booz Allen. Together they founded Athena Women’s Health Clinic, which evolved from a clinic specializing in maternity care to one of the original digital health companies providing cloud-based services and point-of-care clinical and back office tools for providers, later called Athenahealth. In this conversation with a16z general partner Julie Yoo — who is also a digital health builder — JB discusses this evolution, how it mirrors the bigger trend shifts in healthcare, and how it has informed the mission of his new company, Zus, which he compares to a Github for healthtech. JB and Julie cover what’s changed since the launch of Athena, 25 years ago, how to disrupt an entrenched system like healthcare, the role regulation plays in the space, and the under appreciated importance of bottom-up sales. Please note there is some colorful language used in this episode, in case you have young children listening.

Genetic testing is on the cusp of a major revolution, which has the potential to shift not just how we understand our risk for disease, but how we practice healthcare. In the clinic today, genetic testing is used only in cases where we know that mutations have big impact on physiology (BRCA mutations in breast cancer, for example). But our knowledge of how our genetics influences our risk for disease has evolved, and we now know that many (tens of thousands to even millions) small changes in our genes, each of which individually has a tiny effect, combine to influence our risk profile. This new appreciation — coupled with powerful statistical methods and massive datasets — has fueled the creation of a new tool to quantify the risk of a broad range of common diseases: the polygenic risk score. On this episode, which originally aired on January 18, 2021, host Lauren Richardson (@lr_bio) is joined by Peter Donnelly, (@genemodeller Professor of Statistical Science at the University of Oxford and the CEO of Genomics PLC,) and Vineeta Agarwala, (@vintweeta physician-scientist and general partner at a16z), to discuss these scores and how they can reshape healthcare, away from a paradigm of treating illness and towards prevention and maintenance of health. 

When it comes to healthcare, the topic of how expensive it is and what we can do to lower costs is always top of mind. One area with particularly steep costs is the emergency department. These are hospital departments that can take care of pretty much anything from a cut to a car wreck. But going to an emergency department for something as simple as a cut can result in a high bill for both the patient and the insurer. This is where the urgent care center comes in. Urgent care centers are walk-in clinics focused on caring for minor illnesses and injuries — or in medical speak — low acuity conditions. They are way less expensive than a trip to the emergency department, so funneling these low acuity visits from the emergency department to urgent care centers should result in lower healthcare costs… right? On today’s episode, host Lauren Richardson is joined by a16z general partner Vineeta Agawala and bio deal team member Justin Larkin (who are both medical doctors and experts in healthcare), to discuss new research published in the journal Health Affairs, examining this key assumption. The conversation covers the issues with care utilization and care navigation, how urgent care centers impact healthcare costs, and the implications of these results for builders in the digital health space. The article at the center of today's episode is: "Urgent Care Centers Deter Some Emergency Department Visits But, On Net, Increase Spending" by Bill Wang, Ateev Mehrotra, and Ari B. Friedman, published in Health Affairs.

If there is one rule in biology, it is that there is an exception to every rule. This includes even the basic biochemistry of DNA, which was once thought to be universal. On this episode, host Lauren Richardson and Judy Savitskaya (a16z bio deal team member and synthetic biology expert), discuss the results and implications three related articles co-published in Science, which all advance our understanding of a very unique kind of DNA. If you open any biology text book, it will say that the genetic code is made up of 4 bases: Adenine, Thymine, Cytosine, and Guanine, or ATCG. But, back in 1977, scientists discovered a phage — the technical term a virus that infects bacteria — that encodes its genome in ZTCG. Z is a derivative of A that has an extra amino group tagged on, and while that may sound minor, it changes some of the key properties of DNA. These three new articles seek to understand how Z is made and how it is incorporated into DNA. This is essential information for taking Z from a weird, wild bio story into a practical application. The conversation covers what makes Z different than other bases, what these three articles reveal about the synthesis and polymerization of Z, and how we can use use Z in a wide range of applications, from bio-containment to new therapeutics to DNA storage.The three articles discussed are:"A widespread pathway for substitution of adenine by diaminopurine in phage genomes" by  Yan Zhou, Xuexia Xu, Yifeng Wei, Yu Cheng, Yu Guo, Ivan Khudyakov, Fuli Liu, Ping He, Zhangyue Song, Zhi Li, Yan Gao, Ee Lui Ang, Huimin Zhao, Yan Zhang, and Suwen Zhao"A third purine biosynthetic pathway encoded by aminoadenine-based viral DNA genomes" by Dona Sleiman, Pierre Simon Garcia, Marion Lagune, Jerome Loc’h, Ahmed Haouz, Najwa Taib, Pascal Röthlisberger, Simonetta Gribaldo, Philippe Marlière, and Pierre Alexandre Kaminski"Noncanonical DNA polymerization by aminoadenine-based siphoviruses" by  Valerie Pezo, Faten Jaziri, Pierre-Yves Bourguignon, Dominique Louis, Deborah Jacobs-Sera, Jef Rozenski, Sylvie Pochet, Piet Herdewijn, Graham F. Hatfull, Pierre-Alexandre Kaminski, and Philippe Marliere 

This episode was recorded in March of 2019 to celebrate the 20th anniversary of Folding at Home, the distributed computing project for simulating protein dynamics, and originally aired on The a16z Podcast. Folding at Home is run on millions of devices, is the world’s largest supercomputer, and tackles some of biology’s toughest problems, including COVID-19.Proteins are molecular machines that must first assemble themselves to function. But how does a protein, which is produced as a linear string of amino acids, assume the complex three-dimensional structure needed to carry out its job? That's where Folding at Home comes in. Folding at Home is a sophisticated computer program that simulates the way atoms push and pull on each other, applied to the problem of protein dynamics, aka "folding". These simulations help researchers understand protein function and to design drugs and antibodies to target them. Given the extreme complexity of these simulations, they require an astronomical amount of compute power. Folding at Hold solves this problem with a distributed computing framework: it breaks up the calculations in the smaller pieces that can be run on independent computers. Users of Folding at Home — millions of them today — donate the spare compute power on their PCs to help run these simulations. This aggregate compute power represents the largest super computer in the world: currently 2.4 exaFLOPS!Folding at Home was launched in the lab of Vijay Pande at Stanford. In this episode, Vijay (now a general partner at a16z) is joined by his former student and current director of Folding at Home, Greg Bowman, an associate professor at Washington University in St. Louis, and host Lauren Richardson. The conversation covers the origins of the Folding at Home project and the scientific and technical advances needed to solve the complex protein folding and distributed computing problems.To find out more about how Folding at Home is contributing to the COVID-19 pandemic, check out the recenty published article from the Bowman lab, "SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome", published in Nature Chemistry.

On the path from scientific discovery to new drug, the clinical trial is a huge — and critical — hurdle. Clinical trials are themselves experiments, and to make sure that they are doing the best possible job at determining the safety and efficacy of the new drug, we need to be able to do experiments on those experiments. But how do you do that in such a highly regulated space? Host Lauren Richardson talks to James Zou, Assistant Professor of Biomedical Data Science at Stanford University, and a16z general partner Vineeta Agarwala, physician and expert on real world data in healthcare, about new research from the Zou lab that uses AI-powered simulations of clinical trials and real world patient data to understand how different designs influence trial outcomes. In particular, looking for designs that can make trials more inclusive, which is key for getting patients access to potentially life-saving care and for running trials efficiently. The conversation covers the inherited rules and assumptions governing which patients can participate in trials, how Dr. Zou, lead author Ruishan Liu, and colleagues combined real world data and computer simulations to challenge these assumptions via a data-driven approach, and how this can inform smarter trial design. The article at the center of today's episode is: "Evaluating eligibility criteria of oncology trials using real-world data and AI" by Ruishan Liu, Shemra Rizzo, Samuel Whipple, Navdeep Pal, Arturo Lopez Pineda, Michael Lu, Brandon Arnieri, Ying Lu, William Capra, Ryan Copping & James Zou, published in Nature.

They say you should never judge a book by its cover, but can you judge a cell by its shape? On this episode, host Lauren Richardson is joined by Maddison Masaeli (CEO and cofounder of Deepcell), and a16z general partner Vijay Pande (whose lab at Stanford focused on the development of novel computational methods for simulating biology), to discuss what we can learn by characterizing a  cell's shape — also known as its morphology. We've long appreciated that morphology can be used to discriminate cells, for example, cancer cells look very different than the surrounding tissue and can be spotted in a biopsy, and the various classes of immune cells all have distinct appearances. But characterization of cell shape — and what it can tell us about the underlying biology of those cells and the health of the organism that they came from — has been stuck in the low-tech, manual, qualitative era. To unlock the potential of cell morphology, Maddison and her colleagues are leveraging the power of artificial intelligence to assess and learn from cell images to create a quantitative, scaleable technology. The conversation covers the untapped potential of studying cells and their shape, how Maddison and her team at Deepcell are building an AI with seemingly limitless applications, and where this technology could take us.

Neuroscientists have long been trying to determine what happens in the brain during sleep, but to date, they have overlooked a key player: astrocytes. These star-shaped cells were once thought to be the glue that held the brain together, but we are now beginning to appreciate their importance in a variety of brain functions. In this episode, host Lauren Richardson talks to Kira Poskanzer, Assistant Professor at the University of California, San Francisco, about her group's work showing that neurons are only one piece of the larger sleep puzzle. The conversation covers the complexity of sleep, how astrocytes control two key attributes of sleep (depth and duration), the technology and methods employed to uncover this novel mode of regulation, and how appreciating the role of astrocytes in governing sleep could lead to new insights into neuropsychiatric conditions and how to treat them.  The article at the center of today's episode is: “Cortical astrocytes independently regulate sleep depth and duration via separate GPCR pathways” by Trisha V Vaidyanathan, Max Collard, Sae Yokoyama, Michael E Reitman, and Kira E Poskanzer, published in eLife.